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Parallel computing refers to the practice of exploiting parallelism in computing to achieve higher performance. Rough set theory plays a fundamental role in data analysis, which was extensively used in the context of data mining. The lower and upper approximations are the basic tools in rough set theory. The fast calculation of approximations can effectively improve the efficiency of rough set theory-based approaches. In this paper, we propose a new parallel strategy for computing approximations, which is able to exploit parallelism at all levels of the computation. An illustrative example is given to demonstrate the effectiveness and validity of the proposed method.

Attribute core is an important concept of attribute reduction in rough set theory. In this paper, we focus on analyzing the incremental updating method for core computing based on information entropy in set-valued ordered information systems. The definition of attribute core by means of information entropy in set-valued ordered information system is introduced. And the changing mechanisms of information entropy and attribute core are analyzed while the object set varies with time. On the basis of the changing mechanisms, the attribute core can be updated incrementally based on existing results to reduce redundant computation. Finally, an example illustrates the validity of the proposed approach.

Set-valued Information System (SIS) aims at dealing with attributes’ multi-values. It is an extension of the single-valued information system. In traditional SIS, the tolerance and dominance relations were introduced to deal with the relationship between objects. But the partial orders between the attributes values have not been taken into consideration. In this paper, a function set-valued equivalence relation is proposed first considering the applications of SIS. Then the properties of the function set-valued equivalence classes are analyzed. Furthermore, the definitions of approximations under the function SIS are presented and the properties w.r.t. approximations under the tolerance and dominance relations are investigated.

Composite rough set aims to deal with multiple binary relations simultaneously in an information system. In this paper, probabilistic composite rough set is presented by introducing the probabilistic method to composite rough set. Then, the distribution attribute reduction method under probabilistic composite rough set is investigated. Examples are given to illustrate the method.

Local search (LS) is a widely used, general approach for solving hard combinatorial search problems, such as the graph coloring problem (GCP). One main advantage of this method is that effective heuristics for a problem may lead to improvements in solving other problems. Recently, it has been shown that an initial LS algorithm for the Boolean satisfiability problem (SAT) called WalkSAT is extremely effective for random SAT instances. Thus, it is interesting to apply the heuristics in WalkSAT to GCP. This paper proposes a random walk based heuristic, which is inspired by WalkSAT but differs in the tie-breaking mechanism. This new heuristic leads to a new LS algorithm for GCP namely FWLS. The experiments on the DIMACS benchmark show that FWLS finds optimal (or best known) solutions for most instances. Also, when compared to other GCP algorithms, including a greedy one, an LS one and a hybrid one, FWLS exhibits very competitive or better performance.

Stochastic local search SLS algorithms, especially those adopting the focused random walk FRW framework, have exhibited great effectiveness in solving satisfiable random 3-satisfiability 3-SAT instances. However, they are still unsatisfactory in dealing with huge instances, and are usually sensitive to the clause-to-variable ratio of the instance. In this paper, we present a new FRW algorithm dubbed FrwCB, which behaves more satisfying in the above two aspects. The main idea is a new heuristic called CCBM, which combines a recent diversification strategy named configuration checking CC with the common break minimum BM variable-picking strategy. By combining CC and BM in a subtle way, CCBM significantly improves the performance of FrwCB, making FrwCB achieve state-of-the-art performance on a wide range of benchmarks. The experiments show that FrwCB significantly outperforms state-of-the-art SLS solvers on random 3-SAT instances, and competes well on random 5-SAT, random 7-SAT and structured instances.

Understanding the rapid information diffusion process in social media is critical for crisis management. Most of existing studies mainly focus on information diffusion patterns under the word-of-mouth spread mechanism. However, to date, the mass-media spread mechanism in social media is still not well studied. In this paper, we take the emergency event of Wenzhou train crash as a case and conduct an empirical analysis, utilizing geospatial correlation analysis and social network analysis, to explore the mass-meida spread mechanism in social media. By using the approach of agent-based modeling, we further make a quantativiely comparison with the information diffusion patterns under the word-of-mouth spread mechanism. Our exprimental results show that the mass-meida spread mechanism plays a more important role than that of the word-of-mouth in the information diffusion process during emergency events. The results of this paper can provide significant potential implications for crisis management.

The past few years have witnessed the rapid growth of online social networks, which have become important hubs of social activity and conduits of information. Identifying social influence in these newly emerging platforms can provide us with significant insights to better understand the interaction behaviors among online users. However, it is difficult for us to measure the influence quantitatively among user peers, since many key factors such as homophily and heterogeneity, can not be observed in our real world conveniently. More recent work mainly focuses on developing theoretical models based on explicit causal knowledge. Nevertheless, such knowledge is usually not available and often needs to be discovered. In this paper, we introduce a model free approach to formulate causal inferences of behaviors among user peers. Experimental results show that influence measured by our approach could successfully reconstruct the underlying networks structure. Furthermore, two additional case studies based on this approach reveal that influentials wield power through specific venues, which constitute a comparatively small portion of the whole channels.

Despite the growing number of videos featuring electronic cigarettes, there has been no investigation of the portrayal of these videos. This paper presents the first surveillance data of electronic cigarette videos on YouTube. Our results suggest that viewers are primarily being exposed to content promoting the use of these emerging tobacco products and the viewership is global. This study shows that it is critical to develop appropriate health campaigns to inform potential consumers of harms associated with electronic cigarette use.

In real-applications, there may exist missing data and many kinds of data e.g., categorical, real-valued and set-valued data in an information system which is called as a Hybrid Information System HIS. A new Hybrid Distance HD between two objects in HIS is developed based on the value difference metric. Then, a novel fuzzy rough set is constructed by using the HD distance and the Gaussian kernel. In addition, the information systems often vary with time. How to use the previous knowledge to update approximations in fuzzy rough sets is a key step for its applications on hybrid data. The fuzzy information granulation methods based on the HD distance are proposed. Furthermore, the principles of updating approximations in HIS under the variation of the attribute set are discussed. A fuzzy rough set approach for incrementally updating approximations is then presented. Some examples are employed to illustrate the proposed methods.

Multi-granulation rough set is a generalization of Rough Set Theory (RST) in order to adapt to cases that there are multiple relations in the universe. To allow Dominance-based Rough Set Approach (DRSA) being applied in multiple relations cases, we propose a multi-granulation model based on DRSA. A numerical example is employed to validate the rationality and feasibility of our model.

Incremental learning is an efficient technique for knowledge discovery in a dynamic database. Rough set theory is an important mathematical tool for data mining and knowledge discovery in information systems. The lower and upper approximations in the rough set theory may change while data in the information system evolves with time. In this paper, we focus on the incremental updating principle for computing approximations in set-valued ordered information systems. The approaches for updating approximations are proposed when the object set varies over time.