Hicham Hage

A Multi-criteria Collaborative Filtering Approach for Research Paper Recommendation in Papyres

Graduate students, professors and researchers regularly access, review, and use large amounts of literature. In previous work, we presented Papyres, a Research Paper Management Systems, which combines bibliography functionalities along with paper recommender techniques and document management tools, in order to provide a set of functionalities to locate research papers, handle and maintain the bibliographies, and to manage and share knowledge about the research literature. In this work we detail Papyres’ paper recommendation technique. Specifically, Papyres employs a Hybrid recommender system that combines both Content-based and Collaborative filtering to help researchers locate research material. Particularly, in this work special attention is given to the Collaborative filtering process, were a multi-criteria approach is used to evaluate the articles, allowing researchers to denote their interest in specific parts of articles. Moreover, we propose, test and compare several approaches to determine the neighbourhood in the Collaborative filtering process such as to increase the accuracy of the recommendation.

Harnessing Learner's Collective Intelligence: A Web2.0 Approach to E-Learning

Today, learners have access to a wide range of sources where they regularly search for, find and use learning resources outside the scope of regular course material. Although E-learning systems offer a variety of tools and functionalities, there are no specific provisions for learners to easily store and share these valuable resources. In this work we present SHAREK, a Web2.0 inspired approach to allow learners to store and share their resources. Specifically, SHAREK combines Artificial Intelligence techniques, such as Recommendation Systems and Information Retrieval with Web2.0 technologies, including RSS and tagging to allow easy sharing of resources and knowledge. Finally, we report on the implementation and validation of SHAREK.

Papyres: A Research Paper Management System

In the context of a research and development department of an enterprise, researchers regularly access, review, and use large amounts of literature, yet none of the exiting tools and solutions provide the wide range of functionalities required to properly manage these resources. Indeed, bibliography management systems manage the references and citations but fail to help researchers handle and locate resources. On the other hand, research paper recommendation systems and specialized search engines help researchers locate new resources, but again fail to help researchers manage the resources. Finally, Enterprise Content Management systems offer the required functionalities to manage resources and knowledge, but are not designed for research literature. In this work we propose a new class of management systems: Research Paper Management Systems. Moreover, to illustrate our approach we highlight our system Papyres which combines various tools and functionalities, including Web2.0 technique, enabling researchers to maintain and manipulate bibliographies, as well as to manage and share resources and knowledge. Finally, we report on the implementation and validation of Papyres.

Anonymous Credentials for Privacy-Preserving E-learning

E-learning systems have made considerable progress within the last few years. Nonetheless, the issue of learner privacy has been practically ignored. Existing E-learning standards offer some provisions for privacy and the security of E-learning systems offers some privacy protection, but remains unsatisfactory on several levels. On the other hand, privacy preserving solutions that are appropriate and used in E-commerce environments are inadequate and unsuitable to the context of E-learning. Indeed, while in most E-commerce applications different transactions between the client and the system are pretty much independent, in E-learning the interactions between the learner and system are intertwined into a developing process that depends heavily on the path the leaner is following. In this paper, we introduce the anonymous credentials for E- learning systems (ACES), a set of protocols to preserve learners privacy in E-learning environments. In particular, the ACES allows learners to provide anonymous credentials throughout the learning process, such as when they need to prove that they possess the necessary requirements to register for a course, and/or to prove that they are the legitimate owners of an anonymous transcript or an anonymous degree. Although the concept of anonymous credentials is not novel, ACES takes into account the specificities of E-learning. Moreover, in order to prevent the misuse of privacy, ACES prevents the possibility of sharing credentials between learners.

A Framework for Privacy-Preserving E-learning

E-learning systems have made considerable progress within the last few years. Nonetheless, the issue of learner privacy has been practically ignored. The security of E-learning systems offers some privacy protection, but remains unsatisfactory on several levels. In this work, we corroborate the need for privacy in E-learning systems. In particular, we introduce a framework for privacy preserving E-learning to provide the learner with the possibility of combining different levels of Privacy and Tracking to satisfy his personal privacy concerns. This allows the learner to perform learning activities and to prove his achievements (such as with anonymous transcripts and anonymous degrees) without exposing various aspects of his private data. In addition, we introduce the Blind Digital Certificate, a digital certificate that does not reveal the learner’s identity. Finally, we report on the implementation and validation of our approach in the context of an E-testing system.

Preserving Learners’ Privacy

E-learning systems have made considerable progress within the last few years. Nonetheless, the issue of learner privacy has been practically ignored. Existing E-learning standards offer some provisions for privacy and the security of E-learning systems offers some privacy protection. Privacy preserving E-learning solutions fall short and still require further development. Additionally, the advent of E-learning 2.0 introduced a whole new set of challenges with regards to privacy preservation. In this chapter we introduce E-learning systems security and privacy preserving approaches, challenges they still face, as well as the challenges brought forth by E-learning 2.0.

Latent Semantic Analysis for Privacy Preserving Peer Feedback

Today’s e-learning systems enable students to communicate with peers (or co-learners) to ask or provide feedback, leading to more efficient learning. Unfortunately, this new option comes with significantly increased risks to the privacy of the feedback requester as well as the peers involved in the feedback process. In fact, peers may unintentionally disclose personal information which may cause great threats to them like cyber-bullying, which in turn may create an unfavorable learning environment leading individuals to abandon learning. In this paper, we propose an approach to minimize data self-disclosure and privacy risks in e-learning contexts. It consists first of mining peers’ feedback to remove negative comments (reducing bullying and harassment) based on machine learning classifier and natural language processing techniques. Second, it consists of striping sentences that potentially reveal personal information in order to protect learners from self-disclosure risks, based on Latent Semantic Analysis (LSA).

E-Payment Plan: A Conditional Multi-payment Scheme Based on User Personalization and Plan Agreement

In the past two decades, the development of payment solutions has significantly changed the way online retail businesses are conducted and enlarged the scope of numerous payment technologies offered in the market. Despite the multitude of payment solutions, card-based systems are still the most prevalent. While secure, card-based systems still lack privacy protection, user control and supervision. In this paper, we propose a new e-payment framework relying on card-based payment systems, with the aggregation of virtual credit cards and a personalized conditional E-Payment Plan defined by the cardholder. In our framework, the cardholder’s privacy is ensured with the use of Virtual Credit Cards. Moreover, with the E-Payment Plan Service Manager (E-PPSM), our proposed framework brings considerable improvements to the shopping practice. Through this service, cardholders can efficiently control and supervise their online purchases. The proposed framework thus ensures three considerable concentrations: personalization, control, and supervision applicable in multi-purchase checkouts, which are, in addition to privacy protection, our main contributions.

Using information retrieval to detect conflicting questions

Although e-learning has advanced considerably in the last decade, some of its aspects, such as e-testing, are still in the development phase. Authoring tools and test banks for e-tests are becoming an integral and indispensable part of e-learning platforms and with the implementation of e-learning standards, such as IMS QTI, e-testing material can be easily shared and reused across various platforms. With the knowledge available for reuse and exam automation comes a new challenge: making sure that created exams are free of confl icts. A Confl ict exists in an exam if at least two questions within that exam are redundant in content, and/or if at least one question reveals the answer to another question within the same exam. In this paper we propose using information retrieval techniques to detect confl icts within an exam. Our solution, ICE (Identifi cation of Conflicts in Exams), is based on the vector space model relying on tf-idf weighing and the cosine function to calculate similarity. ICE also combines the hybrid recommendation techniques of the EQRS (Exam Question Recommender System) in order to propose replacements for confl icting questions.