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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]

The author reflects on the peer reviewing of papers and using modern technology to create a system in which any paper can be posted and then commented on. He notes that the purpose of peer reviewing is not clear but one of its probable motives include finding obvious errors. In using modern technology, he stresses that authors should be responsible for the quality of the papers they publish and be aware of the effect on their career and reputation. He believes that an online system of publication is more up-to-date and useful.

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

Highlights the changes made to the journal "Signal Processing Letters" (SPL) as of 2003. Move of SPL to an all-electronic submission and review system; Implementation of a new paper classification scheme in 2003; Cases of blatant plagiarism in 2002 and 2003.

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

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]

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

The author reflects on his 1963 article published in the journal "Proceedings of the IEEE" which focused on the subject of writing scientific contributions. According to the author, it is still important to choose an impressive title for a scientific paper. He emphasizes the importance of collaboration for today's young authors. He says self-interest is the deciding factor or motivation for today's research authors.

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.

This paper contributes a new large-scale dataset for weakly supervised cross-media retrieval, named Twitter100k. Current datasets, such as Wikipedia, NUS Wide, and Flickr30k, have two major limitations. First, these datasets are lacking in content diversity, i.e., only some predefined classes are covered. Second, texts in these datasets are written in well-organized language, leading to inconsistency with realistic applications. To overcome these drawbacks, the proposed Twitter100k dataset is characterized by two aspects: it has 100 000 image–text pairs randomly crawled from Twitter, and thus, has no constraint in the image categories; and text in Twitter100k is written in informal language by the users. Since strongly supervised methods leverage the class labels that may be missing in practice, this paper focuses on weakly supervised learning for cross-media retrieval, in which only text-image pairs are exploited during training. We extensively benchmark the performance of four subspace learning methods and three variants of the correspondence AutoEncoder, along with various text features on Wikipedia, Flickr30k, and Twitter100k. As a minor contribution, we also design a deep neural network to learn cross-modal embeddings for Twitter100k. Inspired by the characteristic of Twitter100k, we propose a method to integrate optical character recognition into cross-media retrieval. The experiment results show that the proposed method improves the baseline performance. [ABSTRACT FROM PUBLISHER]

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

This paper introduces the problem of Fine-grained Incident Video Retrieval (FIVR). Given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. FIVR offers a single framework that contains several retrieval tasks as special cases. To address the benchmarking needs of all such tasks, we construct and present a large-scale annotated video dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikipedia and deploy a retrieval pipeline for the automatic selection of query videos based on their estimated suitability as benchmarks. We also devise a protocol for the annotation of the dataset with respect to the four types of video associations defined by FIVR. Finally, we report the results of an experimental study on the dataset comparing five state-of-the-art methods developed based on a variety of visual descriptors, highlighting the challenges of the current problem. [ABSTRACT FROM AUTHOR]

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]

“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players. [ABSTRACT FROM PUBLISHER]

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 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]

This paper introduces the stack for service delivery models and interoperability in the Internet of Things. The main characteristics and functional layers of the IoT stack are described. The applicability of the IoT stack is described based on particular use cases and deployed pilots. The validation of the IoT stack in terms of functionality and adaptation at different IoT particular areas is based on the Virtual Development Kit (VDK) developed and implemented within the framework of the OpenIoT project—OpenIoT project is the awarded Internet of Things open-source rookie of the year by BlackDuck Software Co. (www.github.com/OpenIotOrg ). The methods and standards that boosted OpenIoT-VDK implementation are described in this paper. An instance of the OpenIoT-VDK process is described as the practical use case demonstrating being an IoT platform with autonomic behavior. OpenIoT-VDK creates IoT instances, analyzes the IoT stack dependence, and resolves them following interoperability principles. The OpenIoT-VDK instance deploys IoT service delivery models facilitating the validation of use cases by using the OpenIoT platform. As proof of concept, a delivered IoT service using open data from OpenIoT local instantiation is described. [ABSTRACT FROM PUBLISHER]

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]

The article offers information to authors on paper submission in electronic form final submission format and page charge.

This paper is an extension of research originally reported in. Here, we propose a novel method for summarizing lecture slides to enhance students’ preview efficiency and understanding of the content. Students are often asked to prepare for a class by reading lecture materials. However, because the attention span of students is limited, this is not always beneficial. We surveyed 326 students regarding the preview of lecture materials, revealing a preference for summarized materials to preview. Therefore, we developed an automatic summarization method for condensing original lecture materials into a summarized set. Our proposed approach utilizes image and text processing to extract important pages from lecture materials, optimizing selection of pages in accordance with a specified preview time. We applied the proposed summarization method to a set of lecture slides. In an experiment with 372 students, we compared the effectiveness of the summarized slides and the original materials in terms of quiz scores, preview achievement ratio, and time spent previewing. We found that students who previewed the summarized slides achieved better scores on pre-lecture quizzes, even though they spent less time previewing the material. [ABSTRACT FROM AUTHOR]

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]

With the rapidly growing number of online product models in construction industry, there is an urgent need for developing effective domain-specific information retrieval methods. Explicit semantic analysis (ESA) is a method that automatically extracts concept-based features from human knowledge repositories for semantic retrieval. This avoids the requirement of constructing and maintaining an explicitly formalized ontology. However, since domain-specific knowledge repositories are relatively small, the available terminologies are insufficient and concepts have coarse granularity. In this paper, we propose an enhanced ESA method for product model retrieval in construction industry. The major enhancements for the original ESA method consist of two parts. First, a novel concept expansion algorithm is proposed to solve the problem caused by insufficient terminologies. Second, a reranking algorithm is developed to solve the problem caused by coarse granularity of concepts. Experimental results show that our method significantly improves the performance of product model retrieval and outperforms the state-of-the-art methods. Our method is also applicable to product retrieval in other engineering domain if a specific knowledge repository is provided in that domain. [ABSTRACT FROM PUBLISHER]

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

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]