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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]

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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 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]

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]

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

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]

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]

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]

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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.

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This is the last issue of the year, and the Transactions is making great strides to improve the publication timelines. We are in the process of upgrading the web review system to incorporate processing manuscripts post acceptance. This will provide a convenient portal for Rita Janssen, our industrious adminstrative editor, and authors to remain in communication through the entire process up to publication. Furthermore, the system should enable rapid posting of the manuscripts onto Xplore, which is an enhancement that many students and junior faculty will appreciate. The rapid post will be considered a "publication" even though all the editing and typesetting is not complete. For the researcher, this will mean that credit for your work will be given earlier. And for the editorial board, this will mean that our time from manuscript submission to publication should be reduced by about 6 months. This enhanced web review system will also allow us to perpetually archive all files related to a published paper in one location. We hope that this upgrade goes live by February 2015. [ABSTRACT FROM AUTHOR]

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

User Generated Categories (UGCs) are short but informative phrases that reflect how people describe and organize entities. UGCs express semantic relations among entities implicitly hence serve as a rich data source for knowledge harvesting. However, most UGC relation extraction methods focus on English and heavily rely on lexical and syntactic patterns. Applying them directly to Chinese UGCs poses significant challenges because Chinese is an analytic language with flexible language expressions. In this paper, we aim at harvesting fine-grained relations from Chinese UGCs automatically. Based on neural networks and negative sampling, we introduce two word embedding projection models to identify is-a relations. The accuracy of prediction results is improved via a collective refinement algorithm and a hypernym expansion method. We further propose a graph clique mining algorithm to harvest non-taxonomic relations from UGCs, together with their textual patterns. Two experiments are conducted to validate our approach based on Chinese Wikipedia. The first experiment verifies the is-a relation extraction approach achieves high accuracy, outperforming state-of-the-art methods. The second experiment shows that the proposed method can harvest non-taxonomic relations of large quantity and high accuracy, with minimal human intervention. [ABSTRACT FROM AUTHOR]

Twitter has attracted millions of users to share and disseminate most up-to-date information, resulting in large volumes of data produced everyday. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. By splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. HybridSeg finds the optimal segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments. The stickiness score considers the probability of a segment being a phrase in English (i.e., global context) and the probability of a segment being a phrase within the batch of tweets (i.e., local context). For the latter, we propose and evaluate two models to derive local context by considering the linguistic features and term-dependency in a batch of tweets, respectively. HybridSeg is also designed to iteratively learn from confident segments as pseudo feedback. Experiments on two tweet data sets show that tweet segmentation quality is significantly improved by learning both global and local contexts compared with using global context alone. Through analysis and comparison, we show that local linguistic features are more reliable for learning local context compared with term-dependency. As an application, we show that high accuracy is achieved in named entity recognition by applying segment-based part-of-speech (POS) tagging. [ABSTRACT FROM PUBLISHER]

Books, as a representative of lengthy documents, convey rich semantics. Traditional document modeling methods, such as bag-of-words models, have difficulty capturing such rich semantics when only considering term-frequency features. In order to explore term spatial distributions over a book, a tree-structured book representation is investigated in this paper. Moreover, an efficient learning framework, Tree2Vector, is introduced for mapping tree-structured book data into vectorial space. In particular, we present two types of locality reconstruction (LR) models: Euclidean-type and cosine-type, during the transformation process of tree structures into vectorial representations. The LR is used for modeling the reconstruction process, in which each parent node in a tree is supposed to be reconstructed by its child nodes. The prominent advantage of this Tree2Vector framework is that it solely utilizes the local information within a single book tree. In addition, extensive experimental results demonstrate that Tree2Vector is able to deliver comparable or better performance in comparison to methods that consider the information of all trees in a database globally. Experimental results also suggest that cosine-type LR consistently performs better than Euclidean-type LR in applications of book and author recommendations. [ABSTRACT FROM AUTHOR]

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

Entity linking is one of the problems to be handled in order to process natural language and to enrich the existing unstructured text with metadata. The generation of assignments between knowledge base entities and lexical units is called entity linking. Although a number of systems have been proposed for linking entity mentions in various languages, there is currently no publicly available entity linking system specific to the Turkish language. This paper presents a novel entity linking system—THINKER - for linking Turkish content with entities defined in the Turkish dictionary (tdk.gov.tr) or Turkish Wikipedia (tr.wikipedia.org). Specifically, we first propose a novel machine learning based entity detection algorithm for the Turkish language. Then, we propose a collective disambiguation algorithm which utilizes a set of metrics for the linking task and, which is optimized using a genetic algorithm. The effectiveness of THINKER is validated empirically over generated data sets. The experimental results show that THINKER outperformed the state-of-the-art cross-lingual and multilingual entity linking systems in the literature. High entity linking performance (74.81 percent F1 score) is achieved by extending previous methods with some features specific to Turkish language and by developing a novel method that can learn better representations of entity embeddings. [ABSTRACT FROM PUBLISHER]

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities’ network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time. [ABSTRACT FROM PUBLISHER]

With the unprecedented growth of e-learning, more and more new IT technologies are used to develop e-learning tools. As one of the most common forms of social computing, Wiki technology has been used to develop the collaborative and cooperative learning platform to support multiple users learning online effectively. In this paper, we propose a new software architecture to create a novel wiki-based remote laboratory platform by combining the advantages of both Wiki technology and remote laboratory technology. This platform is implemented based on a unified framework for remote laboratory development and an open source wiki software package, MediaWiki. To illustrate the effectiveness of this platform, two remote experiments, Smart Vibration Platform (SVP) and Proportional-Integral-Derivative (PID) motor speed control, are integrated into this platform. This new wiki-based remote laboratory has been used for teaching Mechanical Engineering courses and creating students’ assignments at the University of Houston. [ABSTRACT FROM PUBLISHER]

This paper investigates the difference in bullwhip effects in online and offline retail supply chains, offering insights into how frequent price discounts in e-commerce influence the bullwhip effect in the online retail supply chain. We consider a two-level online retail supply chain with a manufacturer and an online retailer in which the demand faced by the retailer is price sensitive and based on the price discount. Assuming that the online retailer employs an optimal order-up-to inventory policy with an optimal minimum mean-squared error forecasting technique, we derive the expression of the bullwhip effect in the online retail supply chain and make analysis and comparison. Finally, we develop a dual-channel supply chain model to directly observe the impact of price discounts in e-commerce on the bullwhip effect. The results suggest that price discounts in the online retail market generally amplify the bullwhip effect in the online retail supply chain, but in certain conditions, the bullwhip in the online supply chain may be smaller than that in the offline supply chain. We also find that the relationship between the lead time and the bullwhip effect in the online supply chain presents a distinctive feature contrary to the conclusions of previous studies. Based on the analysis, we develop important managerial insights regarding online retail supply chains. [ABSTRACT FROM AUTHOR]

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

The IEEE Transactions on Aerospace and Electronic Systems is a bimonthly journal that publishes papers concerned with the various aspects of systems for space, air, ocean, or ground environments. The boundaries of acceptable subject matter has been intentionally left flexible so that the Transactions can follow the research activities to better meet the needs of the members of the IEEE Aerospace and Electronic Systems Society. The topics of current interest are best summarized in the titles of the editorial areas published in the inside front cover of the transactions and on our website at http://www.ieee-aess.org. [ABSTRACT FROM AUTHOR]

Self-regulated learning (SRL) environments provide students with activities to improve their learning (e.g., by solving exercises), but they might also provide optional activities (e.g., changing an avatar image or setting goals) where students can decide whether they would like to use or do them and how. Few works have dealt with the use of optional activities in SRL environments. This paper thus analyzes the use of optional activities in two case studies with a SRL approach. We found that the level of use of optional activites was low with only 23.1 percent of students making use of some functionality, while the level of use of learning activities was higher. Optional activities which are not related to learning are used more. We also explored the behavior of students using some of the optional activities in the courses such as setting goals and voting comments, finding that students finished the goals they set in more than 50 percent of the time and that they voted their peers’ comments in a positive way. We also found that gender and the type of course can influence which optional activities are used. Moreover, the relations of the use of optional activities with proficient exercises and learning gains is low when taking out third variables, but we believe that optional activities might motivate students and produce better learning in an indirect way. [ABSTRACT FROM PUBLISHER]

The IEEE Transactions on Aerospace and Electronic Systems is a quarterly journal that publishes papers concerned with the various aspects of systems for space, air, ocean, or ground environments. The boundaries of acceptable subject matter has been intentionally left flexible so that the Transactions can follow the research activities to better meet the needs of the members of the IEEE Aerospace and Electronic Systems Society. The topics of current interest are best summarized in the titles of the editorial areas published in the inside front cover of the transactions and on our website at http://www.ieee-aess.org. [ABSTRACT FROM AUTHOR]

The use of video lectures in distance learning involves the two major problems of searchability and active user participation. In this paper, we promote the implementation and usage of a collaborative educational video annotation functionality to overcome these two challenges. Different use cases and requirements, as well as details of the implementation, are explained. Furthermore, we suggest more improvements to foster a culture of participation and an algorithm for the extraction of semantic data. Finally, evaluations in the form of user tests and questionnaires in a MOOC setting are presented. The results of the evaluation are promising, as they indicate not only that students perceive it as useful, but also that the learning effectiveness increases. The combination of personal lecture video annotations with a semantic topic map was also evaluated positively and will thus be investigated further, as will the implementation in a MOOC context. [ABSTRACT FROM PUBLISHER]