INVESTIGATING CONSUMERS’ ADOPTION OF INTERACTIVE IN-STORE MOBILE SHOPPING ASSISTANT

With smart phones being deployed widely, interactive in-store Mobile Shopping Assistant (MSA) systems can be considered as an effective way for assisting in-store shopping and can become potentially the pervasive personalized services that both consumers and merchant can trust. However, few studies have focused on investigating the adoption of in-store MSA. Therefore, this study examined the consumers’ attitude and acceptance toward in-store MSA services under the framework of the technology acceptance model (TAM). The findings imply that attitude, perceived ease of use, perceived usefulness, environmental variables, perceived quality of the MSA system, social influence, and user satisfaction are some determinant factors. In addition, significant differences exist between female and male consumers

An Improved dynamic Load Balancing Algorithm applied to a Cafeteria System in a University Campus

Load-balancing algorithms play a key role in improving the performance of practical distributed systems that consist of heterogeneous nodes. The performance of any load-balancing algorithms and its convergence-rate is affected by the structural factors of the network that executes the algorithm. The performance deteriorated as the number of system nodes, the network-diameter, the communication-overhead increased. Moreover, additional technical-factors of the algorithm itself significantly affect the performance of rebalancing the load among nodes. Therefore, this paper proposes an approach that improves the performance of load-balancing algorithms by considering the load-balancing technical-factors and the structure of the network executes the algorithm. We applied the proposed method to a cafeteria system in a university campus and compared our approach with two significant methods presented in the literature. Results indicate that our approach considerably outperformed the original neighborhood approach and the nearest neighbor approach in terms of response time, throughput, communication overhead, and movements cost

Accurate traffic flow prediction in heterogeneous vehicular networks in an intelligent transport system using a supervised non-parametric classifier

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy

Role of Blockchain Technology in Combating COVID‐19 Crisis

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Estimation of privacy risk through centrality metrics

[EN] Users are not often aware of privacy risks and disclose information in online social networks. They do not consider the audience that will have access to it or the risk that the information continues to spread and may reach an unexpected audience. Moreover, not all users have the same perception of risk. To overcome these issues, we propose a Privacy Risk Score (PRS) that: (1) estimates the reachability of an user¿s sharing action based on the distance between the user and the potential audience; (2) is described in levels to adjust to the risk perception of individuals; (3) does not require the explicit interaction of individuals since it considers information flows; and (4) can be approximated by centrality metrics for scenarios where there is no access to data about information flows. In this case, if there is access to the network structure, the results show that global metrics such as closeness have a high degree of correlation with PRS. Otherwise, local and social centrality metrics based on ego-networks provide a suitable approximation to PRS. The results in real social networks confirm that local and social centrality metrics based on degree perform well in estimating the privacy risk of users.This work is partially supported by the Spanish Government project TIN2014-55206-R and FPI grant BES-2015-074498.Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2018). Estimation of privacy risk through centrality metrics. Future Generation Computer Systems. 82:63-76. https://doi.org/10.1016/j.future.2017.12.030S63768

ADVANCED DIFFUSION APPROACH TO DYNAMIC LOAD-BALANCING FOR CLOUD STORAGE

Load-balancing techniques have become a critical function in cloud storage systems that consist of complex heterogeneous networks of nodes with different capacities. However, the convergence rate of any load-balancing algorithm as well as its performance deteriorated as the number of nodes in the system, the diameter of the network and the communication overhead increased. Therefore, this paper presents an approach aims at scaling the system out not up - in other words, allowing the system to be expanded by adding more nodes without the need to increase the power of each node while at the same time increasing the overall performance of the system. Also, our proposal aims at improving the performance by not only considering the parameters that will affect the algorithm performance but also simplifying the structure of the network that will execute the algorithm. Our proposal was evaluated through mathematical analysis as well as computer simulations, and it was compared with the centralized approach and the original diffusion technique. Results show that our solution outperforms them in terms of throughput and response time. Finally, we proved that our proposal converges to the state of equilibrium where the loads in all in-domain nodes are the same since each node receives an amount of load proportional to its capacity. Therefore, we conclude that this approach would have an advantage of being fair, simple and no node is privileged

A Blockchain-Based Editorial Management System

Research publications are reaching a stunning growth rate. Therefore, new challenges regarding managing the peer-review activities are presented, such as data security, privacy, integrity, fragmentation, and isolation. Further, because of the emergence of predatory journals and research fraud, there is a need to assess the quality of the peer-review process. This research proposes a fully functional blockchain-based editorial management system, namely, TimedChain, for managing the peer-review process from submission to publication. TimedChain provides secure, interoperable, transparent, and efficient access to manuscripts by publishers, authors, readers, and other third parties. Time-based smart contracts and advanced encryption techniques are employed for governing transactions, controlling access, and providing further security. An incentive mechanism that evaluates publishers’ value respecting their efforts at managing and maintaining research data and creating new blocks is introduced. Extensive experiments were conducted for performance evaluation. Results demonstrate the efficiency of the proposed system in governing a large set of data at low latency

Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI's Extraction in Real Time Traffic Signs Recognition System

Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system. Traffic sign recognition systems consist of an initial detection phase where images and colors are segmented and fed to the recognition phase. The most challenging process in such systems in terms of time consumption is the detection phase. The tradeoff in previous studies, which proposed different methods for detecting traffic signs, is between accuracy and computation time. Therefore, this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression. We used RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of our approach since no color conversion is needed. Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions. The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method

UniChain: A Design of Blockchain-Based System for Electronic Academic Records Access and Permissions Management

Although blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing academic records, is a new mission for recent research to balance the needs for increasing data privacy and the regular interaction among students and universities. In this paper, a design for a blockchain-based system, namely UniChain, for managing Electronic Academic Records (EARs) is proposed. UniChain is designed to improve the current management systems as it provides interoperable, secure, and effective access to EARs by students, universities, and other third parties, while keeping the students’ privacy. UniChain employs timed-based smart contracts for governing transactions and controlling access to EARs. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of universities regarding their efforts on maintaining academic records and creating new blocks. Extensive experiments were conducted to evaluate the UniChain performance, and the results indicate the efficiency of the proposal in handling a large dataset at low latency

A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System

© 2018 IEEE. Computation intelligence plays a major role in developing intelligent vehicles, which contains a Traffic Sign Recognition (TSR) system for increasing vehicle safety. Traffic sign recognition systems consist of an initial phase called Traffic Sign Detection (TSD), where images and colors are segmented and fed to the recognition phase. The most challenging process in TSR systems in terms of time consumption is the detection phase. The previous studies proposed different models for traffic sign detection, however, the computation time of these models still requires improvement for enabling real time systems. Therefore, this paper focuses on the computational time and proposes a novel time efficient color segmentation model based on logistic regression. This paper uses RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of the proposed model, since no color conversion is needed. The trained segmentation classifier is tested on 1000 traffic sign images taken in different lighting conditions. The experimental results show that the proposed model segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation methods