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Inverse frequent itemset mining can be successfully modelled as an instance of the Probabilistic Satisfiability problem. Given a transaction database we can perform a frequent itemset mining algorithm, like the Apriori algorithm, to obtain useful itemset collections such as frequent or closed itemsets. We then use these itemset collections as frequency constraints in order to reconstruct the original database by solving a linear programming problem. There are cases however, where the reconstructed database is not in direct agreement with the original one. In this study, we analyse the degree of similarity between the original database and the reconstructed one, when different variations of condensed itemset representations are used as the initial frequency constraints. We examine how much the initial database properties and itemset relations have been preserved when we emphasize on the frequent itemset border and assess database quality by measuring database distance metrics. As this solution framework presents increased computational cost, we also consider a heuristic approach that is based on the notion that a transaction can be also considered as an itemset and compare the strengths and weaknesses of each framework when the same conditions apply. We manage to improve the efficiency of existing heuristic based approaches to the problem by utilizing smaller initial itemset collections. Our work contributes in (a) privacy preserving data mining and (b) gain in transaction database storage memory savings. [ABSTRACT FROM AUTHOR]

Abstract: The rapid development of modern Information and Communication Technologies (ICTs) in recent years and their introduction into people’s daily lives worldwide, has led to new circumstances at all levels of the social environment. In health care in particular, sensors and data links offer potential for constant monitoring of patient’s symptoms and needs, in real time, enabling physicians to diagnose and monitor health problems wherever the patient is, either at home or outdoors. However, the use of Internet of Things concepts in the health domain does not come without extra data and therefore a data transfer cost overheads. To deal with these overheads, novel metrics, and methods are introduced in an attempt to maximize the capabilities and widen acceptance/usage provided by the Internet of Things. Without losing its generality, the method discussed is experimentally evaluated in the paradigm of the Health domain. The focus is on the need for an overview of available data formats and transmission methods and selection of the optimal combination, which can result to reduction/minimization of costs. An analytic methodology is presented backed with theoretical metrics and evaluated experimentally. [Copyright &y& Elsevier]

Information Systems in the context of the health domain play a critical role. The evolution of the semantic web and knowledge management technologies in the last years set a new context for the exploitation of patient-centric strategies based on well-defined semantics and knowledge. In this paper we have two critical objectives. On the one hand to exploit the state of the art on Semantic Web and Knowledge Management (KM) approaches in the context of the health domain and on the other hand to underline the key challenges within the FP7 framework of the European Union for customised and personalised health services. [ABSTRACT FROM AUTHOR]

An open-source management system of Web-based teaching interventions is presented that can be used in general for science curriculum courses and especially for computer science courses. The proposed solution is called System of Teaching Intelligent Interventions (STII) and facilitates the authoring, deployment, and evaluation of Web-based metaphors. This paper discusses the use of metaphors for assisting students to comprehend the substance of cognitive concepts and their basic background. STII has been applied in a particular educational scenario during the learning process of 228 students attending the course "computer applications." The presentation of results from a pilot evaluation of two metaphors demonstrates the usefulness of the system. The evaluation focuses on the impact of the two metaphors on student learning, and on detecting potential correlations between metaphors and students' groups. STII evaluation conclusions strongly support that multimedia metaphors can be used as an alternative instructional tool to assist students in confronting their cognitive constraints and developing functional understanding of curriculum courses. [ABSTRACT FROM AUTHOR]

The broad acceptance of mobile technology has undoubtedly created new opportunities in communication. The proposed environment attempts to enhance the information flow among the members of a department and, furthermore, to provide a test-bed mobile Web application for students undertaking Internet technologies courses. The key ideas are to support the educational process to provide auxiliary access to educational information sources, such as announcements, course schedules, grades, and user directory details. As a second system integration step, additional mobile Web services were introduced, such as application forms of the department's administration office, project assignment, and discussion groups. Technological evaluation and students' feedback indicate that the proposed solution is both efficient in communication perspective and effective for student involvement in the mobile Web initiative. Future steps include multimedia messages (MMS) integration and third-generation (3G)-based information delivery. [ABSTRACT FROM AUTHOR]

Abstract: Despite the effectiveness of search engines, the persistently increasing amount of web data continuously obscures the search task. Efforts have thus concentrated on personalized search that takes account of user preferences. A new concept is introduced towards this direction; search based on ranking of local set of categories that comprise a user search profile. New algorithms are presented that utilize web page categories to personalize search results. Series of user-based experiments show that the proposed solutions are efficient. Finally, we extend the application of our techniques in the design of topic-focused crawlers, which can be considered an alternative personalized search. [Copyright &y& Elsevier]

This paper addresses the problem of extracting fundamental learning style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems (AIS). We present an unsupervised computational mechanism which exhibits the ability to reveal the inherent group structure of learning patterns that pervade a given set of educational profiles. We rely on the construction of an Artificial Immune Network (AIN) of learning style exemplars by proposing a correlation-based distance metric. This choice is actually imposed by the categoric nature of the underlying data. Our work utilizes an original dataset which was derived during the conduction of an extended empirical study involving students of the Hellenic Open University. The educational profiles of the students were built by collecting their answers on a thoroughly designed questionnaire taking into account a wide range of personal characteristics and skills. The efficiency of the proposed approach was assessed in terms of cluster compactness. Specifically, we measured the average correlation deviation of the students' education profiles from the corresponding artificial memory antibodies that represent the acquired learning style stereotypes. Finally, the unsupervised learning procedure adopted in this paper was tested against a correlation-based version of the k-means algorithm indicating a significant improvement in performance for the AIS-based clustering approach. [ABSTRACT FROM AUTHOR]

Privacy enhancing technologies (PETs) allow to achieve user's transactions unlinkability across different online Service Providers. However, current PETs fail to guarantee unlinkability against the Identity Provider (IdP), which becomes a single point of failure in terms of privacy and security, and therefore, might impersonate its users. To address this issue, OLYMPUS EU project establishes an interoperable framework of technologies for a distributed privacy-preserving identity management based on cryptographic techniques that can be applied both to online and offline scenarios. Namely, distributed cryptographic techniques based on threshold cryptography are used to split up the role of the Identity Provider (IdP) into several authorities so that a single entity is not able to impersonate or track its users. The architecture leverages PET technologies, such as distributed threshold-based signatures and privacy attribute-based credentials (p-ABC), so that the signed tokens and the ABC credentials are managed in a distributed way by several IdPs. This paper describes the Olympus architecture, including its associated requirements, the main building blocks and processes, as well as the associated use cases. In addition, the paper shows how the Olympus oblivious architecture can be used to achieve privacy-preserving M2M offline transactions between IoT devices. [ABSTRACT FROM AUTHOR]