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Human-centered computing is rapidly becoming a major research direction as new developments in sensor technology make it increasingly feasible to obtain signals from human beings. At the same time, the pervasiveness of computing devices is also encouraging more research in human---computer interaction, especially in the direction of personalized and adaptive user interfaces. Among the various research issues, affective computing, or the ability of computers to understand and react according to what a user "feels," has been gaining in importance. In order to recognize the human affect (feeling), computers rely on the analysis of signal inputs captured by a multitude of means. This paper proposes the use of human physiological signals as a new form of modality in determining human affects, in a non-intrusive manner. The principle of non-invasiveness is very important, since it imposes no extra burden on the user, which improves user accessibility and encourages user adoption. This goal is realized via the physiological mouse, as a first step toward the support of affective computing. The conventional mouse is converted with a small optical component for capturing user photoplethysmographic (PPG) signal. With the PPG signal, it is possible to compute and derive human physiological signals. A prototype of the physiological mouse was built and raw PPG readings measured. The accuracy of the approach was evaluated through empirical studies to determine human physiological signals from the mouse PPG data. Finally, pilot experiments to correlate human physiological signals with various modes of human---computer interaction, namely gaming and video watching, were conducted. The trend in physiological signals could be used as feedback to the computer system which in turn adapts to the needs or the mood of the user, for instance change the volume and the light intensity when watching a video or playing a game based on current user emotion. The authors argue that this research will provide a new dimension for multimodal affective computing research, and the pilot study has already shed some light toward this research goal.

The boom of internet technologies, particularly web-based geographic information system (GIS) technologies, is opening new opportunities for use of crime mapping to support crime prevention. The aim of this article is to review the adoption trend of web-based crime mapping in the world's top 100 highest GDP cities and access the current state of the art in the use of web-based crime mapping. We found that the functions provided in web-based crime mapping are less than in most traditional crime mapping software. We conclude that existing works of web-based crime mapping focus on supporting community policing rather than analytical functions such as pattern analysis and prediction. With the current descriptive study, we shed some light on the direction of web-based crime mapping studies. The findings in this article can be treated as a reference for developers, police officers and policymakers to develop, design and implement web-based crime mapping projects.

We propose a novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. Pref applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. Pref takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of Pref against several related algorithms, and suggest that Pref can produce reasonably good results using a small training corpus. We also describe a useful application of Pref as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated Pref with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.

Measuring the structural similarity among XML documents is the task of finding their semantic correspondence and is fundamental to many web-based applications. While there exist several methods to address the problem, the data mining approach seems to be a novel, interesting and promising one. It explores the idea of extracting paths from XML documents, encoding them as sequences and finding the maximal frequent sequences using the sequential pattern mining algorithms. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, a new sequential pattern mining scheme for XML document similarity computation is proposed in this paper. It makes use of a preorder tree representation (PTR) to encode the XML tree’s paths so that both the semantics of the elements and the hierarchical structure of the document can be taken into account when computing the structural similarity among documents. In addition, it proposes a postprocessing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.