An SMDP-based Resource Management Scheme for Distributed Cloud Systems

In this paper, the resource management problem in geographically distributed cloud systems is considered. The Follow Me Cloud concept which enables service migration across federated data centers (DCs) is adopted. Therefore, there are two types of service requests to the DC, i.e., new requests (NRs) initiated in the local service area and migration requests (MRs) generated when mobile users move across service areas. A novel resource management scheme is proposed to help the resource manager decide whether to accept the service requests (NRs or MRs) or not and determine how much resources should be allocated to each service (if accepted). The optimization objective is to maximize the average system reward and keep the rejection probability of service requests under a certain threshold. Numerical results indicate that the proposed scheme can significantly improve the overall system utility as well as the user experience compared with other resource management schemes.Comment: 5 pages, 5 figures, conferenc

Management Emotion and Firm’s Propensity of Strategic IT Investment

Firm’s propensity of strategic IT investment describes the tendency of firms to engage in different strategic roles of IT when IT investment decisions are made. Different from prior IS business value literature that has largely taken rational decision making for granted, this paper considers the irrational characteristics of firms’ decision making and investigated the impacts of management emotion on firms’ propensity of strategic IT investment. Based on 191 annual reports data of 32 companies from three industries in a 6-year period (i.e., 2010-2015 fiscal year), we applied sentiment analysis to retrieve emotion tunes embedded in each report and analyzed the relationship with both the volume and the composition of three types of strategic IT signals (automate, informate and transform). Our results show that positive management emotion promotes firm’s propensity of all types of strategic IT investments, however, informate and/or transform IT gain more weights. With positive management emotion, firms also show propensity of investing in strategic IT different from the industry’s dominant IT strategic rol

Heterogeneous Vehicular Networks

This brief examines recent developments in the Heterogeneous Vehicular NETworks (HETVNETs), integrating cellular networks with Dedicated Short-Range Communication (DSRC) for meeting the communications requirements of the Intelligent Transport System (ITS)services. Along with a review of recent literature, a unified framework of the HetVNET is presented. The brief focuses on introducing efficient MAC mechanisms for vehicular communications, including channel access protocols, broadcast/multicast protocols, the location-based channel congestion control scheme and the content-based resource allocation scheme. The cooperative communication between vehicles is discussed. This brief concludes with a discussion on future research directions, and provides the readers with useful insights into the future designs in the HetVNETs, to motivate new ideas for performance improvements in vehicular networks

Decoding Taste Information in Human Brain: A Temporal and Spatial Reconstruction Data Augmentation Method Coupled with Taste EEG

For humans, taste is essential for perceiving food's nutrient content or harmful components. The current sensory evaluation of taste mainly relies on artificial sensory evaluation and electronic tongue, but the former has strong subjectivity and poor repeatability, and the latter is not flexible enough. This work proposed a strategy for acquiring and recognizing taste electroencephalogram (EEG), aiming to decode people's objective perception of taste through taste EEG. Firstly, according to the proposed experimental paradigm, the taste EEG of subjects under different taste stimulation was collected. Secondly, to avoid insufficient training of the model due to the small number of taste EEG samples, a Temporal and Spatial Reconstruction Data Augmentation (TSRDA) method was proposed, which effectively augmented the taste EEG by reconstructing the taste EEG's important features in temporal and spatial dimensions. Thirdly, a multi-view channel attention module was introduced into a designed convolutional neural network to extract the important features of the augmented taste EEG. The proposed method has accuracy of 99.56%, F1-score of 99.48%, and kappa of 99.38%, proving the method's ability to distinguish the taste EEG evoked by different taste stimuli successfully. In summary, combining TSRDA with taste EEG technology provides an objective and effective method for sensory evaluation of food taste.Comment: 10 pages, 11 figures, 30 references, article is being submitte