Temporal burstiness and collaborative camouflage aware fraud detection

With the prosperity and development of the digital economy, many fraudsters have emerged on e-commerce platforms to fabricate fraudulent reviews to mislead consumers’ shopping decisions for profit. Moreover, in order to evade fraud detection, fraudsters continue to evolve and present the phenomenon of adversarial camouflage and collaborative attack. In this paper, we propose a novel temporal burstiness and collaborative camouflage aware method (TBCCA) for fraudster detection. Specifically, we capture the hidden temporal burstiness features behind camouflage strategy based on the time series prediction model, and identify highly suspicious target products by assigning suspicious scores as node priors. Meanwhile, a propagation graph integrating review collusion is constructed, and an iterative fraud confidence propagation algorithm is designed for inferring the label of nodes in the graph based on Loop Belief Propagation (LBP). Comprehensive experiments are conducted to compare TBCCA with state-of-the-art fraudster detection approaches, and experimental results show that TBCCA can effectively identify fraudsters in real review networks with achieving 6%–10% performance improvement than other baselines.     

Multimodal fake news detection via progressive fusion networks

Multimodal fake news detection methods based on semantic information have achieved great success. However, these methods only exploit the deep features of multimodal information, which leads to a large loss of valid information at the shallow level. To address this problem, we propose a progressive fusion network (MPFN) for multimodal disinformation detection, which captures the representational information of each modality at different levels and achieves fusion between modalities at the same level and at different levels by means of a mixer to establish a strong connection between the modalities. Specifically, we use a transformer structure, which is effective in computer vision tasks, as a visual feature extractor to gradually sample features at different levels and combine features obtained from a text feature extractor and image frequency domain information at different levels for fine-grained modeling. In addition, we design a feature fusion approach to better establish connections between modalities, which can further improve the performance and thus surpass other network structures in the literature. We conducted extensive experiments on two real datasets, Weibo and Twitter, where our method achieved 83.3% accuracy on the Twitter dataset, which has increased by at least 4.3% compared to other state-of-the-art methods. This demonstrates the effectiveness of MPFN for identifying fake news, and the method reaches a relatively advanced level by combining different levels of information from each modality and a powerful modality fusion method.     

Static group synchronization of second-order multi-agent systems via pinning control

This paper investigates the expected static group synchronization problem of the second-order multi-agent systems via pinning control. For directed communication topology with spanning tree, based on Gershgorin disk theorem and the matrix property, a static pinning control protocol with fixed gains is first introduced and some sufficient and necessary static group synchronization criteria are also established. It is worth mentioning that a rigorous proof is also given that only one pinning node is needed to guarantee static group synchronization, which could be inferred that our protocol might be more economical and effective in large scale of multi-agent systems. Then, for weakly connected directed communication topology with nodes of zero in-degree, an adaptive pinning control applied to the node with zero in-degree is also proposed to achieve static group synchronization. Finally, the efficiency of the proposed protocols is verified by two simulation examples.     

Second-order group consensus for multi-agent systems via pinning leader-following approach

This paper addresses the group consensus problem of second-order nonlinear multi-agent systems through leader-following approach and pinning control. The network topology is assumed to be directed and weakly connected. The pinning consensus protocol is designed according to the agent property, that is, the inter-act agent and the intra-act agent. Some consensus criteria are proposed to guarantee that the agents asymptotically follow the virtual leader in each group, while agents in different groups behave independently. Numerical example is also provided to demonstrate the effectiveness of the theoretical analysis.     

Fault detection and diagnosis for stochastic systems via output PDFs

This paper investigates a new type of fault detection and diagnosis (FDD) problem for non-Gaussian stochastic distribution systems via the output probability density function (PDF). The PDF can be approximated by using square root B-spline expansions. In this framework, an optimal fault detection algorithm is presented by introducing the tuning parameter such that the residual is as sensitive as possible to the fault. When the fault occurs, an adaptive network parameter-updating law is designed to approximate the fault. At last, paper-making process example is given to demonstrate the efficiency of the proposed approach.     

The Internet and the Consumer: Countervailing Power Revisited

The Internet is a tool with the potential to enable consumers to effectively participate in collective bargaining in the marketplace. The purpose of this paper is to address the viability of the concept of countervailing power in the Internet era. First, some theoretical perspectives on how on-line consumer networks have the potential to be a new source of countervailing power are introduced. Next, the potential for consumer countervailing power in the Internet era is discussed. A case study that illustrates the viability of the propositions is then presented. Finally, implications and limitations of the paper are briefly discussed: if the potential for countervailing power can be established, then the circumstances under which collective bargaining would occur and the determinants of its outcome would be of vital interest to consumer economists and policy makers.     

On fault detection of discrete-time switched systems via designing time-varying residual generators

This paper investigates the problem of fault detection (FD) for discrete-time switched systems. Under a dwell time constraint, a switching rule that depends on the measured output is constructed for the system. Time-varying residual generators are designed such that the switched system is asymptotically stable and also with the detection performance under this switching rule. The advantages of the proposed technique are threefold: 1) It has the advantages of both slow switching and fast switching. 2) It can extend the classic design of time-invariant residual generator. 3) It can guarantee the switched system still has the desired fault detection performance even if all subsystems are without it. This feature reduces the performance requirements for each subsystem. A numerical example illustrates the effectiveness of the proposed method.     

Event-triggered fault detection filter design for nonlinear networked systems via fuzzy Lyapunov functions

This paper investigates the problem of event-triggered fault detection filter design for nonlinear networked control systems with both sensor faults and process faults. First, Takagi–Sugeno (T–S) fuzzy model is utilized to represent the nonlinear systems with faults and disturbances. Second, a discrete event-triggered communication scheme is proposed to reduce the utilization of limited network bandwidth between filter and original system. At the same time, considering network-induced delays and event-triggered scheme, a novel T–S fuzzy fault detection filter is constructed to generate a residual signal, which has nonsynchronous premise variables with the original T–S fuzzy system. Then, the fuzzy Lyapunov functional based approach and the reciprocally convex approach are developed such that the obtained sufficient conditions ensure that the fuzzy fault detection system is asymptotically stable with H_∞ performance and is less conservative. All the conditions are given in terms of linear matrix inequalities (LMIs), which can be solved by LMI tools in MATLAB environment. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed results.     

Distributed cooperative learning for a group of uncertain systems via output feedback and neural networks

This paper studies the problem of adaptive neural network (NN) output-feedback control for a group of uncertain nonlinear multi-agent systems (MASs) from the viewpoint of cooperative learning. It is assumed that all MASs have identical unknown nonlinear dynamic models but carry out different periodic control tasks, i.e., each agent system has its own periodic reference trajectory. By establishing a network topology among systems, we propose a new consensus-based distributed cooperative learning (DCL) law for the unknown weights of radial basis function (RBF) neural networks appearing in output-feedback control laws. The main advantage of such a learning scheme is that all estimated weights converge to a small neighborhood of the optimal value over the union of all system estimated state orbits. Thus, the learned NN weights have better generalization ability than those obtained by traditional NN learning laws. Our control approach also guarantees the convergence of tracking errors and the stability of closed-loop system. Under the assumption that the network topology is undirected and connected, we give a strict proof by verifying the cooperative persisting excitation condition of RBF regression vectors. This condition is defined in our recent work and plays a key role in analyzing the convergence of adaptive parameters. Finally, two simulation examples are provided to verify the effectiveness and advantages of the control scheme proposed in this paper.     

Perceived value of knowledge: the potential informer's perception

Achieving value from knowledge has been illusive to the organization despite recent attempts to capitalize on it. An individual's willingness to share his or her knowledge is an essential factor in the organization benefiting from the knowledge it collectively possesses. Yet little is understood regarding what knowledge people are willing to share. This paper describes the development of a new construct, perceived value of knowledge (PVK). PVK's relationship with intentions to share knowledge is illustrated (both qualitatively and quantitatively). The qualitative study illustrates that it may influence the enablers and the barriers to knowledge sharing. The quantitative study found that PVK is positively related to intentions to share knowledge, while Uniqueness of Knowledge (a related but separate construct) is negatively related to intentions to share knowledge. Implications of perceived value of knowledge is discussed.     

网上营销的客户信任影响因素实证研究

在网上营销及客户信任已有理论的基础上,分析了影响客户信任的二类因素,建立了网上营销的客户信任影响因素模型,提出了理论假设,根据模型设计了调查问卷,面向大学生进行问卷调查,对调查数据进行分析,得到实证研究的结论,提出促进网上营销客户信任的方法.     

Predicting smartphone brand loyalty: Consumer value and consumer-brand identification perspectives

With the growth and competition of the smartphone industry, developing a better understanding of what drives consumers' loyalty to smartphone brands has become an important issue for academics and practitioners. This study hypothesizes four determinants of smartphone brand loyalty based on the perspectives of consumer value and consumer-brand identification. Furthermore, this study also explores the moderating effects of age and gender differences on the determination process of smartphone brand loyalty. Data collected from 157 respondents was tested against the research model using a partial least squares (PLS) approach. The results indicate that functional value, emotional value, social value, and brand identification have a positive influence on smartphone brand loyalty. Of the two moderators, results show that age enhances the emotional value-brand loyalty and social value-brand loyalty linkages but weakens the brand identification-brand loyalty relationship. However, gender does not play a moderating role in the determination of smartphone brand loyalty. The results of this study provide several important theoretical and practical implications for smartphone brand management.     

Conventional techniques and emerging nanotechnologies for early detection of cancer metastasis via epithelial-mesenchymal transition monitoringOA

The epithelial-mesenchymal transition(EMT) is a critical process for cancer to metastasize by promoting invasiveness and dissemination of cancer cells in the body. Understanding and tracking EMT could improve cancer therapy by intervening in metastasis. Current approaches for investigating and detecting the EMT process often utilize traditional molecular biology techniques like immunohistochemistry, mass spectrometry and sequencing. These approaches have provided valuable insights into understanding signaling pathways and identifying biomarkers. Liquid biopsy analysis using advanced nanotechnologies allows the longitudinal tracking of EMT in patients to become feasible. This review article offers a molecular overview of EMT, summarizes current EMT models used in cancer research, and reviews both traditional techniques and emerging nanotechnologies employed in recent EMT studies. Additionally, we discuss the limitations and prospects of applying nanotechnologies in EMT research. By evaluating this rapidly emerging field, we propose strategies to facilitate the clinical translation of nanotechnologies for early detection and monitoring of EMT.     

Academic discipline and risk perception of technologies: An empirical study

This article brings together two areas of research: studies on risk perception of technologies and studies on vocational/career choice. This is an important link since decisions concerning technologies are influenced by decision makers’ risk perceptions and these in turn may be related to educational and career paths.We analyze students of different academic disciplines with regard to their risk perception of four technologies. The aim is to find out whether there is a relationship between area of study (as a precursor of vocational and career choice) and risk perception of technologies regarding health, environment and society. The four technologies under study are renewable energies, genetic engineering, nanotechnology and information and communication technologies (ICT). Key results are: irrespective of academic discipline risk of genetic engineering on average is rated highest and renewable energies lowest. This holds for all the risks studied (environmental, health, societal risks). On average, students from different academic disciplines differ in their risk perception. Factor analyses show that common dimensions of risk are the technologies and not the kind of risk. Regression analyses show that the variables influencing perceived risks vary between the technological fields.     

品牌形象对品牌延伸评价的影响:消费者产品涉入的调节

从功能性、象征性及经验性这三种不同的品牌形象类型入手,引入产品涉入变量,通过试验设计来探讨品牌形象对品牌延伸评价的作用是否受到产品涉入的调节。发现当品牌延伸到高涉入产品,品牌的功能性形象对延伸评价起到显著的作用,功能性形象对延伸产品感知质量的作用要受到消费者个人涉入程度的调节;而当品牌延伸到低涉入产品时,品牌的象征性形象和经验性形象对延伸评价起到显著作用,经验性形象对延伸产品购买意愿的影响会受到消费者涉入的调节。     

Investigation of behavior and perception of digital library users: A cognitive style perspective

Cognitive style is an influential factor in users’ information seeking. The study presented in this paper examines how users’ cognitive styles affect their behavior and perception in digital libraries. Fifty participants took part in this study. Two dimensions of cognitive styles were considered: (a) Field Dependence/Independence; (2) Verbalizer/Imager. The results showed that Intermediate users and Verbalizers have not only more positive perception, but they also complete the tasks in effective ways. Implications for the design of personalized digital libraries are also discussed.     

专家组:学术活动的一种有效组织形式

<正>长期以来,发挥科协优势,组织专家开展课题研究是各级科协组织服务当地经济社会发展的一项主要工作。胡锦涛总书记在纪念中国科协成立50周年大会上的讲话中对科协组织在一些重大科     

Personalized hybrid recommendation for group of users: Top-N multimedia recommender

Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users’ satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items.