Social forecasting: a literature review of research promoted by the United States National Security System to model human behavior

The development of new information and communication technologies increased the volume of information flows within society. For the security forces, this phenomenon presents new opportunities for collecting, processing and analyzing information linked with the opportunity to collect a vast and diverse amount data, and at the same time it requires new organizational and individual competences to deal with the new forms and huge volumes of information. Our study aimed to outline the research areas funded by the US defense and intelligence agencies with respect to social forecasting. Based on bibliometric techniques, we clustered 2688 articles funded by US defense or intelligence agencies in five research areas: a) Complex networks, b) Social networks, c) Human reasoning, d) Optimization algorithms, and e) Neuroscience. After that, we analyzed qualitatively the most cited papers in each area. Our analysis identified that the research areas are compatible with the US intelligence doctrine. Besides that, we considered that the research areas could be incorporated in the work of security forces provided that basic training is offered. The basic training would not only enhance capabilities of law enforcement agencies but also help safeguard against (unwitting) biases and mistakes in the analysis of data

The application of classical conditioning to the machine learning of a commonsense knowledge of visual events

In the field of artificial intelligence, possession of commonsense knowledge has long been considered to be a requirementto construct a machine that possesses artificial general intelligence. The conventional approach to providing this commonsense knowledge is to manually encode the required knowledge, a process that is both tedious and costly. After an analysis of classical conditioning, it was deemed that constructing a system based upon the stimulusstimulus interpretation of classical conditioning could allow for commonsense knowledge to be learned through a machine directly and passively observing its environment. Based upon these principles, a system was constructed that uses a stream of events, that have been observed within the environment, to learn rules regarding what event is likely to follow after the observation of another event. The system makes use of a feedback loop between three sub-systems: one that associates events that occur together, a second that accumulates evidence that a given association is significant and a third that recognises the significant associations. The recognition of past associations allows for both the creation of evidence for and against the existence of a particular association, and also allows for more complex associations to be created by treating instances of strongly associated event pairs to be themselves events. Testing the abilities of the system involved simulating the three different learning environments. The results found that measures of significance based on classical conditioning generally outperformed a probability-based measure. This thesis contributes a theory of how a stimulus-stimulus interpretation classical conditioning can be used to create commonsense knowledge and an observation that a significant sub-set of classical conditioning phenomena likely exist to aid in the elimination of noise. This thesis also represents a significant departure from existing reinforcement learning systems as the system presented in this thesis does not perform any form of action selection

A Cooperative Approach for Composite Ontology Matching

Ontologies have proven to be an essential element in a range of applications in which knowl-edge plays a key role. Resolving the semantic heterogeneity problem is crucial to allow the interoperability between ontology-based systems. This makes automatic ontology matching, as an anticipated solution to semantic heterogeneity, an important, research issue. Many dif-ferent approaches to the matching problem have emerged from the literature. An important issue of ontology matching is to find effective ways of choosing among many techniques and their variations, and then combining their results. An innovative and promising option is to formalize the combination of matching techniques using agent-based approaches, such as cooperative negotiation and argumentation. In this thesis, the formalization of the on-tology matching problem following an agent-based approach is proposed. Such proposal is evaluated using state-of-the-art data sets. The results show that the consensus obtained by negotiation and argumentation represent intermediary values which are closer to the best matcher. As the best matcher may vary depending on specific differences of multiple data sets, cooperative approaches are an advantage. *** RESUMO - Ontologias são elementos essenciais em sistemas baseados em conhecimento. Resolver o problema de heterogeneidade semântica é fundamental para permitira interoperabilidade entre sistemas baseados em ontologias. Mapeamento automático de ontologias pode ser visto como uma solução para esse problema. Diferentes e complementares abordagens para o problema são propostas na literatura. Um aspecto importante em mapeamento consiste em selecionar o conjunto adequado de abordagens e suas variações, e então combinar seus resultados. Uma opção promissora envolve formalizara combinação de técnicas de ma-peamento usando abordagens baseadas em agentes cooperativos, tais como negociação e argumentação. Nesta tese, a formalização do problema de combinação de técnicas de ma-peamento usando tais abordagens é proposta e avaliada. A avaliação, que envolve conjuntos de testes sugeridos pela comunidade científica, permite concluir que o consenso obtido pela negociação e pela argumentação não é exatamente a melhoria de todos os resultados individuais, mas representa os valores intermediários que são próximo da melhor técnica. Considerando que a melhor técnica pode variar dependendo de diferencas específicas de múltiplas bases de dados, abordagens cooperativas são uma vantagem

An Ontology-Driven Sociomedical Web 3.0 Framework

Web 3.0, the web of social and semantic cooperation, calls for a methodological multidisciplinary architecture in order to reach its mainstream objectives. With the lack of such an architecture and the reliance of existing efforts on lightweight semantics and RDF graphs, this thesis proposes "Web3.OWL", an ontology-driven framework towards a Web 3.0 knowledge architecture. Meanwhile, the online social parenting data and their corresponding websites users known as "mommy bloggers" undergo one of the fastest online demographics growth, and the available literature reflects the very little attention this growth has so far been given and the various deficiencies the parenting domain suffers from; these deficiencies all fall under the umbrella of the scarcity of parenting sociomedical analysis and decision-support systems. The Web3.OWL framework puts forward an approach that relies on the Meta-Object Facility for Semantics standard (SMOF) for the management of its modeled OWL (Web Ontology Language) expressive domain ontologies on the one hand, and the coordination of its various underlined Web 3.0 prerequisite disciplines on the other. Setting off with a holistic portrayal of Web3.OWL’s components and workflow, the thesis progresses into a more analytic exploration of its main paradigms. Out of its different ontology-aware paradigms are notably highlighted both its methodology for expressiveness handling through modularization and projection techniques and algorithms, and its facilities for tagging inference, suggestion and processing. Web3.OWL, albeit generic by conception, proves its efficiency in solving the deficiencies and meeting the requirements of the sociomedical domain of interest. Its conceived ontology for parenting analysis and surveillance, baptised "ParOnt", strongly contributes to the backbone metamodel and the various constituents of this ontology-driven framework. Accordingly, as the workflow revolves around Description Logics principles, OWL 2 profiles along with standard and beyond-standard reasoning techniques, conducted experiments and competency questions are illustrated, thus establishing the required Web 3.0 outcomes. The empirical results of the diverse preliminary decision-support and recommendation services targeting parenting public awareness, orientation and education do ascertain, in conclusion, the value and potentials of the proposed conceptual framework