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A wireless campus environment provides user mobility, as users are no longer tied to fixed locations to access the network. It also offers high network accessibility as network resources remain accessible after office hours. While existing communication applications can work in a wireless network, they are separate applications that often require different devices. This paper describes a personal communications system that integrates various services into a unified platform, providing a one‐stop source for both information access and communication within a wireless campus environment.

With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In this paper, we propose a novel WUL (Web Usage Lattice) based mining approach for mining association access pattern rules for personalized web recommendations. The proposed approach aims to mine a reduced set of effective association pattern rules for enhancing the online performance of web recommendations. We have incorporated the proposed approach into a personalized web recommender system known as AWARS. The performance of the proposed approach is evaluated based on the efficiency and the quality. In the efficiency evaluation, we measure the number of generated rules and the runtime for online recommendations. In the quality evaluation, we measure the quality of the recommendation service based on precision, satisfactory and applicability. This paper will discuss the proposed WUL‐based mining approach, and give the performance of the proposed approach in comparison with the Apriori‐based algorithms.

Although the Internet offers great opportunities for educational purposes, its full potential cannot be realized unless it can be used effectively for lecture delivery across vast distances. However, the best‐effort nature of the current Internet means that many technical issues have to be resolved to ensure that the quality of video and audio play‐out will be acceptable. Presents an end‐to‐end solution for Internet delivery of “live” and recorded lectures based on a one‐to‐many server‐clients topology. The focus is on low‐bandwidth technology to minimize the students’ client side equipment overheads. Describes the development of an integrated quality of service control mechanism comprising both congestion control and error control and how the solution has been implemented as a Web‐based learn‐on‐demand system. Presents a performance analysis of the system.

Traditional document retrieval techniques are ineffective in finding relevant documents due to a lack of semantic understanding of relevance. In this article, two techniques are described – conceptual clustering and co‐word analysis – aimed at injecting intelligence into the retrieval of documents stored in a citation database. Performance analysis has revealed that these techniques are better than traditional lexical analysis in terms of retrieval speed and accuracy.

Research organisations and individual researchers increasingly choose to share their research findings by providing lists of their published works on the World Wide Web. To facilitate the exchange of ideas, the lists often include links to published papers in portable document format (PDF) or Postscript (PS) format. Generally, these publication Web sites are updated regularly to include new works. While manual monitoring of relevant Web sites is tedious, commercial search engines and information monitoring systems are ineffective in finding and tracking scholarly publications. Analyses the characteristics of publication index pages and describes effective automatic extraction techniques that the authors have developed. The authors’ techniques combine lexical and syntactic analyses with heuristics. The proposed techniques have been implemented and tested for more than 14,000 Web pages and achieved consistently high success rates of around 90 percent.

With the proliferation of objectionable materials (e.g. pornography, violence, drugs, etc.) available on the WWW, there is an urgent need for effective countermeasures to protect children and other unsuspecting users from exposure to such materials. Using pornographic Web pages as a case study, this paper presents a thorough analysis of the distinguishing features of such Web pages. The objective of the study is to gain knowledge on the structure and characteristics of typical pornographic Web pages so that effective Web filtering techniques can be developed to filter them automatically. In this paper, we first survey the existing techniques for Web content filtering. A study on the characteristics of pornographic Web pages is then presented. The implementation of a Web content filtering system that combines the use of an artificial neural network and the knowledge gained in the analysis of pornographic Web pages is also given.

The World Wide Web has become an important medium for disseminating scientific publications. To make their research works accessible to other researchers, most research institutions list their publications in an index page that sometimes includes links to online versions of the publications. As the index page is usually updated whenever new research papers are published, researchers need to check these index pages frequently in order to know of any new publications published in the targeted Web site or page. This manual publication monitoring process is tedious and time‐consuming. In this paper, a publication monitoring system, known as PubWatcher, is proposed to automatically track Web publications from user‐specified Web sites or pages. A publication extraction technique has been developed to extract publication information listed in the index pages of the monitored Web sites and pages.

We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined number of semantically meaningful events (categories).

We provide a survey of existing techniques that have been proposed, either directly or indirectly, towards achieving intelligent video categorization. We also compare the performance of two popular ANNs: Kohonen's self‐organizing map (SOM) and fuzzy adaptive resonance theory (Fuzzy ART). In particular, the ANNs are trained offline to form the necessary knowledge base prior to online categorization.

Experimental results show that accurate categorization can be achieved near instantaneously.

The main limitation of this research is the need for a finite set of predefined categories. Further research should focus on generalization of such techniques.

Machine understanding of video footage has tremendous potential for three reasons. First, it enables interactive broadcast of video. Second, it allows unequal error protection for different video shots/segments during transmission to make better use of limited channel resources. Third, it provides intuitive indexing and retrieval for video‐on‐demand applications.

The rapid growth of online social networking sites (“SNS”) such as LinkedIn and Facebook has created new forms of online labor market intermediation that are reconfiguring the hiring process in profound ways; yet, little is understood about the implications of these new technologies for job seekers navigating the labor market, or more broadly, for the careers and lives of workers. The existing literature has focused on digital inequality – workers’ unequal access to or skilled use of digital technologies – but has left unanswered critical questions about the emerging and broad effects of SNS as a labor market intermediary. Drawing on in-depth interviews with unemployed workers this paper describes job seekers’ experiences using SNS to look for work. The findings suggest that SNS intermediation of the labor market has two kinds of effects. First, as an intermediary for hiring, SNS produces labor market winners and losers involving filtering processes that often have little to do with evaluations of merit. Second, SNS filtering processes exert new pressures on all workers, whether winners or losers as perceived though this new filter, to manage their careers, and to some extent their private lives, in particular ways that fit the logic of the SNS-mediated labor market.

With the proliferation of objectionable world wide web (WWW or web) materials such as pornography and violence, there is an increasing need for effective web content filtering tools to protect unsuspecting users from the harmful effect of such materials. This paper aims to discuss this issue.

Using pornographic web materials as a case study, the authors have developed an effective filtering solution that uses machine intelligence to perform offline web page classification into allowed and disallowed web pages.

The results are stored in a database for fast online retrieval whenever access to a web page is requested.

The separation between offline classification and online filtering ensures fast blocking decisions are made from the user's viewpoint.

There is an urgent and continued need for effective measures against the proliferation of objectionable materials on the web. In this paper, the authors describe a possible solution in the form of a complete working system. Future research will focus on adding appropriate modules to tackle other types of objectionable materials than the type described. The basic framework, however, should be applicable to a wide range of materials.