Agent-based architecture for context-aware and personalized event recommendation

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.     

A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers

The aim of this study is to identify and prioritize the solutions of Knowledge Management (KM) adoption in Supply Chain (SC) to overcome its barriers. It helps organizations to concentrate on high rank solutions and develop strategies to implement them on priority. This paper proposes a framework based on fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to identify and rank the solutions of KM adoption in SC and overcome its barriers. The AHP is used to determine weights of the barriers as criteria, and fuzzy TOPSIS method is used to obtain final ranking of the solutions of KM adoption in SC. The empirical case study analysis of an Indian hydraulic valve manufacturing organization is conducted to illustrate the use of the proposed framework for ranking the solutions of KM adoption in SC to overcome its barriers. This proposed framework provides a more accurate, effective and systematic decision support tool for stepwise implementation of the solutions of KM adoption in SC to increase its success rate.     

An adaptive single-point algorithm for global numerical optimization

This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution.     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

An effective node-selection scheme for the energy efficiency of solar-powered WSNs in a stream environment

Previous research on scheduling and solar power issues of wireless sensor networks (WSNs) assumes that the sensors are deployed in a general environment. While monitoring the stream environment, sensors are attached to the stream side to collect the sensed data and transmit the data back to the sink. The stream environment can be scaled in several similar environments. This type of geographic limitation not only exists in a stream environment but also on streets, roads, and trails. This study presents an effective node-selection scheme to enhance the efficiency of saving power and coverage of solar-powered WSNs in a stream environment. Analysis of the sensor deployment in the stream environment permits sensors to be classified into different segments, and then allows the selection of active nodes for building inter-stream connections, inter-segment connections, and intra-segment connections. Based on these connections, the number of active nodes and transmitted packets is minimized. Simulation results show that this scheme can significantly increase the energy efficiency and maintain the monitoring area in solar-powered WSNs.     

Improved migration for mobile computing in distributed networks

A mobile ad hoc network (MANET) is a special type of wireless network in which a collection of mobile nodes with wireless network interfaces may form a temporary network, without the aids of any fixed infrastructure. Security has become a hot research topic in mobile ad hoc networks. In 1998, Volker and Mehrdad proposed a tree-based key management and access control scheme for the mobile agents to manage rights to access its own resources for the visited mobile nodes. Latter, Huang et al. showed that Volker and Mehrdad's scheme needs a large amount of storage and costs for managing and storing secret keys. Huang et al. further proposed a new and efficient scheme based on the elliptic curve cryptosystems to reduce costs and gain better efficiency. However, there is a security leak inherent in Huang et al.'s scheme that the malicious node can overstep his authority to access unauthorized information. This paper will propose a secure, robust, and efficient hierarchical key management scheme for MANETs. Some practical issues and solutions about dynamic key management are also considered and proposed. As compared with Huang et al.'s scheme, our proposed scheme can provide better security assurance, while requiring smaller key-size, lower computational complexities, and constant key management costs which is independent on the number of the confidential files and the visited nodes.     

PPREM: Privacy Preserving REvocation Mechanism for Vehicular Ad Hoc Networks

One of the critical security issues of Vehicular Ad Hoc Networks (VANETs) is the revocation of misbehaving vehicles. While essential, revocation checking can leak potentially sensitive information. Road Side Units (RSUs) receiving the certificate status queries could infer the identity of the vehicles posing the query. An important loss of privacy results from the RSUs ability to tie the checking vehicle with the query's target. We propose a Privacy Preserving Revocation mechanism (PPREM) based on a universal one-way accumulator. PPREM provides explicit, concise, authenticated and unforgeable information about the revocation status of each certificate while preserving the users' privacy.     

Additives to improve the electret properties of isotactic polypropylene

Substituted benzene-1,3,5-tricarboxylic acid trisamides are known to be efficient nucleating agents and in some selected cases are clarifiers for isotactic polypropylene (i-PP). In this paper we expanded the application range of this class of additives to the area of i-PP electret materials. This paper discusses in particular the relation between charge storage properties and additive concentration. Furthermore, attention is directed towards processing conditions, which were found to play an important role and seemed to be related to the dissolution and crystallization behavior of these additives from the i-PP melt. The formation of isolated nanometer-sized supramolecular structures was established to be important. It was found that, with the addition of benzene-1,3,5-tricarboxylic acid-(N-cyclohexyl)-trisamide, the charge storage properties of i-PP films can be improved at concentrations below 0.02 wt% (200 ppm). At such low concentrations, the additive appears to be present as isolated nano-aggregates, which can, therefore, efficiently act as charge traps. A further improvement in electret characteristics can be achieved by increasing the cooling rate of the polymer/additive blends. A clear correlation between nucleation efficiency and charge storage efficiency could not be revealed.     

A measure of information gained through biometric systems

We propose a measure of information gained through biometric matching systems. Firstly, we discuss how the information about the identity of a person is derived from biometric samples through a biometric system, and define the “biometric system entropy” or BSE based on mutual information. We present several theoretical properties and interpretations of the BSE, and show how to design a biometric system which maximizes the BSE. Then we prove that the BSE can be approximated asymptotically by the relative entropy D(f_G(x)∥f_I(x)) where f_G(x) and f_I(x) are probability mass functions of matching scores between samples from individuals and among population. We also discuss how to evaluate the BSE of a biometric system and show experimental evaluation of the BSE of face, fingerprint and multimodal biometric systems.