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Highlights: [•] A new method for environmental impact assessment is proposed. [•] The proposed method is more effective by considering various types of uncertainty. [•] A new representation of uncertain information, called D numbers, is presented. [•] This paper provides a new framework for the environmental impact assessment. [Copyright &y& Elsevier]

Abstract: Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field. [Copyright &y& Elsevier]

Abstract: Radio-frequency identification (RFID) uses non-contact data access mechanism to read or write tag data through the radio frequency transmitted by the readers. Currently, the RFID systems are applied in different areas. However, the data access rate of a RFID system could be interfered by the environment furnishings, barriers, deployment method, and number of readers. The system stability heavily depends on the experiences of the deployment engineers especially for the active readers. This paper proposes an intelligent deployment system for RFID readers. The system uses the furnished simulation of environment and the best deployment of readers to construct an automatic deployment system for the RFID readers. The former constructs a fuzzy interference model of barriers according to the materials and inference degree of the furnishings. The latter uses the reader cluster and genetic algorithm to establish the best deployment plan of readers. The experimental results show that the system can reduce the interference problem of reader deployment as well as improve the reliability and stability of the RFID systems. [Copyright &y& Elsevier]

Abstract: Transfer learning aims to solve the problem that the training data from a source domain and the test data from a target domain follow different distributions. The feature-based method and the case-based method have been widely used in transfer learning. In this paper we propose a knowledge transfer method based on feature representation mapping from the source domain to the target domain. We first construct a new feature subspace, then build a feature representation mapping function and re-weight the source domain and the target domain data to minimize the distance between different distributions. As a result, with the new feature representations in this subspace, we can apply standard machine learning methods to train classifier models in the source domain for use in the target domain. Importantly, different from many previously proposed methods, we combine the feature-based method and the case-based method to construct the knowledge transfer model for solving text classification problems. The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms. [Copyright &y& Elsevier]

Abstract: Watermark is some imperceptible information embedded into the work for later verification and thus it can be used to prove the originality or to protect the work from being illegally copied or modified. proposed a muteness-based watermarking method for audio file. Their method can successfully embed watermarking information into audio file such that the human ears cannot distinguish the covered work from the original one. To improve the efficiency, we proposed a watermarking method for audio files in this paper. By adjusting the length of mute period dynamically, the proposed method can achieve the goal of watermark embedding with little variation of the original work. Experiment has shown that our method has the advantages of efficiency and fidelity as compared with theirs. Furthermore, it does not require the original audio file to derive the embedded watermark. [Copyright &y& Elsevier]

Abstract: Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the fuzzy and the object concepts have been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different quantitative attribute values since they may appear in different transactions. The proposed fuzzy algorithm can be divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. Each linguistic large itemset found in this phase is thought of as a composite item used in phase 2. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets are derived and used to represent the relationship among different kinds of objects. An example is used to illustrate the algorithm. Experimental results are also given to show the effects of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new method for analyzing fuzzy risk based on a new method for ranking generalized fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers. It considers the areas on the positive side, the areas on the negative side and the heights of the generalized fuzzy numbers to evaluate ranking scores of the generalized fuzzy numbers. The proposed method can overcome the drawbacks of some existing methods for ranking generalized fuzzy numbers. Then, we apply the proposed method for ranking generalized fuzzy numbers to develop a new method for dealing with fuzzy risk analysis problems. The proposed method provides us with a useful way to deal with fuzzy risk analysis problems based on generalized fuzzy numbers. [ABSTRACT FROM AUTHOR]

Abstract: Text steam analysis is now of great importance and practical value today. It has several applications such as news group filtering, topic detection & tracking (TDT), user characterized recommendation etc. Clustering is one of the most important methods of analyzing text stream. However, most text stream clustering algorithms rarely consider the possible change of features during a long-time of clustering, which is usually the case, leading to unsatisfactory results of the clustering system. The paper mainly focuses on the problem of adaptive feature selection for clustering text stream. A validity index based method of adaptive feature selection is proposed, incorporating with which a new text stream clustering algorithm is developed. During the clustering process, threshold of cluster valid index is used to automatically trigger feature re-selection in order to ensure the validity of clustering. The experiment using Reuters-21578 text set as the text source shows that the clustering algorithm reaches reasonable results of high quality. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a generalized model for handling prioritized multicriteria decision making systems. First, we present a new method for handling prioritized multicriteria decision making problems, where the weights of the lower priority criteria of each alternative depend on whether each alternative satisfies the requirements of all the higher priority criteria or not. Then, we also present a generalized prioritized multicriteria decision making method for handling multicriteria decision making problems, where some criteria may have equal priority which can be aggregated by the ordered weighted averaging (OWA) operator or the weighted averaging method. The proposed methods can overcome the drawbacks of the methods presented in [Yager, R. R. (2004a). Modeling prioritized multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 34(6), 2396–2404]. The proposed methods can handle multicriteria decision making problems in a more intelligent and more flexible manner. [Copyright &y& Elsevier]