1,721,199 research outputs found
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Course category content values
No full textHere is the excel spreadsheet that lists the ABET course category values for the undergraduate CS courses. There are nothing more than the best guesses of the UG committee, and are subject to revision. Courses 100 and 200 level are listed as core, courses 300 and 400 are listed as advanced
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Learning objectives for courses in the Department of Computer Science, Oregon State University
Full text linkThis document describes the overall learning objectives for courses in the department of computer science at Oregon State University
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Category II course change request
No full textHere is the form for requesting Category II changes to courses. Category II changes are changes to individual courses, not changes to programs
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Course assessment form
Full text linkHere is the course assessment form needed for the ABET/CSAB course evaluation notebooks
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Identifying and minimizing the effects of malicious behavior in SERF
Full text linkCollaborative filtering (CF) algorithms are used in a wide range of internet applications. However the chief objective of using CF algorithms across most of these applications is to discover items that might be of interest to its users. CF algorithms work by obtaining feedback from users on the items that they browse and utilize that feedback to suggest recommendations to other users with similar tastes. CF algorithms rely heavily on input provided by humans and thus it is vital to verify that this information is appropriate. In this paper, we analyze various mechanisms by which users can enter malicious data to a CF system called SERF (System for Electronic Recommendation Filtering). We explore how bad data can be propagated through the system and can be used to manipulate the quality of recommendations. We also explore some techniques to counter the effects of bad data on the system. We report the results of our experiment with two simulated systems - a reputation system that utilizes a user's agreement and disagreement history to predict the trust that can be attributed to a user and a word weighting scheme based on word co-occurrence.Keywords: malicious behavior, System for Electronic Recommendation Filtering, SERF, Collaborative filteringKeywords: malicious behavior, System for Electronic Recommendation Filtering, SERF, Collaborative filterin
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Multiparadigm extensions to Java
Full text linkIn 1995 my students and I developed Leda, a multiparadigm language based on the Pascal model. Leda allowed programmers to create abstractions in an object-oriented, functional, or logic programming style. More recently we have been interested in recreating this work, but this time using Java as the language basis. The objective to to add as few new operations as possible, and to make these operations seem as close to Java as possible, so that they seem to fit naturally into the language. To date we have proposed facilities for breaking apart composed objects (sometimes called unboxing), for functions as first-class values, for pass-by-name parameters, and for relational (or logic) programming
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A state-transition model for accessing local resources for a standard Windows system
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Proposed metrics for transfer learning
Full text linkSummary: Four proposed metrics:
[1] average relative reduction in training time (sample size, number of training experiences)
[2] jumpstart (initial advantage of transfer algorithm)
[3] handicap (how long it takes the no-transfer algorithm to overcome the jumpstart)
[4] asymptotic advantage (how much better the transfer learning algorithm does in the limit of large sample sizes)Version
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Machine learning for activity recognition
No full textThis paper surveys the activity recognition task from a machine learning perspective. I give a definition of this problem, and I classify different activity recognition problems into two categories. I show the activities can be hierarchical, and based on such hierarchies I synthesize a language to describe activities. I give a general criteria set to evaluate activity recognition methods. I summarize some off-the-shelf machine learning methods for activity recognition and evaluate them based on this criteria set. Finally, I discuss some methods that I believe can improve the activity recognition performance
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