Jiangxi University of Finance and Economics

About: Jiangxi University of Finance and Economics is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Fuzzy logic & China. The organization has 2865 authors who have published 3556 publications receiving 41567 citations.

Towards a Trust Prediction Framework for Cloud Services Based on PSO-Driven Neural Network

Abstract: Trustworthiness is an important indicator for service selection and recommendation in the cloud environment. However, predicting the trust rate of a cloud service based on its multifaceted quality of services (QoSs) is not an easy task due to the complicated and non-linear relations between service’s QoS values and the final trust rate of the service. According to the existing studies, the adoption of intelligent technique is a rational way to attack this problem. Neural network (NN) has been validated as an effective way to predict the trust rate of the service. However, the parameter setting of NN, which plays an important role in its prediction performance, has not been properly addressed yet. In the paper, particle swarm optimization (PSO) is introduced to enhance NN by optimizing its initial settings. In the proposed hybrid prediction algorithm named PSO-NN, PSO is used to search the appropriate parameters for NN so as to realize accurate trust prediction of cloud services. In order to investigate the effectiveness of PSO-NN, extensive experiments are performed based on public QoS data set, as well as in-depth comparison analysis. The results show that our proposed approach has better performance than basic classification methods in most cases, and significantly outperforms the basic NN in the terms of prediction precision. In addition, PSO-NN demonstrates better stability than the basic NN.

Exploring Consumers’ Buying Behavior in a Large Online Promotion Activity: The Role of Psychological Distance and Involvement

Abstract: As a key marketing tool, online sales promotion has been widely used by online retailers to increase sales of products and brands. Most previous researches on online sales promotion have ignored the effect of consumers’ psychological factors and the heterogeneity of product and consumers. The purpose of this study is to examine the role of psychological distance and involvement on consumers’ buying behavior in large online promotion activities. The research model was examined using empirical analysis of data obtained from consumer surveys after the Double 11 promotion. Our results indicate that temporal distance has positive impact on purchase decision of high involvement products, while having negative impact on purchase decision of low involvement products. Social distance has negative impact on consumers’ purchase decision. Temporal distance is positively associated with consumers’ purchase-decision involvement, and then purchase-decision involvement positively impacts consumers’ total consumption. Social distance has no impact on consumers’ purchase decision involvement. These findings not only advance the understanding of the role of psychological distance and involvement in online sales promotion but also offer implications regarding strategies that online retailers can employ to publish their promotions at different times and encourage consumers more to share promotional information among their friends.

Exploring Rich and Efficient Spatial Temporal Interactions for Real-Time Video Salient Object Detection

Abstract: We have witnessed a growing interest in video salient object detection (VSOD) techniques in today’s computer vision applications In contrast with temporal information (which is still considered a rather unstable source thus far), the spatial information is more stable and ubiquitous, thus it could influence our vision system more As a result, the current main-stream VSOD approaches have inferred and obtained their saliency primarily from the spatial perspective, still treating temporal information as subordinate Although the aforementioned methodology of focusing on the spatial aspect is effective in achieving a numeric performance gain, it still has two critical limitations First, to ensure the dominance by the spatial information, its temporal counterpart remains inadequately used, though in some complex video scenes, the temporal information may represent the only reliable data source, which is critical to derive the correct VSOD Second, both spatial and temporal saliency cues are often computed independently in advance and then integrated later on, while the interactions between them are omitted completely, resulting in saliency cues with limited quality To combat these challenges, this paper advocates a novel spatiotemporal network, where the key innovation is the design of its temporal unit Compared with other existing competitors (eg, convLSTM), the proposed temporal unit exhibits an extremely lightweight design that does not degrade its strong ability to sense temporal information Furthermore, it fully enables the computation of temporal saliency cues that interact with their spatial counterparts, ultimately boosting the overall VSOD performance and realizing its full potential towards mutual performance improvement for each The proposed method is easy to implement yet still effective, achieving high-quality VSOD at 50 FPS in real-time applications

Computing projection depth and its associated estimators

Abstract: To facilitate the application of projection depth, an exact algorithm is proposed from the view of cutting a convex polytope with hyperplanes. Based on this algorithm, one can obtain a finite number of optimal direction vectors, which are x-free and therefore enable us (Liu et al., Preprint, 2011) to compute the projection depth and most of its associated estimators of dimension p?2, including Stahel-Donoho location and scatter estimators, projection trimmed mean, projection depth contours and median, etc. Both real and simulated examples are also provided to illustrate the performance of the proposed algorithm.