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Twitter, a microblogging platform, has developed into an increasingly invaluable information source, where millions of users post a great quantity of tweets with various topics per day. Heterogeneous information networks consisting of multi-type objects and relations are becoming more and more prevalent as an organization form of knowledge and information. The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets. However, the entity mention is ambiguous. Additionally, tweets are short and informal, making it difficult to mine enough information from a single tweet for entity linking. In this paper, we propose an unsupervised iterative clustering framework TELHIN to link multiple similar tweets with a heterogeneous information network jointly. Our framework takes three dimensions of tweet similarity into consideration: (1) content similarity, (2) temporal similarity, and (3) user similarity. The appropriate weights of different similarity dimensions for each entity mention are learned iteratively based on the metric learning algorithm by leveraging the pairwise constraints generated automatically. Experiments on real data demonstrate the effectiveness of our framework in comparison with the baselines. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

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The article outlines the journal's statement of editorial policy and provides information for authors on matters including electronic publishing, copyright and open access publishing.

The article offers information about the journal including the electronic publishing format of the journal, the processing of manuscripts for publication and copyright.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

In the sharing economy system, consumer-to-consumer product-exchange (C2C-PE) for digital products is commonly seen. In this paper, we analytically explore the C2C-PE problem for digital products. To be specific, we consider the presence of a digital product developer (DPD) who develops and sells a product to consumers in the market. Consumers possess random valuation toward the digital product and the DPD needs to decide the optimal selling price for the product. We study the impacts brought by C2C-PE. We try to uncover whether (and when) DPDs and consumers will be benefited by the presence of C2C-PE. In the basic model, when all parties are risk neutral, we find that the presence of C2C-PE is always beneficial to the DPD and consumers. To show the robustness of results in the basic model, we further extend the analysis to cover a few cases and prove that this conclusion holds irrespective of the DPD’s risk averse attitude, the consumers’ risk averse attitude, as well as whether the utility gained from C2C-PE is effort dependent or not. Hence, we conclude that C2C-PE, which seems to be harmful to DPDs, is in fact a beneficial scheme to both the DPDs and consumers. [ABSTRACT FROM AUTHOR]

The article presents information for the authors of periodical "IEEE Transactions on Broadcasting" which includes information on transactions paper, brief paper, letter and electronic publishing format.

Electronic publishing through IEEE Xplore offers the possibility of augmenting papers with multimedia examples - including audio, image, and video files - which provide concrete illustrations of the utility of a new algorithm. It is encouraged for all authors to take advantage of these opportunities, which are especially relevant to many problems in signal processing and can increase the impact of their work. To make the papers more easily reproducible, it is encouraged to submit code that can be accessed through IEEE Xplore with the paper. IEEE Xplore is also now augmenting the paper presentation with links to related content and citation information, and more developments are in the works to make the presentation more useful to readers. [ABSTRACT FROM PUBLISHER]

The article provides issue information, which includes list of editors in chief for the periodical published by the organization IEEE Transactions on Microwave Theory & Techniques, which includes contact details of Luca Perregrini, information for authors, and the rules of submitting paper in electronic form such as size of the electronic file, website address where the paper is to be uploaded, and charges for submission.

As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state-of-the-art models, even data-driven models, is fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, $\mathsf{AutoPhrase}$ , which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, $\mathsf{AutoPhrase}$ has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, $\mathsf{AutoPhrase}$ can be extended to model single-word quality phrases. [ABSTRACT FROM AUTHOR]

The July President Page is hosting an article by Gianluca Setti on Open Access. Open Access is a hot topic among researchers, editors, and research funding institutions, and has reached the legislation level in several countries. It is also crucial for IEEE, an institution that receives most of its revenues from IPs related to archival publications. It is then a pleasure and, I think, a good service to ComSoc members to introduce Gianluca and his paper. [ABSTRACT FROM AUTHOR]

Focuses on efforts made by the journal "IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics" to address the opportunities and challenges presented by electronic dissemination of scholarly work. Benefits of accepting paper submission only through a specific web site; Information about web sites through with all paper submission is accepted; Impact of world wide web and Internet on the publication of scholarly work.

Most recent approaches for Text Simplification (TS) have drawn on insights from machine translation to learn simplification rewrites from the monolingual parallel corpus of complex and simple sentences, yet their effectiveness strongly relies on large amounts of parallel sentences. However, there has been a serious problem haunting TS for decades, that is, the availability of parallel TS corpora is scarce or not fit for the learning task. In this paper, we will focus on one especially useful and challenging problem of unsupervised TS without a single parallel sentence. To the best of our knowledge, we present the first unsupervised text simplification system based on phrase-based machine translation system, which leverages a careful initialization of phrase tables and language models. On the widely used WikiLarge and WikiSmall benchmarks, our system respectively obtains 39.08 and 25.12 SARI points, even outperforms some supervised baselines. [ABSTRACT FROM AUTHOR]

The article offers information about the journal including the submission of a transaction paper, the style of manuscript and the color photo charges.

The article offers information about the journal including the electronic publishing forum of broadcasting industry, the submission of a technical manuscript and the copyright.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

Provides a listing of board members, committee members and society officers. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

The article offers information on the procedure for submission of a technical manuscript to the periodical along with information on electronic publishing format, style for illustrations and manuscripts, and electronic submission.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

An introduction to the journal is presented in which the editor acknowledges the people who have contributed to the issue, cites the plans of the journal for future publications and offers information on the online submission and review system of the journal.

Power optimization has become a key challenge in the design of large-scale enterprise data centers. Existing research efforts focus mainly on computer servers to lower their energy consumption, while only few studies have tried to address data center networks (DCNs), which can account for 10-20 percent of the total energy consumption of a data center. In this paper, we propose CARPO, a correlation-aware power optimization algorithm that dynamically consolidates traffic flows onto a small set of links and switches in a DCN and then shuts down unused network devices for energy savings. In sharp contrast to existing work, CARPO is designed based on a key observation from the analysis of real DCN traces that the bandwidth demands of different flows do not peak at exactly the same time. As a result, if the correlations among flows are considered in consolidation, more energy savings can be achieved. In addition, CARPO integrates traffic consolidation with link rate adaptation for maximized energy savings. We implement CARPO on a hardware testbed composed of 10 virtual switches configured with a production 48-port OpenFlow switch and 8 servers. Our empirical results with traces from Wikipedia and Yahoo! data centers demonstrate that CARPO can save up to 50 percent of network energy for a DCN, while having only negligible delay increases. CARPO also outperforms two state-of-the-art baselines by 19.6 and 95 percent on energy savings, respectively. Our simulation results with a large-scale DCN also show that CARPO can achieve more energy savings than the baselines for typical DCN topologies, such as fat tree and BCube. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

The article presents point to remember for authors for paper submission in electronic format in the journal.

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

In this paper, we introduce “task trail” to understand user search behaviors. We define a task to be an atomic user information need, whereas a task trail represents all user activities within that particular task, such as query reformulations, URL clicks. Previously, web search logs have been studied mainly at session or query level where users may submit several queries within one task and handle several tasks within one session. Although previous studies have addressed the problem of task identification, little is known about the advantage of using task over session or query for search applications. In this paper, we conduct extensive analyses and comparisons to evaluate the effectiveness of task trails in several search applications: determining user satisfaction, predicting user search interests, and suggesting related queries. Experiments on large scale data sets of a commercial search engine show that: (1) Task trail performs better than session and query trails in determining user satisfaction; (2) Task trail increases webpage utilities of end users comparing to session and query trails; (3) Task trails are comparable to query trails but more sensitive than session trails in measuring different ranking functions; (4) Query terms from the same task are more topically consistent to each other than query terms from different tasks; (5) Query suggestion based on task trail is a good complement of query suggestions based on session trail and click-through bipartite. The findings in this paper verify the need of extracting task trails from web search logs and enhance applications in search and recommendation systems. [ABSTRACT FROM AUTHOR]

This paper studies the problem of mining named entity translations by aligning comparable corpora. Current state-of-the-art approaches mine a translation pair by aligning an entity graph in one language to another based on node similarity or propagated similarity of related entities. However, they, building on the assumption of “symmetry”, quickly deteriorate on “weakly” comparable corpora with some asymmetry. In this paper, we pursue two directions for overcoming relation and entity asymmetry respectively. The first approach starts from weakly comparable corpora (for high recall) then ensures precision by selective propagation only to entities of symmetric relations. The second approach starts from parallel corpora (for high precision) then enhances recall by extending the translation matrix based on node similarity and contextual similarity. Our experimental results on English-Chinese corpora show that both approaches are effective and complementary. Our combined approach outperforms the best-performing baseline in terms of F1-score by up to 0.28. [ABSTRACT FROM AUTHOR]

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Heterogeneous information networks that consist of multi-type, interconnected objects are becoming increasingly popular, such as social media networks and bibliographic networks. The task of linking named entity mentions detected from unstructured Web text with their corresponding entities in a heterogeneous information network is of practical importance for the problem of information network population. This task is challenging due to name ambiguity and limited knowledge existing in the network. Most existing entity linking methods focus on linking entities with Wikipedia and cannot be applied to our task. In this paper, we present SHINE+, a general framework for linking named entitieS in Web free text with a Heterogeneous I nformation NEtwork. We propose a probabilistic linking model, which unifies an entity popularity model with an entity object model. As the entity knowledge contained in the information network is insufficient, we propose a knowledge population algorithm to iteratively enrich the network entity knowledge by leveraging the context information of mentions mapped by the linking model with high confidence, which subsequently boosts the linking performance. Experimental results over two real heterogeneous information networks (i.e., DBLP and IMDb) demonstrate the effectiveness and efficiency of our proposed framework in comparison with the baselines. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

As technological, economic, and social factors drive scientific publishing toward electronic formats, opportunities open beyond traditional reading and writing frameworks. Journal articles now, and in the future, can increasingly include a variety of supplemental multimedia and interactive materials for augmented reading that will impact both the nature and presentation of scientific research. The IEEE Computer Society is preparing for this evolution. The first Web extra at http://youtu.be/06J3yg0SV8o is a video that serves as a short abstract of the paper "Augmented Reading: The Present and Future of Electronic Scientific Publications," presented by the two authors with the support of two young promising scientists. The second Web extra at http://youtu.be/86nN1gCMc1E is a video by David Alan Grier (IEEE Computer Society President, 2013) that discusses the Computer Society and its plans for scientific publications. The third Web extra at http://youtu.be/GCCMYR5Zy70 is a video that collects the personal views of Dejan Milosevic (IEEE CS President, 2014), David A. Grier (IEEE CS President, 2013), Sorel Reisman (IEEE CS President, 2012), Jon Rokne (IEEE CS Publications), Fabrizio Lombardi (EIC IEEE Transactions on Emerging Topics in Computing and member of the IEEE CS Board of Governors), and Steve Woods (IEEE CS Senior Manager Multimedia, Production) about the future of scientific publications. The fourth Web extra at http://youtu.be/7cLNV4SNdjY is a video presentation of the multimedia enhancements Computer magazine offers to its readers each month. The fifth Web extra at http://youtu.be/4csCKb685XQ is a video that presents a collection of definitions for the term "supplemental material." The sixth Web extra at http://youtu.be/d7a7XxMT6eY is a video that reviews the announcement from the Journal of Neuroscience that authors can no longer submit supplemental material. The seventh Web extra at http://youtu.be/NMFwLJhv11c is a video in which author Paolo Montuschi summarizes the key concepts of the article "Augmented Reading: The Present and Future of Electronic Scientific Publications". [ABSTRACT FROM PUBLISHER]

Presents the inside back cover for this issue of the publication. [ABSTRACT FROM AUTHOR]

New entities are being created daily. Though the novelty of these entities naturally attracts mentions, due to lack of prior knowledge, it is more challenging to collect knowledge about such entities than pre-existing entities, whose KBs are comprehensively annotated through LBSNs and EBSNs. In this paper, we focus on knowledge harvesting for emerging spatial entities (ESEs), such as new businesses and venues, assuming we have only a list of ESE names. Existing techniques for knowledge base (KB) harvesting are primarily associated with information extraction from textual corpora. In contrast, we propose a multimodal method for event detection based on the complementary interaction of image, text, and user information between multi-source platforms, namely Flickr and Twitter. We empirically validate our harvesting approaches improve the quality of KB with enriched place and event knowledge. [ABSTRACT FROM AUTHOR]

The article offers information about the journal "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems." It provides several critical topics the authors should consider when publishing manuscripts including methods, techniques, and tools for the automated design of integrated circuits and systems. Information about electronic submission is presented.

In this paper, we present a novel approach for semiautomatic question generation to support academic writing. Our system first extracts key phrases from students' literature review papers. Each key phrase is matched with a Wikipedia article and classified into one of five abstract concept categories: Research Field, Technology, System, Term, and Other. Using the content of the matched Wikipedia article, the system then constructs a conceptual graph structure representation for each key phrase and the questions are then generated based the structure. To evaluate the quality of the computer generated questions, we conducted a version of the Bystander Turing test, which involved 20 research students who had written literature reviews for an IT methods course. The pedagogical values of generated questions were evaluated using a semiautomated process. The results indicate that the students had difficulty distinguishing between computer-generated and supervisor-generated questions. Computer-generated questions were also rated as being as pedagogically useful as supervisor-generated questions, and more useful than generic questions. The findings also suggest that the computer-generated questions were more useful for the first-year students than for second or third-year students. [ABSTRACT FROM PUBLISHER]

Early studies of electronic journals (e-journals) viewed the web as a technological discontinuity that would deem traditional academic publishing null This article uses the industry life cycle framework as a lens to examine the formation of academic e-journals. The article proposes five factors affecting the academic publishing dominant design selection process, and examines these factors against six longitudinal case studies. The study concludes that from technical/functional, economic and market perspectives, e-journals are superior to paper journals (p journals) However academic social norms and the managerial processes needed to establish and maintain e-journals are more influential on their formation than the capabilities provided by the new technology. In addition, the long-term sustainability and survivability of e-journals depend on the administrative and managerial processes established by its management team. This study demonstrates that although the web enables a large number of new entrants and a variety of business and technical models, e-journals are a competency-enhancing innovation that relies on the existing expertise of professional publishers and the journal's ability to support the social and cultural needs of academia. [ABSTRACT FROM AUTHOR]