Abstract Traditional Digital Libraries require human editorial control over the lifecycles of digital objects contained therein. This imposes an inherent (human) overhead on the maintenance of these digital libraries, which becomes unwieldy once the number of important information units in the digital library becomes too large. A revised framework is needed for digital libraries that takes the onus off the editor... Traditional Digital Libraries require human editorial control over the lifecycles of digital objects contained therein. This imposes an inherent (human) overhead on the maintenance of these digital libraries, which becomes unwieldy once the number of important information units in the digital library becomes too large. A revised framework is needed for digital libraries that takes the onus off the editor and allows the digital library to directly control digital object lifecycles, by employing a set of transformation rules that operate directly on the digital objects themselves. In this paper we motivate and describe a revised digital library framework that utilises transformation rules to automatically optimise system resources. We evaluate this library in three scenarios and also outline how we could apply concepts from this revised framework to address other challenges for digital libraries and digital information access in general. Read More Read Less

Abstract Preserving privacy and managing data access have become crucial requirements for enterprises who deal with personal data. Hippocratic databases (Agrawal et al,2002) and the Chain method (Fedaghi,2007) have been proposed to support this through candid database design that includes responsibility for the privacy of data as a founding tenet. We identify, study, and implement several privacy- preserving features that extend... Preserving privacy and managing data access have become crucial requirements for enterprises who deal with personal data. Hippocratic databases (Agrawal et al,2002) and the Chain method (Fedaghi,2007) have been proposed to support this through candid database design that includes responsibility for the privacy of data as a founding tenet. We identify, study, and implement several privacy- preserving features that extend the previous work on Limiting Disclosure in the Chain method. These features include the support of Ontology in the database design, Ontology design in OWL and applying a hybrid approach of Chains and Ontologies to a Health-care use case. The proposed approach tries to bring the Chain method one step closer to fi tting real-world scenarios. We present the design and implementation guidelines of each of the proposed features. The evaluation of the design shows how the ontology has simplifi ed the complicated work of designing the traditional Hippocratic and chain methods for real databases. Read More Read Less

Abstract Phishing attacks have become a serious problem for users of online banking and e-commerce websites. Many anti- Phishing approaches have been proposed to detect and prevent Phishing. One such approach is the anti-Phishing tips published by many governmental and private organizations to help users themselves to detect and prevent the attacks. This paper examines the effectiveness of the most common... Phishing attacks have become a serious problem for users of online banking and e-commerce websites. Many anti- Phishing approaches have been proposed to detect and prevent Phishing. One such approach is the anti-Phishing tips published by many governmental and private organizations to help users themselves to detect and prevent the attacks. This paper examines the effectiveness of the most common of the many different tips for detecting Phishing websites. A novel effectiveness criteria is proposed and used to examine each tip and to rank it based on its effectiveness score, thus revealing the most effective tips to enable users to detect Phishing attacks. Thus, proponents of anti-Phishing tips can focus on those tips which are most helpful to users in detecting Phishing attacks Read More Read Less

Abstract One of the most important measures for estimating the impact of scientific publications is the number of citations they have received. Today, there exist several tools and metrics to evaluate the relative importance of individual papers and publication venues based on their citation distribution. Despite their acknowledged usefulness, most of the existing techniques rely on quantitative rather than qualitative aspects... One of the most important measures for estimating the impact of scientific publications is the number of citations they have received. Today, there exist several tools and metrics to evaluate the relative importance of individual papers and publication venues based on their citation distribution. Despite their acknowledged usefulness, most of the existing techniques rely on quantitative rather than qualitative aspects of the cita- tion analysis and thus they are inherently limited in conveying any specific information about the author opinions towards the papers they cite. In this paper, we introduce a method that combines text mining and lexical analysis in order to elucidate the authors’ attitude towards the works they cite in their publica- tions. We have applied our method on a set of 4,520 citations that span to 40 publications and tried to shed some light on the following issues. How often authors express an opinion about the papers they cite? Do all authors who cite the same publication share a common understanding on that publication’s impact? Does the citations’ context in fluence people’s perception of the referred papers and how we can take context into consideration? Can we define a qualitative measure for estimating the impact of scientific publications? Our evaluation shows that although authors do not always express their personal opinions about the papers they cite, their judgments (when articulated) have a great influence on the papers’ perceived importance. Read More Read Less

Abstract Learning by discussion when applied to on-line collaborative learning settings can provide signifi cant benefi ts for students in education in general. Indeed, the discussion process plays an important social task in collaborative learning practices. Participants can discuss about the activity being performed, collaborate with each other through the exchange of ideas that may arise, propose new resolution mechanisms, justify... Learning by discussion when applied to on-line collaborative learning settings can provide signifi cant benefi ts for students in education in general. Indeed, the discussion process plays an important social task in collaborative learning practices. Participants can discuss about the activity being performed, collaborate with each other through the exchange of ideas that may arise, propose new resolution mechanisms, justify and refi ne their own contributions, and as a result, acquire new knowledge. Considering these benefi ts, current educational organizations incorporate on-line discussions into web-based courses as part of the very rationale of their pedagogical models. However, in-class collaborative assignments are usually greatly participated and contributed, which makes the monitoring and assessment tasks by tutors and moderators time-consuming, tedious and error-prone. Specially hard if not impossible by human tutors is to manually deal with the sequences of hundreds of contributions making up the discussion threads and the relations between these contributions. Consequently, tutoring tasks during on-line discussions usually restrict to offer evaluation results of the contributing effort and quality after the collaborative learning activity takes place and thus neglect the essential issue of constantly considering the process of knowledge building while it is still being performed. In this paper, we propose a multidimensional model based on data analysis from online collaborative discussion interactions that provides a fi rst step towards an automatic evaluation in just-in-time fashion. The context of this study is a real on-line discussion experience that took place at the Open University of Catalonia. Read More Read Less

Abstract Website Information Architecture (IA) is primarily treated as a new discipline that is concerned with design principles and architecture of information in digital landscape. Proliferation of revolutionary models for interdisciplinary applications of IA, however lack theoretical perspectives, empirical evidence and cultural context. Here, we proposed a new model called Web Architectural-Inducing Model (WA-IM). The model was developed based from existing... Website Information Architecture (IA) is primarily treated as a new discipline that is concerned with design principles and architecture of information in digital landscape. Proliferation of revolutionary models for interdisciplinary applications of IA, however lack theoretical perspectives, empirical evidence and cultural context. Here, we proposed a new model called Web Architectural-Inducing Model (WA-IM). The model was developed based from existing literature and available architectural proposition from prior work. We tested the model suggesting that website IA being conceptualized as multidimensional constructs by exploring the applicability of WA-IM to web localization process from a socio-cognitive cultural perspective. The research methodology involves a web-based survey on Muslim online user as the cultural case study where 427 usable international responses were accumulated. The construct validation of the multifactor structure of website IA was assessed via confi rmatory factor analysis (CFA) by using structural equation modeling. AMOS 7.0 was used to evaluate the fi ve factor hypothesis goodness fi t model. The result revealed that website IA is indeed a multidimensional construct composed of fi ve factors: content-information, content-trust, navigation-traits, navigation-wayfi ndings and context. We conclude our study by providing empirical evidence for WA-IM in a form of goodness-fi t-structure as part of the localization process towards the creation of cultural information structure. Read More Read Less

Abstract Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user’s current work task, i.e. the current work context a user resides in. In this work we present and evaluate machine learning based work task detection methods. By viewing a work task as sequence of digital interaction patterns of mouse clicks and key strokes, we... Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user’s current work task, i.e. the current work context a user resides in. In this work we present and evaluate machine learning based work task detection methods. By viewing a work task as sequence of digital interaction patterns of mouse clicks and key strokes, we present (i) a methodology for recording those user interactions and (ii) an in-depth analysis of supervised classification models for classifying work tasks in two different scenarios: a task centric scenario and a user centric scenario. We analyze different supervised classification models, feature types and feature selection methods on a laboratory as well as a real world data set. Results show satisfiable accuracy and high user acceptance by using relatively simple types of features. Read More Read Less

Abstract The presentation of a book or a digital document is an important factor when designing the interface of a digital catalogue or an online bookstore. One way to attract readers and customers is to show a thumbnail or a preview of the book cover, a feature that can be extended to include a selection of the book’s most appealing pages... The presentation of a book or a digital document is an important factor when designing the interface of a digital catalogue or an online bookstore. One way to attract readers and customers is to show a thumbnail or a preview of the book cover, a feature that can be extended to include a selection of the book’s most appealing pages as well. If the number of documents handled by the system is large then the need to automatise the page selection task arises. This paper details an efficient and easily implementable image-based technique to choose a given number of pages to appear as thumbnails on a document list interface. The algorithm is based on a combination of rudimentary layout analysis and colour saliency computa- tion in order to extract pages with attractive features such as large, colourful images and text elements. An arbitrary amount of spread can be injected in the selection process through an iterativefilter function. A preliminary user study of the algorithm shows that its results are satisfying and provides insight as to how the technique can be improved. Read More Read Less

Abstract Smart Electronic Visitor Information System (SE-VIMS) is an application to replace traditional visitor registration and information management activities. SEVIMS able to record visitor information during visitor registration by using visitor’s Malaysia Government Multipurpose Card (MyKad). An imple- mentation of mobile smart card reader with biometric verification has increase the high level of security and access control. With additional features, the... Smart Electronic Visitor Information System (SE-VIMS) is an application to replace traditional visitor registration and information management activities. SEVIMS able to record visitor information during visitor registration by using visitor’s Malaysia Government Multipurpose Card (MyKad). An imple- mentation of mobile smart card reader with biometric verification has increase the high level of security and access control. With additional features, the identity of visitor can be verify based on matching of his/her thumbprint with the thumbprint image stored in MyKad. The benefits of sophisticated SEVIMS are enhancing the level of security enforced in premises, providing an organized view of visitor records and reducing the time spent on managing visitor information. Read More Read Less