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		<title>Peruvian government captures alleged Chilean spy</title>
		<link>http://www.theamericaspostes.com/4182/peruvian-government-captures-alleged-chilean-spy/</link>
		<comments>http://www.theamericaspostes.com/4182/peruvian-government-captures-alleged-chilean-spy/#comments</comments>
		<pubDate>Fri, 03 Feb 2012 01:26:53 +0000</pubDate>
		<dc:creator>tlc</dc:creator>
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		<category><![CDATA[Chile Peru espionage]]></category>
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		<description><![CDATA[Peru&#8217;s government said Wednesday it is investigating an alleged Chilean spy arrested in the vicinity of a military airbase in the north, in a case that could cast a shadow over relations between Lima and Santiago. Defense Minister Alberto Otárola, said when Luis Maximiliano Seraín was arrested he had in his possession a CD,  a [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_4183" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2012/02/Chilean-spy.jpg"><img class="size-medium wp-image-4183 " title="The Americas Post - The alleged Chilean spy is the one wearing the Jack Daniels t-shirt" src="http://www.theamericaspostes.com/wp-content/uploads/2012/02/Chilean-spy-300x167.jpg" alt="" width="300" height="167" /></a><p class="wp-caption-text">The Americas Post - The alleged Chilean spy is the one wearing the Jack Daniels t-shirt</p></div>
<p><span><span>Peru&#8217;s government said Wednesday it is investigating an alleged Chilean spy arrested in the vicinity of a military airbase in the north, in a case that could cast a shadow over relations between Lima and Santiago.</span></span></p>
<p><span><span>Defense Minister Alberto Otárola, said when </span></span>Luis Maximiliano Seraín was arrested he had in his possession a CD,  a USB memory stick and &#8220;some writings&#8221; that are being investigated by the local public prosecutor.</p>
<p><span><span>&#8220;From what we know he is a Chilean citizen who was arrested near the barracks of El Pato military base in Talara.  I can not provide or confirm more details because I repeat is in full investigation,&#8221; Otárola told journalists.</span></span></p>
<p><span><span>In 2009, Peru and Chile engaged in a diplomatic row after the arrest of a Peruvian Air Force officer, who was accused of sending classified information to Chile.  </span></span>After a year of research, a military court in Peru convicted the officer, formerly employed in the Peruvian Embassy in Santiago, to 25 years in prison.</p>
<p><span><span>This new espionage case involving Chile, a major investor in Peru, comes at a time when both South American countries are in international court disputing differences in their maritime boundaries.</span></span></p>
<p><span><span>Chilean government spokesman Andrew Chadwick said in Santiago they are fully confident that the person arrested in Peru has no link with Chilean state activities.</span></span></p>
<p><span><span>&#8220;We are absolutely clear on the country&#8217;s foreign policy in relation to our neighbors.  We never want to risk any type of situation that could harm our relations,&#8221; he said.</span></span></p>
<p><span><span>&#8220;As a government we deal with these situations calmly and wisely,&#8221; he added.</span></span></p>
<p><span><span>Relations between Peru and Chile, both major mineral exporters, have gone through ups and downs since they fought a war in the late nineteenth century.  </span></span>Despite the friction, trade links and business between the two countries have grown rapidly in recent years.</p>
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		<title>Cyber News: U.S. Defense computers tied to &#8220;the cloud&#8221;.</title>
		<link>http://www.theamericaspostes.com/4139/cyber-news-u-s-defense-computers-tied-to-the-cloud/</link>
		<comments>http://www.theamericaspostes.com/4139/cyber-news-u-s-defense-computers-tied-to-the-cloud/#comments</comments>
		<pubDate>Fri, 20 Jan 2012 01:32:21 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
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		<category><![CDATA[U.S. military information assets cloud]]></category>

		<guid isPermaLink="false">http://www.theamericaspostes.com/?p=4139</guid>
		<description><![CDATA[To defend the U.S. military&#8217;s information assets, Pentagon leaders say defense computers must be tied to the cloud &#8212; meaning an online environment that can be centrally locked down. Yet it&#8217;s difficult to police parts of that environment manufactured or even housed in countries that stand accused of cyberespionage, experts say. The shift of military [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_4140" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2012/01/The-Americas-Post.-U.S.-Armed-Forces-including-this-fighter-shift-operations-to-the-cloud.-Photo-Credit-iStockPhoto.jpg"><img class="size-medium wp-image-4140  " title="The Americas Post Security News. U.S. Armed Forces, including this fighter, shift operations to the cloud. Photo Credit iStockPhoto" src="http://www.theamericaspostes.com/wp-content/uploads/2012/01/The-Americas-Post.-U.S.-Armed-Forces-including-this-fighter-shift-operations-to-the-cloud.-Photo-Credit-iStockPhoto-300x139.jpg" alt="" width="300" height="139" /></a><p class="wp-caption-text">The Americas Post Security News. U.S. Armed Forces, including this fighter, shift operations to the cloud. Photo Credit iStockPhoto</p></div>
<p>To defend the U.S. military&#8217;s information assets, Pentagon leaders say defense computers must be tied to the cloud &#8212; meaning an online environment that can be centrally locked down. Yet it&#8217;s difficult to police parts of that environment manufactured or even housed in countries that stand accused of cyberespionage, experts say.</p>
<p>The shift of military operations to the cloud  will require protecting electronics manufactured in Asia from supply chain tampering, say some private security auditors. But that won&#8217;t necessarily mean inspecting every network component made in China</p>
<p>&#8220;Our clouds are running off of hardware that&#8217;s built in China,&#8221; said&#8230;<a href="http://www.linkedin.com/news?viewArticle=&amp;articleID=5561135943525597201&amp;gid=1864210&amp;type=member&amp;item=88143116&amp;articleURL=http%3A%2F%2Fwww.nextgov.com%2Fnextgov%2Fng_20120106_5015.php%3Foref%3Dtopstory&amp;urlhash=-HzL&amp;goback=.gde_1864210_member_88143116"><strong>READ MORE HERE</strong></a></p>
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		<title>Chavez rejects World Bank oil decision in advance</title>
		<link>http://www.theamericaspostes.com/4111/chavez-rejects-world-bank-oil-decision-in-advance/</link>
		<comments>http://www.theamericaspostes.com/4111/chavez-rejects-world-bank-oil-decision-in-advance/#comments</comments>
		<pubDate>Tue, 10 Jan 2012 03:54:20 +0000</pubDate>
		<dc:creator>tlc</dc:creator>
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		<guid isPermaLink="false">http://www.theamericaspostes.com/?p=4111</guid>
		<description><![CDATA[Venezuelan President Hugo Chavez announced on Sunday that his nation will not recognize any decision by a World Bank tribunal in a multibillion-dollar arbitration case against Exxon Mobil Corporation. Exxon has called Venezuela before the World Bank&#8217;s International Center for Settlement of Investment Disputes, or ICSID, demanding $12 billion in compensation for nationalization of the Cerro [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_4112" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2012/01/ExxonMobilVenezuelaCrude.jpg"><img class="size-medium wp-image-4112" title="The Americas Post - That isn't the Exxon Mobil logo on the side of that storage tank" src="http://www.theamericaspostes.com/wp-content/uploads/2012/01/ExxonMobilVenezuelaCrude-300x200.jpg" alt="" width="300" height="200" /></a><p class="wp-caption-text">The Americas Post - That isn&#39;t the Exxon Mobil logo on the side of that storage tank</p></div>
<p>Venezuelan President Hugo Chavez announced on Sunday that his nation will not recognize any decision by a World Bank tribunal in a multibillion-dollar arbitration case against Exxon Mobil Corporation.</p>
<p>Exxon has called Venezuela before the World Bank&#8217;s International Center for Settlement of Investment Disputes, or ICSID, demanding $12 billion in compensation for nationalization of the Cerro Negro oil project by Chavez in 2007.</p>
<p>&#8220;I tell you now: we will not recognize any decision by ICSID,&#8221; Chavez said in a televised speech.   He repeated accusations that the U.S. oil producer has used unfair practices to rob the South American OPEC member of its resources.</p>
<p>&#8220;They are immoral &#8230; How much could they steal in 50 years? Who would dare launch this madness without any foundation?  They wanted $12 billion. From where?&#8221; he said.</p>
<p>&#8220;We are not going to bow before imperialism and its tentacles, understand that &#8230; They are trying the impossible: to get us to pay them. We are not going to pay them anything.&#8221;</p>
<p>Exxon declined to comment.</p>
<p>Some interpreted the statement to mean Venezuela would reject rulings in the 20 other cases pending before the World Bank&#8217;s tribunal, responses to multiple state takeovers in recent years.  They include separate multi-billion dollar claims filed by another U.S. oil company, Conoco Phillips.  Two statements issued later however, by Venezuela&#8217;s Petroleum and Mining Ministry and by its state oil company PDVSA, only mentioned the Exxon case.</p>
<p>Last week an arbitration panel at the International Chamber of Commerce, awarded Exxon $908 million in a separate case about the Cerro Negro nationalization.  On Saturday, Venezuelan Oil Minister Rafael Ramirez said he did not expect a verdict in Exxon&#8217;s World Bank case before the end of this year.</p>
<p>Both cases are being monitored by the industry for precedents in future disputes between companies and producing states, which have demanded a larger share of oil profits as prices increase and new reserves grow scarce.</p>
<p>For years, Venezuela&#8217;s socialist president has accused foreign oil companies of looting that nation&#8217;s resources, while still maintaining close ties with many of them.</p>
<p>Exxon says the World Bank case is to compensate for its assets, and experts say it could result in a larger award.  The government has insisted Exxon receive only slightly more than the $750 million it said was invested in the project.  In September, Venezuela offered to settle for $1 billion.</p>
<p>For years, Chavez has hit oil companies with tax hikes and contract revisions  to fund public anti-poverty programs.</p>
<p>Venezuela&#8217;s push for more control over its oil has been emulated by other producing nations.  Critics say it has scared investors away from that country and reduced production volume.  Regardless, some companies remain eager to invest in Venezuela&#8217;s Orinoco extra heavy oil belt, one of the world&#8217;s largest untapped reserves of crude.  Chevron and Spain&#8217;s Repsol both signed deals in 2010 for new projects there.</p>
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		<title>U.S.-Latin American Trade Policy in the Bush Era</title>
		<link>http://www.theamericaspostes.com/4031/u-s-latin-american-trade-policy-in-the-bush-era/</link>
		<comments>http://www.theamericaspostes.com/4031/u-s-latin-american-trade-policy-in-the-bush-era/#comments</comments>
		<pubDate>Mon, 28 Nov 2011 02:01:49 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
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		<category><![CDATA[Bush’s trade negotiatorX 1994 Miami SummitX Andean Trade Preference Act (ATPA)X Asia Pacific Economic Cooperation (APEC)X Brazilian President Fernando Henrique CardosoX by President Vicente Fox and ]]></category>

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		<description><![CDATA[Dear Readers of our blog: Starting now, TheAmericasPost.com will publish papers and essays from prestigious academics and decision makers within the US intelligentsia. Those documents will focuse on interamerican relations, and will bring us light into the way of thinking of the United States in relation to The Americas. In this case,  this working paper [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_4032" class="wp-caption alignleft" style="width: 160px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2011/11/skype-photo.jpg"><img class="size-thumbnail wp-image-4032" title="Victor Bjorgan - Publisher of The Americas Post" src="http://www.theamericaspostes.com/wp-content/uploads/2011/11/skype-photo-150x150.jpg" alt="" width="150" height="150" /></a><p class="wp-caption-text">Victor Bjorgan - Publisher of The Americas Post</p></div>
<p>Dear Readers of our blog: Starting now, TheAmericasPost.com will publish papers and essays from prestigious academics and decision makers within the US intelligentsia. Those documents will focuse on interamerican relations, and will bring us light into the way of thinking of the United States in relation to The Americas. In this case,  this working paper of 2003 will enlight us about US policy toward The Americas during the Bush Administration.  Hope you will enjoy this paper written by RICHARD FEINBERG.</p>
<p>Mr. Feinberg served as President Bill Clinton’s Senior Adviser for Inter-American Affairs and was a principal architect of the first Summit of the Americas in 1994. He is now Professor of Political Economy at the University of California, San Diego. Professor of International Political Economy; Director of the APEC Study Center University of California San Diego; Chair of the Global Leadership Institute. He is a Ph.D., Stanford University, 1978 (international economics) University College, University of London. Concentration in British history and a  B.A.in Brown University, 1969 (cum laude, European history). <strong>As The Publisher, our TheAmericasPost.com gives full credit for this paper to Mr. Richard Feinberg and to the University of California, San Diego.</strong></p>
<h2>Policy Issues<br />
Regionalism and Domestic Politics:<br />
U.S.-Latin American Trade Policy<br />
in the Bush Era</h2>
<div>
<dl id="attachment_835">
<dt><a href="http://vbjorgan.wordpress.com/files/2009/12/richard-feinberg.jpg"><img title="Dr. Richard Feinberg" src="http://vbjorgan.wordpress.com/files/2009/12/richard-feinberg.jpg" alt="Dr. Richard Feinberg" width="130" height="140" /></a></dt>
<dd>Dr. Richard Feinberg</dd>
</dl>
</div>
<p>by Richard E. Feinberg</p>
<p><strong>ABSTRACT<br />
With remarkable success, Latin Americans have sought to impose<br />
their free trade policy agenda on a very reluctant and internally fractious<br />
United States. They have an ally in President George W. Bush,<br />
whose senior appointments notably support hemispheric trade integration<br />
even as political pressures sometimes have yielded protectionist<br />
outcomes. Bush’s trade negotiator, Robert Zoellick, pursues a<br />
doctrine of competitive liberalization while accepting some linkage<br />
between trade and social and political goals. In negotiating the Free<br />
Trade Area of the Americas (FTAA), the administration will have to<br />
balance many domestic pressures without alienating Latin America.<br />
Ultimately, FTAA ratification will signal a new Western Hemisphere<br />
economic-security alliance for the twenty-first century.</strong></p>
<p>The United States has long resisted what geography would seem to<br />
dictate: a special relationship with Latin America. Frequent rhetorical<br />
and occasional real concessions to the idea of hemispheric solidarity<br />
notwithstanding, U.S. foreign policy has generally preferred to focus<br />
on other regions of the world––notably Europe and Asia––or to eschew<br />
regional favorites altogether in favor of a global reach. These preferences<br />
have been deeply rooted in the ethnic origins of the long-dominant<br />
East Coast foreign policy establishment, and in global power relations<br />
that located the nation’s major challengers and allies in those<br />
distant theatres. After World War II, U.S. hegemony seemed to dictate a<br />
universalist perspective that favored global institutions and policies as<br />
against a more parochial and local regionalism.</p>
<p>This denial of geography often had been reciprocated by Latin<br />
American elites throughout the nineteenth and twentieth centuries<br />
(Feinberg and Corrales 1999). Nationalists of the right and left preferred<br />
to limit their dependence on U.S. power by diversifying their relations<br />
through stronger ties to Europe or other Latin American nations, or<br />
simply by erecting nationalist barriers against foreign commerce and<br />
capital. There have been moments in history, however, when Latin<br />
America has reached out to the United States and the United States has<br />
responded affirmatively. We are living in one such moment.</p>
<p>The idea of a hemispheric free trade zone is not new. It can be<br />
traced back at least to Simón Bolívar (whose integrationist vision sometimes<br />
excluded but sometimes seemed to include North America), and<br />
it was discussed at the time of the founding of the Pan-American Union<br />
at the end of the nineteenth century. Presidents Ronald Reagan<br />
(1980–88) and George Bush (1988–92) made rhetorical references to the<br />
idea of a hemispheric free trade zone.</p>
<p>This vague aspiration did not become a hard policy option, however,<br />
until it was advanced with energy and persistence during the 1990s<br />
in a series of Latin American initiatives. The Latin Americans––acting out<br />
of a series of economic, political, and diplomatic motives––have sought<br />
to impose their free trade policy agenda on a very reluctant United<br />
States. The Latin Americans have worked to take advantage of the sharp<br />
divisions within the Washington bureaucracy, Congress, and public<br />
opinion to tip the balance in their favor. Just as U.S. policy has so often<br />
altered the course of history in Latin American nations by playing on<br />
internal divisions and decisively strengthening the hand of its internal<br />
allies, so now the Latin Americans have worked their will on a fragmented<br />
U.S. body politic, seeking to alter the constellation of forces at<br />
the margin that made a difference.</p>
<p>In President George W. Bush, the Latin American integrationists<br />
have a professed friend. The 43rd U.S. president inherited his preference<br />
for free trade from his Eastern Establishment family, his elite New<br />
England schooling, and his Republican Party roots. He took up the<br />
cause of free trade in the Western Hemisphere after he saw how<br />
exchange with the Mexican economy benefited the Texan economy,<br />
and he was among the first politicians to grasp that better relations with<br />
Latin America could help garner Latino votes. He learned the benefits of<br />
the North American Free Trade Agreement (NAFTA) from his business<br />
associates and Mexican friends, and when he became president his new<br />
counterparts in Latin America repeatedly told him that the reputation of<br />
the United States as a reliable friend and partner hung on his completion<br />
of the Free Trade Area of the Americas (FTAA).</p>
<p>Bush wants to please and support his friends south of the border as a gesture of generosity<br />
and reciprocity that exemplifies his unique combination of Eastern<br />
noblesse and Texas hospitality.</p>
<p>Bush’s selection to direct his trade diplomacy, Robert Zoellick, is<br />
another true believer in hemispheric integration. Indeed, the administration’s<br />
trade team is a remarkably homogeneous collection of freetrade<br />
advocates who also support regional trade initiatives. Yet even<br />
with well-placed sympathizers in the executive branch, the Latin Americans<br />
cannot be certain that their historic gamble will pay off. Only aca-<br />
demic theorists rooted in the rigid realist tradition of state security studies<br />
would be surprised to discover that trade policy does not arise from<br />
some unitary “national interest” embodied in the presidency but rather<br />
boils up from the many strands of domestic politics. And U.S. society is<br />
deeply divided on trade policy, especially with regard to developing<br />
countries. Protectionists, mercantilists, social welfare advocates, and<br />
other opponents of freer trade in general and the FTAA in particular<br />
have strong influence in the U.S. Congress, which, in turn, has a powerful<br />
voice in the making of U.S. trade policy. These forces also carry<br />
weight in the more political circles in the White House and presumably<br />
with President Bush himself, as evident recently in the joint executive legislative<br />
branch complicity to grant protection and enriched subsidies<br />
to steel and agriculture. In the summer of 2002, after a prolonged struggle,<br />
President Bush finally persuaded Congress to grant him the authority<br />
to negotiate trade agreements that Congress would not seek to<br />
amend, an authority denied to President Clinton since 1994, even as this<br />
trade promotion authority was approved by a razor-thin margin in the<br />
House of Representatives.</p>
<p>To cross the finish line––to negotiate and then ratify a free trade<br />
pact, with all that will imply for U.S.-Latin American relations––the Latin<br />
Americans will have to devise strategies with their embattled U.S. allies<br />
that build a willing coalition in U.S. public opinion and the U.S. Congress<br />
while simultaneously keeping Latin American elite support behind<br />
a vision of a more integrated Western Hemisphere. The outcome of this<br />
highly disputatious, complex, multilevel game will determine the future<br />
of U.S.-Latin American relations.</p>
<p><strong>MADE IN LATIN AMERICA</strong></p>
<p>Scholars typically attribute the emergence of regionalism in the<br />
post–Cold War period to the shifting trade strategies of the hegemonic<br />
powers, the specter of competition among great trading blocs in a more<br />
pluralistic world, or the structure and interests of domestic actors in the<br />
key industrial countries. Smaller nations are depicted as mere pawns in<br />
these struggles of the giants or, at best, as lemmings scurrying to follow<br />
the major powers. Rarely is the drive toward regional integration perceived<br />
to be a bottom-up affair, in which the smaller, developing nations<br />
are a driving force in history. In reality, by the early 1990s, structural<br />
shifts in Latin American economies and polities and in Latin Americans’<br />
interpretation of their own interests had altered the region’s traditional<br />
aversion to integration with the United States. Seizing control of their<br />
own destiny, Latin American actors have successfully engaged the<br />
United States to negotiate what could amount to a strategic alliance for<br />
the twenty-first century: the FTAA.</p>
<p>As the process of hemispheric trade integration has unfolded, the<br />
key initiatives have come from Latin America (Feinberg 1997). In 1990,<br />
it was President Carlos Salinas de Gortari, returning from Europe disillusioned<br />
with the lack of interest in Mexican markets, who proposed an<br />
FTA to President George Bush. It was the leaders of the Andean countries,<br />
at a minisummit in Cartagena, Colombia, who first urged Bush to<br />
consider a post–Cold War economic policy toward the region that<br />
yielded the Enterprise for the Americas Initiative (EAI), a forerunner of<br />
the FTAA. It was the Chileans who have pressed three successive U.S.<br />
administrations for an FTA. It was Latin Americans who proposed to the<br />
Clinton White House that the U.S. convene a post-NAFTA meeting of<br />
hemispheric leaders to diffuse the spirit of NAFTA southward, who<br />
insisted that the centerpiece of the subsequent 1994 Miami Summit be a<br />
free trade pact, and who cornered the United States into accepting a<br />
firm end date for negotiations (Feinberg 1997, 76).</p>
<p>The United States acceded to Latin American pressures only at the<br />
last moment, when the credibility of the Clinton summit hinged on the<br />
announcement of a certain date (Feinberg 1997, 77–78). Without the<br />
Miami Summit, regionwide free trade would probably have remained a<br />
vague aspiration, not a hemispheric consensus. Most recently, in early<br />
2002, it was Central America’s turn to twist the U.S. arm to open negotiations<br />
for a plurilateral FTA, as a building bloc toward Central America’s<br />
final goal, the Free Trade Area of the Americas.</p>
<p>The Latin Americans have good reasons to pursue hemispheric<br />
integration. We can group their motives in three clusters: trade and<br />
investment, macroeconomic issues, and political-strategic matters.</p>
<p><strong>Trade and Investment</strong></p>
<p>As a consequence of the 1980s debt crisis, among other factors, Latin<br />
American economies have adopted a more outward-oriented growth<br />
strategy, and regional integration models have shifted from inward-looking<br />
and protective to forms that couple domestic liberalization with an<br />
opening to global markets (“open regionalism”). By enlarging their markets,<br />
Latin Americans have hoped to attract more international investment<br />
and, with it, technology transfer––as Latin America has increasingly<br />
turned away from viewing a north-south division of the world<br />
toward perceiving its future as tied to global capital and technology<br />
flows. Entering into a free trade pact with the United States would signal<br />
to investors a more stable policy environment and a warmer and more<br />
predictable business climate.</p>
<p>Latin American trade strategists saw another, specific advantage in<br />
an FTAA: the opportunity to gain the secure access to the world’s<br />
largest and most dynamic market that they had never really extracted<br />
from negotiations in the General Agreement on Tariffs and Trade<br />
(Tussie 1998).</p>
<p><strong>Macroeconomic Reform</strong></p>
<p>In regional integration with the United States, macroeconomic reformers<br />
in Latin America perceived additional leverage in their domestic<br />
political struggles. In their constant battles against the remnants of the<br />
import substitution industrialization model, reformers saw international<br />
treaties as ways to lock in the steps already taken toward liberalization,<br />
or at least to significantly raise the costs of backsliding.</p>
<p>More concretely, international competition could overwhelm and destroy the inefficient<br />
and reactionary “national” industry sectors, as Chile and then Mexico<br />
had demonstrated. A regional free trade alliance would also focus<br />
national policy debates on the next stage of reforms required to achieve<br />
international competitiveness, including further market liberalization,<br />
effective regulation and competition policies, and modern infrastructure<br />
in, for example, telecommunications, energy, and transportation<br />
(Salazar-Xirinachs and Tavares de Araujo, 1999). As the antiglobalization<br />
backlash has gained momentum in some countries, this search for international<br />
leverage becomes all the more salient.<br />
<strong><br />
Democratic Consolidation and<br />
Strategic Bargaining</strong></p>
<p>Politically, Latin America’s democrats were looking to Washington and to<br />
interamerican institutions to bolster their position against the everpresent<br />
authoritarian tendencies in Latin American polities. During the 1990s, the<br />
effective exercise of this collective defense of democracy scored several<br />
impressive victories and gained prestige and legitimacy. The oft-repeated<br />
phrase “Democracy is the only legitimate form of government in the<br />
Americas” took on substance and probably altered the correlation of<br />
forces in several countries when democracy was in the balance.</p>
<p>As César Gaviria, secretary-general of the Organization of American<br />
States, has noted, “The FTAA was conceived from the beginning as part<br />
of a broader effort at rapproachement” (2001). Shrewd Latin American<br />
diplomats recognized that a hemispheric free trade alliance would give<br />
them leverage over the United States on a range of interdependence<br />
issues. They saw how Mexico again and again sought advantage from<br />
NAFTA to squeeze indulgences out of Washington, a strategy raised to<br />
an art form by President Vicente Fox and his wily foreign minister, Jorge<br />
Castañeda.</p>
<p>Just one example of this leverage: Fox asked Bush to overrule<br />
his advisers and join him at the Monterrey Conference on Development<br />
Finance, transforming that U.N.-sponsored meeting into a major<br />
international gathering and a huge diplomatic success for Mexico and<br />
for Fox himself.</p>
<p><strong>IN THE UNITED STATES,<br />
REACTIVE CONVERGENCE</strong>This Latin American push for hemispheric integration has not been<br />
entirely well received in the United States, as most of its diplomats, generals,<br />
and merchants have preferred to ply their trades in more powerful<br />
and prosperous parts of the world. During the 1980s and 1990s,<br />
however, the appeal of Latin America did gain ground in some circles,<br />
even as the debate on trade became increasingly polarized.</p>
<p>Economic Interests</p>
<p>Latin American economies expanded during the late 1980s and through<br />
most of the 1990s. Latin America’s weight in U.S. exports rose during the<br />
1990s, from 17 percent in 1992 to nearly 21 percent in 1998. The exposure<br />
of U.S. multinational firms, lenders, and portfolio investors<br />
expanded in many markets. U.S. direct investment in Latin America<br />
increased from $71 billion in 1990 to $172 billion in 1997, or 20 percent<br />
of U.S. overseas holdings.</p>
<p>The attractiveness of an FTAA to such market participants is evident.<br />
A fulsome FTAA could make such transactions less risky and possibly<br />
more profitable. An FTAA could lower tariff and other trade barriers,<br />
eliminate or reduce some bothersome regulations, and enhance predictability<br />
and transparency.</p>
<p>According to one calculation, an FTAA could result in the doubling<br />
of U.S. trade with Brazil in the short term alone (Schott and Hufbauer<br />
1999; Hufbauer et al. 1999). What’s more, the density of merchandise<br />
trade flows within a country is estimated to be at least ten times greater<br />
than trade flows that cross international borders, holding constant the<br />
economic size and distance between the source and destination (Schott<br />
and Hufbauer 1999). Seen from this perspective, U.S.-Brazilian<br />
trade––only $25 billion in 1997––is far below its potential.</p>
<p>An FTAA, moreover, has the additional advantage that it would<br />
grant U.S. firms preferential treatment––as against, for example, competitors<br />
from Europe and Asia. Or, as other nations sign free trade pacts<br />
with Latin American nations and as the Latin Americans themselves<br />
weave a web of intraregional trading arrangements, an FTAA would<br />
guarantee U.S. firms at least a level playing field.</p>
<p>Many U.S. traders and investors active in the region have developed<br />
business as well as personal relations with their Latin American counterparts.<br />
They interact regularly with them in various incountry business<br />
associations (for example, chambers of commerce) and in the New<br />
York–based Americas Society and its affiliate, the Council of the Americas.<br />
Sensitive to the interests of the internationalized Latin American<br />
corporate and financial sectors, these U.S. executives serve as conduits,<br />
spokespersons, and lobbyists for their regional partners. These channels<br />
are particularly influential in the Republican Party and in the editorial<br />
pages of business-oriented media. It could be argued that the flow of<br />
influence runs in the opposite direction––from U.S.-based firms to Latin<br />
America. Perhaps we can best talk of a convergence of interests, a<br />
transnational alliance among the internationalized interests in the hemisphere’s<br />
private markets.</p>
<p>Trade-bargaining Strategies</p>
<p>Pursuit of a regional free trade pact in the Western Hemisphere offers<br />
other advantages to U.S. trade negotiators. As seen from a trade-bargaining<br />
perspective, plurilateralism or regionalism can strengthen the<br />
hand of U.S. negotiators engaged in global or other regional forums.<br />
First the Canadian and then the Mexican FTAs were designed in part to<br />
encourage negotiations taking place within the GATT (Elliot and Hufbauer<br />
2002, 403). The Asia Pacific Economic Cooperation (APEC) forum<br />
served to spur the wrap-up of the Uruguay Round. Regionalism also<br />
offers opportunities to set precedents that may later be advanced in<br />
global forums––as APEC did in 1996 with its international telecommunications<br />
agreement, later accepted by the World Trade Organization.</p>
<p>Regionalism, that is, can spur globalization. The Bush administration has<br />
adopted the phrase “competitive liberalization” to describe this positive<br />
interaction among bilateral, plurilateral, and global trade forums.</p>
<p>An FTAA could be a positive spur to global freer trade. But it could<br />
also be seen as building a fallback option, should other forums fail and<br />
global trade contract. It is interesting that this component of strategic<br />
“hemispherism” crept into President Bush’s language at the third Summit<br />
of the Americas in Quebec City in April 2001. Speaking extemporaneously<br />
at the closing press conference, Bush asserted that freer trade<br />
would better enable the Western Hemisphere to compete against Asia<br />
and Europe. This seemed to put Asia and Europe on notice that Bush<br />
would be willing to contemplate turning inward toward his own hemisphere<br />
should relations elsewhere deteriorate. At the very least, Bush<br />
appears willing to play one region off against another, to the potential<br />
benefit of U.S. bargaining power and strategic interests. In a similar but<br />
more positive vein, U.S. Trade Representative Robert Zoellick told a<br />
gathering of business executives with interests in Latin America, “If the<br />
Americas are strong, the United States will be better positioned to<br />
pursue its aims around the world” (Zoellick 2001a).</p>
<p><strong>Positive Externalities</strong></p>
<p>Beyond trade and investment flows, successive U.S. administrations<br />
have recognized an interest in supporting the internationalized sectors<br />
in Latin American economies and polities. Many U.S. policymakers have<br />
understood that free trade pacts could enhance the legitimacy and leverage<br />
of those sectors, help to lock in their macroeconomic reforms, and<br />
accelerate steps toward market liberalization. The standard U.S. rhetoric<br />
linking free markets and free societies thus has taken on real form in a<br />
region struggling to consolidate democratic institutions. Furthermore, on<br />
issues from counternarcotics to counterterrorism, environmental protection<br />
to energy cooperation, free trade was widely understood as generating<br />
positive externalities or spillover effects in diplomatic negotiations.<br />
Countries linked together in an FTAA could also serve as useful allies in<br />
global forums, whether in the WTO or elsewhere.</p>
<p><strong>A FRAGMENTED NATION</strong></p>
<p>Not all U.S. citizens have been persuaded, however, by these arguments<br />
of economic interest, bargaining tactics, and issue linkage. During the<br />
1990s in the United States, opponents of the FTAA and of freer trade in<br />
general grew increasingly vociferous. Their arguments were many.</p>
<p>Trade specialists in and outside the bureaucracy favored globalism over<br />
regionalism––although as time went on, they grew more accustomed to<br />
regionalism, less frightened that it might divide the world into hostile<br />
trading blocs, more willing to see regionalism as a building block or catalyst<br />
to global freer trade. They continued to question the urgency of<br />
the FTAA project nevertheless, pointing out that most of the alleged<br />
gains from hemispheric free trade had already been realized in NAFTA<br />
and that once Canada and Mexico were excluded from the numbers, the<br />
near-term gains from more open trade with Central and South America<br />
would be modest.</p>
<p>Some critics continued to question the wisdom of entering into such<br />
a close arrangement with Latin America. In the bureaucratic and policy<br />
community, Europe, Asia, and the Middle East continued to hold sway<br />
as regions of primary importance and attention––even as George W.<br />
Bush’s extraordinary focus on the Western Hemisphere dampened, at<br />
least temporarily, those attitudes in the executive branch that disdained<br />
Latin America. As several South American countries, notably Venezuela<br />
and Argentina, experienced renewed political or economic instability<br />
between 2000 and 2002, concerns surfaced once again about the<br />
region’s reliability as a diplomatic ally and economic partner.</p>
<p>The loudest opposition to the FTAA came from the “blue-green<br />
alliance”––that de facto coalition between organized labor and many<br />
progressive nongovernmental organizations (NGOs) advocating environmental<br />
protection, human rights, poverty alleviation, and other social<br />
concerns.</p>
<p>The unionists worried that an FTAA would expose them to<br />
competition from lower-wage areas, and they sought to protect their<br />
jobs––if even at the expense of Latin American workers––by linking<br />
trade sanctions to alleged violations of workers’ rights. Most of the<br />
NGOs had other motives; they saw in trade accords one more arrow<br />
they could add to their quiver as they sought leverage over developing country<br />
social policies to internationalize good practices and to prevent<br />
a “race to the bottom” that could endanger hard-won gains in the United<br />
States.</p>
<p>Distrusting governments’ vague statements of good intentions,<br />
both the unions and the NGOs sought hard sanctions to enforce their<br />
high standards of behavior.</p>
<p>Politically, many of the blues and greens were linked to the Democratic<br />
Party; and as the ardor of their opposition to freer trade pacts<br />
grew, more and more congressional Democrats began to vote against<br />
trade legislation.5 When the renewal of fast-track negotiating authority<br />
came to a vote in 1998, it went down to defeat in the U.S. House of Representatives<br />
by 243 to 180, with over 80 percent of Democrats voting<br />
against the measure. This blue-green alliance was joined by 32 percent<br />
of the Republicans, many located in the traditionally protectionist South,<br />
to form a rejectionist majority.</p>
<p>By 2000, it appeared that the Clinton administration’s decision in<br />
1994 in favor of an FTAA had been a disequilibrium solution, in the<br />
sense that it lacked sufficient domestic political support to reach the<br />
final goal. In the run-up to the Miami Summit, the pressures from Latin<br />
Americans and the summit itself temporarily pushed U.S. decision makers<br />
off the true equilibrium path––which perhaps would have led to a<br />
gradual and incomplete expansion of NAFTA or the signing of disconnected<br />
bilateral FTAs, well short of a comprehensive FTAA. At moments<br />
when the trade debate turned essentially domestic and Latin American<br />
preferences failed to register, as in the 1999 fast-track authority vote, the<br />
oppositionists demonstrated a blocking majority.</p>
<p><strong>BUSH-ZOELLICK TEAM</strong>THE</p>
<p>The Bush trade team came charging out of the gates during its first year<br />
in office, presenting a seemingly united determination to advance trade<br />
liberalization. Zoellick made strong speeches laying out his strategic<br />
vision, pursued international negotiations on many fronts, and strove to<br />
build a winning coalition in the Congress. But in the administration’s<br />
second year, as the midterm congressional elections approached,<br />
domestic political interests increasingly impinged on trade policy, slowing<br />
international negotiations, dragging out the debates in Congress,<br />
and driving the administration itself to advocate or acquiesce to some<br />
highly visible protectionist measures.</p>
<p>Having criticized Clinton during the 2000 presidential campaign for<br />
dropping the ball on the FTAA and paying insufficient attention to Latin<br />
America, and driven by a calendar that fixed the third Summit of the<br />
Americas just three months into his term, Bush quickly jumped into<br />
Latin American relations. In his first year in office, Bush had 26 meetings<br />
with Western Hemisphere heads of state, not including his attendance<br />
at the Summit in Quebec (Feinberg and Rosenberg 2002).</p>
<p>He has given several speeches devoted to U.S.-Latin American relations, one at<br />
the OAS and another at the Inter-American Development Bank (IDB),<br />
two venues symbolizing multilateralism in hemispheric relations. Bush<br />
White House aides report that he regularly pushes them to keep working<br />
on various Latin American issues of special interest to him, including<br />
the FTAA; and his national security adviser, Condoleezza Rice, has<br />
beefed up her Latin Americanist staff to keep pace with her boss’s interest<br />
in hemispheric affairs.</p>
<p><strong>Bureaucratic Homogeneity</strong></p>
<p>Bush’s senior appointments were notable for their homogeneity in their<br />
belief in free trade. If pockets of protectionism, disdain for Latin America,<br />
or distaste for regionalism remained, they were submerged by the<br />
president’s compelling affirmations of his intention to complete the<br />
FTAA. In his appointment of Zoellick to direct the U.S. Trade Representative<br />
Office (USTR), Bush found a seasoned policymaker who, during<br />
the elder Bush’s administration, had worked on the Uruguay Round,<br />
NAFTA, and APEC, and was comfortable with the FTAA. Zoellick combines<br />
a sharp intellect with operational skills honed as an aide to James<br />
Baker III during the earlier Bush administration in the departments of<br />
Treasury and State and at the White House.</p>
<p>Among the various agencies with equities in trade, Bush has given<br />
Zoellick and the USTR the lead over U.S. policy and has blessed him with<br />
appointees in the other key agencies who have a decidedly pro–free trade<br />
bias. At the Treasury, the leading international post is in the hands of the<br />
undersecretary for international affairs, John Taylor, a Stanford University<br />
economist who served under free-marketeer John Boskin (a Stanford University<br />
colleague) on the Council of Economic Advisers during the previous<br />
Bush tenure.</p>
<p>The Treasury deputy secretary is Kenneth Dam, whose<br />
credentials include a standard text on the WTO (Dam 1970).<br />
At the State Department, Colin Powell is inclined toward freer markets,<br />
as is his chief economic officer, Under Secretary for Economic,<br />
Business, and Agricultural Affairs Alan Larson. At the Commerce Department,<br />
the views of Under Secretary for International Trade Grant<br />
Aldonas are less easy to classify, but he had experience advocating free<br />
trade policies while serving previously as special assistant to the under<br />
secretary of state for economic affairs and at USTR as director of South<br />
American and Caribbean affairs. He was also viewed as a supporter of<br />
more open markets when he was a Republican Trade Counsel on the<br />
Senate Finance Committee.</p>
<p>At the White House, the National Economic Council, under Harvard<br />
professor Lawrence Lindsey, in its role as White House coordinator of<br />
economic policies, potentially could challenge USTR for primacy. But<br />
the NEC’s international role was diluted in a Bush reorganization that<br />
stripped the NEC of a separate international staff, creating instead a<br />
small international office under a deputy, Gary Edson, that reports<br />
jointly to NEC Director Lindsey and National Security Adviser Condoleezza<br />
Rice. In any case, Edson has impressive free-trade credentials:<br />
he worked for then-deputy secretary of state Kenneth Dam during the<br />
Reagan administration and USTR Carla Hills during the first Bush administration,<br />
and he holds an MBA from the University of Chicago. Another<br />
White House player is Deputy Chief of Staff Josh Bolten, a former USTR<br />
general counsel known in Washington policy circles as a sophisticated<br />
proponent of market-opening trade policies.</p>
<p>Zoellick reportedly confers routinely with his cabinet<br />
peers––notably, Treasury Secretary Paul O’Neill, Commerce Secretary<br />
Donald Evans, and National Security Adviser Rice––who generally defer<br />
to him in his spheres of primary jurisdiction: trade policy initiation and<br />
negotiation.7 With the self-confidence of a Robert McNamara, when<br />
challenged at interagency meetings Zoellick does not hesitate to establish<br />
his superior grasp of both strategy and details. As a result of this<br />
policy homogeneity, delegated leadership, and aggressive style, Zoellick<br />
got off to a quick start during 2001.</p>
<p>The early Bush-Zoellick trade offensive benefited from a certain<br />
bureaucratic momentum that had built up behind the FTAA process after<br />
the 1994 Miami Summit. There is nothing like the repetition of boilerplate<br />
language––asserting that the FTAA favored a laundry list of U.S.<br />
interests––to persuade its drafters and articulators little by little of the<br />
virtues of that policy direction. What some once saw as an unfamiliar<br />
and dangerous innovation of trade policy become part of the everyday<br />
landscape. The same familiarization process occurred in other countries,<br />
as leaders and ministers reaffirmed their commitment to the FTAA project<br />
at meeting after meeting.</p>
<p><strong>Institutional Momentum</strong><br />
The Bush-Zoellick offensive also benefited from an institutional momentum<br />
embedded in several international forums and institutions.</p>
<p>As negotiations on the FTAA proceeded, there evolved an elaborate web of negotiating<br />
committees and working groups that networked with as many as<br />
a thousand trade officials. Through repeated interaction and learning,<br />
they gradually developed mutual knowledge and trust. As their understanding<br />
of each other’s interests became more sophisticated and realistic,<br />
some ill-founded prejudices evaporated, professional friendships<br />
developed, and a stake in a successful outcome took shape (Salazar-Xirinachs<br />
2001). These government experts became a form of epistemic community<br />
with an interest in regional cooperation and the FTAA.</p>
<p>Other sources of this institutional momentum were the premier<br />
regional institutions, the OAS and the IDB, with their built-in inclination<br />
to favor regional integration projects. These multilateral agencies became<br />
increasingly attached to the FTAA project. Their respective leaders, César<br />
Gaviria and Enrique Iglesias, spoke out repeatedly in favor of the FTAA,<br />
and their bureaucracies became the FTAA’s de facto think tank, churning<br />
out supportive databases and policy studies that tracked the direction of<br />
regional trade, monitored the various regional trade pacts, and analyzed<br />
the remaining obstacles facing the FTAA negotiators.</p>
<p>Yet another source of institutional momentum was the summit<br />
process, as set in motion by the 1994 Miami meeting and carried forward<br />
in Santiago in 1998 and Quebec City in 2001. These hemispheric summits<br />
have served as markers for negotiators and occasions for leaders to<br />
reaffirm their devotion to the FTAA vision. The Quebec Summit, coming<br />
only three months after Bush took office, compelled his administration<br />
to focus early on hemispheric trade issues and gave Bush and Zoellick<br />
occasions to voice their strong support for the regionalist option.</p>
<p>Together, these various institutional locations and procedures took on a<br />
momentum of their own, somewhat autonomous from those national<br />
leaders who had originally set the FTAA project in motion––just as institutionalists<br />
might have predicted and some FTAA opponents had feared.</p>
<p>Some Favorable Domestic Trends<br />
Certain developments in U.S. domestic politics and opinion also supported<br />
Bush’s intuitive preference for the FTAA project. In the Republican<br />
Party during the 1990s, it appeared as though the protectionist wing<br />
was growing stronger. But Bush’s own preferences and his reliance on<br />
his father’s circle of senior advisers––committed free traders––promised<br />
to pull the party leadership and congressional majority back into the<br />
free trade camp. In the trade policy community––which, until the early<br />
1990s, sided with the USTR in strongly preferring global trade liberalization<br />
as a first-best solution––regionalism became an increasingly<br />
acceptable second-best option, especially if conceived as part of a “competitive<br />
liberalization” strategy.</p>
<p>In addition, Hispanics emerged as an increasingly potent political<br />
force, courted by both Republicans and Democrats. Clinton had<br />
believed that Latinos were primarily interested in local “bread and butter<br />
issues” (other than the Cuban American community, with its passionate<br />
hatred of Fidel Castro), but Bush intuited that a subtle link existed<br />
between U.S. policy toward Latin America and Latino sentiment.</p>
<p>Most Latinos might not pay attention to the details of U.S. policies toward the<br />
hemisphere, but they were offended by the sort of verbal assaults on<br />
Latin America that seemed to evoke racist imagery, as disseminated by<br />
some anti-NAFTA voices and as repeated during the debates over trade<br />
promotion authority. By adopting a more positive tone toward Latin<br />
America and by seeking to embrace it as a worthy partner for the United<br />
States, Bush was indirectly flattering the Latinos and making them feel<br />
more welcome in their chosen land. This interpretation of Latino politics<br />
gave the FTAA a new and increasingly influential constituency in the<br />
domestic political realm.</p>
<p>Paradoxically, the business community presented a problem for the<br />
Bush team. It generally supported the Bush administration and freer<br />
trade, and business alliances such as the U.S. Trade Coalition worked on<br />
behalf of the administration’s market-opening trade initiatives. Many<br />
corporate lobbyists, however, were not placing trade at the top of their<br />
congressional agenda (Alden 2002). Some sectors, such as telecommunications<br />
and pharmaceuticals, had satisfied many of their trade goals in<br />
previous negotiations.</p>
<p>Other sectors, such as agriculture, were increasingly<br />
divided on trade policy, as some big commodity producers now<br />
feared fresh competition from cheap imports. Many corporate executives<br />
preferred to focus on tax reforms or on the current quarter’s earnings,<br />
rather than to spend political capital on potential long-term gains<br />
from trade liberalization. Nor had the executive branch been particularly<br />
adept at making tangible and visible the projected gains for U.S. business<br />
from future trade accords.</p>
<p>More generally, the current Bush administration, like previous<br />
administrations, has not engaged in a comprehensive and systematic<br />
effort to educate the U.S. public about the value to the United States of<br />
an engaged and market-opening trade policy. The critics of trade agreements<br />
and more open markets have done a much better job than the<br />
proponents, post-NAFTA, of getting their message out to the public. This<br />
has significantly complicated the congressional debate on trade.</p>
<p><strong>THE BUSH-ZOELLICK TRADE STRATEGY</strong></p>
<p>Against this complex domestic political backdrop, Zoellick has articulated<br />
a trade policy with two central characteristics: the simultaneous<br />
pursuit of trade agreements at multiple levels in ways that definitively<br />
bestow legitimacy on regional trading arrangements (RTAs); and the<br />
linkage of international trade with other social, political, and strategic<br />
objectives. (For his major speeches, see Zoellick 2001a, b, c; and his<br />
congressional testimony, Zoellick 2002.)</p>
<p>Multiple Fronts and RTAs</p>
<p>Zoellick is pursuing global, regional, and bilateral trade agreements, to<br />
be negotiated simultaneously and to be seen as potentially complementary<br />
and mutually reinforcing. In phrases that echo military tactics<br />
and international political economy, Zoellick has stated with candor, “By<br />
moving on multiple fronts, we can create a competition in liberalization<br />
that will promote open markets around the world” (Zoellick 2001b).</p>
<p>In following this multiple-front strategy, Zoellick initially accepted the<br />
hand dealt him by the outgoing Clinton administration, or as dictated by<br />
prescheduled international meetings, including FTA negotiations with<br />
Singapore and Chile, regional negotiations with APEC, the FTAA, and the<br />
renewed efforts to launch the new WTO round.</p>
<p>As far as can be determined, at the outset of the Bush administration there was no “bottom-up”<br />
review of U.S. trade policy that might have questioned these negotiations<br />
and forums in any fundamental manner. In this sense, Zoellick represents<br />
continuity in U.S. trade policy as it evolved during the 1990s. But he<br />
raised this multiple-front strategy approach to the level of trade doctrine.<br />
It is significant that RTAs are no longer anomalies to be explained<br />
away as fitting unique circumstances; rather, they have been fully<br />
accepted and incorporated into the heart of U.S. trade policy. The USTR<br />
has buried its historical dedication to globalism and the GATT-WTO as<br />
being not only the first-best but the only legitimate trade strategy. For<br />
the Bush administration, RTAs can serve a variety of purposes. As Zoellick<br />
succinctly told Congress in February 2002:</p>
<p>[Bilateral free trade agreements] can open up a new front for free<br />
trade. They can create models of success that help reformers, break<br />
new ground for liberalization in changing or emerging sectors (e.g.,<br />
biotech, high tech––including IPR-related [intellectual property<br />
rights] sectors––and services), build friendly coalitions to promote<br />
trade objectives in other contexts (e.g., biotech, SPS topics), add to<br />
America’s trade leverage globally, underpin links with other<br />
nations, and energize and expand the support for trade. Next trade<br />
agreements also present fresh opportunities to find common<br />
ground at home, and with our trading partners, on the nexus<br />
among trade, growth and improved environmental and working<br />
conditions. (Zoellick 2002)</p>
<p>Beyond pursuing the FTAs with Singapore and Chile inherited from<br />
the Clinton administration, the Bush administration has begun talks with</p>
<p>Central America and is considering FTAs with a number of other nations,<br />
including Uruguay, Australia, South Africa, Morocco, and possibly others<br />
in sub-Saharan Africa. While this more aggressive pursuit of FTAs is a<br />
new element in U.S. trade policy, Zoellick realizes that the United States<br />
is playing catch-up with the rest of the world. As the USTR lamented in<br />
its “2001 International Trade Legislative Agenda” (USTR 2001), “There are<br />
over 130 preferential trade agreements in the world today––and the<br />
United States is a party to only two of them.” As Zoellick pointedly<br />
moaned, moreover, “There are 30 free trade agreements in the Western<br />
Hemisphere; the United States belongs to only one” (Zoellick 2001c).</p>
<p><strong>Linkage Politics</strong></p>
<p>Another trend that had emerged during the Clinton administration was<br />
codified in the Bush-Zoellick trade strategy: that of linkage politics. In a<br />
retreat from USTR purism, which held that trade policy should be kept<br />
neat and separate from other policy issues, Zoellick accepts that trade<br />
cannot be quarantined from other variables in the international political<br />
economy. “We need to align the global trading system with our values,”<br />
he says (2001b).</p>
<p>Specifically, Zoellick has argued that trade policy can show respect<br />
for core labor standards and environmental protectionism, as well as<br />
democracy and the rule of law––but “without slipping into fear-based<br />
campaigns and protectionism” (2001b). In an effort to move away from<br />
a sanctions-based approach to advancing trade-related issues, Zoellick<br />
has proposed a “toolkit” of measures to advance trade and environmental<br />
objectives.</p>
<p>These measures consist primarily of technical and financial<br />
assistance that can be extended to the relevant ministries in developing<br />
countries by bilateral and international agencies, such as the U.S. Agency<br />
for International Development, the World Bank, the International Labor<br />
Organization, and various United Nations agencies (USTR 2002b). Zoellick<br />
has also floated the idea of applying monetary fines against violators<br />
of agreed-on standards as an alternative to trade sanctions.</p>
<p>To persuade Congress to grant it authority to negotiate trade<br />
accords and then present them for an up or down vote with no amendments<br />
and limited debate time (formerly known as fast-track authority,<br />
renamed trade promotion authority, or TPA, by the Bush administration),<br />
the executives agreed to accept as overall negotiating objectives<br />
the following: to promote respect for worker rights consistent with the<br />
core labor standards of the International Labor Organization (ILO), to<br />
ensure that trade and the environment are mutually supportive, and to<br />
seek to protect the environment.</p>
<p>This TPA legislation also bound the administration to seek appropriate<br />
penalties in a given violation of these requirements (while not adversely<br />
affecting interests not party to the dispute). It remains to be seen exactly<br />
how far the Bush administration will attempt to push these issues in<br />
international negotiations, whether inthe WTO round, the FTAA, or<br />
bilateral accords, and whether it can find compromises that satisfy the<br />
more accommodationist elements in the blue-green coalition without<br />
alienating Republican free traders and foreign governments.</p>
<p><strong>Trade and Democracy</strong></p>
<p>The Bush administration has also accepted a linkage between trade and<br />
democracy. At the Quebec Summit, Bush and the other 33 hemispheric<br />
leaders adopted a democracy clause that “establishes that any unconstitutional<br />
alteration or interruption of the democratic order in a state of<br />
the Hemisphere constitutes an insurmountable obstacle to the participation<br />
of that state’s government in the Summits of the Americas<br />
process” (OAS 2001).</p>
<p>It was understood that the phrase “Summits of the<br />
Americas process” encompasses the FTAA. That is, the FTAA became a<br />
tool of international political economy, a potential trade sanction to<br />
deter would-be authoritarians and to punish those adventurous enough<br />
to violate democratic norms.</p>
<p>This linking of potential trade sanctions to democracy puts teeth<br />
into the hemisphere’s collective defense of democracy. Secretary of<br />
State Colin Powell traveled to Lima (by coincidence on September 11,<br />
2001) to join his fellow foreign ministers in signing the Inter-American<br />
Democracy Charter, which codified the Quebec democracy clause into<br />
hemispheric dogma. The Democracy Charter defines the “essential elements”<br />
of democracy and records the procedures by which a member<br />
state could be suspended from the OAS, although it does not detail procedures<br />
for suspension from the prospective FTAA.</p>
<p>Of course, the idea of inserting a democracy clause in a trade agreement<br />
hardly originated in Washington. The European Union requires<br />
members to be democracies respectful of human rights. In 1996, the<br />
MERCOSUR countries (Brazil, Argentina, Uruguay, and Paraguay)<br />
agreed that “the full effectiveness of democratic institutions is an essential<br />
condition for cooperation in the framework of the Treaty of Asunción”<br />
(MERCOSUR 1996). Led by Brazil, MERCOSUR threatened to<br />
invoke the agreement’s democracy clause to quell political instability in<br />
Paraguay. Brazilian President Fernando Henrique Cardoso warned the<br />
Paraguayan authorities that the interruption of the constitutional order<br />
would result in Paraguay’s expulsion from MERCOSUR (Feinberg and<br />
Bates 2001). This pressure was a key factor in the resignation of then president<br />
Raúl Cubas.</p>
<p>When his domestic opponents sought to oust Venezuelan President<br />
Hugo Chávez in April 2002, many parties to the drama, both foreign and<br />
domestic, sought to justify their actions with reference to the newly<br />
minted Inter-American Democracy Charter. By chance, a meeting of<br />
FTAA negotiators was scheduled to begin shortly on the Venezuelan<br />
island of Margarita, and a movement began among governments to boycott<br />
the gathering, in effect to sanction Venezuela for its interruption of<br />
democratic procedures.</p>
<p>The quick restoration of Chávez, however,avoided this test.<br />
For its part, the Bush State Department badly bungled<br />
by publicly endorsing what quickly proved to be a short-lived and<br />
unconstitutional change of government, even as it sought to justify its<br />
ill-considered posture based on its reading of the principles of the Inter-<br />
American Charter (U.S. Department of State 2002).</p>
<p><strong>Responding to Latin American Pressures</strong></p>
<p>To keep the trade ball rolling forward in the Western Hemisphere, the<br />
Bush administration has continued the bilateral FTA negotiations with<br />
Chile initiated by the Clinton administration and has acquiesced to Central<br />
American pressures to launch FTA negotiations, while engaging in<br />
the ongoing FTAA negotiations as best it could until Congress finally<br />
granted trade-negotiating authority in August 2002.</p>
<p>The administration also sought and won congressional renewal of the Andean<br />
Trade Preference Act (ATPA), which gives the Andean nations duty-free treatment<br />
for certain exports, and of the Generalized System of Preferences (GSP),<br />
which provides duty-free benefits to the vast range of developing countries<br />
and to those in South America that do not benefit from the ATPA<br />
or CBI programs.</p>
<p>In a speech at the OAS on January 16, 2002, President Bush<br />
announced that the United States would explore a free trade agreement<br />
with the five countries of Central America. In explaining the timing of<br />
the announcement, a senior USTR official said, “First of all, we were<br />
responding to continual expressions of interest by Central America”<br />
(USTR 2002c), most recently reaffirmed to Bush administration officials<br />
at the Quebec Summit. Once again, the initiative came from Latin America.<br />
Sounding like an official from the Treasury Department, the USTR<br />
official added, “Secondly, following a recent cycle of elections, there are<br />
new governments in Central America particularly interested in reform<br />
and we want to enhance that” (USTR 2002c).</p>
<p>Opening a new trade front also fit the administration’s “competition<br />
in liberalization” strategy, as well as increasing the momentum in the<br />
hemisphere toward completing the FTAA by 2005 (USTR 2002a). It<br />
could serve to line up the Central American parties behind U.S. positions<br />
in the FTAA talks. The announcement was timed to offset another<br />
move: in order to garner votes in the House of Representatives for the<br />
fast-track decision in December, the administration had rescinded some<br />
concessions made in the enhanced Caribbean Basin Initiative (CBI), to<br />
the distress of the Central Americans.</p>
<p>The President’s speech, scheduled partly to dispel allegations<br />
that Bush had lost sight of Latin America in<br />
the wake of the terrorist incidents of September 11, served to focus<br />
senior-level attention and to provide a deadline for a decision.<br />
In mid-February 2002, Uruguayan president Jorge Batlle visited<br />
Washington and appeared to be following up on his earlier public<br />
expressions of interest in an FTA with the United States. If so, he was<br />
disappointed, as the Bush administration did not want to appear to be<br />
dismembering MERCOSUR at a time when it was seeking Brazil’s cooperation<br />
in both global and regional trade negotiations. Instead, the Bush<br />
administration agreed with Batlle to establish a Joint Commission on<br />
Trade and Investment. The United States held out hope for Uruguay by<br />
noting that it had created a similar mechanism with Chile in 1998 (USTR<br />
2002b). The U.S.-Uruguay bilateral talks will contemplate the compatibility<br />
of an FTA with MERCOSUR’s common external tariff; but one<br />
senior U.S. official admitted, “It will be hard to square that circle.”</p>
<p><strong>CONGRESSIONAL WARS</strong></p>
<p>Despite certain of the aforementioned favorable domestic political<br />
trends and its own greater homogeneity and commitment, the Bush<br />
administration still has had to struggle mightily to wrest trade promotion<br />
and fast-track authority from the U.S. Congress. To win the first test in<br />
the House, the administration had to promise temporary import relief<br />
for steel manufacturers, offer guarantees to the citrus and textile industries,<br />
and claw back some of the trade benefits granted in 2000 to CBI<br />
countries and to sub-Saharan Africa (Alden 2001). Even with the<br />
enhanced weight of the post-9/11 presidency, the administration triumphed<br />
by a single vote, 215–214, in a largely partisan tally at the end<br />
of the 2001 congressional session. Democrats voted against, by 189–23;<br />
Republicans voted in favor by 194–21 (New York Times 2001). Still, this<br />
outcome was a marked swing from the 1998 tally, which the Clinton<br />
administration lost by 63 votes.</p>
<p>The administration won the next grueling battle for TPA in the<br />
Senate by a more comfortable margin (66–30), but only after the<br />
Democrats extracted major concessions in trade adjustment assistance<br />
(TAA). In legislation passed in late May, the Senate sharply expanded<br />
the existing TAA program and set new precedents for coverage of tradedisplaced<br />
workers (U.S. Senate 2002; Chen and King 2002). Income benefits<br />
would be extended by six months, and funds available for training<br />
programs would jump to $300 million. For the first time, displaced<br />
workers would receive a tax credit for purchasing qualified group health<br />
insurance coverage. TAA coverage would be extended to an estimated<br />
60,000 workers, including some secondary workers who supply importimpacted<br />
plants. Under a pilot program, older workers would be eligible<br />
for wage insurance; workers over 50 who took lower-paying jobs<br />
could receive, for two years, as much as $5,000 a year in wage insurance.<br />
Another innovative program would help communities develop<br />
strategic plans following job losses, providing such communities with<br />
technical assistance, loans, and grants.</p>
<p>The overall cost of the proposed TAA was estimated at $12 billion<br />
over ten years, or about triple the financing levels of the existing assistance<br />
programs (Mitchell 2002). Perhaps this better balancing of trade<br />
liberalization with worker protection would contribute, if not immediately<br />
than in the long run, to recreating a bipartisan majority behind<br />
freer and fairer trade.</p>
<p>After some intense partisan jockeying and personal squabbles, just<br />
before midnight on July 25, a joint House-Senate conference committee<br />
reached agreement on the Trade Act of 2002 that contained the key<br />
accords that each chamber had been approved separately. The following<br />
day, Bush made a rare visit to Capitol Hill to personally lobby House<br />
Republicans to rally behind the measure.</p>
<p>In the early morning hours of July 27, the House passed TPA by 215–212<br />
(with 190 Republicans and 25 Democrats in favor, 27 Republicans<br />
and 183 Democrats plus 2 independents in opposition).<br />
President Bush joked that the margin of victory had<br />
tripled since the December vote (from 1 vote to three). In its last vote<br />
before leaving Washington for an August recess, the Senate passed the bill<br />
by a secure margin of 64–34, with 20 Democrats joining 43 Republicans<br />
and 1 independent to give the President trade promotion authority for five<br />
years, with renewal possible for an additional two years.</p>
<p>In the White House signing ceremony on August 6, Bush acknowledged<br />
the role that Latin America had played in the internal U.S. political<br />
struggle. He thanked the diplomats present in the East Room,<br />
addressing by name the ambassadors from Ecuador and Peru.<br />
I appreciate your hard work on sending the message of trade to<br />
members of our Congress. I want to thank you for your diligence,<br />
and I want to thank your Presidents for their care and concern about<br />
this incredibly important initiative. . . . I want to thank the ambassadors<br />
for their role in getting this bill passed. . . . (Bush 2002)</p>
<p>It was an extraordinarily candid recognition by the President of the<br />
United States of the transnational political alliance at work among the<br />
integrationists throughout the hemisphere.</p>
<p>While the passage of TPA was a major legislative victory, during<br />
2002 the free-trade credentials of the Bush administration were tarnished<br />
by the rush of domestic politics. White House political advisers,<br />
focused on gaining Republican majorities in both houses of Congress in<br />
the midterm elections, responded to political pressures that might swing<br />
key congressional contests and repeatedly trumped economic officials<br />
concerned with market efficiency or fiscal soundness. The administration<br />
itself initiated protectionist measures for the steel industry in the<br />
hope of bolstering the prospects of Republicans in steel-producing districts,<br />
even as Zoellick argued that this was the price for critical congressional<br />
votes for TPA.</p>
<p>More egregiously, Congress passed a farm bill that sharply<br />
increased agricultural subsidies, embarrassing U.S. trade officials who<br />
for years had been chastising the Europeans and Japanese for protecting<br />
their domestic farmers at the expense of global efficiency and developing-<br />
country producers. Some administration spokespersons, including<br />
those at the Department of Agriculture, initially opposed this<br />
trade-distorting measure; but Bush signed the bill on May 13, 2002, fearing<br />
that a veto could compromise Republican chances in agricultural<br />
districts in the November elections, and even his own reelection.</p>
<p>The Latin Americans were alarmed at the many compromises the<br />
Bush team was making with domestic constituencies––whether for the<br />
noble objective of gaining a congressional majority behind TPA or for<br />
partisan advantage––in agriculture, textiles, citrus, and steel, among other<br />
products of great interest to regional producers. Bush trade officials<br />
sought to persuade their foreign counterparts that trade negotiations<br />
would be the best way to cajole Congress to rescind these measures and<br />
to forestall new protectionisms, but foreigners wondered whether U.S.<br />
trade negotiators would have sufficient political power to negotiate balanced<br />
agreements that constrained their own trade restrictions and also<br />
made reciprocal concessions to foreign producer interests.</p>
<p><strong>THE ENDGAME</strong></p>
<p>The TPA vote was a high––perhaps the highest––domestic hurdle facing<br />
U.S. proponents of the FTAA. During the prospective FTAA bargaining sessions,<br />
U.S. trade negotiators will have to consult closely with a multiplicity<br />
of interest groups, NGOs, and members of Congress; but once an agreement<br />
is signed, it will be difficult for the U.S. Congress to vote it down. If<br />
Jimmy Carter could persuade two-thirds of the U.S. Senate to pass the<br />
Panama Canal Treaties, with all of their negative symbolism, George W.<br />
Bush or his successor in about 2005 should be able to garner the necessary<br />
simple majorities behind the positive imagery of the world’s largest<br />
free-trade pact, stretching from Alaska to Argentina. In addition to all the<br />
economic self-interest arguments, the Executive Branch will play its trump<br />
card––the security rationale––with powerful effect as it paints the FTAA as<br />
the centerpiece of U.S. strategic policies toward Latin America.</p>
<p>In this final push, the U.S. President will be accompanied on Capitol<br />
Hill by as many as 33 democratically elected leaders, underscoring how<br />
critical the FTAA is to maintaining economic liberalization and democratic<br />
reforms in their countries, how it is precisely in times of turbulence that<br />
the region needs the stabilizing anchor of a major trade accord (Feinberg<br />
2002). They will also explain how they would prefer to cooperate with the<br />
United States on a host of important issues, from antiterrorism to environmental<br />
protection, and how they look forward to celebrating the Western<br />
Hemisphere’s new economic-security alliance for the twenty-first century––<br />
an alliance that will serve as a strong and stable platform from which<br />
the United States will be able to project its power around the world.<br />
During its first two years in office, and especially in 2002, the Bush<br />
administration’s trade policy has been heavily preoccupied with<br />
domestic politics.</p>
<p>Now that it has extracted trade promotion authority<br />
from Congress, it will have to concentrate on negotiating with Latin<br />
America. These negotiations, which will encompass a broad agenda of<br />
trade and trade-related matters, will be arduous. The administration will<br />
have to balance many pressures as it seeks middle ground on a range<br />
of contentious issues in order to satisfy U.S. constituencies without<br />
alienating Latin America. It will have to factor in the simultaneous WTO<br />
Millennium Round talks, also scheduled to finish in 2005. Global talks<br />
may take some pressure off the FTAA by tackling such contentious<br />
issues as agricultural export subsidies and antidumping, but FTAA<br />
negotiators may withhold concessions pending 11th-hour bargaining in<br />
the global round.</p>
<p>As the largest South American country, and with the most astute<br />
negotiators, Brazil will play a leading role in the FTAA endgame, which<br />
Brazil and the United States will cochair.10 It is not surprising that the<br />
two nations with the largest domestic markets and with globally diversified<br />
trading patterns face the greatest protectionist pressures and have<br />
been the most ambivalent about regional integration. The 2002 U.S.<br />
farm bill, which boosted subsidies in several product areas of interest to<br />
Brazil, caused Brazil to question whether the U.S. political system could<br />
deliver on freer trade, but also gave Brazil additional incentives to enter<br />
into a regional trade pact, the rules and understandings of which might<br />
roll back such protectionism and make it less likely in the future. As a<br />
tough bargainer, Brazil can be expected to resist certain measures, such<br />
as trade sanctions tied to social behavior, that Latin Americans fear<br />
would open the door to a new protectionism.</p>
<p>Should political forces that have been vociferously critical of free<br />
trade gain the presidency in Brazil, its negotiators will be even tougher<br />
in their defense of particular national economic interests. But in the end,<br />
it is likely that Brazil will not want to be excluded from a hemispheric<br />
accord or to risk jeopardizing its cherished leadership role by abandoning<br />
at the final moment the quest that Latin America has pursued with<br />
such purposefulness for so long.<br />
As the demandeurs and with so much to gain, Latin American negotiators<br />
will have to be realistic about the ultimate asymmetries of power.<br />
As Mexico did in negotiating the NAFTA, they will have to accede to<br />
many U.S. pressures, including those designed to shield politically sensitive<br />
sectors from competitive imports. Such a flawed Free Trade Area<br />
of the Americas should be understood as an ongoing project and as a<br />
historic accomplishment for Latin American diplomacy. There will be<br />
time enough in the future to revisit the agreement’s many imperfections.</p>
<p><strong>NOTES</strong><br />
In preparing this paper, the author benefited from informal conversations<br />
and interviews with many officials in the U.S. government, Latin American governments,<br />
and international institutions, who preferred to remain anonymous.<br />
For their valuable comments on earlier drafts, the author wishes to thank Jorge<br />
Domínguez, Gary Hufbauer, Jeffrey Schott, Jonathan Hueneman, and Kati<br />
Suominen. Elizabeth Erwin provided able editorial assistance. Portions of this<br />
article draw on a paper presented at the conference “Competing Regionalisms<br />
in the Americas,” organized by Oxford University and El Colegio de México,<br />
Mexico City, March 14, 2002.<br />
1. The Central Americans had been pressing the United States for an FTA<br />
at least since 1997, as noted in Salazar-Xirinachs 2002. For an early example of<br />
the intellectual case, presented at a Washington, DC, conference in 1995 by two<br />
leading Central American economists, see Lizano and Salazar-Xirinachs 1997.<br />
2. For overviews, see Salazar-Xirinachs and Tavares de Araujo 1999;<br />
Tussie 1998; Bernal 1996; Devlin and Ffrench-Davis 1999. See also the essays in<br />
Jatar and Weintraub 1997; and Burki et al. 1998.<br />
3. The Chileans, for example, sought an FTA with the United States to<br />
enhance their nation’s worldwide image. See Butelmann and Meller 1995.<br />
4. For a succinct survey of the views of greens and blues, see Roett 2001;<br />
see also Korzeniewicz and Smith 2001.<br />
5. For a detailed political analysis, see Baldwin and Magee 2000, especially<br />
table 1, p. 7.<br />
6. For Zoellick’s thoughts before taking office, see Zoellick 1999–2000, 2000.<br />
7. The White House and other agencies would play more central roles in<br />
the domestic political task of guiding trade legislation through Congress, where<br />
Zoellick’s strong personality sometimes ruffled feathers. In the March 2002 decision<br />
on protection for the steel industry, with its important implications for<br />
domestic politics and the November 2002 congressional elections, the president’s<br />
chief political adviser, Karl Rove, reportedly advocated stiff tariffs to protect<br />
steelmaking jobs. Predictably, Zoellick sought a compromise that balanced<br />
free-trade precepts with such political realities (Kahn and Sanger 2002).<br />
8. For a discussion of the role of the state and institutions in advancing<br />
trade policy, see Hurrell 2001.<br />
9. There were, however, some purist holdouts. See Bhagwati 2002.<br />
10. On the dangers of Brazil’s overestimating its bargaining power, see<br />
Mackay 2002, 15.</p>
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Feinberg, Richard E., and Robin Rosenberg, 2002. Bush Getting It Right in Latin<br />
America So Far. Miami Herald. January 27.<br />
Gaviria, César. 2001. Integration and Interdependence in the Americas. In<br />
Toward Free Trade in the Americas, ed. José M. Salazar-Xirinachs and<br />
Maryse Robert. Washington, DC: Organization of American States/Inter-<br />
American Dialogue. 303–15.<br />
Hufbauer, Gary C., Jeffrey Schott, and Barbara Kotschwar. 1999. U.S. Interests in<br />
Free Trade in the Americas. In The United States and the Americas: A<br />
Twenty-first Century View, ed. Albert Fishlow and James Jones. New York:<br />
W. W. Norton/American Assembly. 58–78.<br />
Hurrell, Andrew. 2001. The Politics of Regional Integration in MERCOSUR. In<br />
Bulmer-Thomas 2001. 194–211.<br />
Jatar, Ana Julia, and Sidney Weintraub, eds. 1997. Integrating the Hemisphere:<br />
Perspectives from Latin America and the Caribbean. Washington, DC: Inter-<br />
American Dialogue.<br />
Kahn, Joseph, and David Sanger. 2002. Bush Officials Meet to Seek a Compromise<br />
on Steel Tariffs. New York Times, March 1: C1.<br />
Korzeniewicz, Roberto Patricio, and William C. Smith. 2001. Protest and Collaboration:<br />
Transnational Civil Society Networks and the Politics of Summitry<br />
and Free Trade in the Americas. Agenda Paper 51. Coral Gables: North-<br />
South Center. September.<br />
Lizano, Eduardo, and José M. Salazar-Xirinachs. The Central American Common<br />
Market and Hemispheric Free Trade. In Jatar and Weintraub 1997. 111–35.<br />
Mackay, Donald. 2002. Challenges Confronting the Free Trade Area of the Americas.<br />
Policy Paper FPP-02-7. Ottawa: Canadian Foundation for the Americas.<br />
MERCOSUR. 1996. Presidential Declaration on the Democratic Commitment in<br />
MERCOSUR. June.<br />
Mitchell, Alison. 2002. White House and Senate in Trade Accord. New York<br />
Times, May 10: A24.<br />
New York Times. 2001. December 7: A20.<br />
Office of the United States Trade Representative (USTR). 2001. International<br />
Trade Legislative Agenda. Washington, DC: USTR.<br />
––––. 2002a. Proposed U.S.-Central America Free Trade Agreement. Fact sheet.<br />
Washington, DC: USTR.<br />
––––. 2002b. Statement Regarding Recent Meeting Between USTR Robert Zoellick<br />
and the President of Uruguay, Jorge Batlle. February 15.<br />
––––. 2002c. Senior USTR official. Author telephone interview. March.<br />
Organization of American States (OAS). General Assembly. 2001. Inter-American<br />
Democracy Charter. September 11. Washington, DC: OAS.<br />
Roett, Riordan. 2001. U.S. Policy Toward Western Hemisphere Integration. In<br />
Bulmer-Thomas 2001. 212–33.<br />
Salazar-Xirinachs, José M. 2002. A Free Trade Agreement Between the United<br />
States and Central America: Background and Prospects. Remarks made at<br />
the conference “Negotiating Free Trade in the Hemisphere,” sponsored by<br />
the Inter-American Dialogue. Washington, DC, February 20.<br />
Salazar-Xirinachs, José M., and José Tavares de Araujo, Jr. 1999. The Free Trade<br />
Area of the Americas: A Latin American Perspective. World Economy 22, 6<br />
(August): 783–98.<br />
Schott, Jeffrey, and Gary C. Hufbauer. 1999. Whither the Free Trade Area of the<br />
Americas? World Economy 22, 6 (August): 778–82.<br />
150 LATIN AMERICAN POLITICS AND SOCIETY 44: 4<br />
Tussie, Diana. 1998. Globalization and World Trade: From Multilateralism to<br />
Regionalism. Oxford Development Studies 26, l.<br />
U.S. Department of State. 2002. Venezuela: Change of Government. Press<br />
release. April 12.<br />
U.S. Senate. Finance Committee. 2002. Fact Sheet on Baucus-Grassley fast track<br />
legislation. May 10.<br />
Zoellick, Robert. 1999–2000. Congress and the Making of U.S. Foreign Policy.<br />
Survival 41, 4 (Winter).<br />
––––. 2000. Campaign 2000: A Republican Foreign Policy. Foreign Affairs 79, 1<br />
(Spring).<br />
––––. 2001a. Free Trade and Hemispheric Hope. Remarks Before the Council of<br />
the Americas. Washington, DC, May 7.<br />
––––. 2001b. The United States, Europe, and the World Trading System. Remarks<br />
before The Kangaroo Group. Strasbourg, May 15.<br />
––––. 2001c. American Trade Leadership: What Is at Stake. Remarks Before the<br />
Institute for International Economics. Washington, DC, September 24.<br />
––––. 2002. Statement Before the Committee on Finance, U.S. Senate. February<br />
6.</p>
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		<title>Joe Parry says: In Intelligence Infographics &#8221; a good sketch is better than a long speech&#8221;.</title>
		<link>http://www.theamericaspostes.com/3798/joe-parry-says-in-intelligence-infographics-a-good-sketch-is-better-than-a-long-speech/</link>
		<comments>http://www.theamericaspostes.com/3798/joe-parry-says-in-intelligence-infographics-a-good-sketch-is-better-than-a-long-speech/#comments</comments>
		<pubDate>Sat, 24 Sep 2011 10:11:44 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
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		<description><![CDATA[How should we communicate the results of our analysis to decision-makers? How to visualize key information for decision makers?  The talk of Joe Parry argued that visualisations and infographics play a very important role, not only for analytical processes of data analysts, but also for explaining the analytical results to decision-makers at the highest of [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_3801" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2011/09/Joe-Parry-from-Cambridge-Intelligence-and-Victor-Bjorgan-CEO-og-Global-Security-Services-LLC-during-the-EISIC-2011-Conference..jpg"><img class="size-medium wp-image-3801" title="Security: Joe Parry from Cambridge Intelligence and Victor Bjorgan, CEO og Global Security Services LLC and Publisher of TheAmericasPost.com, during the EISIC 2011 Conference." src="http://www.theamericaspostes.com/wp-content/uploads/2011/09/Joe-Parry-from-Cambridge-Intelligence-and-Victor-Bjorgan-CEO-og-Global-Security-Services-LLC-during-the-EISIC-2011-Conference.-300x176.jpg" alt="" width="300" height="176" /></a><p class="wp-caption-text">Security: Joe Parry from Cambridge Intelligence and Victor Bjorgan, CEO og Global Security Services LLC and Publisher of TheAmericasPost.com, during the EISIC 2011 Conference.</p></div>
<p>How should we communicate the results of our analysis to decision-makers? How to visualize key information for decision makers?  The talk of Joe Parry argued that visualisations and infographics play a very important role, not only for analytical processes of data analysts, but also for explaining the analytical results to decision-makers at the highest of levels.</p>
<p>Some care must be taken to avoid various common pitfalls when designing such visuals: the talk will cover bad examples as well as good in order to uncover design guidelines and practical advice for those wishing to pursue a more visual approach.</p>
<p>in that sense, Joe Parry initiated his keynote that these visualization tools are specially important for law enforcement and National Security. In that sense he recalled an old saying &#8221; a good sketch is better than a long speech&#8221;.</p>
<p>What is visualitation? asked Parry to the conferencists. Well , a good visualitation does not consist in just good pictures.</p>
<p>About graphs and numbers, Parry said that the use of interactive data and vissualy representation of data is used to amplify cognition. Parry briefly described how a good visualization helps in the Intel analysis. Described some common pitfalls in data visualization. he recommend the reading of the book written by Westell, Duncan and Weeks on visualization in Intel analyIntel analysissis.</p>
<p>Parry also described a model of intelligence analyzing processes. The final product of such analysis is a report.</p>
<p>In the reports, he does not recommend the use of colours to describe the visual variables. He recommend the use of Kinley Visual variables.</p>
<p>To start the visualization of the Analysis in its low phase, Parry mentioned the visualization of pattern of life, but said this is a analytical tool, not a decission tool. He also said that this analysis must be simplified, not use numbers.</p>
<p>During his keynote speech, Joe Parry showed a photograph of former President George Bush looking a wall full of sketches with the networks and interrelations of all individual terrorists participants in the attacks of September 9/11. It was a very confusing material, President Bush was looking at in this photo. Not the best intelligence visualization report.  President Bush was not looking in the photo at a screen with the three followings ingredients that are important in any report: geovisualization, timeline visualization and network visualization.</p>
<p>Parry said, that if only using that photo, it was impossible for former President Bush tot ake any intelligence decission, because there is no report, but only information.</p>
<p>In things not to do, Joe Parry also told participants that it is no good to put pies in the reports, neither 3D presentations. The intelligence reports for decission makers must avoid the use of colour since there is often a color blind among the public. He recommends not to use labels, simplicity instead  of complexity, no use of black backgrounds, no use of pie charts, not use of misleading scales.</p>
<p>Joe Parry showed enthusiasm while talking about the infographics reports of the New York Times NYT. He said the NYT infographics show all needed info to understand what is happening.</p>
<p>Summarizing, Parry described some of what he called as &#8221; design guidelines&#8221; for any Intel report, like  first an overview, always  aim for clarity, show context, show how the situation has changed, make comparison easy, all visual elements must be backed by data, and that the report should be so clear and convincing that the decission makers will act backed by the report.</p>
<p>To know more about the subject, Joe Parry from Cambridge Intelligence mentioned some sites that can be of interest like:</p>
<p><a href="http://flowingdata.com/"><strong>flowingdata.com</strong></a></p>
<p><a href="http://infosthetics.com/"><strong>infosthetics.com</strong></a> (slow uploading, be patient..)</p>
<p><a href="http://www.visualcomplexity.com/vc/"><strong>visualcomplexity.com</strong></a></p>
<p><a href="http://twitter.com/#!/nytgraphics"><strong>nytgraphics.com</strong></a></p>
<p><a href="http://stamen.com/"><strong>stamen.com</strong></a></p>
<p><a href="http://www.improving-visualisation.org/"><strong>visualization.org</strong></a></p>
<p>Finally, Joe Parry ended his keynote speech saying to all participants: &#8220;Back Up everything you say!&#8221;</p>
<p>Joe Parry´s email address is joe@cambridge-intelligence.com</p>
<p>His twitter is @parry_joe</p>
<p>Phone nr: 07973 787 233</p>
<p>His address is:</p>
<p>idea Space</p>
<p>The Entrepreneurship Centre</p>
<p>3 Charles Babbage Road</p>
<p>Cambridge CB 3 OGT</p>
<p>Biographical Details:<br />
Joe Parry has worked on visualization and graphics systems for intelligence work for the last thirteen years.<br />
During that time he has done software development, design, systems architecture and more experimental<br />
research projects. He has worked with the intelligence communities of the UK, US and other countries. His<br />
recent professional interests include social network analysis and web-based visualisation systems. This year he started his own software company which is producing what he hopes will be part of a new wave of<br />
investigation software.</p>
]]></content:encoded>
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		<title>EISIC, Dr. Nasrullah Memon and Computational Criminology: Early Warning Informatics System could predict Terrorist Threats</title>
		<link>http://www.theamericaspostes.com/3794/eisic-dr-nasrullah-memon-and-computational-criminology-early-warning-informatics-system-could-predict-terrorist-threats/</link>
		<comments>http://www.theamericaspostes.com/3794/eisic-dr-nasrullah-memon-and-computational-criminology-early-warning-informatics-system-could-predict-terrorist-threats/#comments</comments>
		<pubDate>Thu, 22 Sep 2011 21:19:23 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
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		<guid isPermaLink="false">http://www.theamericaspostes.com/?p=3794</guid>
		<description><![CDATA[On the occasion of the EISIC (September 12-14,2011) held in Athens, we interviewed the Program Chair of the event Dr. Nasrullah Memon, professor at the University of Southern Denmark and member of the Steering Committee of the European Intelligence &#38; Security Informatics Conference EISIC on Counterterrorism and Criminology. The event was organized jointly with The [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_3795" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2011/09/DSC05226.jpg"><img class="size-medium wp-image-3795" title="Dr.Nasrullah Memon, Program Chair of EISIC 2011 in exclusive interview with TheAmericasPost.com" src="http://www.theamericaspostes.com/wp-content/uploads/2011/09/DSC05226-300x200.jpg" alt="" width="300" height="200" /></a><p class="wp-caption-text">Dr.Nasrullah Memon, Program Chair of EISIC 2011 in exclusive interview with TheAmericasPost.com</p></div>
<p>On the occasion of the EISIC (September 12-14,2011) held in Athens, we interviewed the Program Chair of the event Dr. Nasrullah Memon, professor at the University of Southern Denmark and member of the Steering Committee of the European Intelligence &amp; Security Informatics Conference EISIC on Counterterrorism and Criminology. The event was organized jointly with The International Symposium on Open Source Intelligence and Web Mining (OSINT-WM 2011), and having as Academic Sponsors The University of Arizona, University of Southern Denmark,  and The Hellenic American University, as technical co-sponsor the IEEE Computer Society, as local organizer the Hellenic American University and SPRINGER as the Industry Sponsor.</p>
<p>During the interview, Dr. Memon gave details about EISIC (past, present and future plans) , how the Intelligence Community can take advantage of the Intelligence &amp; Security Informatics discipline, the relationship between counterterrorism and computational criminology, the role of Higher Education, as well as differents kinds of Crime involving information and communication technologies. The privacy problem was also analized during the conversation.</p>
<p>Here is an excerpt of a very interesting interview with one of the world´s most brilliant minds in Defense, Counterterrorism and Security Informatics, Dr. Nasrullah Memon.</p>
<p><strong>Dear Dr. Memon, could you please tell us what is EISIC 2011? Please elaborate on its creation, mission, objectives and members.</strong></p>
<p>&nbsp;</p>
<p>EISIC is the European chapter of Intelligence and Security Informatics (ISI) series of conferences. The conference is dynamic and allows academicians, researchers and practitioners to keep abreast of new tools and methodologies in the area of Intelligence Security and Informatics. It is also a venue that fosters networking opportunities for people working in this scientific area.</p>
<p>After my graduation (PhD Defense), Professor Hsinchun Chen, Director, Artificial Lab, University of Arizona, the founder of ISI series of conferences encouraged us to organize the European chapter of ISI. In this context, the first event was organized under the name of EUROISI 2008 at Esbjerg, Denmark.  That event was organized more as a workshop than a conference and it was not very well attended.</p>
<p>In September 2010, while Professor Hsinchun Chen visited University of Southern Denmark, we discussed how to re-organize the European chapter of ISI as an annual event in order to create a consortium involving academic researchers in information technologies, computer science, public policy, criminology, and social and behavior studies as well as local, national, and European law enforcement and intelligence experts, and information technology industry consultants and practitioners to support counterterrorism and national/international security missions of anticipation, interdiction, prevention, preparedness and response to terrorist acts. In other words the mission and objectives for the organization of EISIC series of conferences that came out from the discussions with Prof. Chen are to provide opportunities to establish a European network in the area of Intelligence and Security Informatics.</p>
<p>The organization of the conference was a very difficult task and we started working for the success of the event from September 2010 with the collaboration of Hellenic American University, University of Southern Denmark and University of Arizona. We formed a team from the above mentioned academic institutes and Hellenic American University accepted to host EISIC 2011 in Athens, Greece while University of Southern Denmark accepted to host EISIC 2012 in Odense, Denmark.  As per program chairs, Daniel Zeng and me invited around 100 researchers from academia and industry to work as program committee members. We received 111 submissions for EISIC 2011 and we accepted 27% of high quality papers as LONG papers based on the peer-review process.  The research articles were received from 41 countries from all sub-continents.</p>
<p>The founding members of EISIC 2011 are: Prof. Hsinchun Chen, Prof. George J. Hagerty, Professor Uffe Kock Wiil, Professor Triant Flouris, Dr. Panagiotis Karampelas and myself.</p>
<p>&nbsp;</p>
<p><strong>What is your assessment of the conference in Athens?</strong></p>
<p>As per feedback from the audience we found it was a very successful event. The keynote speeches as well as the paper presentations were very well attended by the participants and there were a lot of positive comments on the quality of the presentations. There were also a lot of opportunities for networking between the participants and we witnessed several discussions about future collaborations between the participants. I would like to mention at this point that the host organization played a vital role in the success of the conference sponsoring the expenses of keynote speakers and finding alternatives of certain problems we faced because of unforeseen incidents such as strikes, etc.</p>
<p><strong> </strong></p>
<p><strong> </strong></p>
<p><strong>What activities does EISIC plan to carry out in the near future?</strong></p>
<p>We have a number of long and short term plans in the area of Intelligence and Security Informatics.  We have established the Counterterrorism Research Lab at University of Southern Denmark in October 2009.  Behind the lab there is a small group of researchers (around 10, one Professor, one Associate Professor and several PhD students) working in the area of ISI. We are publishing our research articles in the area in various conferences and journals and we try to establish connections with various law enforcement bodies and intelligence services providing them with our cutting edge technology and experience. Concerning EISIC, we plan to organize EISIC 2012 at our University at Odense on August 22-24, 2012. We also received a number of informal proposals for the organization of collocated events. As soon as we receive formal proposals, we’ll decide accordingly. We have also received informal proposals for the organization of EISIC 2013 and EISIC 2014 in Italy and Sweden respectively. The steering committee will decide as soon as we receive formal proposals for the organization of EISIC and related events in future.</p>
<p><strong>In regard to the Intel discipline, where do you classify the Intelligence Security Informatics ISI? Is it a separate intelligence discipline in and of itself, or part of other intel disciplines like HUMINT, SIGINT, IMINT, etc.?</strong></p>
<p>&nbsp;</p>
<p>ISI is a discipline where INTEL disciplines could be benefited. Let us take an example; we are working on a research project (sponsored by the Faculty of Engineering, University of Southern Denmark): Developing an Early Warning System to predict Terrorist Threats. Mostly we use OSINT (Open Source Intelligence), but there is room for counterterrorism experts/INTEL to work. We do not have yet a formal collaboration with INTEL agencies, but I’m sure INTEL people could be benefited as well from ISI research.</p>
<p>&nbsp;</p>
<p><strong>What role does higher education play in ISI?</strong></p>
<p>&nbsp;</p>
<p>As this is a new inter-disciplinary area, higher education can play a vital role in encouraging students from Sociology, Anthropology, Psychology, Criminology, Computer Science, and Applied Mathematics to adopt research in the area of ISI in order to educate experts to help us in building a <strong>SAFE AND SECURE WORLD</strong>. It is also a need of the hour to train INTEL people with this emerging area of research.</p>
<p>&nbsp;</p>
<p><strong>What is the relationship between counter-terrorism and computational criminology?</strong><strong></strong></p>
<p>&nbsp;</p>
<p>Computational criminology like Countering terrorism is an emerging blend of criminology, anthropology, social computing, computer science and applied mathematics. Modern concerns about public safety and security include a focus on a range of events from less serious everyday crimes like shoplifting to personal violent crimes like homicide and ultimately terrorism. Underlying all of these events is a decision process or a chain of steps in target identification, steps that focus first on rough and vague decisions and move towards the precise plot. The fields of counterterrorism and computational criminology involves the use of computational power to identify: (1) crime patterns and emerging patterns; (2) crime generators and attractors; (3) terrorism, organized crime and gang social and spatial networks as well as co-offending networks; and, (4) cybercrime/cyber terrorism. Algorithms are developed using computational topology, hyper-graphs, Social Network Analysis (SNA), Knowledge Discovery and Data-mining (KDD), agent based simulations, dynamic information systems analysis and more for detecting organized crime and predicting terrorist threats.</p>
<p>The methods and models used for counter terrorism and computational criminology can provide information about pattern theory and identification. In short, we treat terrorism as an organized crime, and therefore, it would be possible to use some of the traditional methods to detect terrorism evidences, but also new models can be developed looking into the new type of terrorism of 21<sup>st</sup> century.</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p><strong>What is</strong><strong> c</strong><strong>ybercrime</strong><strong> </strong><strong>and what current challenges does it pose</strong><strong>?</strong></p>
<p>&nbsp;</p>
<p>Crime involving information and communication technologies (ICT), for example:</p>
<p>·        ICT as an instrument, where ICT can be used as primary tool to commit the offence (Identity theft, Internet scams and Fraud-misappropriation of funds are some of the examples)</p>
<p>·        ICT as target, where ICT is the target of the offence (Hacking, Misuse of  ICT resources, Denial of service, Stealing information)</p>
<p>·        ICT as Ancillary Resource, where conventional crime can be assisted by ICT; in other words technology can be used to commit conventional crime or technology can be used to store information about crime (Fraud, Money laundering, etc., are known examples)</p>
<p>&nbsp;</p>
<p>As the cybercrime has border-less and transnational reach, therefore, there is urgent need to establish competency in predictive cyber analysis and to develop trusted relationships to encourage information sharing among the INTEL agencies.  There are number of challenges in cybercrime, for example</p>
<p>&nbsp;</p>
<p>·        Enforcing extraterritorial/ trans-border law enforcement activity</p>
<p>·        Many offences are never detected</p>
<p>·        Many detected offences are never reported</p>
<p>·        Difficult to quantify the offence</p>
<p>·        Difficult to “Police” the cyber space</p>
<p>·        Evidence can be intangible</p>
<p>·        Issuing warrant without knowledge of the precise location of data (evidence) can be problematic</p>
<p>·        Evidence can be destroyed during search</p>
<p>·        Encryption and other concealment technologies are available to offenders</p>
<p>·        Human rights and privacy issue, etc.</p>
<p><strong>How can</strong><strong> </strong><strong>the right</strong><strong> </strong><strong>to individual privacy</strong><strong> </strong><strong>be balanced against the need for protection from</strong><strong> </strong><strong>cybercrime</strong><strong>?</strong></p>
<p>&nbsp;</p>
<p>It is a very difficult question; I think security is more important than privacy of individual person in some cases.  But according to the laws of each country, the privacy problem should be dealt in treating with cyber criminals.</p>
]]></content:encoded>
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		<title>Sensor Urban Environments can prevent and fight robberies, terrorism attacks, natural catastrophes and other emergencies.</title>
		<link>http://www.theamericaspostes.com/3752/sensor-urban-environments-can-prevent-and-fight-robberies-terrorism-attacks-natural-catastrophes-and-other-emergencies/</link>
		<comments>http://www.theamericaspostes.com/3752/sensor-urban-environments-can-prevent-and-fight-robberies-terrorism-attacks-natural-catastrophes-and-other-emergencies/#comments</comments>
		<pubDate>Wed, 14 Sep 2011 21:49:45 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
				<category><![CDATA[Ajax]]></category>
		<category><![CDATA[Counter Terrorism]]></category>
		<category><![CDATA[CRIME]]></category>
		<category><![CDATA[Cybercrime]]></category>
		<category><![CDATA[Events Crime]]></category>
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		<category><![CDATA[Preventive Actions]]></category>
		<category><![CDATA[Prison System]]></category>
		<category><![CDATA[Related Business and Market]]></category>
		<category><![CDATA[Surveillance]]></category>
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		<guid isPermaLink="false">http://www.theamericaspostes.com/?p=3752</guid>
		<description><![CDATA[This document was published by the criminology scientists Charalampos Doulaverakis, Nikolaos Konstantinou, Thomas Knape, Ioannis Kompatsiaris and John Soldatos. Doulaverakis was the keynote speaker at the EISIC 2011 SecuAll scientists are related to three institutions: a)  Informatics and Telematics Institute, Centre for Research and Technology Hellas  in Thessaloniki, Greece  b) the Autonomic and Grid Computing [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_3753" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2011/09/DSCF0009.jpg"><img class="size-medium wp-image-3753" title="Keynote Speaker Charalampos Doulaverakis Eng MSc after his speech in Athens, met with our Publisher Victor Bjorgan." src="http://www.theamericaspostes.com/wp-content/uploads/2011/09/DSCF0009-300x225.jpg" alt="" width="300" height="225" /></a><p class="wp-caption-text">Keynote Speaker Charalampos Doulaverakis Eng MSc after his speech in Athens, met with our Publisher Victor Bjorgan.</p></div>
<p>This document was published by the criminology scientists Charalampos Doulaverakis, Nikolaos Konstantinou, Thomas Knape, Ioannis Kompatsiaris and John Soldatos.</p>
<p>Doulaverakis was the keynote speaker at the EISIC 2011 SecuAll scientists are related to three institutions: a)  Informatics and Telematics Institute, Centre for Research and Technology Hellas  in Thessaloniki, Greece  b) the Autonomic and Grid Computing Laboratory, Athens Information Technology, Athens, Greece</p>
<p>c) Data Fusion International, Dublin, Ireland.<br />
<strong>Abstract—This paper introduces a novel sensor information</strong><br />
<strong>fusion system enabling security and surveillance in large scale</strong><br />
<strong>sensor saturated urban environments. The system is built over</strong><br />
<strong>state-of-the art sensor networks middleware and provides information</strong><br />
<strong>fusion at multiple layers. A distinguishing characteristic</strong><br />
<strong>of the system is that it support seamless integration with semantic</strong><br />
<strong>web middleware (including ontologies and inference mechanisms),</strong><br />
<strong>which enable intelligent high-level accurate reasoning.</strong><br />
<strong>This is a key functionality for efficient surveillance in large scale</strong><br />
<strong>environment, where manual inspection of individual tracking</strong><br />
<strong>systems becomes extremely resourceful and overall impractical.</strong><br />
<strong>A proof-of-concept implementation of the system manifests its</strong><br />
<strong>benefits and technical challenges, while also outlining lessons</strong><br />
<strong>learnt.</strong><br />
I. INTRODUCTION<br />
The proliferation of ubiquitous computing and the parallel<br />
decrease of the price of sensors has recently allowed sensor<br />
applications to make their appearance in a variety of domains.<br />
Especially, sensor applications with emphasis on large scale<br />
deployments in urban environments and the emerging wave of<br />
participatory sensing applications such as WikiCity [1], City-<br />
Sense [2], and Google Latitude [3] has offered fundamental<br />
changes and advancements both in the academia as well as in<br />
the heart of the society itself. Nowadays, more than ever, we<br />
are witnessing the materialization of visions and concepts such<br />
as the Internet-of-Things (IoT) [4] and M2M communications<br />
[5].<br />
Recent incidents have manifested that modern cities are<br />
very susceptible to terrorist attacks. For instance, the collapse<br />
of New York’s Twin Towers on 11th September 2001, the<br />
bombing of packed commuter trains in Madrid on 11th March<br />
2004, the London bombings in July 2005, or the Moscow<br />
metro in March 2010 demonstrate that prominent applications<br />
need to be devised in order to serve critical surveillance and<br />
security needs in urban environments.<br />
However, numerous challenges are associated with such<br />
efforts. The large scale nature of both the geographically<br />
dispersed environment as well as of the volume of the data,<br />
the multiple distributed heterogeneous components that need<br />
to be assembled, spanning sensor, sensor processing, signal<br />
processing (including A/V) components, often from multiple<br />
vendors are some of the issues that need to be addressed. Additionally,<br />
the need for automation, since manual observation<br />
of multiple camera feeds is not possible, and the inclusion of<br />
high-level intelligent reasoning for event inference are features<br />
without which the added value of the system is limited.<br />
In order to tackle these issues in global sensor networks, various<br />
frameworks have been developed, offering programmable<br />
and configurable solutions, e.g. [6]–[8]. Among the most<br />
important in these frameworks is the limited support for perceptual<br />
processing components – with the exception of some<br />
more heavyweight frameworks [9] – and limited support for<br />
semantics embodiment, inference, and high-level reasoning.<br />
Employment of Semantic Web technologies in sensor networks<br />
research focuses mainly on sensor modelling in order<br />
to enable higher level processing for event/situation analysis.<br />
Initiatives such as [10] use SUMO as their core ontology under<br />
which they define a sensor ontology for annotating the sensors<br />
and it can be then queried for sensor discovery. Other frameworks,<br />
such as the one proposed in [11], define ontologies for<br />
sensor measurement and sensor description. The framework<br />
emerges from the W3C Semantic Sensor Network XG1 which<br />
aims at providing ontologies and semantic annotations that<br />
define capabilities of sensors and sensor networks.<br />
Ontologies have been proposed for situation awareness<br />
(SAW) in sensor fusion applications where their ability to<br />
model a domain or a “part of the world” is utilized. Several<br />
approaches have been proposed as in [12] where the authors<br />
define a core ontology for SAW which can be used as a basis<br />
from which to build separate ontologies for arbitrary situations<br />
that are able to express objects, relations and their evolution<br />
over time.<br />
In [13], we  the authors used an ontology to create a unified<br />
expression of the Situation Theory [14], [15]. The Situation<br />
Theory Ontology (STO) is expressed in OWL which enables<br />
situations to be described using a formal language, thus<br />
allowing inference through a reasoning engine or by using<br />
appropriate rules.<br />
A framework for the designing of ontology for SAW is<br />
presented in [16] where a six step guide to design ontologies</p>
<p>based on the Basic Formal Ontology (BFO) is presented. The<br />
authors test their framework in a situation assessment in a postdisaster<br />
environment context and claim that their BFO-based<br />
ontology was able to capture the complexities of the testing<br />
context and provide adequate inferential capabilities for higher<br />
level fusion.<br />
All the above methods do not propose an architecture which<br />
will integrate sensors and low level processing modules with<br />
higher level fusion process for situation awareness. In order<br />
to address this limitation, a solution is presented in this paper<br />
that<br />
∙ Comprises a multi-level fusion system, at all JDL (see<br />
Section II-B) levels<br />
∙ Seamlessly blends ontologies with low-level information<br />
databases<br />
∙ Combines semantic web middleware with sensor networks<br />
middleware<br />
The structure of the paper is as follows: Section II analyses<br />
the architectural approach that is followed, Section III demonstrates<br />
the Low Level Fusion (LLF) capabilities, Section IV<br />
the semantic and reasoning capabilities, Section V presents<br />
implementation details and example use cases, while Section<br />
VI concludes the paper by presenting out comments and<br />
remarks.<br />
<strong>II. PROPOSED APPLICATION AND ARCHITECTURE</strong><br />
In order to test our approach, a suitable environment for<br />
setting up and deploying the proposed sensor analysis architecture<br />
had to be selected. Security surveillance environments<br />
offer an ideal set up in which the main characteristics that<br />
make it distinguishing are:<br />
∙ Usually security surveillance areas are sensor saturated<br />
environments with electro-optical (visual and IR cameras)<br />
and acoustic sensors being more common while<br />
others like temperature or RFID, etc sensors can also<br />
be found. The abundance of available sensors makes it<br />
more possible to capture the various events that take<br />
place in a surveillance session. However, the higher the<br />
number of sensors, the more difficult it is to manage and<br />
observe them. Additionally, it makes it harder to filter out<br />
information that is irrelevant or discover information that<br />
could potentially be useful.<br />
∙ Surveillance environments are deployed in areas that are<br />
densely populated in terms of people but also in assets,<br />
e.g. buildings or vehicles, hence many events that could<br />
be of interest are taking place. This fact, coupled with<br />
the higher chance of these events to be captured by the<br />
sensors (the previous characteristic), makes discovery of<br />
important events difficult.<br />
∙ Multiple processing algorithms and context-acquisition<br />
components, which are used for extracting information<br />
and detecting events, require a method for managing the<br />
data that they produce.<br />
∙ Due to the variety of sensor modalities that are deployed<br />
there is a large degree of sensor and data heterogeneity</p>
<p><img 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" alt="" /></p>
<p><strong>Fig. 1. Architecture of the proposed system</strong><br />
that need to be tackled by the sensor management and<br />
analysis system.<br />
Public security in urban areas can be a challenging task to<br />
security personnel. Threats posed to citizens include crimes<br />
such as robberies, terrorism attacks, natural catastrophes and<br />
other emergencies. Being aware of threatening situations either<br />
in the moment or being able to forecast events from security<br />
monitoring applications enables security personnel to take the<br />
right actions in context. Fusion of data from appropriate sensor<br />
types supports inferring threatening situations in the context of<br />
situation specific parameters. An example of such a deployed<br />
sensor network is the case of urban surveillance in an urban<br />
war environment where data feeds from multiple sensors, e.g.<br />
visible spectrum and IR cameras, acoustic sensors, seismic<br />
sensors, etc, are directed to a command center and critical<br />
decisions have to be made in short time regarding the threat<br />
level in a situation.<br />
Taking into account the above facts, a method is required<br />
that will enable efficient information processing and management.<br />
Semantic web and ontologies can efficiently handle<br />
heterogeneous information through semantic description of<br />
knowledge. Additionally, ontologies are used to model domain<br />
knowledge through class definitions and relations between<br />
classes. The knowledge model and the underlying data can<br />
then be used for supporting reasoning services in order to infer<br />
new knowledge that is not explicitly stated. These features<br />
can be utilized in a sensor network in order to provide the<br />
backbone for intelligent sensor fusion.<br />
<strong>A. System architecture</strong><br />
Figure 1 illustrates the approach followed in the hereby<br />
presented work. At the bottom layer are the sensors, that are<br />
translating their perception of the world into raw sensor data.<br />
This data is processed by the leaf nodes, that are operating<br />
at the LLF layer which adds structure to the data. The nodes<br />
are named “Leaf” nodes because, if the architecture can be<br />
considered as tree-like, then these nodes can be considered<br />
as leaves, in the sense that they do not have children nodes<br />
of inferior capabilities or at a lower level. When deemed<br />
necessary, signal processing components can be hosted in<br />
dedicated hardware since, specifically in A/V processing, algorithms<br />
may be extremely resource-hungry. At the high-level<br />
fusion (HLF) layer, intelligence is added to the system, by</p>
<p>mapping the collected data into ontology concepts, achieving<br />
thus uniform information representation throughout the system<br />
and enabling reasoning.<br />
The Central Control layer that is logically on top of the<br />
infrastructure offers monitoring and control capabilities. In<br />
essence, it comprises:<br />
∙ A database (DB) where high-level information such as<br />
events and threats is stored<br />
∙ An Environmental Service (ES) that enables geospatial<br />
services<br />
∙ The Semantic Fusion component that performs analysis<br />
on the system-wide high-level collected information in<br />
order to infer events and potential risks and threats<br />
∙ The Common Operational Picture (COP) that offers a<br />
visualization of the system state with regard to deployed<br />
sensors, detected objects, sensed events and inferred<br />
threats.<br />
In the following sections, we analyse how the aforementioned<br />
layers and components function and interoperate in<br />
order to achieve intelligent information fusion in an urban<br />
environment.<br />
<strong>B. JDL model for sensor fusion</strong><br />
In order to improve communications among military researchers<br />
and system developers, the Joint Directors of Laboratories<br />
(JDL) Data Fusion Working Group began an effort<br />
to define the terminology related to data fusion. The result<br />
of that effort was the creation, in 1986, of a process model<br />
for data fusion and a data fusion lexicon [17]. The JDL<br />
process model is a paper model of data fusion and is intended<br />
to be very general and useful across multiple application<br />
areas. The JDL data fusion process model is a conceptual<br />
model which identifies the processes, functions, categories of<br />
techniques, and specific techniques applicable to data fusion<br />
(Figure 2). According to the model data fusion process is<br />
conceptualized by sensor inputs, human-computer interaction,<br />
database management, source preprocessing, and four key<br />
subprocesses:<br />
1) Level 1 (Object Refinement): is aimed at combining<br />
sensor data together to obtain a reliable estimation of<br />
an entity position, velocity, attributes, and identity;<br />
2) Level 2 (Situation Refinement): dynamically attempts<br />
to develop a description of current relationships among<br />
entities and events in the context of their environment;<br />
3) Level 3 (Threat Refinement): projects the current situation<br />
into the future to draw inferences about enemy<br />
threats, friendly and enemy vulnerabilities, and opportunities<br />
for operations;<br />
4) Level 4 (Process Refinement): is a meta-process which<br />
monitors the overall data fusion process to assess and<br />
improve the real-time system performance.<br />
Revisions of the model suggest the addition of 2 more<br />
levels: A lower Level 0 (Source preprocessing/Sub-object<br />
refinement) which aims to estimate the state of sub-object<br />
entities such as signals and features, and a higher Level 5</p>
<p>(Cognitive or User Refinement) which introduces man in the</p>
<p>fusion loop where the aim is to generate fusion information<br />
according to the needs of the system user.</p>
<p><img 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" alt="" /></p>
<p>Fig. 2. The JDL model levels and how they are related to the proposed<br />
system’s layers and nodes<br />
The current paper focuses in the definition of a sensor fusion<br />
architecture which addresses matters that correspond to the<br />
higher JDL levels 2, 3 and 4, i.e. situation, threat assessment<br />
and process refinement respectively, as is depicted in Figure<br />
2. Ontologies, with their inherent ability to model relations,<br />
can be employed to provide enough specificity in describing<br />
the higher level concepts of JDL but also to describe the<br />
relations between these concepts [18]. The ontology provides<br />
the standardized form in which situations are defined so<br />
that appropriate algorithms for situation assessment can be<br />
formulated.<br />
III. LOW-LEVEL FUSION CAPABILITIES<br />
In order to perform Low-Level Fusion, our work relies on<br />
the GSN2 middleware. The middleware constitutes an opensource,<br />
Java-based implementation. It was designed in order<br />
to allow processing from a large number of sensors and as<br />
such, it covers the functionality requirements of low level<br />
fusion in sensor data streams. In order to acquire information,<br />
GSN introduces the concept of “virtual sensor”. Any data<br />
provider, not only sensors can provide data to a GSN instance,<br />
as long as a virtual sensor configuration file (in the form of<br />
an XML) defines the the processing class, the sliding window<br />
size, the datasource and the output fields. GSN Servers can<br />
communicate between them, thus forming a network where<br />
information is collected, communicated, fused and integrated<br />
in order to produce the desired results.<br />
As far as it concerns data acquisition, each GSN node can<br />
support input from more than one data stream. In order to<br />
combine the information, an SQL-like procedural language is<br />
offered. This allows to the user to define LLF functions in the<br />
following manner, while GSN takes care behind the scenes<br />
about crucial issues such as thread safety, synchronization,<br />
etc.<br />
SELECT source1.S1 AS NumberOfPersons, source2.S2<br />
AS ExistenceOfSmoke<br />
FROM source1, source2<br />
WHERE NumberOfPersons&gt;0 AND ExistenceOfSmoke=&#8221;true&#8221;<br />
In the example above, there are two data providers for one<br />
GSN Server.</p>
<p>This virtual sensor definition will produce events only in<br />
the case when the WHERE condition is satisfied. The example<br />
above demonstrates the concept of fusion: results are provided<br />
by taking into account the inputs from both the sensors.<br />
Using the abovementioned approach, a variety of signal<br />
processing components can be integrated into one unifying<br />
architecture. For instance, the example above can be supported<br />
using a Body Tracker [19], [20] and a Smoke Detector, in order<br />
to produce alerts when persons are detected near smoke. In the<br />
same manner, results by processing components such as face<br />
detectors [21], unusual event detectors [22] can be fused in<br />
order to achieve the desired system functionality.<br />
Communication with these processing components constitutes<br />
a difficult to tackle problem, mostly in terms of<br />
implementation since:<br />
∙ Two worlds have to be brought together: image processing<br />
and the distributed systems. The former typically processes<br />
images or videos that are fundamentally different<br />
in nature from streaming data. As such, the component’s<br />
input has to be modified in order to take into account<br />
streams that may lack synchronization, be erroneous, or<br />
overwhelming the network with data.<br />
∙ Integration is hardly a trivial task. Prototypes in Matlab,<br />
algorithms implemented in C++ will need to communicate<br />
with the java-based GSN servers, running either<br />
locally or remotely. Web services/socket interfaces can<br />
be developed, imposing though, additional processing<br />
overheads.<br />
Next, in order to allow GSN-GSN node communication, the<br />
information produced can be forwarded to other GSN nodes<br />
either in push or in pull mode. Data streams can be forwarded<br />
in order to be processed at remote GSN nodes. In order to<br />
forward these data streams, GSN supports subscriptions, onceoff<br />
queries or even simple data forwarders. Data is forwarded<br />
using a RESTful approach (remote wrappers return XML over<br />
HTTP).<br />
<strong>IV. SEMANTICS AND REASONING</strong><br />
The Higher Level Fusion (HLF) layer of the proposed<br />
architecture consists of the underlying ontology which will<br />
be used as the backbone for performing the reasoning that<br />
is required for situation assessment. OpenLink Virtuoso3 has<br />
strong support for Semantic Web technologies and provides<br />
services like RDF triple storage, a SPARQL compiler, rulebased<br />
reasoning and can expose relational data as RDF.<br />
Virtuoso acts as a federation layer between the relational and<br />
semantic data thus enabling their seamless integration. A block<br />
diagram of the HLF approach is presented in Figure 3 (Virtuoso Universal Server: http://virtuoso.openlinksw.com/).</p>
<p><img 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" alt="" /><br />
<strong> </strong></p>
<p style="text-align: center;"><strong>Fig. 3. The High Level Fusion process</strong></p>
<p><strong>A. Ontology for high level fusion</strong><br />
The ontology is the main information gathering point in the<br />
proposed architecture. It is defined according to the domain<br />
where the sensor network is placed and it defines the entities<br />
that are taking place in a situation, the events and the relations</p>
<p>between them. All data that are produced by the processing<br />
modules and by the LLF nodes are eventually stored here<br />
where reasoning is performed in order to infer new knowledge<br />
which corresponds to events and situation assessment that<br />
cannot be performed at the LLF level.<br />
In order to take advantage of research that has been conducted<br />
in the area of semantic enabled situation awareness, the<br />
current system uses as its core ontology the Situation Theory<br />
Ontology (STO) [13] which has been developed specifically<br />
for that purpose and is based on Situation Theory. In short<br />
STO is written in OWL and models the events/objects and<br />
their relationships in a way that can be extended using either<br />
OWL axioms and properties or in combinations with rules<br />
for supporting complex cause-effect relations that cannot be<br />
expressed in OWL alone. The main classes of STO are<br />
displayed in Figure 4.</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p>&nbsp;</p>
<p><strong>Fig. 4. Situation Theory Ontology with its main classes and properties</strong><br />
Situation is the central class. Instances of this class are<br />
specific situations. The second class is the Individual class,<br />
which is a counterpart of the individuals in situation theory.<br />
Similarly, Relation captures the n-ary relations. Attribute is<br />
a generalization of locations and time instants in situation<br />
theory. Instances of this class are attributes of individuals<br />
and situations. An attribute may have a dimension associated<br />
with it so the class Dimensionality represents this fact. The<br />
Polarity class has only two instances that correspond to the<br />
two possible values associated with a tuple, either that a<br />
given tuple holds or that it does not hold.</p>
<p>Classes of STO are related through a number of OWL properties.</p>
<p>Situations are linked with four kinds of entities. First, the property<br />
relevantIndividual captures the individuals that participate in a<br />
situation. The property relevantRelation is used to assert that<br />
a given kind of relation is relevant to a given situation. Since<br />
situations are objects, they can have attributes of their own.<br />
The STO is extended with classes and relations that correspond<br />
to the actual application scenario. In order to be able<br />
to use it in a real sensor fusion environment two additional<br />
ontologies are integrated. These are the “Time ontology”</p>
<p>(Time Ontology in OWL: http://www.w3.org/TR/owl-time/</p>
<p>which holds the timestamp of any concept instance that<br />
is stored during runtime and the “WGS84 Geo Positioning<br />
ontology” (Basic Geo (WGS84 lat/long) Vocabulary: http://www.w3.org/2003/01/geo/5)</p>
<p>which holds the latitude and longitude values of<br />
entities.<br />
<strong>B. Mapping relational data to RDF</strong><br />
As explained in Section III, the data that are produced by<br />
the processing modules and by the LLF nodes are stored in the<br />
relational database that backs GSN. In order to forward these<br />
data to the ontology they have to be translated into semantic<br />
notations. This task is performed by the mapping layer which<br />
maps the relational schema to the semantic schema. There<br />
are two strategies for accomplishing this transformation, using<br />
either a push or a pull method.<br />
The push method forwards the data to the ontology using<br />
semantic notation as soon as they are generated. This has to be<br />
implemented in the lower level layer with the semantic layer<br />
having a passive role in the process. The advantage of this<br />
method is that the transformations are executed fast and the<br />
ontology is always up-to-date. The disadvantages is that each<br />
lower level node will have to implement its own push method<br />
while there is the risk that the ontology will be populated with<br />
data even when no query is sent to the semantic layer.<br />
The pull method transforms the relational data to semantic<br />
on request, i.e. during query time. Virtuoso supports this<br />
functionality through “RDF Views” where mappings, simple<br />
or complex, between relational database tables and ontology<br />
concepts and properties are defined. During query time, the<br />
mapping process is triggered and data are transformed on<br />
the fly. The advantages of the pull method is that the actual<br />
mapping is defined at the higher semantic level rather than the<br />
lower levels and that data are transformed on request so that<br />
the ontology will accumulate instances that are needed for the<br />
actual query evaluation. The disadvantage of this method is<br />
that it could lead to longer response times during queries.<br />
In the proposed system, the pull method which utilizes<br />
Virtuoso’s “RDF Views” is used as it is best to separate the<br />
semantic layer from lower level processing. This will make<br />
extension of the system by adding more sensors and sensor<br />
processing modules easier without having to mix the lower<br />
level processing level with the high level semantic fusion level.<br />
Additional information can be integrated by the HLF layer<br />
in order to derive situations.</p>
<p>Information coming from sources like environmental services which could provide details about<br />
the geographical locations of specific structures or points can<br />
be queried, as long as they expose I/O interfaces, and these<br />
data can be used for inferencing.<br />
<strong>C. Reasoning</strong></p>
<p><strong> </strong><br />
Reasoning uses the ontology structure and the stored instances<br />
in order to draw conclusions about the ongoing situations.<br />
Under this approach relations between classes like<br />
rdfs:subClassOf, properties like owl:sameAs and relations<br />
that are defined by experts in the ontology are utilized<br />
for inference. The knowledge base can further be extended by<br />
using rules to describe situations/events that are too complex<br />
to be defined using OWL notation only.<br />
For utilizing the above, two different reasoners are integrated<br />
in the system, Virtuoso’s internal reasoning engine<br />
and the Jena Semantic Web Framework (The Jena Semantic Web Framework: http://jena.sourceforge.net/.)</p>
<p>Virtuoso provides inference capabilities using OWL only thus does not provide<br />
support for an external rule set. However, Virtuoso implements<br />
a powerful geospatial inference mechanism which is a crucial<br />
feature when dealing with sensor networks and sensor fusion.<br />
On the other hand, Jena supports external rules, advanced<br />
reasoning capabilities and seamless integration with Virtuoso<br />
where in this case, Virtuoso is treated only as a triple store.<br />
Both of them can be employed simultaneously on the same<br />
dataset and their activation is managed programmatically by<br />
the application.<br />
Another issue that had to be dealt with is that sensor analysis<br />
modules and the LLF processes can generate a significant<br />
amount of data over time, enough to make the reasoning<br />
service not responsive. To overcome this problem, the solution<br />
that is proposed is to use a time window where reasoning is<br />
performed using only facts in a specific time interval. Jena<br />
and Virtuoso can support this type of inference by assigning<br />
a RDF triples to a specific “context” type, thus dealing with<br />
RDF quads.<br />
The reasoning service can also use information and data<br />
from external services in order to use them in the inference<br />
process. In the proposed architecture, an Environmental<br />
Service provides the location information of sensors, events,<br />
waypoints and critical or important landmarks which are<br />
subsequently used for geospatial reasoning.<br />
It should be noted here that in a real world application the<br />
different information sources that provide data to the proposed<br />
fusion architecture could make use of different ontologies<br />
for their purposes. In order to enable a seamless integration<br />
of these sources an ontology mapping process between the<br />
information source ontology and the HLF ontology would have<br />
to be defined.<br />
<strong>V. IMPLEMENTATION AND EXPERIMENTAL RESULTS</strong><br />
Implementation is based mostly on two types of GSN-based<br />
servers that communicate with the sensor world, with a central<br />
node that monitors and controls the network. As illustrated</p>
<p>in Figure 1, the Leaf Nodes are the ones that consume the</p>
<p>sensory information. These ones are in fact GSN-based servers<br />
configured to process incoming data from the sensor layer.<br />
In order to integrate Signal Processing components as in<br />
Figure 5, our implementation allows the use of standalone<br />
hosts. This happens because the components can either be<br />
resource-hungry in terms of processing capabilities to the<br />
extend that they utilize the whole processing power for their<br />
efficient operation or simply because the implementation is<br />
not portable, as in linux/windows-specific libraries.<br />
A. System Implementation<br />
In the scope of a proof-of-concept implementation and<br />
also as a testbed for our experiments, the snapshot of the<br />
architecture above that was implemented employs 1 computer<br />
node that hosts a Leaf Node and two processing components:<br />
a Smoke Detector and a Body Tracker, a camera that streams<br />
its feed using RTP, a node that hosts a Semantic Node that<br />
offers HLF capabilities and, finally, the Central Node that has<br />
the overall system supervision as already depicted in Figure<br />
1.<br />
Bottom-up, the system can be described as follows:<br />
First, the camera generates an RTP feed with its perception.<br />
The feed is processed by both the signal processing<br />
components (the Body tracker and the Smoke detector).<br />
The first component generates a stream containing at<br />
all times the NumberOfPersons detected by the component.<br />
The second one, simple however, is not as straightforward:<br />
it splits the image into particles and reports the<br />
NumberOfSmokeParticles detected. Then, the LLF Virtual<br />
sensor, according to the example presented in Section<br />
III fuses the data. Figure 5, illustrates the behavior of the<br />
BodyTracker.</p>
<p>&nbsp;</p>
<p><img 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" alt="" /></p>
<p style="text-align: center;">Fig. 5. Body Tracker Sequence Diagram</p>
<p>The sequence is initiated by the Body Tracker wrapper,<br />
which is polling for results the Body Tracker component<br />
host at fixed time intervals. The host, when it receives<br />
the POLL command – note that the component may<br />
support a number of functions such as START, STOP,<br />
REQUEST_VIDEO_DETAILS etc. – it returns a string containing<br />
a description in XML of the number of persons tracked.<br />
This description is of the following form:</p>
<p>&lt;frame&gt;<br />
&lt;frame_counter&gt;1534&lt;/frame_counter&gt;<br />
&lt;person n=&#8221;1&#8243;&gt;<br />
&lt;trackID&gt;25&lt;/trackID&gt;<br />
&lt;x&gt;587&lt;/x&gt;<br />
&lt;y&gt;493&lt;/y&gt;<br />
&lt;w&gt;79&lt;/w&gt;<br />
&lt;h&gt;130&lt;/h&gt;<br />
&lt;quality_detection&gt;2.8480&lt;/quality_detection&gt;<br />
&lt;quality_color&gt;7.6249&lt;/quality_color&gt;<br />
&lt;quality_motion&gt;5.1601&lt;/quality_motion&gt;<br />
&lt;/person&gt;<br />
&lt;person n=&#8221;2&#8243;&gt;<br />
&lt;trackID&gt;30&lt;/trackID&gt;<br />
&lt;x&gt;1024&lt;/x&gt;<br />
&lt;y&gt;491&lt;/y&gt;<br />
&lt;w&gt;41&lt;/w&gt;<br />
&lt;h&gt;83&lt;/h&gt;<br />
&lt;quality_detection&gt;42.8867&lt;/quality_detection&gt;<br />
&lt;quality_color&gt;6.7535&lt;/quality_color&gt;<br />
&lt;quality_motion&gt;9.0603&lt;/quality_motion&gt;<br />
&lt;/person&gt;<br />
&#8230;<br />
&lt;/frame&gt;</p>
<p>Note, next, that sampling the video source takes place<br />
asynchronously. This happens because the camera will be<br />
streaming at 25 fps, while for the needs of the LLF, 500<br />
ms may suffice between two consecutive polls. In addition,<br />
tracking a person is a more demanding task than detecting<br />
it, since the former implies comparing consecutive frames for<br />
differences between them while for the latter processing single<br />
frames is enough. Therefore, the asynchronous behaviour is<br />
explained from the fact that the camera fps is not aligned<br />
with the messages per second that the component produces.<br />
Under any circumstances, the LLF Wrapper receives a<br />
notification from the BodyTracker. The same information flow<br />
applies to the Smoke detection component. The LLF Wrapper<br />
generates results only in the case when the fusion conditions<br />
are true. Of course, more processing components and more<br />
complex fusion conditions can be added in the processing<br />
scheme hereby described.<br />
For the HLF, the major difference is in the introduction of<br />
an ontology and reasoning procedures where more complex<br />
conditions for fusion can be applied. HLF only deals with<br />
higher level detections and events meaning that it communicates<br />
directly with the LLF and individual perception modules,<br />
pulling data in order to make inferences. An example situation<br />
where an alarm would be triggered by a rule, expressed either<br />
as a Jena rule or as a SPARQL construct query, which would<br />
state ”If smoke is detected near an object then raise an alarm”<br />
with objects being a Person or Vehicle. The classes that are<br />
inserted are<br />
Smoke rdfs:subClassOf Non-RigidObject<br />
Person rdfs:subClassOf RigidObject<br />
Vehicle rdfs:subClassOf RigidObject<br />
Alarms can then be retrieved with a simple SPARQL query<br />
issued to Virtuoso as<br />
SPARQL<br />
SELECT ?x WHERE {?x rdf:type STO:Alarm}<br />
This example illustrates the use of the subclass inference in</p>
<p>order to define situations that hold for a class of objects<br />
which is something that cannot be defined using the LLF<br />
layer only. In a similar way, the owl:sameAs property can<br />
be used for reasoning. The property can be evaluated during<br />
runtime. For example if two distinct detection instances is<br />
decided that are actually the same entity, e.g. if they have<br />
the same geographical locations, then by inference through<br />
owl:sameAs the rules and properties that apply to one also<br />
apply to the other thus enhancing the reasoning capabilities of<br />
the system.<br />
B. Advanced inference<br />
In order to demonstrate the inference capabilities of the<br />
proposed architecture, a scenario was set-up which makes<br />
full use of the data chain from low level sensor data to high<br />
level complex event detection and situation awareness/threat<br />
assessment. The example demonstrates how external services<br />
can be utilized in order to detect “critical” situations. For<br />
setting up a realistic usage scenario, the Environmental Service<br />
which exposes an interface where all interest points, e.g.<br />
buildings or sensors, positions in absolute lat/long coordinates<br />
are registered, is employed. These locations can be queried<br />
in real time by the HLF layer and are used for inferencing<br />
through geospatial reasoning. These data along with the lower<br />
level processing modules are used by the HLF layer in order<br />
to derive hypothesis about the criticality of a situation.<br />
To demonstrate the use of sensors typically deployed in<br />
urban environment we selected video cameras which can be<br />
used to detect persons and also incidents, such as smoke, in<br />
an area of interest. Sensor locations are associated with a<br />
priori knowledge during reasoning. Detected smoke at a petrol<br />
station marks a significant threat to public safety based on<br />
a priori knowledge about the location and can be associated<br />
with a clear emergency action plan. A public square with a<br />
highly varying number of persons and smoke allows inferring<br />
various situations which can be controlled as necessary based<br />
on a security agenda. A specific public area can be subject<br />
to an event schedule which can provide significant semantic<br />
input to inferring on the threat level associated with detected<br />
situations. A gathering of locals celebrating with a bond fire<br />
on a public square is different from an unplanned detected<br />
smoke event during mid-day hours.<br />
1) “Smoke detection in critical location” situation: Reasoning<br />
over triples in Virtuoso requires that the underlying<br />
triple store was populated with inferred event data from the<br />
smoke detector and body tracker. RDF views have been<br />
developed and used to load the underlying data from the<br />
sensor inference database. Using the scheduler component in<br />
Virtuoso we trigger a procedural logic which updates the triple<br />
store via RDF views from the sensor inference database and<br />
subsequently invoke on the reasoning.<br />
In the example of smoke detection at the petrol station<br />
the relevant smoke event together with location data of the<br />
video camera is forwarded to emergency personnel with the<br />
relevant information. The reasoning process associates the<br />
smoke detection event with a criticality factor according to</p>
<p>events modelled in STO and information coming from the<br />
ES. The process of modelling events in STO is described in<br />
detail in [23]. Figure 6 illustrates the model with instance<br />
data by example. We used the STO:FocalSituation class<br />
to mark significant situations that prompt action by security<br />
personnel. The class interlinks the data associated with events<br />
which we query via SPARQL and forward to emergency via<br />
a Web service call.</p>
<p><img src=&#8221;data:image/png;base64,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&#8221; alt=&#8221;" /></p>
<p style=&#8221;text-align: center;&#8221;>Fig. 6. Modelling of the “significant situations” in STO</p>
<p>The query of Figure 7a (Fig. 7. a) Query for retrieving focal situations and 7b) the results of the query<br />
run against the modelled “significant situation ) retrieves focal situations with their<br />
event related details such as time location and further textual<br />
descriptions and was run against the model above with the<br />
results shown in Figure 7b. Predefined SPARQL queries are<br />
invoked on in Virtuoso/PL.<br />
Integration with emergency departments can be achieved<br />
by sending details on significant threats found in SPARQL<br />
result sets to a Web service endpoint exposed in an emergency<br />
department.<br />
SELECT ?focalsituation ?sentTxt ?timeTxt ?locTxt<br />
WHERE {<br />
?focalsituation STO:focalRelation ?event .<br />
?event STO:hasAttribute ?time .<br />
?event STO:hasAttribute ?location .<br />
?time rdf:type STO:Time .<br />
?location rdf:type STO:Location .<br />
?sentence rdf:type STO:Sentence .<br />
?location rdfs:label ?locTxt .<br />
?time time:inXSDDateTime ?timeTxt .<br />
?sentence rdfs:label ?sentTxt</p>
<p><img 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&#8221; alt=&#8221;" /></p>
<p style=&#8221;text-align: center;&#8221;><strong>b)</strong></p>
<p style=&#8221;text-align: left;&#8221;><strong>VI. CONCLUSIONS</strong></p>
<p style=&#8221;text-align: left;&#8221;>
In this paper we have presented a framework for implementing<br />
intelligent information fusion in a sensor network<br />
environment. The framework deals with all aspects above the<br />
sensor layer i.e. it deals with perception modules integration,<br />
communication of the perception modules with GSN, low<br />
level fusion, high level fusion with integration of semantic<br />
description of information, communication with external services,<br />
situation assessment and alert generation. It is a generic<br />
framework that can be applied to any sensor network and<br />
it is not restricted to the security and surveillance area that<br />
was demonstrated. Illustrative examples of how the framework<br />
would be deployed in realistic scenarios have also been<br />
demonstrated where inference capabilities are shown. The core<br />
advantages of the proposed framework are it’s extensibility<br />
with pluggable perception modules integration and the ease of<br />
defining rules for LLF and HLF, using SQL-like syntax and<br />
semantic rules/queries respectively.<br />
Future work will be focused on implementing and integrating<br />
probabilistic reasoning, probably through fuzzy-DL,<br />
in order to drive inference as opposed to the deterministic<br />
reasoning that is applied up to now, thus enabling resolution<br />
of issues such as conflicts or missing detections in situation<br />
assessment.</p>
<p>ACKNOWLEDGMENT<br />
Part of this work has been carried out in the scope of the<br />
MEDUSA project (Multi sEensor Data fusion grid for Urban<br />
Situational Awareness, co-funded by the European Defense<br />
Agency (MDS-MoM-A026RTGC-C-0003-VCS). The authors<br />
acknowledge help and contributions from all partners of the<br />
project.<br />
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<p>[7] C. Sharp, E. Brewer, and D. Culler, “Hood: A neighbourhood sensor<br />
networks,” in Proc. of MobiSYS’04, 2004.<br />
[8] I. Chatzigiannakis, G. Mylonas, and S. Nikoletseas, “50 ways to build<br />
your application: A survey of middleware and systems for wireless sensor<br />
networks,” in Proc. of IEEE Conference on Emerging Technologies<br />
and Factory Automation, ETFA, 2007.<br />
[9] N. Dimakis, J. Soldatos, L. Polymenakos, P. Fleury, J. Curin, and<br />
J. Kleindienst, “Integrated development of context-aware applications<br />
in smart spaces,” IEEE Pervasive Computing, vol. 7, no. 4, pp. 71–79,<br />
2008.<br />
[10] M. Eid, R. Liscano, and A. E. Saddik, “A universal ontology for sensor<br />
networks data,” in IEEE International Conference on Computational<br />
Intelligence for Measurement Systems and Applications, Ostuni, Italy,<br />
2007.<br />
[11] H. Neuhaus and M. Compton, “The semantic sensor network ontology:<br />
A generic language to describe sensor assets,” in AGILE International<br />
Conference on Geographic Information Science, Hannover, Germany,<br />
2009.<br />
[12] C. J. Matheus, M. M. Kokar, and K. Baclawski, “A core ontology<br />
for situation awareness,” in Proceedings of the Sixth International<br />
Conference of Information Fusion, 2003, pp. 545–552.<br />
[13] M. M. Kokar, C. J. Matheus, and K. Baclawski, “Ontology-based<br />
situation awareness,” Information Fusion, Special Issue on High-level<br />
Information Fusion and Situation Awareness, vol. 10, no. 1, pp. 83–98,<br />
January 2009.<br />
[14] J. Barwise, “Scenes and other situations,” Journal of Philosophy, vol. 78,<br />
no. 7, pp. 369–397, 1981.<br />
[15] M. R. Endsley, “Theoretical underpinnings of situation awareness: a<br />
critical review,” in Situation Awareness Analysis and Measurement.<br />
Mahawah, NJ, USA: Lawrence Erlbaum Associates, 2000.<br />
[16] E. G. Little and G. L. Rogova, “Designing ontologies for higher level<br />
fusion,” Information Fusion, Special Issue on High-level Information<br />
Fusion and Situation Awareness, vol. 10, no. 1, pp. 70–82, January<br />
2009.<br />
[17] D. L. Hall and J. Llinas, Handbook of Multisensor Data Fusion. CRC<br />
Press, 2001.<br />
[18] J. Llinas, C. Bowman, G. Rogova, A. Steinberg, E. Waltz, and F. White,<br />
“Revisiting the JDL data fusion model II,” in Proceedings of the<br />
Seventh International Conference on Information Fusion, P. Svensson<br />
and J. Schubert, Eds., 2004, pp. 1218–1230.<br />
[19] K. Bernardin, R. Stiefelhagen, A. Pnevmatikakis, O. Lanz, A. Brutti,<br />
J. R. Casas, and G. Potamianos, Computers in the Human Interaction<br />
Loop. Springer, 2009, ch. Person Tracking, pp. 11–23.<br />
[20] A. Pnevmatikakis and F. Talantzis, “Person tracking in enhanced cognitive<br />
care: A particle filtering approach,” in The 18th European Signal<br />
Processing Conference (EUSIPCO 2010), Aalborg, Denmark, August<br />
2010.<br />
[21] N. Katsarakis and A. Pnevmatikakis, “Face validation using 3d information<br />
from single calibrated camera,” in The 16th International<br />
Conference on Digital Signal Processing (DSP 2009), Santorini, Greece,<br />
July 2009.<br />
[22] K. Avgerinakis, A. Briassouli, and I. Kompatsiaris, “Real time illumination<br />
invariant motion change detection,” in Proceedings of the ACM<br />
Multimedia 2010 Workshop &#8211; 1st ACM ARTEMIS2010 International<br />
Workshop, October 2010.<br />
[23] M. M. Kokar, J. J. Letkowski, R. Dionne, and C. J. Matheus, “Situation<br />
tracking: The concept and a scenario.” in Situation Management<br />
Workshop: SIMA’08. IEEE, MILCOM, 2008.</p>
<p>&nbsp;</p>
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		<title>Athens Cyber Security Conference, Dr. Thuraisingham´s &#8220;Data Mining for Security&#8221;.</title>
		<link>http://www.theamericaspostes.com/3741/athens-cyber-security-conference-dr-thuraisingham%c2%b4s-data-mining-for-security/</link>
		<comments>http://www.theamericaspostes.com/3741/athens-cyber-security-conference-dr-thuraisingham%c2%b4s-data-mining-for-security/#comments</comments>
		<pubDate>Mon, 12 Sep 2011 21:23:48 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
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		<guid isPermaLink="false">http://www.theamericaspostes.com/?p=3741</guid>
		<description><![CDATA[On September 12th, and during the first day of the conference EISIC 2011 &#8220;European Intelligence &#38; Security Informatics Conference, on Counterterrorism and Criminology,&#8221; the first keynote speech was given by the expert Dr. Bhavani Thuraisingham Cyber ​​Security (BT) (*). The title of his presentation was &#8220;Data Mining for Malicious Code Detection and Security Applications&#8221;. Among [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_3743" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2011/09/eisic-2011-003.jpg"><img class="size-medium wp-image-3743" title="Dr. Bhavani Thuraisingham and our Publisher Victor Bjørgan during EISIC 2011, European Intelligence &amp; Security Informatics Conference on Counterterrorism and Criminology." src="http://www.theamericaspostes.com/wp-content/uploads/2011/09/eisic-2011-003-300x230.jpg" alt="" width="300" height="230" /></a><p class="wp-caption-text">Dr. Bhavani Thuraisingham and our Publisher Victor Bjørgan during EISIC 2011, European Intelligence &amp; Security Informatics Conference on Counterterrorism and Criminology.</p></div>
<p>On September 12th, and during the first day of the conference EISIC 2011 &#8220;European Intelligence &amp; Security Informatics Conference, on Counterterrorism and Criminology,&#8221; the first keynote speech was given by the expert Dr. Bhavani Thuraisingham Cyber ​​Security (BT) <strong><a href="http://www.utdallas.edu/~bxt043000/">(*)</a>.</strong> The title of his presentation was &#8220;Data Mining for Malicious Code Detection and Security Applications&#8221;. Among the highlights of his academic dissertation BT defined the meaning of saying that data mining &#8220;is the process of posing queries and extracting patterns different from data using techniques&#8221;. About its use, she said the technology can be used in national security as well aganist cybercrime and security, like f.e. like to Prevent buildings, destroying critical infrastructure (power, telecom). Dr Thuraisingham said that also can Data Mining find out who the bad guys are, capable of carrying out those Terrorist Activities.<br />
Defining Cyber ​​Security BT said it is a technology to Protect the computer and network systems due to Against Corruption last generation of malware like Trojan horses, worms and viruses, including the ultradangerous malware called RAMAL (Radioactive Adaptive Malware), as well as intrusion detection and auditing.</p>
<p>During the first part of the presentation, BT described her research (together with Prof Latifur Khan and students of the University of Texas) and said that some techniques like the Link Analysis technology can be used to trace the viruses to the perpetrators. Another technology called Classification can prevent future attacks depending on the data mining learned about the terrorists through emails and phone conversations. The technology can also separate between real threats and non threats at all, by reducing false positives and false negatives.</p>
<p>More into details of her speech, BT said that the researched techniques like the CFB Program can extract the code blocker malware from data, and make a control flow analysis. She also compared her System with another already in the market , the code blocker SigFree, and assured her system is better, performs better.   Her System can detect Malware that is evolving continuosly, even every milisecond, like the RAMAL (Radioactive Adaptive Malware). Currently, all last generation malware evolve continuosly and it is difficult to prevent for regular firewalls. Dr. Bhavani Thuraisimgham defined her anti RAMAL malware tech as the NCD Novel Class Detection, and the tool is the system based on NCD, the so called SNOD or SNODMAL).</p>
<p>Currently, the most advanced Malware goes undetected because a continuos change in behaviours , every milisecond, and the regular anti malware software can not keep up that speed.</p>
<p>BT assured that her SNOD hast the ability to detect new classes of malware and its changes. She used the SNODMAL, malware detector using SNOD.</p>
<p>She classified the Malware in two categories: Benign and Novel.</p>
<p>The usefullness of SNODMAL will extend to detect multiple novel malware classes and quarantine them.</p>
<p>Summarizing, BT also revealed that they are working to find the best way to detect where this malware attack comes from, and to be able to attribute the attack, where it come from with 100% certainty (to avoid false accusations). Several countries have been attacjed by these novel malware.</p>
<p>In regard to the privacy matter, BT affirmed that the extract of results of the data mining should be private, this is a legal matter, not only an ethical one.</p>
<p>Once her speech finalized and the round of questions ended, Dr. Bhavani Thuraisimgham met Victor Bjoergan , CEO of the U.S. based Global Security Services LLC,  also Publisher of TheAmericasPost.com and EuropeSecurityNews (this under construction). Both discussed the importance of developing these technologies, and its role anti Cybercrime and the strengthening of global security against terrorism.</p>
<p><a href="http://www.utdallas.edu/~bxt043000/"><strong>(*) READ MORE ABOUT DR. BHAVANI THURAISINGHAM</strong></a></p>
<p><a href="http://www.wpafb.af.mil/news/story.asp?id=123209377"><strong>(**) MORE ON DR.THURAISINGHAM</strong></a></p>
<p>&nbsp;</p>
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		<title>Biometrics: $4 billion market for private industry in airport security.</title>
		<link>http://www.theamericaspostes.com/3728/from-stoppin-weapons-to-finding-people-4-billion-market-for-private-industry-in-airport-security/</link>
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		<pubDate>Thu, 08 Sep 2011 12:32:31 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
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		<guid isPermaLink="false">http://www.theamericaspostes.com/?p=3728</guid>
		<description><![CDATA[The U.S. Government agency that manages airport security shifts its strategic emphasis from stopping weapons to finding people. It’s a potential $4 billion market for private industry, based on what the Transportation Security Administration, or TSA, currently spends on aviation security. “Businesses that are good at data analysis, behavior-pattern recognition, big data handling, data-visualization — [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_3729" class="wp-caption alignleft" style="width: 310px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2011/09/Airport-Security.jpg"><img class="size-medium wp-image-3729" title="New business alternatives within Airport Security" src="http://www.theamericaspostes.com/wp-content/uploads/2011/09/Airport-Security-300x249.jpg" alt="" width="300" height="249" /></a><p class="wp-caption-text">New business alternatives within Airport Security</p></div>
<p>The U.S. Government agency that manages airport security shifts its strategic emphasis from stopping weapons to finding people.</p>
<p>It’s a potential $4 billion market for private industry, based on what the Transportation Security Administration, or TSA, currently spends on aviation security.</p>
<p>“Businesses that are good at data analysis, behavior-pattern recognition, big data handling, data-visualization — all ought to come out ahead,” said Stewart Baker, who headed the Department of Homeland Security from 2005 through 2009.</p>
<p>“We could also see an increase in demand for identification tools, such as biometric and facial-recognition scanners,” he said in an interview.</p>
<p>Stepping up to the plate are well known publicly-traded companies such as&#8230;<a href="http://www.marketwatch.com/story/a-decade-after-911-airport-security-shifts-focus-2011-09-08?dist=beforebell"><strong>READ MORE HERE</strong></a></p>
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		<title>Mexican police in Nuevo León/Monterrey using high tech against crime</title>
		<link>http://www.theamericaspostes.com/3687/mexican-police-in-nuevo-leonmonterrey-using-high-tech-against-crime/</link>
		<comments>http://www.theamericaspostes.com/3687/mexican-police-in-nuevo-leonmonterrey-using-high-tech-against-crime/#comments</comments>
		<pubDate>Tue, 30 Aug 2011 09:24:56 +0000</pubDate>
		<dc:creator>Carbonero</dc:creator>
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		<guid isPermaLink="false">http://www.theamericaspostes.com/?p=3687</guid>
		<description><![CDATA[In Mexico, 1500 new police officers are reinforcing the fight against crime in the state of Nuevo Leon. The police are using the latest military technology, as reported by the Federal Ministry of Public Security SSPF to international news agency Prensa Latina PL. According to PL reports , the Federal agents were endowed with these resources to gain [...]]]></description>
			<content:encoded><![CDATA[<div id="attachment_3688" class="wp-caption alignleft" style="width: 295px"><a class="highslide" onclick="return vz.expand(this)" href="http://www.theamericaspostes.com/wp-content/uploads/2011/08/Police-High-Technology-Unit-in-Mexico.jpg"><img class="size-full wp-image-3688" title="Police High Technology Unit in Monterrey, Mexico." src="http://www.theamericaspostes.com/wp-content/uploads/2011/08/Police-High-Technology-Unit-in-Mexico.jpg" alt="" width="285" height="213" /></a><p class="wp-caption-text">Police High Technology Unit in Monterrey, Mexico.</p></div>
<p>In Mexico, 1500 new police officers are reinforcing the fight against crime in the state of Nuevo Leon. The police are using the latest military technology, as reported by the Federal Ministry of Public Security SSPF to international news agency Prensa Latina PL.</p>
<p>According to PL reports , the Federal agents were endowed with these resources to gain an operational advantage over organized criminal groups, which have bought and/or stolen military weapons smuggled from the United States into Mexico.</p>
<p>According to the SSPF, the troops have the support of armored units, motorcycles, ambulances, police cars, high tech communications and helicopters Black Hawk, all equipped with latest instruments, as well as surveillance cameras, a high tech police headquarters, etc.</p>
<p>With these tools, new units in the northern state can operate in extreme conditions, using automated controls and infrared systems for advanced search.</p>
<p>The SSPF also announced that they have implemented at Monterrey and other cities in the northern state various operations, dynamic patrols, designation of safe corridors and use of intelligence units.</p>
<p>SSPF  also activated the Emergency Response Center (see photo)  and set up checkpoints and fixed phones in critical areas or high incidence, on instructions of President Felipe Calderón.</p>
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