20360552
Internet attitudes and Internet use: some surprising findings from the HomeNetToo project
HomeNetToo is a longitudinal field study to examine the antecedents and consequences of home Internet use in low-income families. Among the antecedents considered are attitudes about the Internet and their ability to predict Internet use. Participants in the project were 117 adults who completed attitude measures at pre-trial, 3 months, 9 months and post-trial (16 months) and had their Internet use automatically recorded. Ethnographic accounts of their experiences with the Internet were also obtained. Findings indicate that attitudes about privacy and reliability of information on the Internet predict Internet use, but not as expected. Participants who believed less in privacy and reliability of information used the Internet more, even after the contributions of demographic characteristics (race and age), pre-trial experience using the Internet, and actual Internet use during the preceding time period were considered. Attitudes about the potential harm to children and health from Internet use predicted less use. Implications for efforts to reduce the digital divide, the importance of gathering both quantitative and qualitative data, and directions for future research are discussed.
27884919
Writing photo captions for the Web
Photographs are rarely self-sufficient. They need captions. A caption tells us something about the person or thing photographed, also something about the photographer. In this article, we discuss how to write photo captions for the Web. We provide examples from adultso and childrenos work.
31784785
Tiger: Educational Research in the Age of the Internet
14081236
Potential Advantages of Semantic Web for Internet Commerce
Past decade saw much hype in the area of information technology. The emerging of semantic Web makes us ask if it is another hype. This paper focuses on its potential application in Internet commerc ...
29045473
On Reduct Construction Algorithms
This paper critically analyzes reduct construction methods at two levels. At a high level, one can abstract commonalities from the existing algorithms, and classify them into three basic groups based on the underlying control structures. At a low level, by adopting different heuristics or fitness functions for attribute selection, one is able to derive most of the existing algorithms. The analysis brings new insights into the problem of reduct construction, and provides guidelines for the design of new algorithms
17699590
Adapting K-Means Algorithm for Discovering Clusters in Subspaces
Subspace clustering is a challenging task in the field of data mining. Traditional distance measures fail to differentiate the furthest point from the nearest point in very high dimensional data space. To tackle the problem, we design minimal subspace distance which measures the similarity between two points in the subspace where they are nearest to each other. It can discover subspace clusters implicitly when measuring the similarities between points. We use the new similarity measure to improve traditional k-means algorithm for discovering clusters in subspaces. By clustering with low-dimensional minimal subspace distance first, the clusters in low-dimensional subspaces are detected. Then by gradually increasing the dimension of minimal subspace distance, the clusters get refined in higher dimensional subspaces. Our experiments on both synthetic data and real data show the effectiveness of the proposed similarity measure and algorithm.
15518950
Effective and Scalable Authorship Attribution Using Function Words
Techniques for identifying the author of an unattributed document can be applied to problems in information analysis and in academic scholarship. A range of methods have been proposed in the research literature, using a variety of features and machine learning approaches, but the methods have been tested on very different data and the results cannot be compared. It is not even clear whether the differences in performance are due to feature selection or other variables. In this paper we examine the use of a large publicly available collection of newswire articles as a benchmark for comparing authorship attribution methods. To demonstrate the value of having a benchmark, we experimentally compare several recent feature-based techniques for authorship attribution, and test how well these methods perform as the volume of data is increased. We show that the benchmark is able to clearly distinguish between different approaches, and that the scalability of the best methods based on using function words features is acceptable, with only moderate decline as the difficulty of the problem is increased.
14675910
Generalized dimension-reduction framework for recent-biased time series analysis
Recent-biased approximations have received increased attention recently as a mechanism for learning trend patterns from time series or data streams. They have shown promise for clustering time series and incrementally pattern maintaining. In this paper, we design a generalized dimension-reduction framework for recent-biased approximations, aiming at making traditional dimension-reduction techniques actionable in recent-biased time series analysis. The framework is designed in two ways: equi-segmented scheme and vari-segmented scheme. In both schemes, time series data are first partitioned into segments and a dimension-reduction technique is applied to each segment. Then, more coefficients are kept for more recent data while fewer kept for older data. Thus, more details are preserved for recent data and fewer coefficients are kept for the whole time series, which improves the efficiency greatly. We experimentally evaluate the proposed approach, and demonstrate that traditional dimension-reduction techniques, such as SVD, DFT, DWT, PIP, PAA, and landmarks, can be embedded into our framework for recent-biased approximations over streaming time series.