Proceedings of the 2004 ONR Decision-Support Workshop Series: Interoperability

In August of 1998 the Collaborative Agent Design Research Center (CADRC) of the California Polytechnic State University in San Luis Obispo (Cal Poly), approached Dr. Phillip Abraham of the Office of Naval Research (ONR) with the proposal for an annual workshop focusing on emerging concepts in decision-support systems for military applications. The proposal was considered timely by the ONR Logistics Program Office for at least two reasons. First, rapid advances in information systems technology over the past decade had produced distributed collaborative computer-assistance capabilities with profound potential for providing meaningful support to military decision makers. Indeed, some systems based on these new capabilities such as the Integrated Marine Multi-Agent Command and Control System (IMMACCS) and the Integrated Computerized Deployment System (ICODES) had already reached the field-testing and final product stages, respectively. Second, over the past two decades the US Navy and Marine Corps had been increasingly challenged by missions demanding the rapid deployment of forces into hostile or devastate dterritories with minimum or non-existent indigenous support capabilities. Under these conditions Marine Corps forces had to rely mostly, if not entirely, on sea-based support and sustainment operations. Particularly today, operational strategies such as Operational Maneuver From The Sea (OMFTS) and Sea To Objective Maneuver (STOM) are very much in need of intelligent, near real-time and adaptive decision-support tools to assist military commanders and their staff under conditions of rapid change and overwhelming data loads. In the light of these developments the Logistics Program Office of ONR considered it timely to provide an annual forum for the interchange of ideas, needs and concepts that would address the decision-support requirements and opportunities in combined Navy and Marine Corps sea-based warfare and humanitarian relief operations. The first ONR Workshop was held April 20-22, 1999 at the Embassy Suites Hotel in San Luis Obispo, California. It focused on advances in technology with particular emphasis on an emerging family of powerful computer-based tools, and concluded that the most able members of this family of tools appear to be computer-based agents that are capable of communicating within a virtual environment of the real world. From 2001 onward the venue of the Workshop moved from the West Coast to Washington, and in 2003 the sponsorship was taken over by ONR’s Littoral Combat/Power Projection (FNC) Program Office (Program Manager: Mr. Barry Blumenthal). Themes and keynote speakers of past Workshops have included: 1999: ‘Collaborative Decision Making Tools’ Vadm Jerry Tuttle (USN Ret.); LtGen Paul Van Riper (USMC Ret.);Radm Leland Kollmorgen (USN Ret.); and, Dr. Gary Klein (KleinAssociates) 2000: ‘The Human-Computer Partnership in Decision-Support’ Dr. Ronald DeMarco (Associate Technical Director, ONR); Radm CharlesMunns; Col Robert Schmidle; and, Col Ray Cole (USMC Ret.) 2001: ‘Continuing the Revolution in Military Affairs’ Mr. Andrew Marshall (Director, Office of Net Assessment, OSD); and,Radm Jay M. Cohen (Chief of Naval Research, ONR) 2002: ‘Transformation ... ’ Vadm Jerry Tuttle (USN Ret.); and, Steve Cooper (CIO, Office ofHomeland Security) 2003: ‘Developing the New Infostructure’ Richard P. Lee (Assistant Deputy Under Secretary, OSD); and, MichaelO’Neil (Boeing) 2004: ‘Interoperability’ MajGen Bradley M. Lott (USMC), Deputy Commanding General, Marine Corps Combat Development Command; Donald Diggs, Director, C2 Policy, OASD (NII

Project 10: Training and Education Research and Implementation Strategies for Homeland Security Intelligence Community

Intelligence is vital to national security. Since 2001, there has been a significant movement to protecting U.S. borders and citizens from experiencing the devastating effects of terrorism, among other national security threats. Since its inception in 2002, the Department of Homeland Security has founded the creation of a national security framework based on intelligence. However, there remains significant gaps in the standardization of intelligence training and education. This may be due in part due to the differing missions of DHS components under the overarching umbrella of national security. From experience, it is known that homeland security not only encompasses counterterrorism, but also border protection, emergency management, cyber security, and more. Due to the multifaceted and ever evolving nature of homeland security, there are 17 DHS components to approach the broader issue of national security. Scholars debate on how intelligence education and training should be taught, and who should teach this curriculum. When intelligence training was in its initial stages, most of it was conducted in-house by government agencies. As the demand for homeland security efforts have increased following 9/11, universities have developed homeland security intelligence programs to accommodate the instruction gap. A major issue with two separate entities creating courses to fulfill the intelligence demand is the variation in education and training content. While some scholars believe that a greater professionalization of intelligence careers would help better establish core competencies, others argue that not all levels and types of analysis require the same types of competencies (Bruce and George, 2015, p. 4; Moore and Krizan, 2009). There not only exists a lack of education standardization in the intelligence community, but also in core competency definitions. Due to the overall lack of IC standards in both IC in-house training and university education, some programs fail to include content that is relevant to a professional intelligence career, which creates employee pipeline issues for DHS intelligence needs. This slows the hiring process and exacerbates the issues that come with understaffing, which include low employee morale, high turnover, and demand for more versatile employees. A lack of DHS-wide core competencies only feeds this issue with variation of DHS component missions. In response to the uneven education that employees may receive either from a university or instruction in-house, some agencies have established their own schoolhouses with separate competencies and standard training. Through ethnographic interviews with Intelligence Community members including many DHS participants, as well as in-depth research and domain analysis drawing on scholarly literature and published government reports, Project 10 researchers found a lack of benchmarks for core competencies associated with intelligence analysis as well as multiple gaps in the current implementation of intelligence training and education. There was very little research and literature pertaining to intelligence analysis standards that also mapped how competencies are measured, implemented, and organized. With little guidance or uniformity, the intelligence community entry-level workforce talent demonstrates how knowledge, skills, and abilities vary in similar positions when core competencies are not utilized or enforced. The absence of standardization and structure highlights the need for core competency framework across the entire intelligence community that not only establishes intelligence analysis core competencies but also recommends how these practices and standards could be integrated in a meaningful manner that would positively affect DHS’ mission performance. 5 Based on this analysis, the research team recommends the intelligence analyst working within DHS and its components should have the basic six Core Intelligence Analysis Competencies: Analytical Writing, Communication, Critical Thinking and Reasoning Methods, Collaboration, Project Management, and Basic Technology. In addition to the Core Intelligence Analysis Core Competencies, it is desirable for the intelligence analyst to have Intelligence Fundamentals Skills – this includes familiarity with national intelligence structures and policy, intelligence cycle, and intelligence writing and analytic tools. Despite recommendations provided in both the 2010 Common Competencies for State, Local, and Tribal Intelligence Analysts document by SLT Working Group and the 2015 Analyst Professional Development Road Map, there is no still no baseline standard of competencies that define the role and function of all entry-level intelligence analysts within DHS and its components. To this day, it remains fragmented and siloed, with each component providing only in-house specialized training that is relevant to their unique mission. Echoing the calls to action by both the academic works the research team reviewed and intelligence enterprise practitioners the team interviewed, our analysis demonstrates that being able to standardize this set of competencies is critical to the DHS’s ability to provide and integrate timely intelligence and information, and not merely just a question of hiring and promoting potential job candidates. Furthermore, the team found that the development and inclusion of a standardized Core Intelligence Analyst Competency Matrix that is integrated into the DHS Performance and Learning Management System, and utilizes the Intelligence Community Centers for Academic Excellence can increase the employment pipeline and academic needs, and improve retention and merit-based advancements through educational opportunities

An eco-friendly hybrid urban computing network combining community-based wireless LAN access and wireless sensor networking

Computer-enhanced smart environments, distributed environmental monitoring, wireless communication, energy conservation and sustainable technologies, ubiquitous access to Internet-located data and services, user mobility and innovation as a tool for service differentiation are all significant contemporary research subjects and societal developments. This position paper presents the design of a hybrid municipal network infrastructure that, to a lesser or greater degree, incorporates aspects from each of these topics by integrating a community-based Wi-Fi access network with Wireless Sensor Network (WSN) functionality. The former component provides free wireless Internet connectivity by harvesting the Internet subscriptions of city inhabitants. To minimize session interruptions for mobile clients, this subsystem incorporates technology that achieves (near-)seamless handover between Wi-Fi access points. The WSN component on the other hand renders it feasible to sense physical properties and to realize the Internet of Things (IoT) paradigm. This in turn scaffolds the development of value-added end-user applications that are consumable through the community-powered access network. The WSN subsystem invests substantially in ecological considerations by means of a green distributed reasoning framework and sensor middleware that collaboratively aim to minimize the network's global energy consumption. Via the discussion of two illustrative applications that are currently being developed as part of a concrete smart city deployment, we offer a taste of the myriad of innovative digital services in an extensive spectrum of application domains that is unlocked by the proposed platform

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by projects from the Spanish Ministry of Education and Competitivity TIN2015-65100-R and DIIM2.0 (PROMETEOII/2014/001)

Suspect Development Systems: Databasing Marginality and Enforcing Discipline

Algorithmic accountability law—focused on the regulation of data-driven systems like artificial intelligence (AI) or automated decision-making (ADM) tools—is the subject of lively policy debates, heated advocacy, and mainstream media attention. Concerns have moved beyond data protection and individual due process to encompass a broader range of group-level harms such as discrimination and modes of democratic participation. While a welcome and long overdue shift, the current discourse ignores systems like databases, which are viewed as technically “rudimentary” and often siloed from regulatory scrutiny and public attention. Additionally, burgeoning regulatory proposals like algorithmic impact assessments are not structured to surface important –yet often overlooked –social, organizational, and political economy contexts that are critical to evaluating the practical functions and outcomes of technological systems. This Article presents a new categorical lens and analytical framework that aims to address and overcome these limitations. “Suspect Development Systems” (SDS) refers to: (1) information technologies used by government and private actors, (2) to manage vague or often immeasurable social risk based on presumed or real social conditions (e.g. violence, corruption, substance abuse), (3) that subject targeted individuals or groups to greater suspicion, differential treatment, and more punitive and exclusionary outcomes. This framework includes some of the most recent and egregious examples of data-driven tools (such as predictive policing or risk assessments), but critically, it is also inclusive of a broader range of database systems that are currently at the margins of technology policy discourse. By examining the use of various criminal intelligence databases in India, the United Kingdom, and the United States, we developed a framework of five categories of features (technical, legal, political economy, organizational, and social) that together and separately influence how these technologies function in practice, the ways they are used, and the outcomes they produce. We then apply this analytical framework to welfare system databases, universal or ID number databases, and citizenship databases to demonstrate the value of this framework in both identifying and evaluating emergent or under-examined technologies in other sensitive social domains. Suspect Development Systems is an intervention in legal scholarship and practice, as it provides a much-needed definitional and analytical framework for understanding an ever-evolving ecosystem of technologies embedded and employed in modern governance. Our analysis also helps redirect attention toward important yet often under-examined contexts, conditions, and consequences that are pertinent to the development of meaningful legislative or regulatory interventions in the field of algorithmic accountability. The cross-jurisdictional evidence put forth across this Article illuminates the value of examining commonalities between the Global North and South to inform our understanding of how seemingly disparate technologies and contexts are in fact coaxial, which is the basis for building more global solidarity