Compensation paradox: the influence of monetary rewards on user behaviour

Many e-commerce companies collect users’ personal data for marketing purposes despite privacy concerns. Information-collecting companies often offer a monetary reward to users to alleviate privacy concerns and ease the collection of personal information. This study focused on the negative effects of monetary rewards on both information privacy concerns (IPC) and information disclosure. A survey approach was used to collect data and 370 final responses were analysed using a two-way analysis of variance and a binomial logistic regression model. The results show that monetary rewards increase IPC when an information-collecting company requires sensitive information. Additional results indicate that building trust is a more effective way of collecting personal data. This study identifies how organisations can best execute information-collection activities and contributes additional insights for academia and practitioners.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

Improved average consensus algorithm based distributed cost optimization for loading shedding of autonomous microgrids

This paper addresses a new distributed cost optimization (DCO) method for load shedding (LS) of an islanded microgrid considering cost. A two-layer improved average consensus algorithm (IACA) of multi-agent system (MAS) is proposed, and the consensus characteristic of which is analyzed in detail. With the global information discovered in the first layer of the IACA, the DCO of LS can be solved by using the synchronization processing of the IACA in the second layer. PSCAD/EMTDC-based simulation models are built to study the value settings of consensus constants and the performances of the proposed DCO method. Simulation results verified the convergence improvement of the IACA and the effectiveness of the proposed DCO.     

Integrated smart grid systems security threat model

The smart grid (SG) integrates the power grid and the Information and Communication Technology (ICT) with the aim of achieving more reliable and safe power transmission and distribution to the customers. Integrating the power grid with the ICT exposes the SG to systems security threats and vulnerabilities that could be compromised by malicious users and attackers. This paper presents a SG systems threats analysis and integrated SG Systems Security Threat Model (SSTM). The reference architecture of the SG, with its components and communication interfaces used to exchange the energy-related information, is integrated with the results of SG systems security threat analysis to produce a comprehensive, integrated SG SSTM. The SG SSTM in this paper helps better depict and understand the vulnerabilities exploited by attackers to compromise the components and communication links of the SG. The SG SSTM provides a reference of the systems security threats for industrial security practitioners, and can be used for design and implementation of SG systems security controls and countermeasures.     

电信运营商信息安全管控架构及集中策略研究

本文对运营商信息安全集中管控架构演进和信息安全管控策略面临的问题进行了深入分析，提出了信息安全集中管控架构的演进方向建议及对信息安全监控策略工作优化的若干意见。     

Formal Approach to Workflow Application Fragmentations Over Cloud Deployment Models

Workflow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments. Especially, such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workflow processes and applications with scalable on-demand services. In this paper, we focus on the distribution paradigm and its deployment formalism for such very large-scale workflow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments. We propose a formal approach to vertically as well as horizontally fragment very large-scale workflow processes and their applications and to deploy the workflow process and application fragments over three types of cloud deployment models and architectures. To concretize the formal approach, we firstly devise a series of operational situations fragmenting into cloud workflow process and application components and deploying onto three different types of cloud deployment models and architectures. These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workflow process and applications as an implementing component for cloud workflow management systems. Finally, we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workflow processes and applications to be deployed on cloud deployment models and architectural computing environments as well.     

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.     

DEM: Deep Entity Matching Across Heterogeneous Information Networks

Heterogeneous information networks, which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects, are ubiquitous in the real world. In this paper, we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network, and we propose a new method named DEM short for Deep Entity Matching. In contrast to the traditional entity matching methods, DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching. Importantly, we incorporate DEM with the network embedding methodology, enabling highly efficient computing in a vectorized manner. DEM’s generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly. To illustrate its functionality, we apply the DEM algorithm to two real-world entity matching applications: user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks. Extensive experiments on real-world datasets demonstrate DEM’s effectiveness and rationality.

     

Temporal information based GoP adaptation for linear video delivery schemes

An original wireless video transmission scheme called SoftCast has been recently proposed to deal with the issues encountered in conventional wireless video broadcasting systems (e.g. cliff effect). In this paper, we evaluate and optimize the performance of the SoftCast scheme according to the transmitted video content. Precisely, an adaptive coding mechanism based on GoP-size adaptation, which takes into account the temporal information fluctuations of the video, is proposed. This extension denoted Adaptive GoP-size mechanism based on Content and Cut detection for SoftCast (AGCC-SoftCast) significantly improves the performance of the SoftCast scheme. It consists in modifying the GoP-size according to the shot changes and the spatio-temporal characteristics of the transmitted video. When hardware capacities, such as buffer or processor performance are limited, an alternative method based only on the shot changes detection (AGCut-SoftCast) is also proposed. Improvements up to 16 dB for the PSNR and up to 0.55 for the SSIM are observed with the proposed solutions at the cut boundaries. In addition, temporal visual quality fluctuations are reduced under 1dB in average, showing the effectiveness of the proposed methods.