In this paper, a new layered architecture is proposed for big data-driven processing and management of future smart homes. The proposed Representational State Transfer (REST)-based architecture includes seven layers: physical, fog-computing, network, cloud-computing, service, session, and application for efficient data exchange and processing tasks of future smart homes. The smart home physical layer includes all the sensing technologies and smart devices within the smart home, which monitors the home environment and its residents. The data of these sensors will be sent to the smart home fog-computing layer that can do limited data storage and processing. Then, all the required data will be sent to the cloud-computing layer using smart home network layer. The cloud-computing layer provides the scalable solution for data processing and storage. The processed data in the cloud-computing layer will be provided as the data-driven services to different smart home and third-party (e.g., smart city) applications via smart home service layer. Based on the proposed architecture, the applications will utilize the session layer and RESTFUL APIs to use the data-driven services of the smart home. The proposed smart home architecture can provide a ubiquitous and shared data environment as the key aspect of Internet-of-Things systems. INDEX TERMS Big data management, Internet of Things, smart home, layered architecture, data-driven services.

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The purpose of this paper is to analyze the relationship between positive psychological quality and college students' sense of responsibility for innovation and entrepreneurship from the perspective of positive psychology, to explore the cultivation model that can effectively improve college students' sense of responsibility for innovation and entrepreneurship, and to promote their success in entrepreneurship. In this study, a total of 1,500 college students were selected for questionnaire survey. ANOVA was used to analyze the differences of innovation and entrepreneurship responsibilities in demographic variables; factor analysis models were used to explore the factors that influence college students' sense of responsibility for innovation and entrepreneurship; and Spearman correlation and linear regression were used to analyze the relationship between college students' positive quality and innovation and entrepreneurship. The results showed that the average scores of individual responsibility, team responsibility, and social responsibility were 3.290, 3.624, and 3.720, respectively; individual responsibility differed significantly at the grade level; group responsibilities and social responsibilities were significantly different at the grade and gender levels; the linear fitting between benevolence, super-excellence, bravery, restraint, and wisdom with team responsibilities all reached significant levels, among which the wisdom coefficient was the highest; the linear fitting between syngroup, excellence, bravery, modesty, and wisdom with social responsibility reached a significant level, among which the wisdom coefficient was the highest; the linear fitting between syngroup, excellence, bravery, modesty, and wisdom with personal responsibility reached a significant level, among which the coefficient of excellence was the highest. This indicated that positive psychological qualities such as syngroup, excellence, modesty, benevolence, superexcellence, bravery, restraint, and wisdom were the influencing factors of college students' sense of responsibility for innovation and entrepreneurship. Among them, the role of wisdom is the most noteworthy in predicting social and group responsibilities, and super-excellent is the most significant predictor for individual responsibility.

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This paper is concerned with the optimality of a trend following trading rule. The underlying market is modeled as a bull-bear switching market in which the drift of the stock price switches between two states: the uptrend (bull market) and the downtrend (bear market). Such switching process is modelled as a hidden Markov chain. This is a continuation of our earlier study reported in Dai et al. [5] where a trend following rule is obtained in terms of a sequence of stopping times. Nevertheless, a severe restriction imposed in [5] is that only a single share can be traded over time. As a result, the corresponding wealth process is not self-financing. In this paper, we relax this restriction. Our objective is to maximize the expected log-utility of the terminal wealth. We show, via a thorough theoretical analysis, that the optimal trading strategy is trend-following. Numerical simulations and backtesting, in support of our theoretical findings, are also reported.