Abstract:With the development of science and technology, it is possible to analyze residents' daily behaviors for the purpose of smart healthcare in the smart home environment. Many researchers have begun to detect residents' anomalous behaviors and assess their physical condition, but these approaches used by the researchers are often caught in plight caused by a lack of ground truth, one-sided analysis of behavior, and difficulty of understanding behaviors. In this paper, we put forward a smart home visual analysis system (SHVis) to help analysts detect and comprehend unusual behaviors of residents, and predict the health information intelligently. Firstly, the system classifies daily activities recorded by sensor devices in smart home environment into different categories, and discovers unusual behavior patterns of residents living in this environment by using various characteristics extracted from those activities and appropriate unsupervised anomaly detection algorithm. Secondly, on the basis of figuring out the residents' anomaly degree of every date, we explore the daily behavior patterns and details with the help of several visualization views, and compare and analyze residents' activities of various dates to find the reasons why residents act unusually. In the case study of this paper, we analyze residents' behaviors that happened over two months and find unusual indoor behaviors and give health advice to the residents.

One of the relations used with granularity is indistinguishability, where distinguishable entities in a finer-grained granule are indistinguishable in a coarser-grained granule. This relation is a subtype of equivalence relation, which is used in the other direction to create finer-grained granules. Together with the notion of similarity, we formally prove some intuitive properties of the indistinguishability relation for both qualitative and quantitative granularity, that with a given granulation there must be at least two granules (levels of granularity) for it to be granular, and derive a strict order between finer and coarser granules. Based on these results, granulation hierarchy is defined as extra assisting structure to augment implementations.

The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node to core nodes, then makes judgement if the node should be classified in the community or not, and finally form the process of community detection. The experimental results show that the proposed method can mine the social structure efficiently with a low computational complexity. IntroductionCommunity discovery is one of the core research areas in social network analysis [1] which is also benefit to other related research areas, such as public opinion monitoring, advertising precision delivery, impact analysis. As early as in 1927, Stuart Rice [2] proposed the discovery of small political groups based on voting patterns. Until 2002, Newman et al. proposed the GN algorithm [2] , and community analysis began to flourish. The GN algorithm is a classical graph splitting algorithm, which uses the edge betweenness to measure the importance of the edge in the entire network, the larger betweenness the edge has, the edge is more likely to connect two different communities.Newman proposed a fast agglomeration algorithm based on the GN algorithm [3] via repeatedly calculating the network shortest path to update the edge betweennesses until the network is divided into appropriate community structures, for an unweighted graph with n nodes and m edges, the time complexity is as high as ( 3 ) . Through continuously merging nodes and communities, the highest modularity of the community is generated. The efficiency of the algorithm is greatly improved. In order to further reduce the computational complexity, many improved algorithms, such as CNM algorithm [4] and FastUnfolding algorithm [5] based on modularity optimization, have largely sacrificed the quality of the results in order to ensure the speed of computation. Rosvall et al. [6] proposed the Infomap algorithm, a method based on

No abstract

Variation of δ 18 O, δD, and 3 H in the Yellow River water were analyzed based on the isotope test results of water samples in 18 sections of the Yellow River in rainy and dry seasons. The results show the trend of the ratios of the stable isotopes increases while the 3 H concentration decreases progressively from the river source to the estuary. The main factors affecting the isotopes in the river water are the mixing of foreign water bodies, evaporation, and human activities. River runoff mostly comes from the source and the middle reaches of the river. The changes of the isotopes in the river water could be served as a good indicator of the recharge of different foreign water to the Yellow River.