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In view of the real-time and distributed features of Internet of Things (IoT) safety system in water conservancy engineering, this study proposed a new safety system architecture for water conservancy engineering based on cloud/fog computing and put forward a method of data reliability detection for the false alarm caused by false abnormal data from the bottom sensors. Designed for the South-North Water Transfer Project (SNWTP), the architecture integrated project safety, water quality safety, and human safety. Using IoT devices, fog computing layer was constructed between cloud server and safety detection devices in water conservancy projects. Technologies such as real-time sensing, intelligent processing, and information interconnection were developed. Therefore, accurate forecasting, accurate positioning, and efficient management were implemented as required by safety prevention of the SNWTP, and safety protection of water conservancy projects was effectively improved, and intelligential water conservancy engineering was developed. BackgroundWater conservancy engineering presents the national fundamental industry and is vital in national economic development. The construction of water conservancy projects is generally large in scale, with high investment, wide geographical distribution, and decentralized management, in a long construction period and contains a large amount of information. It may be also affected by unfavorable terrain conditions, complex geological structure, and flood, which increase the difficulty in supervision. Therefore, the construction of Internet safety information system is of great significance for improving safety monitoring and ensuring project safety and water quality safety in water conservancy engineering.With the rapid development of Internet of Things (IoT) technology in recent years, an important source of big data is the tremendous amount of data that are collected, transferred, stored, and processed through the IoT system [1][2][3]. Due to the general dense geographical distribution of water conservancy projects, the IoT-based data in water conservancy engineering have typical geographical attributes.Taking the Middle Route of the SNWTP as an example, its main line starts at Taocha Sluice of Danjiangkou Reservoir, crosses the Yangtze River, Huaihe River, Yellow River, and Haihe River, passes through Provinces of Henan, Hebei, Beijing and Tianjin, and provides water for tens of large-and medium-sized cities, like Pingdingshan, Xuchang, Zhengzhou, Jiaozuo, Xinxiang, Hebi, Anyang, Handan, Xingtai, Shijiazhuang, Baoding, Beijing, Tianjin, and so on. Therefore, it is necessary to build the safety information network system for the water conservancy project.Combined with the research work of the National Major Project "Research and Development of Sensor Network Technology Oriented to the Safety of the South-North Water Transfer Project (SNWTP)," this study investigated the cloud/fog network system architecture for project safety of the Middle Route of SNWTP. This paper first ana...

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The number of mobile applications(APPs) has increased dramatically with the development of mobile Internet. It becomes challenging for users to identify these APPs they are really interested in. Existing mobile APP recommendation methods focus on learning users' preference and recommending high visibility APPs. However, some low visibility APPs may satisfy users and even surprise them. If those low visibility APPs have the opportunity to show to the user, they will not only improve the user's satisfaction, but also provide a fair competitive market for APP providers. Furthermore, it will improve the vitality of the APP market. To this end, we present a fairness-aware APP recommendation method named FARM. The principal study of this method emphasizes on the fairness issue during the recommendation process. In this method, APP candidates are divided into high visibility and low visibility APPs, and implement recommendation algorithm respectively. For low visibility APPs, we set a fairness factor for everyone, and use the user's latest feedback to make a dynamic adjustment. Based on the fairness factor, the recommendation is implemented by roulette-wheel. For high visibility APPs, we employ the fuzzy analytic hierarchy process to implement the recommendation. The evaluation results show that FARM outperforms baselines in terms of recommendation fairness.

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Adaptive Game AI improves adaptability of opponent AI as well as the challenge level of the gameplay; as a result the entertainment of game is augmented. Opponent game AI is usually implemented by scripted rules in video games, but the most updated algorithm of UCT (Upper Confidence bound for Trees) which perform excellent in computer go can also be used to achieve excellent result to control non-player characters (NPCs) in video games. In this paper, the prey and predator game genre of Dead End is used as a test-bed, the basic principle of UCT is presented, and the effectiveness of its application to game AI development is demonstrated. The experiment compares the performance of different NPCs control approaches: given a 300 milliseconds for each simulation step, the approach of UCT with recognized player's strategy pattern is better than the one of UCT without recognized player's strategy pattern, the worst one is Monte-Carlo approach.