It has been an important task of discovering frequent fragments as particular patterns from large sequence databases generated from a variety of applications. In general, the patterns to be discovered may partially an...
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It has been an important task of discovering frequent fragments as particular patterns from large sequence databases generated from a variety of applications. In general, the patterns to be discovered may partially and asynchronously exist in sequences, and even contain gaps. In addition, it is necessary to collect the information regarding the locations and frequencies of the patterns. How to enumerate candidate patterns for evaluation without exponentially increasing the computation is another problem. In this paper, the modified periodicity transform is proposed to meet the requirements mentioned above. Also, a distributed computing framework is implemented to perform the mining task more efficiently. Both synthetic and biological sequences are utilized to examine the approach. The experimental results demonstrate the efficiency and effectiveness the system.
Secure Progressively Updatable Traffic Emergency Response System (SPUTERS) is a framework for collecting traffic-surveillance data in crash-prone areas of roadways. SPUTERS receives as input video, audio, and text-bas...
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The extensive use of computers and networks for exchange of information has also had ramifications on the growth and spread of crime through their use. Law enforcement agencies need to keep up with the emerging trends...
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Inoue et al. introduced an automaton on a two-dimensional tape, which decides acceptance or rejection of an input tape by scanning the tape from various sides by various automata which move one way, and investigated t...
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Automated analysis of object-oriented design models can provide insight into the quality of a given software design. Data obtained from automated analysis, however, is often too complex to be easily understood by a de...
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Today, we witness a transformation of radio access network topologies from strictly tree-structured towards meshed architectures. Yet, these edge networks follow mostly circuit-switched paradigms to support quality of...
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The discovery of the association between terms and a specified topic is a difficult task. A new data mining technique, topic-oriented mining and reasoning, is presented for this task. The technique consists of two thr...
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The full-bridge converter with current-doubter synchronous rectifier (FB-CDSR) is a promising topology to implement low-voltage high-current power supplies. This paper proposes a novel control for this converter, whic...
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distributed collaboration over the Internet has become increasingly common in recent years, supported by various technologies such as virtual workspace systems. Often such collaboration is ad-hoc, and virtual workspac...
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ISBN:
(纸本)9781604235531
distributed collaboration over the Internet has become increasingly common in recent years, supported by various technologies such as virtual workspace systems. Often such collaboration is ad-hoc, and virtual workspaces are set up anew for each new instance of collaboration. We propose that much of the ad-hoc collaboration can be captured and transformed into patterns for reuse in future collaboration. This paper presents the results of the past five years of our work in this area. We introduce the notion of patterns of virtual collaboration;present a framework for extracting patterns of work in virtual workspace systems;and introduce an information model of virtual collaboration. We then present an overview of our data and process mining methods and reverse engineering techniques for discerning work processes carried out through virtual workspace systems. Finally we present our visual mining techniques that we use to discern aspects of work processes in virtual workspaces.
This paper describes an empirical study that investigates the knowledge that computer Science students have about the extent of their own previous learning. The study compares self-generated estimates of performance w...
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ISBN:
(纸本)1595930248
This paper describes an empirical study that investigates the knowledge that computer Science students have about the extent of their own previous learning. The study compares self-generated estimates of performance with actual performance on a data structures quiz taken by undergraduate students in courses requiring data structures as a pre-requisite. The study is contextualized and grounded within a research paradigm in Psychology called calibration of knowledge that suggests that self-knowledge across a range of disciplines is highly unreliable. Such self-knowledge is important because of its role in meta-cognition, particularly in cognitive self-regulation and monitoring. It is also important because of the credence that faculty give to student self-reports. Our results indicate that computer Science student self-estimates correlate moderately with their performance on a quiz, more so for estimates provided after they have taken the quiz than before. The pedagogical implications are that students should be provided with regular opportunities for empirical validation of their knowledge as well as being taught the metacognitive skills of regular self-testing in order to overcome validation bias. Copyright 2005 ACM.
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