One effective way for organizations to capture expert knowledge and experience is to encapsulate it within an expert system (ES) and make that system available to others. While ES users have access to the system's knowledge, they shoulder the difficult task of appropriately incorporating the ES recommendations into the decision-making process.One proposed application of an ES is in the realm of deception detection. Humans are inherently poor at recognizing deception when it occurs and their confidence in their judgments is poorly calibrated to their performance. An ES has the potential to significantly improve deception detection; however, joining an ES and a human decision maker creates many important questions that must be addressed before such a system will be useful in a field environment. These questions concern changes in decision outcomes, decision processes, and the decision maker that result from ES use.To examine these questions, a prototype system was created that implements new and unobtrusive methods of deception detection. Kinesic analysis examines the body movement of a potential deceiver and linguistic analysis reviews the structure of utterances from a potential deceiver. This prototype, complete with explanations, was utilized in two experiments that examined the effects of access to the prototype, accuracy level of the prototype, user training in deception detection, and novice or professional lie-catcher status of the users.Use of the prototype system was found to significantly improve professional and novice accuracy rates and confidence alignment. Training was found to have no effect on novice accuracy rates. Accuracy level of the prototype significantly elevated accuracy rates and confidence alignment among novices; however, this improvement was imperceptible to the novices. Novices using the prototype performed on a level equivalent to professionals using the prototype. Neither professional nor novice users of the prototype exceeded the performance of the prototype system alone. Implications of these findings include emphasizing the development of computer-based tools to detect deception and defining a new role for human users of such tools.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-03T20:09:00Z No. of bitstreams: 1 LESANDRO PONCIANO DOS SANTOS - DISSERTAÇÃO PPGCC 2015..pdf: 4083903 bytes, checksum: d3e795a6363845dac05f1dd6ff0cf964 (MD5) / Made available in DSpace on 2018-05-03T20:09:00Z (GMT). No. of bitstreams: 1 LESANDRO PONCIANO DOS SANTOS - DISSERTAÇÃO PPGCC 2015..pdf: 4083903 bytes, checksum: d3e795a6363845dac05f1dd6ff0cf964 (MD5) Previous issue date: 2015-11-23 / Capes / Computação por humanos (human computation) é um modelo de computação que se baseia na coordenação de seres humanos para resolver problemas para os quais o sistema cognitivo humano é mais rápido ou preciso que os atuais sistemas computacionais baseados em processadores digitais. Em sistemas de computação por humanos, ao invés de máquinas, os processadores que realizam as computações são seres humanos. Usar adequadamente o poder cognitivo provido por tais seres humanos é fundamental para o sucesso desse tipo de sistema. Entretanto, pouco se sabe sobre as características de oferta de poder cognitivo e de como o sistema pode utilizar essa oferta de forma otimizada. Este estudo visa avançar esse conhecimento. Como referencial teórico-conceitual, propõe-se uma articulação de teorias e conceitos sobre computação por humanos, engajamento, credibilidade e otimização de desempenho Considerando essa articulação, são propostas métricas para analisar a oferta de poder cognitivo em termos do engajamento e da credibilidade dos participantes. Como estudo de caso de estratégia de otimização de desempenho, propõe-se um algoritmo de replicação de tarefas que visa melhorar o uso do poder cognitivo levando em conta informações de credibilidade dos participantes. Por meio de análise de distribuições, correlações, regressões, classificação e agrupamento, os comportamentos de engajamento e credibilidade são caracterizados usando dados de seis sistemas reais. Entre os resultados obtidos, destacam-se diversos padrões comportamentais identificados na caracterização. Há duas classes de engajamento de participantes: os transientes, que atuam no sistema em apenas um dia e não retornam, e os regulares, que apresentam um engajamento mais duradouro. Os regulares são a minoria, mas são os mais importantes por agregarem maior tempo de computação ao sistema. Eles também não são homogêneos; subdividem-se em cinco grandes perfis,que podem ser rotulados como: empenhados, espasmódicos, persistentes, duradouros e moderados. A credibilidade dos participantes, por sua vez, pode ser medida usando várias métricas baseadas no nível de concordância entre eles. Tal credibilidade está negativamente correlacionada com a dificuldade das tarefas. Por fim, simulações do algoritmo de replicação proposto mostram que ele melhora o uso do poder cognitivo provido pelos participantes e permite tratar diversos compromissos entre diferentes requisitos de qualidade de serviço. / Human computation is a computing approach that draws upon human cognitive abilities to solve computational tasks for which there are so far no satisfactory fully automated solutions. In human computation systems, the processors performing the computations are humans rather than machines. The effectiveness of this kind of system relies on its ability to optimize the use of the cognitive power provided by each human processor. However, little is known about how humans provide their cognitive power in these systems and how these systems can use such cognitive power properly. This study aims at advancing knowledge in this direction. To guide this study, we articulate a framework of theories and concepts about human computation, human engagement, human credibility, and the optimization of computational systems. Based on this theoretical-conceptual framework, we propose metrics to characterize the cognitive power available in a human computation system in terms of the engagement and the credibility of the participants. As case study of system optomization, we also propose a task replication algorithm that optimizes the use of the available cognitive power taking into account information about the credibility of participants. By using correlations, regressions, and clustering algorithms, we characterize the engagement and credibility of participants in data collected from six real systems. Several behavioral patterns are identified in such characterization. Participants can be divided into two broad classes of engagement: the transients, those who work in the system in just one day; and the regulars, those who exhibit a more lasting engagement. Regulars are the minority of participants, but they aggregate the larger amount of cognitive power to the system. They can be subdivided into five groups, labeled as: hardworking, spasmodic, persistent, lasting and moderate. The credibility of participants can be measured by using several different metrics based on the level of agreement among them. Regardless of the metric used, the credibility is negatively correlated with the degree of difficulty of the tasks. Results from simulation show that the proposedtaskreplicationalgorithmcanimprovetheabilityofthesystemtoproperlyusethe cognitive power provided by participants. It also allows one to address trade-offs between differentquality-of-servicerequirements.