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These instructions give guidelines [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]

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]

The task of assigning geographic coordinates to textual resources plays an increasingly central role in geographic information retrieval. The ability to select those terms from a given collection that are most indicative of geographic location is of key importance in successfully addressing this task. However, this process of selecting spatially relevant terms is at present not well understood, and the majority of current systems are based on standard term selection techniques, such as $(\chi^2)$ or information gain, and thus fail to exploit the spatial nature of the domain. In this paper, we propose two classes of term selection techniques based on standard geostatistical methods. First, to implement the idea of spatial smoothing of term occurrences, we investigate the use of kernel density estimation (KDE) to model each term as a two-dimensional probability distribution over the surface of the Earth. The second class of term selection methods we consider is based on Ripley's K statistic, which measures the deviation of a point set from spatial homogeneity. We provide experimental results which compare these classes of methods against existing baseline techniques on the tasks of assigning coordinates to Flickr photos and to Wikipedia articles, revealing marked improvements in cases where only a relatively small number of terms can be selected. [ABSTRACT FROM AUTHOR]

Extending the keyword search paradigm to relational data has been an active area of research within the database and IR community during the past decade. Many approaches have been proposed, but despite numerous publications, there remains a severe lack of standardization for the evaluation of proposed search techniques. Lack of standardization has resulted in contradictory results from different evaluations, and the numerous discrepancies muddle what advantages are proffered by different approaches. In this paper, we present the most extensive empirical performance evaluation of relational keyword search techniques to appear to date in the literature. Our results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption precludes many search techniques from scaling beyond small data sets with tens of thousands of vertices. We also explore the relationship between execution time and factors varied in previous evaluations; our analysis indicates that most of these factors have relatively little impact on performance. In summary, our work confirms previous claims regarding the unacceptable performance of these search techniques and underscores the need for standardization in evaluations--standardization exemplified by the IR community. [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 discusses shortcomings in the current state of academic publishing. Although a great deal of published material is available on the Internet, most of it is still organized similar to print media. Persons searching for information on a particular subject need to be conversant with that field's jargon. Internet publishing could do much more to make material accessible by aiding the interdisciplinary search process. One helpful innovation is the overlay journal, which addresses a particular theme and provides related links that cut across academic disciplines.

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]

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]

In this article the author acknowledges the contributors of this issue of the periodical "IEEE Transactions on Knowledge And Data Engineering."

In this paper, we study the problem of video transcoding from MPEG-2 to Windows Media Video (WMV) format, together with several desired functionalities such as bit-rate reduction and spatial resolution downscaling. Based on in-depth analysis of error propagation behavior, we propose two architectures (for different typical application scenarios) that are unique in their complexity scalability and adaptive drifting error control, which in return provide a mechanism to achieve a desired tradeoff between complexity and quality. We perform extensive experiments for various design targets such as complexity, scalability, performance tradeoff, and drifting control effect. The proposed transcoding architectures can be straightforwardly applied to the MPEG-2 to MPEG-4 transcoding applications due to the significant overlap between the MPEG-4 and WMV coding technology. [ABSTRACT FROM AUTHOR]

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 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]

Vandalism, the malicious modification of articles, is a serious problem for open access encyclopedias such as Wikipedia. The use of counter-vandalism bots is changing the way Wikipedia identifies and bans vandals, but their contributions are often not considered nor discussed. In this paper, we propose novel text features capturing the invariants of vandalism across five languages to learn and compare the contributions of bots and users in the task of identifying vandalism. We construct computationally efficient features that highlight the contributions of bots and users, and generalize across languages. We evaluate our proposed features through classification performance on revisions of five Wikipedia languages, totaling over 500 million revisions of over nine million articles. As a comparison, we evaluate these features on the small PAN Wikipedia vandalism data sets, used by previous research, which contain approximately 62,000 revisions. We show differences in the performance of our features on the PAN and the full Wikipedia data set. With the appropriate text features, vandalism bots can be effective across different languages while learning from only one language. Our ultimate aim is to build the next generation of vandalism detection bots based on machine learning approaches that can work effectively across many languages. [ABSTRACT FROM PUBLISHER]

During the last decades, educational contexts have transformed into complex technological and social ecologies, with mobile devices expanding the scope of education beyond the traditional classroom, creating so-called Ubiquitous Learning Environments (ULEs). However, these new technological opportunities entail an additional burden for teachers, who need to manage and coordinate the resources involved in such complex educational scenarios in a process known as “orchestration”. This paper presents the evaluation of the orchestration support provided by GLUEPS-AR, a system aimed to help teachers in the coordination of across-spaces learning situations carried out in ULEs. The evaluation, following an interpretive research perspective, relied on a study where a pre-service teacher designed and enacted an authentic across-spaces learning situation in a primary school. The situation, which illustrates the orchestration challenges of ULEs, was aimed at fostering orienteering skills. It spanned five sessions taking place in the classroom, in the school's playground and at a nearby park, using multiple technologies and devices. The evaluation showed that GLUEPS-AR helped the teacher in the multiple aspects of orchestration, including implementation of his pedagogical ideas, adaptation in runtime, and sharing of orchestration load with students. Teacher awareness during outdoor activities was the main aspect to improve upon. [ABSTRACT FROM PUBLISHER]

It is nowadays well-established that the construction of quality domain ontologies benefits from the involvement in the modelling process of more actors, possibly having different roles and skills. To be effective, the collaboration between these actors has to be fostered, enabling each of them to actively and readily participate to the development of the ontology, favoring as much as possible the direct involvement of the domain experts in the authoring activities. Recent works have shown that ontology modelling tools based on wikis’ paradigm and technology could contribute in meeting these collaborative requirements. This paper investigates, both at the theoretical and empirical level, the effectiveness of wiki features for collaborative ontology authoring in supporting teamworks composed of domain experts and knowledge engineers, as well as their impact on the entire process of collaborative ontology modelling and entity lifecycle. [ABSTRACT FROM AUTHOR]

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 three IEEE Communications Society magazines are all very healthy, especially in terms of the number of submissions, their impact factor, and the number of downloads from IEEE Xplore. IEEE Communications Magazine is quickly becoming the publication of record for the 5G wireless topics. I want to take this opportunity to update our Communications Society members and magazine subscribers on some of the new initiatives and activities that are in progress to further strengthen our magazines. [ABSTRACT FROM AUTHOR]

In this paper, we propose a survival modeling approach to promoting ranking diversity for biomedical information retrieval. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using Wikipedia to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, we further evaluate our approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that our approach can achieve promising performance improvements. [ABSTRACT FROM PUBLISHER]

This paper describes a new approach for a course and laboratory designed to allow students to develop low-cost prototypes of robotic and other embedded devices that feature Internet connectivity, I/O, networking, a real-time operating system (RTOS), and object-oriented C/C++. The application programming interface (API) libraries provided permit students to work at a higher level of abstraction. A low-cost 32-bit SOC RISC microcontroller module with flash memory, numerous I/O interfaces, and on-chip networking hardware is used to build prototypes. A cloud-based C/C++ compiler is used for software development. All student files are stored on a server, and any Web browser can be used for software development. Breadboards are used in laboratory projects to rapidly build prototypes of robots and embedded devices using the microcontroller, networking, and other I/O subsystems on small breakout boards. The commercial breakout boards used provide a large assortment of modern sensors, drivers, display ICs, and external I/O connectors. Resources provided include eBooks, laboratory assignments, and extensive Wiki pages with schematics and sample microcontroller application code for each breakout board. [ABSTRACT FROM PUBLISHER]

Topic models have achieved big success in recent years. To detect topics in a text stream, various online topic models have been proposed in the literature. The limitations of these works include that: 1) most of them run with fixed topic numbers and 2) the overlaps between the topics may enlarge in the evolving process. Hierarchical topic model is a candidate solution to these problems since it can reveal many useful relationships between the topics. These relationships can help to find high quality topics and reduce topic overlaps. In this paper, a knowledge-based semisupervised hierarchical online topic detection framework is proposed. The proposed framework can detect topics in an online hierarchical way. In addition, it has been proven that introducing external knowledge can improve the performance of text mining. Therefore, the knowledge from external knowledge sources and human experts are also integrated in the proposed framework. Experiments are conducted to evaluate the proposed framework with different metrics. The results show that compared with the baseline methods, our framework can achieve better performance with competitive time efficiency. [ABSTRACT FROM AUTHOR]

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

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

With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact. [ABSTRACT FROM AUTHOR]

Editor in chief Forrest Shull describes the decisions made at the magazine's most recent editorial board meeting and the exciting changes to come for this publication's digital edition. He also highlights winners of recent IEEE Software-sponsored awards. [ABSTRACT FROM PUBLISHER]

This article introduces the readers of the Journal "IEEE Tansactions on Vehicular Technology," to its new Editor-in-Chief Tan F. Wang. Wang had been serving as associate Editor for past several years. It further informs about the technological progress, the journal had made. For past 3 years, it's peer review process has migrated from a paper-based system to a web-based system and as a result, reviews are conducted by a diverse group of international experts. It also thanks all those who are involved with the publishing process of the journals.