94,529 research outputs found

    Lies in Disguise. An experimental study on cheating

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    In this paper we present a new design which allows us to draw inferences on the distribution of lying behavior among the population. Participants received a dice in order to determine their payoff anonymously. Whatever they reported to have rolled, they received as payoff. 39% of the subjects were honest and maximally 22% of them were lying completely. Interestingly we found subjects who lied but who did not maximize their income by doing so. Using additional experiments, we can show that a compelling explanation for this behavior is the desire to maintain a favorable self-concept, including honesty and non-greediness.Lie detection, honesty, deception, experimental design

    Between overt and covert research: concealment and disclosure in an ethnographic study of commercial hospitality

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    This article examines the ways in which problems of concealment emerged in an ethnographic study of a suburban bar and considers how disclosure of the research aims, the recruitment of informants, and elicitation of information was negotiated throughout the fieldwork. The case study demonstrates how the social context and the relationships with specific informants determined overtness or covertness in the research. It is argued that the existing literature on covert research and covert methods provides an inappropriate frame of reference with which to understand concealment in fieldwork. The article illustrates why concealment is sometimes necessary, and often unavoidable, and concludes that the criticisms leveled against covert methods should not stop the fieldworker from engaging in research that involves covertness

    Detecting and Explaining Causes From Text For a Time Series Event

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    Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.Comment: Accepted at EMNLP 201

    Situational awareness and safety

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    This paper considers the applicability of situation awareness concepts to safety in the control of complex systems. Much of the research to date has been conducted in aviation, which has obvious safety implications. It is argued that the concepts could be extended to other safety critical domains. The paper presents three theories of situational awareness: the three-level model, the interactive sub-systems approach, and the perceptual cycle. The difference between these theories is the extent to which they emphasise process or product as indicative of situational awareness. Some data from other studies are discussed to consider the negative effects of losing situational awareness, as this has serious safety implications. Finally, the application of situational awareness to system design, and training are presented

    Explanation-Based Auditing

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    To comply with emerging privacy laws and regulations, it has become common for applications like electronic health records systems (EHRs) to collect access logs, which record each time a user (e.g., a hospital employee) accesses a piece of sensitive data (e.g., a patient record). Using the access log, it is easy to answer simple queries (e.g., Who accessed Alice's medical record?), but this often does not provide enough information. In addition to learning who accessed their medical records, patients will likely want to understand why each access occurred. In this paper, we introduce the problem of generating explanations for individual records in an access log. The problem is motivated by user-centric auditing applications, and it also provides a novel approach to misuse detection. We develop a framework for modeling explanations which is based on a fundamental observation: For certain classes of databases, including EHRs, the reason for most data accesses can be inferred from data stored elsewhere in the database. For example, if Alice has an appointment with Dr. Dave, this information is stored in the database, and it explains why Dr. Dave looked at Alice's record. Large numbers of data accesses can be explained using general forms called explanation templates. Rather than requiring an administrator to manually specify explanation templates, we propose a set of algorithms for automatically discovering frequent templates from the database (i.e., those that explain a large number of accesses). We also propose techniques for inferring collaborative user groups, which can be used to enhance the quality of the discovered explanations. Finally, we have evaluated our proposed techniques using an access log and data from the University of Michigan Health System. Our results demonstrate that in practice we can provide explanations for over 94% of data accesses in the log.Comment: VLDB201
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