94,529 research outputs found
Recommended from our members
A comparative survey of integrated learning systems
This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework
Lies in Disguise. An experimental study on cheating
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
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
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
Recommended from our members
Investigating the Intelligibility of a Computer Vision System for Blind Users
Computer vision systems to help blind usersare becoming increasingly common yet often these systems are not intelligible. Our work investigates the intelligibility of a wearable computer vision system to help blind users locate and identify people in their vicinity. Providing a continuous stream of information, this system allows us to explore intelligibility through interaction and instructions, going beyond studies of intelligibility that focus on explaining a decision a computer vision system might make. In a study with 13 blind users, we explored whether varying instructions (either basic or enhanced) about how the system worked would change blind usersâ experience of the system. We found offering a more detailed set of instructions did not affect how successful users were using the system nor their perceived workload. We did, however, find evidence of significant differences in what they knew about the system, and they employed different, and potentially more effective, use strategies. Our findings have important implications for researchers and designers of computer vision systemsfor blind users, as well more general implications for understanding what it means to make interactive computer vision systems intelligible
Situational awareness and safety
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
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
- âŠ