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Most disaster responses require manual processing to eliminate errors, which is currently handled by the government. Although this process ensures quality of the information, considerable time delays occur. With the rapid development of the Internet and mobile devices, crowd-based platforms have been developed and used for disaster responses. However, three obvious drawbacks exist with crowdsourcing: errors, duplications, and unstructured formats. This study aims to develop a computational method, called the Artificial and Crowd Intelligence (ACI) filter, to overcome these drawbacks. To verify the ACI filter, 876 responses collected from an actual flood that occurred on June 10th, 2012, in Taiwan, were used; 284 volunteers from the Internet were recruited for the testing. The results show that the ACI filter eliminates 26.25% of the responses. The percentage of mistaken validations was 0.00% and the percentage of mistaken eliminations was 3.91%. This demonstrates that the ACI filter, by combining machine and human intelligence, can successfully improve crowd response accuracy. [ABSTRACT FROM AUTHOR]

Admiral Stansfield Turner, a former chief of the U.S. Intelligence Community (formally, the Director of Central Intelligence or DCI), died in early 2018. He served as DCI during the Carter Administration, where he was known to be an advocate of stronger intelligence accountability. This previously unpublished interview with him from 1991 discloses his views on a range of intelligence topics, as he reflected back on his experiences a decade after his tenure as DCI. [ABSTRACT FROM AUTHOR]

Social media producers are currently having a fierce competition worldwide to increase their revenues. To achieve this goal, they are investigating alternative ways to attract more users, generate new user activities, and collect valuable data for personalizing contents and services. One such alternative is crowd computing. Our vision is based on the great potential of a well-coordinated and controlled joint utilization of human intelligence and computer systems that can help solve problems that would be difficult to do with individual capabilities alone. To achieve this vision, which we summarize under our concept of crowd social media computing, we investigate and model the characteristics of the social media ecosystem, we discuss the characteristics of crowd computing, and then we demonstrate how crowd computing can play a pivotal role in emerging social media applications. We also propose a new approach to evaluate the impact of crowd computing on the issue of social media Return of Investment (ROI). [ABSTRACT FROM AUTHOR]

In the age of ‘Big Data’, the potential value of open-source information for intelligence-related purposes is widely recognised. Of late, progress in this space has increasingly become associated with software that can expand our ability to gather, filter, interrelate and manipulate data through automated processes. The trend towards automation is both innovative and necessary. However, techno-centric efforts to replace human analysts with finely crafted algorithms across the board, from collection to synthesis and analysis of information, risk limiting the potential of OSINT rather than increasing its scope and impact. Effective OSINT systems must be carefully designed to facilitate complementarity, exploit the strengths, and mitigate the weaknesses of both human analysts and software solutions, obtaining the best contribution from both. Drawing on insights from the field of cognitive engineering, this article considers at a conceptual level how this might be achieved. [ABSTRACT FROM AUTHOR]

The article focuses on limitations in speech processing and semantics capabilities and the use of language has been considered an essential aspect of human intelligence. It mentions compelling goal of artificial intelligence (AI) research from its inception and need for computational models of collaborative activities to support dialogue processing. It also mentions research on natural language processing with personal assistant systems and chatbots becoming pervasive.

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns. [ABSTRACT FROM AUTHOR]

An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g , emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g , represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation. [ABSTRACT FROM AUTHOR]

Rapid developments in detection technology led to the extensive use of new military geographical intelligence systems (GIS). These systems were developed to support military tactical and strategic operation by data collection, processing, analysis, and distribution. This paper reports a case study of ergonomic intervention on the design of GIS. In particular, effects of icon type, icon size, and map background clutter are discussed. This design was specifically used to enhance surveillance performance in the use of interface. Twelve participants carried out a visual search task using the mock GIS. The best surveillance performance was obtained in an interface with large icons and topography not displayed (TND) map background clutter. The results recommended an improved GIS interface design and help interface designers to create efficient and comfortable interfaces for the reduction workload of surveillance radar operators. [ABSTRACT FROM AUTHOR]

Given that it is quite possible that in the near future artificial intelligence equal to or exceeding human intelligence may be created, there are all the grounds to recognize the “e-person (personality)” as the subject of relations under criminal law and the subject (perpetrator) of a crime. The purpose of this article is to study the characteristics of artificial intelligence in the context of criminal law and the relevant doctrine, this being a promising task for this branch with a potential to facilitate the establishment and development of systemic relations with other related areas as a timely response to the need of preventing socially dangerous challenges of the present day and the future. The status “e-person (personality)” given to artificial intelligence as the subject of legal relations should not encounter any resistance from the perspective of legal doctrine and at the legislative level, in the same manner as this issue has been stabilized with regard to legal entities. Therefore, it is quite predictable that in the Ukrainian Criminal Code the section No. XIV-2 has been introduced under the provisional title “Measures at criminal law in respect of electronic persons (personalities)”. The features of artificial intelligence as the subject of a crime de lege ferenda may be as follows: 1) e-person (personality); 2) the ability to understand own actions (or omissions) and control them at the time of committing a crime. At the same time, it may be appropriate and justified to recognize artificial intelligence as the subject of relations under criminal law and the subject (perpetrator) of a crime only subject to a thorough analysis of proposals for a complete readjustment of the entire system, and subject to their harmonization at the level of branch and inter-branch relations, in the inseparable unity with the fundamental constitutional principles and international commitments of Ukraine. Besides, an important aspect is the task of protecting the rights of a victim who uses artificial intelligence- based implants, and combating criminal activities of individuals who may be more socially dangerous due to the use of similar implants based on up-todate technologies. [ABSTRACT FROM AUTHOR]

The building of reliable covers has always been of the utmost importance for clandestine Humint. Using both primary and secondary sources, this study seeks to examine how classical authors and modern scholars have dealt with this topic, and which kind of covers have been the most used throughout history in different sociopolitical contexts, and what are the new perspectives for today’s challenges. Findings suggest that a careful reading of the political milieu in which intelligence officers are required to work, and a certain degree of creativity, are the essential premises for the construction of a plausible cover; that some apparently outdated disguises such as merchants, itinerant monks and philosophers should be understood more broadly today to include business people, humanitarian NGOs and academics; that undercover practices have been theoretically and historically recognized as necessary and convenient by a great number of societies, often with scant regard for ethical considerations. [ABSTRACT FROM PUBLISHER]

The coupling of machines and human intelligence has generated a lot of interest lately to solve the problems that computers cannot yet do. Human participation shows that it is possible to perform tasks at time with a lower cost in a wide range of applications. The emergence of the Crowdsourcing paradigm becomes an important opportunity for performing fastidious and time-consuming tasks because of their complexity and lack of human resources. Combined with automatic image recognition analysis, crowdsourcing can be a decision support tool to effectively solve problems related to the investigation and identification of suspects. However, the infrastructure currently available to support crowdsourcing tasks is very limited. There is an extra non-trivial work that the developer has to do to get the desired design. For this, we present a suspect investigation framework that significantly and adaptively optimizes the use of human and automated software resources to maximize overall the task performance. This framework includes a model that integrates human and material capabilities that supports flexibility when triggering the suspect identification process. This framework defines an acceleration context of the proposed process and constitutes a support for decision-making essential for the suspect's identification. A case study shows the effectiveness of the proposed framework. As a result, the integration of human skills into software engineering greatly improves the performance of the results. The case study consists of two different experiences: the evaluation of the usefulness of a crowd of 83 non-specialized people to handle facial identification tasks; and the implementation of a fully automatic solution based solely on machine components based on strong face detection and recognition algorithms. [ABSTRACT FROM AUTHOR]

This paper is a short introduction on (Big) Data Science and Intelligence for the RDA educational corner. Its purpose is to motivate a greater discussion of what is Big Data, how it is transforming the future of finance and what are the essential opportunities and concerns when using Big Data. "Intelligence" in Big Data is used to emphasize that mathematics is an essential part of the algorithmic and the statistical approaches we use when searching, estimating or seeking answers to our problems. When we use the power of IT, Mathematical and Statistical Intelligence embedded in numerous applications and studies seek to bridge theoretical constructs and their computational realizations. Their integration is a complete system of automatic and learning know how (we may call AI, Learning Machines or what not and by any other name). It is now expanded by systemic computing, data analytics and management to do much more with a lot less. However, in the long run, doing more without Intelligence, replacing intentionality by machine rationality, lead to an evolution where choices are no longer made but instead, are imposed by a data complexity and expert systems that may embed far greater risks than we can expect. In this case, without the power of a human intelligence and a mathematical (objective) rationality, our use of BIG data without science are similar to seeking to go from one place to another without a map. [ABSTRACT FROM AUTHOR]

Research suggests that the process of explaining influences causal reasoning by prompting learners to favor hypotheses that offer 'good' explanations. One feature of a good explanation is its simplicity. Here, we investigate whether prompting children to generate explanations for observed effects increases the extent to which they favor causal hypotheses that offer simpler explanations, and whether this changes over the course of development. Children aged 4, 5, and 6 years observed several outcomes that could be explained by appeal to a common cause (the simple hypothesis) or two independent causes (the complex hypothesis). We varied whether children were prompted to explain each observation or, in a control condition, to report it. Children were then asked to make additional inferences for which the competing hypotheses generated different predictions. The results revealed developmental differences in the extent to which children favored simpler hypotheses as a basis for further inference in this task: 4-year-olds did not favor the simpler hypothesis in either condition; 5-year-olds favored the simpler hypothesis only when prompted to explain; and 6-year-olds favored the simpler hypothesis whether or not they explained. [ABSTRACT FROM AUTHOR]

This article examines human intelligence collection in wartime, and offers a methodology to help determine the relative success or failure of the detainee interrogation mission at Guantanamo Bay, Cuba (GTMO). It surveys the relevant background on GTMO, a brief history on interrogations during World War II and Vietnam, and draws comparisons among them to be considered in potential future mass-interrogation missions. Drawing from documentary research and two dozen original interviews, it argues that, to the extent an intelligence mission was one of the purposes of transferring detainees to GTMO, the relevant metric of success is whether collectors were able to obtain more or ‘better’ intelligence from the detainees than they would have been able to in-theater. At the strategic level, then, GTMO’s value to intelligence collectors is to be assessed at the margins, rather than the absolute value of the information educed. [ABSTRACT FROM PUBLISHER]

Botany is the scientific examination of plants characters. Plant disease can be caused either by living organisms having wide range as harmful or benign ones which grow specifically on the horticultural plant leaves or deficiency of minerals e.g. iron, phosphate, nitrogen, etc. Modern agriculture requires smart and feasible techniques to replace the human intelligence with machine intelligence. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical horticultural field at a mid-day summer Mediterranean day in produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not. [ABSTRACT FROM AUTHOR]

Purpose In previous laboratory-based work, the Scharff technique has proved successful for gathering intelligence from human sources. However, little is known about whether the technique can be taught to practitioners, and whether Scharff-trained practitioners will interview more effectively than colleagues using their conventional approaches and tactics. Method We examined professional handlers from the Norwegian Police ( n = 64), all experienced in interacting with informants. Half received training in the Scharff technique, and their performance was compared against handlers receiving no Scharff training and free to use the approaches they saw fit. All handlers received the same case file describing a source holding information about a future terrorist attack and were given the same interview objectives. Police trainees ( n = 64) took on the role of semicooperative sources and were given incomplete information about the attack. Results The trained handlers adhered to the Scharff training as they (1) aimed to establish the illusion of 'knowing-it-all', (2) posed claims to collect information, and (3) asked few (if any) explicit questions. In contrast, the untrained handlers tried to evoke the sources' motivation to reveal information and asked a high number of explicit questions. Scharff-trained handlers were perceived as less eager to gather information, but collected comparatively more new information. Conclusions The Scharff-trained interviewers utilized more specific elicitation tactics (e.g., posing claims) and fewer general interview strategies (e.g., evoking motivation), and they collected comparatively more new information. This captures the essence of the Scharff technique: It is subtle, yet effective. [ABSTRACT FROM AUTHOR]

This commentary on the JSIS Special Issue on datification focuses on two key themes selected from among the many topics discussed by Special Issue authors: 1) the debate over algorithmic intelligence versus human intelligence, and 2) the consequences of strategic performance systems. The current algorithmic versus human intelligence debate echoes earlier discussions in our field about whether expert systems should replace or support human experts. As appealing as it is to assume that algorithms support expert workers, research suggests that people are not very effective at monitoring and overriding automation. In addition, post-automation work tends to evolve toward lower human knowledge and skill. These observations should caution datification researchers against simplistic theories and should guide researchers to study the multilevel sociotechnical conditions and stakeholders involved in the design, use, and consequences of algorithms in organizations. Strategic performance measurement and ranking systems are also not new, but what is new is the belief that they will not just inform, but also transform, human behavior. In this respect, performance systems resemble the career tournaments that promote intense competition and create great inequality in executive pay and promotion. In addition, performance systems increasing serve as infrastructures, operating at multiple levels of analysis simultaneously. These observations imply that designing and studying such systems involve ethical choices, intensifying the demands on datification researchers. [ABSTRACT FROM AUTHOR]

The article discusses strategies used by security agencies for accurate intelligence acquisition. It elaborates on the human intelligence gathering methods such as interviewing and interrogation and techniques involving rapport and respect-building for quality information gathering. Topics include efficacy of intelligence officer in context manipulation, emotional provocation and understand deception cues.

We administered the Verbal IQ (VIQ) part of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III) to the ConceptNet 4 artificial intelligence (AI) system. The test questions (e.g. “Why do we shake hands?”) were translated into ConceptNet 4 inputs using a combination of the simple natural language processing tools that come with ConceptNet together with short Python programs that we wrote. The question answering used a version of ConceptNet based on spectral methods. The ConceptNet system scored a WPPSI-III VIQ that is average for a four-year-old child, but below average for 5–7 year olds. Large variations among subtests indicate potential areas of improvement. In particular, results were strongest for the Vocabulary and Similarities subtests, intermediate for the Information subtest and lowest for the Comprehension and Word Reasoning subtests. Comprehension is the subtest most strongly associated with common sense. The large variations among subtests and ordinary common sense strongly suggest that the WPPSI-III VIQ results donotshow that “ConceptNet has the verbal abilities of a four-year-old”. Rather, children’s IQ tests offer one objective metric for the evaluation and comparison of AI systems. Also, this work continues previous research on psychometric AI. [ABSTRACT FROM PUBLISHER]

Research on investigative interviewing has only recently started to compare the efficacy of different techniques for gathering intelligence from human sources. So far the research has focused exclusively on sources interviewed once, thus overlooking that most sources are interviewed multiple times. The present study attempts to remedy this gap in the literature. Students (N = 66) took on the role of semi-cooperative sources, holding incomplete information about an upcoming terrorist attack. The sources were informed that they would be interviewed at least once, and that additional interviews might follow. Half of the sources were interviewed on three occasions with the Scharff technique (consisting of five tactics), and the other half was interviewed on three occasions using the so-called direct approach (i.e. open-ended and specific questions). Collapsing the outcome over the three interviews, the Scharff technique resulted in significantly more new information compared to the direct approach. Furthermore, sources interviewed by the direct approach overestimated how much new information they had revealed, whereas the sources interviewed by the Scharff technique underestimated their contribution (although not significantly so). The current study advances previous research by further contextualizing the tests of the efficacy of human intelligence gathering techniques. [ABSTRACT FROM PUBLISHER]

Purpose This paper aims to show that systems intelligence (SI) can be a useful perspective in knowledge management, particularly in the context of the socialization, externalization, combination and internalization (SECI) model. SI is a recently developed systemic concept, a certain kind of human intelligence based on a systems thinking perspective.Design/methodology/approach This paper first provides an overview of the related literature, and then conceptually discusses the role of SI in organizational knowledge creations.Findings SI can work as a powerful momentum in each stage as well as the whole process of SECI.Originality/value This paper is the first application of SI to the field of knowledge management. It provides us with a new perspective to touch human factors in knowledge management processes, which are considered to be essential in the SECI model. [ABSTRACT FROM AUTHOR]

Framing strongly influences actions among technology proponents and end-users. Underlying much debate about artificial intelligence (AI) are several fundamental shortcomings in its framing. First, discussion of AI is atheoretical, and therefore has limited potential for addressing the complexity of causation. Second, intelligence is considered from an anthropocentric perspective that sees human intelligence, and intelligence developed by humans, as superior to all other intelligences. Thus, the extensive post-anthropocentric research into intelligence is not given sufficient consideration. Third, AI is discussed often in reductionist mechanistic terms. Rather than in organicist emergentist terms as a contributor to multi-intelligence (MI) hybrid beings and/or systems. Thus, current framing of AI can be a self-validating reduction within which AI development is focused upon AI becoming the single-variable mechanism causing future effects. In this paper, AI is reframed as a contributor to MI. [ABSTRACT FROM AUTHOR]

In a software system, most of the runtime failures may come to light only during test execution, and this may have a very high cost. To help address this problem, a symbolic execution engine called RTEHunter, which has been developed at the Department of Software Engineering at the University of Szeged, is able to detect runtime errors (such as null pointer dereference, bad array indexing, division by zero) in Java programs without actually running the program in a real-life environment. Applying the theory of symbolic execution, RTEHunter builds a tree, called a symbolic execution tree, composed of all the possible execution paths of the program. RTEHunter detects runtime issues by traversing the symbolic execution tree and if a certain condition is fulfilled the engine reports an issue. However, as the number of execution paths increases exponentially with the number of branching points, the exploration of the whole symbolic execution tree becomes impossible in practice. To overcome this problem, different kinds of constraints can be set up over the tree. E.g. the number of symbolic states, the depth of the execution tree, or the time consumption could be restricted. Our goal in this study is to find the optimal parametrization of RTEHunter in terms of the maximum number of states, maximum depth of the symbolic execution tree and search strategy in order to find more runtime issues in a shorter time. Results on three open-source Java systems demonstrate that more runtime issues can be detected in the 0 to 60 basic block-depth levels than in deeper ones within the same time frame. We also developed two novel search strategies for traversing the tree based on the number of null pointer references in the program and on linear regression that performs better than the default depth-first search strategy. [ABSTRACT FROM AUTHOR]

Talent or human capital is the most important single factor that determines business success or failure. In this respect, the topic of Human Intelligence (HUMINT), or what is more popularly referred to as people, talent, human capital, was and remain one of the hottest topic of the intelligence history. Today, HUMINT may be defined as the collection of information by a trained HUMINT collector, from people and their associated documents. During the entire interwar period, the chief of Romanian Secret Service (SSI) strived to introduce modern principles for conducting the Romanian intelligence activity, as archival documents state. One of the modern principles applied was the permanent concern for recruitment of human sources or agents. Having in mind the contemporary definition of HUMINT, this paper aim to provide a historical perspective on the Romanian Interwar Secret Service's policy of recruitment, trying to identify inside the structure of the SSI the defining elements of intelligence. [ABSTRACT FROM AUTHOR]

Component based development was formerly dependent on propriety/closed source software’s (CSS) components. Open Source software components has attracted noteworthy attention and become an operational alternative of proprietary software because of OSS security, cost effectiveness, quality, flexibility and freedom. Due to the increased attention on component-based development in the past decades, companies have widely adopted open source software (OSS), with the view that using the right software is critical to project success. The availability of Internet as a marketplace for components and wide adoption of OSS has introduced new challenges for selection of software components. Source Forge, other general and domain specific software repositories, different software foundations and individual OSS providers offer an abundance of OSS components. Identification, evaluation and selection of best possible OSS Components for the required need is a quite challenging job. As a reaction to these challenges different methods have been proposed for OSS maturity measurements. E.g. Capgemini-Open Source Maturity Model(C-OSSM), Navicasoft-Open Source Maturity Model (N-OSSM), Qualification and Selection of Open Source (QSOS), Open Business Readiness Rating (Open BRR) and Easiest Open Source (E-OSS).In this paper we compare different Open Source software maturity models available in the market that will help user in OSS component selection. [ABSTRACT FROM AUTHOR]

Pedestrian density is one of the important factors in designing visual surveillance and intelligent transportation systems, but it is challenging to obtain accurate and robust estimates because of both inconsistent crowd patterns in the scenes and target drift caused by imbalanced data distribution. Most of existing global regression frameworks focus on the former challenge to improve the robustness of regression learning, but very few work concerns on mitigating the suffering from the latter one. This paper proposes a novel counting-by-regression framework to utilize the importance of training samples to improve the robustness against inconsistent feature-target relationship based on a recently-proposed learning paradigm—learning with privileged information. To this end, the concept of back-propagation is for the first time considered to select more informative samples contributed to robust fitting performance. Moreover, the direction of target drift along the continuously-changing target dimension is discovered by learning local classifiers under different situation of pedestrian density, which can thus be exploited in our algorithm to further boost the performance. Experimental evaluation on the public UCSD and shopping Mall benchmarks verifies that our approach significantly beats the state-of-the-art counting-by-regression frameworks. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the proliferation of mobile crowdsourcing that brings a new opportunity to leverage human intelligence and movement behaviors to wider application areas. In parallel with the development of online centralized platforms, we look into the realization of self-organized mobile crowdsourcing drawing on opportunistic networks, and propose the Crowd Foraging framework, in which a mobile task requester can proactively recruit a massive crowd of opportunistic encountered mobile workers in real time for quick and high-quality results. We present a comprehensive framework model that fully integrates human behavior factors for modeling task profile, worker arrival, and work ability, and then introduce a service quality concept to indicate the expected service gain that a requester can enjoy when she recruits an arrival worker by jointly considering the work ability of workers as well as timeliness and reward of tasks. Furthermore, we formulate a sequential worker recruitment problem as an online multiple stopping problem to maximize the expected sum of service quality, and accordingly derive an optimal worker recruitment policy through the dynamic programming principle, which exhibits a nice threshold-based structure. We provide data-driven case studies to validate the assumptions used in the policy design, and conduct extensive trace-driven numerical evaluations, which demonstrate that our policy can achieve superior performance (e.g., improve more than 30% performance over classic policies). Besides, our Android prototype shows that the Crowd Foraging framework is cost-efficient, such as requiring less than 7 s and 6 J in terms of time and energy consumption for the optimal threshold calculation in our policy in most cases. [ABSTRACT FROM PUBLISHER]

Effective human-machine collaboration is a long standing problem within rehabilitation robotics. Machines that affect peer-to-peer interaction hold promise. Agents can serve as the basis for such machines. Belief-desire-intention (BDI) paradigm is used to achieve human-like intelligence. However, the BDI model does not emulate human-like qualities such as coordination or collaborative planning. Therein lies the motivation to extend the basic BDI agent model. cBDI-an extension to the BDI architecture supporting human-machine collaboration is presented. Two different applications for the cBDI agent are highlighted: (1) human-cBDI agent collaboration to achieve block stacking and (2) cBDI-based collaborative navigation for an intelligent wheelchair (IW). Under scenarios such that the block stacking task cannot be completed by either human or agent alone, we show how the cBDI is tuned to collaborate and accomplish the task. The cBDI-based collaborative navigation controller is for affecting assistance-as-required in an IW. The effectiveness of the cBDI-based collaborative architecture is evaluated within a ROS-USARSim framework. [ABSTRACT FROM AUTHOR]

We introduce Refinery, an open source platform for exploring large text document collections with topic models. Refinery is a standalone web application driven by a graphical interface, so it is usable by those without machine learning or programming expertise. Users can interactively organize articles by topic and also refine this organization with phrase-level analysis. Under the hood, we train Bayesian nonparametric topic models that can adapt model complexity to the provided data with scalable learning algorithms. The project website http://daeilkim.github.io/refinery/ contains Python code and further documentation. [ABSTRACT FROM AUTHOR]

University-based leadership development programs have long included a focus on human intelligence and emotional intelligence to foster subject and relationship competence, respectively. In response to the need to develop leaders with a strong sense of individual purpose, vision, and values who can meet the challenges of a volatile business environment, spiritual intelligence, a measure of inner resilience, is being added to executive training curricula. All three elements were addressed in a four-year leadership development program conducted through the University of Alberta for the Alberta Heath Services and the Royal Canadian Mounted Police. The results of post-program self-assessments and 360-degree evaluations of the 160 program participants highlight the value of this three-tier training method in molding a new generation of resilient leaders. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In modern times, Lamarck's view of evolution, based oninheritance of acquired traitshas been superseded by neo-Darwinism, based onrandom DNA mutations. This article begins with a series of observations suggesting that Lamarckian inheritance is in fact operative throughout Nature. I then launch into a discussion of human intelligence that is the most important feature of human evolution that cannot be easily explained by mutational selection. Thus, we are smarter than demanded by our evolutionary experience as hunter-gatherers. The difficulty lies in the inability of neo-Darwinism to satisfactorily answer the following question: How can a large energy-costly set of genes, each member of which has little apparent benefit when first created individually, all gather into a permanent existence within a short time period in each and every member of a small population (that was dispersed and geographically isolated over a huge planet) who had a low reproductive output, a low rate of beneficial mutations, and a low level of genetic contact? The article concludes with a speculative but far-reaching epigenetic theory of intelligence that does not require DNA mutation as the exclusive source of evolutionary change. Instead,cranial feedbackrelating brain chemistry, as affected by brain activity including education, with the genome. When it comes to the fast rate of evolution, and the dissemination of the intelligence trait worldwide, cranial feedback could make all the difference. [ABSTRACT FROM AUTHOR]

We combined diffusion tension imaging (DTI) of prefrontal white matter integrity and neuropsychological measures to examine the functional neuroanatomy of human intelligence. Healthy participants completed the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) along with neuropsychological tests of attention and executive control, as measured by Trail Making Test (TMT) and Wisconsin Card Sorting Test (WCST). Stochastic tractography, considered the most effective DTI method, quantified white matter integrity of the medial orbital frontal cortex (mOFC) and rostral anterior cingulate cortex (rACC) circuitry. Based on prior studies, we hypothesized that posterior mOFC–rACC connections may play a key structural role linking attentional control processes and intelligence. Behavioral results provided strong support for this hypothesis, specifically linking attentional control processes, measured by Trails B and WCST perseverative errors, to intelligent quotient (IQ). Hierarchical regression results indicated left posterior mOFC–rACC fractional anisotropy (FA) and Trails B performance time, but not WCST perseverative errors, each contributed significantly to IQ, accounting for approximately 33.95–51.60% of the variance in IQ scores. These findings suggested that left posterior mOFC–rACC white matter connections may play a key role in supporting the relationship of executive functions of attentional control and general intelligence in healthy cognition. [ABSTRACT FROM AUTHOR]

In counterinsurgency, agent networks are double-edged swords. They are useful tools for degrading insurgent influence and protecting the population. However, they also endanger the population in some ways, as we have seen with mass executions of suspected agents and agent misdirection of raids. Identifying how/why this occurs is critical for developing intelligence practices to more effectively implement COIN strategies. This exploratory study uses three recent counterinsurgency cases – for which significant secondary open-source agent network documentation is available – to identify, describe, organize and analyse patterns of noncombatant-targeted violence associated with human intelligence networks. Identified cases of noncombatant-targeted violence in Operation Enduring Freedom – Afghanistan, Operation Iraqi Freedom, and the – Israeli–Palestinian conflict are used to develop a theoretical framework that models intelligence-related violence incentivization, which also draws from theoretical literature on human intelligence, civil war violence, police states, community policing, and the sociology of betrayal. [ABSTRACT FROM PUBLISHER]

Most pedestrian evacuation literatures are about routing algorithm, human intelligence and behavior etc. Few works studied how to fully explore the function of authority/security figures, who know more of the environment by simply being there every day. To evaluate the effect of authority figure (AF) in complex buildings, this paper fully investigates the AF related factors that may influence the evacuation effect of crowd, such as the number and locations of AFs, their spread of direction, calming effect and distribution strategies etc. Social force based modeling and simulation results show that these factors of AFs play important roles in evacuation efficiency, which means fewer AFs with right guiding strategy can have good evacuation performance. For our case study, Zhichun Avenue station, the conclusion is that deployment of four AFs is a good choice to achieve relatively high evacuation performance yet save cost. [ABSTRACT FROM AUTHOR]

Attribution of malicious cyber activities is a deep issue about which confusion and disquiet can be found in abundance. Attribution has many aspects— technical, political, legal, policy, and so on. A number of well-researched and executed papers cover one or more of these aspects, but integration of these aspects is usually left as an exercise for the analyst. This paper distinguishes between attribution of malicious cyber activity to a machine, to a specific perpetrator (often a human being pressing the keys) initiating that activity, and to an adversary that is deemed ultimately responsible for that activity. Which type of attribution is relevant depends on the goals of the relevant decisionmaker. Further, attribution is a multi-dimensional issue that draws on all sources of information available, including technical forensics, human intelligence, signals intelligence, history, and geopolitics, among others. From the perspective of the victim, some degree of factual uncertainty attaches to any of these types of attribution, although the last type—attribution to an ultimately responsible party—also implicates to a very large degree legal, policy, and political questions. But from the perspective of the adversary, the ability to conceal its identity from the victim with high confidence is also uncertain. It is the very existence of such risk that underpins the possibility of deterring hostile actions in cyberspace. [ABSTRACT FROM AUTHOR]

Interrogation represents a crucial moment for prisoners of war, and this article examines the deep influence that cultural issues had on those who experienced interrogation during the Second World War. Memory, gender and race, are considered in relation to the collection of human intelligence not simply from the point of view of military and security studies, but — drawing on historical and memory studies — the impact on the people involved is examined as well. The wider aim is to analyse the relationship between war and culture in the Second World War and its aftermath. The article is part of a wider research on interrogation and questioning in the Second World War which received funding by the Kluge Center (Library of Congress), Washington, DC, and it is based on sources from the Veterans History Project collections at Library of Congress, Washington DC, and the US Holocaust Memorial Museum, Washington, DC. [ABSTRACT FROM PUBLISHER]

This research combines machine learning and human intelligence to analyze 2654 Facebook messages initiated by 26 hospitality companies to develop the taxonomy of Facebook messages in business-to-consumer (B2C) communications. Facebook messages can be classified into two broad message types: Sales/Marketing messages, with five sub-categories of Social Responsibility, Direct Boasting, Indirect Boasting, Product Highlight, and Campaign/Sales, and Conversational messages, with four sub-categories of Call for Action, Provoke Feedback, Advice/Suggestions, and Updates. By comparison, Conversational messages received more “Likes” and comments than Sales/Marketing messages. Direct Boasting, Product Highlight, Call for Action, Provoke Feedback, Advice/Suggestions, and Updates received more “Likes” than other types; Provoke Feedback and Call for Action received more comments. As compared to current literature, the results allow managers to advance more specific strategies to better engage with Facebook users and provide a more thorough taxonomy for additional analysis on companies’ B2C messages on other social media websites. [ABSTRACT FROM AUTHOR]

Online data collection has begun to revolutionize the behavioral sciences. However, conducting carefully controlled behavioral experiments online introduces a number of new of technical and scientific challenges. The project described in this paper, psiTurk, is an open-source platform which helps researchers develop experiment designs which can be conducted over the Internet. The tool primarily interfaces with Amazon's Mechanical Turk, a popular crowd-sourcing labor market. This paper describes the basic architecture of the system and introduces new users to the overall goals. psiTurk aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences. [ABSTRACT FROM AUTHOR]

The article proposes the application of the Design, Evaluation, Justification, and Integration (DEJI) systems engineering model to intelligence applications. Topics discussed include the use of HUMINT (human intelligence) to augment SIGINT (signals/sensor intelligence) from a systems perspectives, the design aspects of the DEJI model, and the ability of the proposed model to change the threat, target vulnerability or consequences for each terrorist attack scenario.

Automatically detecting anomaly in surveillance videos is a crucial issue for social security. Motion instability based online abnormal behaviors detection has been developed in an unsupervised way. The motion instability is composed of direction randomness and motion intensity of particles gotten by optical flow based consecutive motion feature extraction. The direction randomness is gotten based on weighted average of a circular variance of all particles. The motion intensity is determined according to average energy of all particles considering the camera perspective effect. A feature tracking based scheme has been employed to extract spatial-temporal motion features from videos to increase the processing speed. An adaptive dynamic thresholding strategy is developed to detect deviation of the track from the patterns observed both in direction randomness and motion intensity. Besides a double-threshold inference strategy is adopted to determine the range of the motion instability. A state transition model is used to reduce false alarm for confirming anomaly. The anomaly in the video is fast online detected in an ordinary hardware from a cluttered scene without any hypothesis for the scenario contents in advance. Comparative study with state-of-the-arts has indicated the superior performance of the developed approach. [ABSTRACT FROM AUTHOR]

Civil unrest events (protests, strikes, and "occupy" events) are common occurrences in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizens express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. We report on a system for doing that in this article. It uses a combination of key-phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions. We illustrate the application of our system to 10 countries in Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant trade-offs. [ABSTRACT FROM AUTHOR]

The present study examines non-coercive interview techniques aimed for eliciting intelligence from human sources. Two versions of the Scharff technique were compared against the direct approach (a combination of open-ended and specific questions). The Scharff conditions were conceptualised into four tactics and differed with respect to when the confirmation tactic was implemented: before or after an initial open-ended question. Participants (n= 93) took the role of a source in a phone interview and were instructed to strike a balance between not revealing too little or too much information. In general, the Scharff technique outperformed the direct approach on all important measures. The sources in the Scharff conditions revealed more new information, and found it more difficult to understand the interviewer's information objectives. Importantly, the sources interviewed by the Scharff technique underestimated how much new information they revealed, whereas the sources interviewed by the direct approach overestimated the amount of new information revealed. Although no clear order effects of the Scharff tactics were found, we introduce an alternative method for implementing the confirmation tactic. [ABSTRACT FROM AUTHOR]