Social semantic cloud of tags: semantic model for folksonomies

A growing number of tagging applications have begun to provide users the ability to socialise their own keywords. Tagging, which assigns a set of keywords to resources, has become a powerful way for organising, browsing, and publicly sharing personal collections of resources on the Web. It is called folksonomies. These systems on current social websites, however, have deficiencies in defining tag's meaning, and are often blocked to users in order to reuse, share, and exchange the tags across heterogeneous websites. In this paper, we describe a semantic model for expressing folksonomies in social websites. This model, called Social Semantic Cloud of Tags, aims to provide a consistent format of representing folksonomies and some features in terms of tagging activities. We describe core concepts and relevant properties such as a popularity and usage of tags, along with deduced relationships between tags. We will discuss how this model helps to reduce drawbacks regarding tag sharing between users, applications, or folksonomies.     

A data-driven subspace predictive control method for air-cooled data center thermal modelling and optimization

This paper presents a data-driven predictive control method for optimizing the energy consumption of air-cooled data centers with unknown system model parameters. First, based on the measurable data of the studied system, the subspace predictive control (SPC) method is adopted to improve the energy use efficiency of the data center by regulating the power allocation of the server racks and the supply temperature of cold air, while ensuring the safe operating environment of the electronic equipment. Furthermore, a reasonable event-triggered law is designed to solve the problem of the low computational efficiency of the conventional SPC method. The simulation results illustrate that the designed event-triggered law can improve the computational efficiency of the algorithm while maintaining the control performance of the algorithm, which verifies its application prospect in practice.     

数据挖掘—大数据时代的重要工具

介绍了数据挖掘的定义、过程以及数据挖掘的功能、应用和发展前景。     

Multi-candidate reduction: Sentence compression as a tool for document summarization tasks

This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization—a “parse-and-trim” approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations demonstrate that sentence compression is a valuable component of a larger multi-document summarization framework.     

Neural embedding-based indices for semantic search

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.     

Microfluidics as a functional tool for cell mechanics

Living cells are a fascinating demonstration of nature’s most intricate and well-coordinated micromechanical objects. They crawl, spread, contract, and relax—thus performing a multitude of complex mechanical functions. Alternatively, they also respond to physical and chemical cues that lead to remodeling of the cytoskeleton. To understand this intricate coupling between mechanical properties, mechanical function and force-induced biochemical signaling requires tools that are capable of both controlling and manipulating the cell microenvironment and measuring the resulting mechanical response. In this review, the power of microfluidics as a functional tool for research in cell mechanics is highlighted. In particular, current literature is discussed to show that microfluidics powered by soft lithographic techniques offers the following capabilities that are of significance for understanding the mechanical behavior of cells: (i) Microfluidics enables the creation of in vitro models of physiological environments in which cell mechanics can be probed. (ii) Microfluidics is an excellent means to deliver physical cues that affect cell mechanics, such as cell shape, fluid flow, substrate topography, and stiffness. (iii) Microfluidics can also expose cells to chemical cues, such as growth factors and drugs, which alter their mechanical behavior. Moreover, these chemical cues can be delivered either at the whole cell or subcellular level. (iv) Microfluidic devices offer the possibility of measuring the intrinsic mechanical properties of cells in a high throughput fashion. (v) Finally, microfluidic methods provide exquisite control over drop size, generation, and manipulation. As a result, droplets are being increasingly used to control the physicochemical environment of cells and as biomimetic analogs of living cells. These powerful attributes of microfluidics should further stimulate novel means of investigating the link between physicochemical cues and the biomechanical response of cells. Insights from such studies will have implications in areas such as drug delivery, medicine, tissue engineering, and biomedical diagnostics.     

Green chemistry: a tool for the sustainable development of the chemical industry

<正>Most manufactured products involve one or more chemical processes.We cannot imagine what our life will be like without the products produced by the chemical industry.However,on average,only a small proportion of the resources we take from the Earth is converted into the desired products in current chemical processes,and large amounts of wastes and hazardous materials are generated.How to supply humanity with enough food,     

Going beyond technology: Knowledge sharing as a tool for enhancing customer-oriented attitudes

Numerous articles, books and essays have shown that ongoing efforts to meet customers’ needs characterize market leaders. For a firm to successfully develop customer-oriented attitudes throughout the entire organization, employees at all levels and sections need to be committed to providing a high level of service. It is therefore essential to understand what drives employees – especially those who do not interact daily with customers – to adopt a customer-oriented attitude. The aim of this study is to shed some light on one facet of this relationship, namely the mechanism of knowledge sharing that may impact employee customer orientation. The underlying notion is that technology, in its various advanced forms of CRM software, enhances and enables client “knowing.” In other words, it leads to a greater ability on the part of employees to share knowledge, which brings about real customer orientation.This paper provides empirical evidence, based on a sample of 345 Hi-Tech workers from a telecommunication company, of a link between sharing explicit knowledge and tacit knowledge to enhance customer orientation. The findings indicate that sharing tacit knowledge has a positive effect on increasing employee customer orientation and increases sharing of explicit knowledge, while also strengthening communication about customers in the organization. It is concluded that firms should encourage tacit knowledge sharing and its transformation by technology into explicit knowledge to augment employees’ customer-oriented attitudes.     

SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

Automatic text summarization attempts to provide an effective solution to today’s unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL is used to achieve sentence semantic parsing whose word tokens are represented as a vector of weighted Wikipedia concepts using ESA method. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC 2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. On the other hand, the testing also demonstrates the scalability of the system, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectorial semantic representation when combined with the crowd-sourced knowledge base in Wikipedia.     

A knowledge-based semantic framework for query expansion

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.     

p2pDating: Real life inspired semantic overlay networks for Web search

We consider a network of autonomous peers forming a logically global but physically distributed search engine, where every peer has its own local collection generated by independently crawling the Web. A challenging task in such systems is to efficiently route user queries to peers that can deliver high quality results and be able to rank these returned results, thus satisfying the users’ information need. However, the problem inherent with this scenario is selecting a few promising peers out of an a priori unlimited number of peers. In recent research a rather strict notion of semantic overlay networks has been established. In most approaches, peers are connected to other peers based on a rigid semantic profile by clustering them based on their contents. In contrast, our strategy follows the spirit of peer autonomy and creates semantic overlay networks based on the notion of “peer-to-peer dating”. Peers are free to decide which connections they create and which they want to avoid based on various usefulness estimators. The proposed techniques can be easily integrated into existing systems as they require only small additional bandwidth consumption as most messages can be piggybacked onto established communication. We show how we can greatly benefit from these additional semantic relations during query routing in search engines, such as Minerva, and in the JXP algorithm, which computes the PageRank authority measure in a completely decentralized manner.     

Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study

This paper proposes collaborative filtering as a means to predict semantic preferences by combining information on social ties with information on links between actors and semantics. First, the authors present an overview of the most relevant collaborative filtering approaches, showing how they work and how they differ. They then compare three different collaborative filtering algorithms using articles published by New York Times journalists from 2003 to 2005 to predict preferences, where preferences refer to journalists’ inclination to use certain words in their writing. Results show that while preference profile similarities in an actor’s neighbourhood are a good predictor of her semantic preferences, information on her social network adds little to prediction accuracy.     

Inaccuracy as a privacy-enhancing tool

The accuracy principle is one of the key standards of informational privacy. It epitomises the obligation for those processing personal data to keep their records accurate and up-to-date, with the aim of protecting individuals from unfair decisions. Currently, however, different practices being put in place in order to enhance the protection of individuals appear to deliberately rely on the use of ‘inaccurate’ personal information. This article explores such practices and tries to assess their potential for privacy protection, giving particular attention to their legal implications and to related ethical issues. Ultimately, it suggests that the use of ‘inaccurate’ data can potentially play a useful role to preserve the informational autonomy of the individual, and that any understandings of privacy or personal data protection that would tend to unduly limit such potential should be critically questioned.     

中国城镇居民生活能源消费影响因素及其效应分析——基于八区域的静态面板数据模型

探究城镇居民生活能源消费的影响因素可为引导城镇居民生活能源合理消费提供依据。本文以中国八大区域为研究对象,建立静态面板数据模型挖掘2000-2013年节能政策、居民实际消费支出、能源价格对城镇居民生活能耗的影响。研究结果显示：城镇居民生活能耗与居民实际消费支出、能源价格、节能政策之间存在长期协整关系;能源价格对城镇居民生活能耗的影响最大,且具有显著负向作用,而实际消费支出对生活能耗具有显著正向作用,但城镇居民生活能源消费增长速度小于实际消费支出增长速度;节能政策的滞后期为2年,即政策颁布2年后对生活能耗具有一定抑制作用但该作用较小;由于经济发展水平及环境气候引起的居民取暖制冷需求的不同,节能政策、实际消费支出、能源价格对于生活能耗的影响呈现出显著的空间差异。由此建议：应通过调整居民实际消费支出结构、适当提高能源价格、加强节能政策的宣传与执行等方式降低生活能耗。     

A text-mining-based patent network: Analytical tool for high-technology trend

Patent documents are an ample source of technical and commercial knowledge and, thus, patent analysis has long been considered a useful vehicle for R&D management and technoeconomic analysis. In terms of techniques for patent analysis, citation analysis has been the most frequently adopted tool. In this research, we note that citation analysis is subject to some crucial drawbacks and propose a network-based analysis, an alternative method for citation analysis. By using an illustrative data set, the overall process of developing patent network is described. Furthermore, such new indexes as technology centrality index, technology cycle index, and technology keyword clusters are suggested for in-depth quantitative analysis. Although network analysis shares some commonality with conventional citation analysis, its relative advantage is substantial. It shows the overall relationship among patents as a visual network. In addition, the proposed method provides richer information and thus enables deeper analysis since it takes more diverse keywords into account and produces more meaningful indexes. These visuals and indexes can be used in analyzing up-to-date trends of high technologies and identifying promising avenues for new product development.