Query: Exploring the Effects of Word Roots for Arabic Sentiment Analysis

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Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences <=SEP=> Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion question answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective being expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classification (upwards of 97% precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91% accuracy).

Thumbs up? Sentiment Classification using Machine Learning Techniques <=SEP=> We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging. Publication info: Proceedings of EMNLP 2002, pp. 79–86.

Determining the Sentiment of Opinions <=SEP=> Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.

Development and Use of a Gold-Standard Data Set for Subjectivity Classifications <=SEP=> This paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using statistical techniques. Bias-corrected tags are formulated and successfully used to guide a revision of the coding manual and develop an automatic classifier. 1 Introduction This paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using the statistical techniques presented in (Bruce and Wiebe, 1998; Bruce and Wiebe, to appear). Our approach is data driven: we refine our understanding and presentation of the classification scheme guided by the results of the intercoder analysis. We also present the results of a probabilistic classifier developed on the resulting annotations. Much research in discourse processing has focused on task-oriented and instructional dialogs. The task addressed here comes to the fore in other genres, especially news reporting. The task is to distinguish sentences used to objectively present factual information from sentences used to present opinions and evaluations. There are many applications for which this distinction promises to be important, including text categorization and summarization. This research takes a large step toward developing a reliably annotated gold standard to support experimenting with such applications. This research is also a case study of analyzing and improving manual tagging that is applicable to any tagging task. We perform a statistical analysis that provides information that complements the information provided by

New Avenues in Opinion Mining and Sentiment Analysis <=SEP=> The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.

Inferring 3D layout of building facades from a single image <=SEP=> In this paper, we propose a novel algorithm that infers the 3D layout of building facades from a single 2D image of an urban scene. Different from existing methods that only yield coarse orientation labels or qualitative block approximations, our algorithm quantitatively reconstructs building facades in 3D space using a set of planes mutually related by 3D geometric constraints. Each plane is characterized by a continuous orientation vector and a depth distribution. An optimal solution is reached through inter-planar interactions. Due to the quantitative and plane-based nature of our geometric reasoning, our model is more expressive and informative than existing approaches. Experiments show that our method compares competitively with the state of the art on both 2D and 3D measures, while yielding a richer interpretation of the 3D scene behind the image.

Suicide rates among physicians: a quantitative and gender assessment (meta-analysis). <=SEP=> OBJECTIVE
Physicians' suicide rates have repeatedly been reported to be higher than those of the general population or other academics, but uncertainty remains. In this study, physicians' suicide rate ratios were estimated with a meta-analysis and systematic quality assessment of recent studies.


METHOD
Studies of physicians' suicide rates were located in MEDLINE, PsycINFO, AARP Ageline, and the EBM Reviews: Cochrane Database of Systematic Reviews with the terms "physicians," "doctors," "suicide," and "mortality." Studies were included if they were published in or after 1960 and gave estimates of age-standardized suicide rates of physicians and their reference population or reported extractable data on physicians' suicide; 25 studies met the criteria. Reviewers extracted data and scored each study for quality. The studies were tested for heterogeneity and publication bias and were stratified by publication year, follow-up, and study quality. Effect sizes were pooled by using fixed-effects (women) and random-effects (men) models.


RESULTS
The aggregate suicide rate ratio for male physicians, compared to the general population, was 1.41, with a 95% confidence interval (CI) of 1.21-1.65. For female physicians the ratio was 2.27 (95% CI=1.90-2.73). Visual inspection of funnel plots from tests of publication bias revealed randomness for men but some indication of bias for women, with a relative, nonsignificant lack of studies in the lower right quadrant.


CONCLUSIONS
Studies on physicians' suicide collectively show modestly (men) to highly (women) elevated suicide rate ratios. Larger studies should help clarify whether female physicians' suicide rate is truly elevated or can be explained by publication bias.

Designs of fully on-chip antennas in (Bi)CMOS technology <=SEP=> The paper presents several feasible millimeter wave on-chip antenna designs suitable to be fabricated in CMOS technology without any additional process. The results are listed and compared with state-of-the-art designs in the literature. The difficulties in designing high efficiency antenna on CMOS chip are discussed.

LOMAC: Low Water-Mark Integrity Protection for COTS Environments <=SEP=> We hypothesizethat a form of kernel-residentaccesscontrol-based integrity protection can gain widespread acceptancein Commer cial Off-The-Shelf(COTS)environmentsprovidedthat it couplessomeusefulprotectionwith a high degreeof compatibilitywith existing software, configurations,andpractices.To testthis hypothesis, wehave developeda highly-compatiblefreeopen-sour ceprototype calledLOMAC, andreleasedit on theInternet.LOMAC is a dynamicallyloadableextensionfor COTSLinux kernels thatprovidesintegrity protectionbasedonLowWater-Mark accesscontrol. We presenta classificationof existing accesscontrol modelswith regard tocompatibility, concluding thatmodelssimilar to LowWater-Mark areespeciallywellsuitedto high-compatibilitysolutions.Wealsodescribeour practical strategiesfor dealingwith thepathological cases in the Low Water-Mark model’s behavior , which includea smallextensionof themodel,andanunusualapplicationof

Experience in System Design for Human-Robot Teaming in Urban Search and Rescue <=SEP=> The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search & Rescue. A human-robot team consists of several semi-autonomous robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system.

End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression <=SEP=> When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users , enabling them to point out which features are important for a class, rath er than provide new training instances. We first evaluate our algorithms against other feature labeling algorit hms under idealized conditions using feature labels generated by an oracle . In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling invo lvi g a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best perfo rmers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end u sers in our study. We also perform an analysis to investigate the relative gains of incorporatin g the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by e d users is both viable and effective for allowing end users to improve the learnin g algorithm behind their customized applicati ons. * Corresponding author E-mail address: dassh@eecs.oregonstate.edu (Shubhomoy Das, 1148 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, Ph: 1-541-908-6949). 1 Early versions of portions of this work appeared in [36, 37] S. Das, T. Moore, W-K. Wong et. al./Artificial Intelligence © 2012 Elsevier B.V. All rights reserved.

A method for Automatic Text Summarization using Consensus of Multiple Similarity Measures and Ranking Techniques <=SEP=> In the era of information overload, text summarization can be defined as the process of extracting useful information from a large space of available content using traditional filtering methods. One of the major challenges in the domain of extraction based summarization is that a single statistical measure is not sufficient to produce efficient summaries which would be close to human-made ‘gold standard’, since each measure suffers from individual weaknesses. We deal with this problem by proposing a text summarization model that combines various statistical measures so that the pitfalls of an individual technique could be compensated by the strengths of others. Experimental results are presented to demonstrate the effectiveness of the proposed method using the TAC 2011 Multiling pilot dataset for English language and ROUGE summary evaluation tool.

Multilevel Inverter Topologies With Reduced Device Count: A Review <=SEP=> Multilevel inverters have created a new wave of interest in industry and research. While the classical topologies have proved to be a viable alternative in a wide range of high-power medium-voltage applications, there has been an active interest in the evolution of newer topologies. Reduction in overall part count as compared to the classical topologies has been an important objective in the recently introduced topologies. In this paper, some of the recently proposed multilevel inverter topologies with reduced power switch count are reviewed and analyzed. The paper will serve as an introduction and an update to these topologies, both in terms of the qualitative and quantitative parameters. Also, it takes into account the challenges which arise when an attempt is made to reduce the device count. Based on a detailed comparison of these topologies as presented in this paper, appropriate multilevel solution can be arrived at for a given application.

TopicShop: enhanced support for evaluating and organizing collections of Web sites <=SEP=> TopicShop is an interface that helps users evaluate and organize collections of web sites. The main interface components are site profiles, which contain information that helps users select high-quality items, and a work area, which offers thumbnail images, annotation, and lightweight grouping techniques to help users organize selected sites. The two components are linked to allow task integration. Previous work [2] demonstrated that subjects who used TopicShop were able to select significantly more highquality sites, in less time and with less effort. We report here on studies that confirm and extend these results. We also show that TopicShop subjects spent just half the time organizing sites, yet still created more groups and more annotations, and agreed more in how they grouped sites. Finally, TopicShop subjects tightly integrated the tasks of evaluating and organizing sites. INTRODUCTION In previous work [2], we motivated an important task for web users – gathering, evaluating, and organizing information resources for a given topic. Current web tools do not support this task well; specifically, they do not make it easy to evaluate collections of web sites to select the best ones or to organize sites for future reuse and sharing. Users have to browse and view sites one after another until they are satisfied they have a good set, or, more likely, they get tired and give up. Browsing a web site is an expensive operation, both in time and cognitive effort. And bookmarks, the most common form of keeping track of web sites, are a fairly primitive organizational technique. We designed and implemented the TopicShop system to provide comprehensive, integrated support for this task. TopicShop aids users in finding a set of relevant sites, in narrowing down the set into a smaller set of high quality sites, and in organizing sites for future use. TopicShop has evolved through a number of design iterations, driven by extensive user testing. We report here on lessons we learned from a pilot study, how these lessons re-shaped our understanding of the task and led to a significant re-design, and the results of a second, larger user study. RELATED WORK Our research program investigates the major information problems faced by users of the World Wide Web: • finding collections of items relevant to their interests; • identifying high-quality items within a collection; • finding items that contain a certain category of information, e.g., episode guides (for a television show) or song lyrics (for a musician); • organizing personalized subsets of items. We have addressed these problems by developing algorithms, implementing them in web crawling and analysis tools, creating interfaces to support users in exploring, comprehending, and organizing collections of web documents, and performing user studies [2, 3, 4, 15]. The work reported here focuses on understanding the user tasks of evaluating and organizing collections of web sites, as ill uminated by the design, evaluation, and re-design of interfaces to support these tasks. Other researchers have investigated these issues. Much recent work has been devoted to algorithms for adding meta-information to collections of web sites to enhance user comprehension, typically by analyzing the structure of links between sites. This approach builds on the intuition that when the author of one site chooses to link to another, this often implies both that the sites have similar content and that the author is endorsing the content of the linked-to site. Pirolli , Pitkow and colleagues [12, 13] experimented with link-based algorithms for clustering and categorizing web pages. Kleinberg’s HITS algorithm [8] defines authoritative and hub pages within a hypertext collection. Authorities and hubs are mutually dependent: a good authority is a page that is linked to by many hubs, and a good hub is one that links to many authorities. After evaluating items and selecting the interesting ones, users must organize the items for future use. Card, Robertson, and Mackinlay [5] introduced the concept of information workspaces to refer to environments in which information items can be stored and manipulated. A departure point for most such systems is the file manager popularized by the Apple Macintosh and then in Microsoft Windows. Such systems typically include a list view, which shows various properties of items, and an icon view, which lets users organize icons representing the items in a 2D space. Mander, Salomon, and Wong [10] enhanced the basic metaphor with the addition of “piles” . Users could create and manipulate piles of items. Interesting interaction techniques for displaying, browsing, and searching piles were designed and tested. Bookmarks are the most popular way to create personal information workspaces of web resources. Bookmarks consist of lists of URLs; typically the title of the web page is used as the label for the URL. Users may organize their bookmarks into a hierarchical category structure. Abrams, Baecker, and Chignell [1] carried out an extensive study of how several hundred web users used bookmarks. They observed a number of strategies for organizing bookmarks, including a flat ordered list, a single level of folders, and hierarchical folders. They also made four design recommendations to help users manage their bookmarks more effectively. First, bookmarks must be easy to organize, e.g., via automatic sorting techniques. Second, visualization techniques are necessary to provide comprehensive overviews of large sets of bookmarks. Third, rich representations of sites are required; many users noted that site titles are not accurate descriptors of site content. Finally, tools for managing bookmarks must be well i ntegrated with web browsers. Many researchers have created experimental information workspace interfaces, often designed expressly for web documents. Card, Robertson, and York [5] describe the WebBook, which uses a book metaphor to group a collection of related web pages for viewing and interaction, and the WebForager, an interface that lets users view and manage multiple WebBooks. Mackinlay, Rao, and Card [9] developed a novel user interface for accessing articles from a citation database. The central UI object is a “Butterfly” , which represents an article, its references, and its citers. The interface makes it easy for users to browse among related articles, group articles, and generate queries to retrieve articles that stand in a particular relationship to the current article. The Data Mountain of Robertson et al [14] represents documents as thumbnail images in a 3D virtual space. Users can move and group the images freely, with various interesting visual and audio cues used to help users arrange the documents. In a study comparing the use of Data Mountain to Internet Explorer Favorites, Data Mountain users retrieved items more quickly, with fewer incorrect or failed retrievals. Our research shares goals with much of the previous work. We focus on designing interfaces that make automatically extracted information about web sites readily accessible to users. We show that this increases users’ abilit y to select high-quality sites. Through ongoing user studies and redesign, we developed easy-to-use annotation and grouping techniques that let users organize items better and more quickly. Finally, we learned how users interleave work on various tasks and re-designed our interface to support such task interleaving. TOPICSHOPEXPLORER, VERSION 1 The TopicShop Explorer is implemented in C++ and runs on Microsoft Windows platforms. Version 1 was based directly on the Macintosh file manager / MS Windows Explorer metaphor (see [3] for detail of TopicShop Version 1 and the pilot study). Accordingly, users could view collections in either a details (Figure 1) or icons (Figure 2) view. The details view showed site profile information (see below) to help users evaluate sites, and the icons view let users organize sites spatially. Figure 1: TopicShop Explorer (version 1), details view. Each web site is represented by a small thumbnail image, the site title, and profile data including the links to/from other sites in the collection, and the number of pages, images, and audio files on the site. Users can sort by a property by clicking on the appropriate column. Figure 2: TopicShop Explorer (version 1), icons view. Each site is represented by a large thumbnail image and the site title. Users can organize sites by arranging them spatially, a technique especially useful in the early stages of exploration. The collections of sites and site profile data used in TopicShop are obtained by running a webcrawler/analyzer. The crawler takes a user-specified set of seed sites as input, and follows links from the seeds to construct a graph of the seed sites, pages contained on these sites, and, optionally, sites determined to be related based on their textual and hyperlink connections to the seeds. Site profiles are built by fetching a large number of pages from each site. Profiles contain content data, including the page title, an estimate of the page count, and a roster of audio files, movie files, and images; they also record links between sites in the collection. In addition, a thumbnail image of each site’s root page is constructed. The first goal of TopicShop is to help users evaluate and identify high quality sites. We sought to achieve this goal by providing site profile data and interface mechanisms for viewing and exploring the data. Making this data visible means that users no longer had to select sites to browse based solely on titles and (sometimes) brief textual annotations. (A chief complaint of subjects in the Abrams et al [1] study was that titles were inadequate descriptors of site content — and that was for sites that users already had browsed and decided to bookmark.) Instead, users may visit only sites that have been endorsed (linked to) by many other sites or sites that are rich in

Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model <=SEP=> Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which 1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts; 2) automatically identifies the number of clusters; 3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.

CBSE as Novel Approach for IDS <=SEP=> This paper gives a new approach called Component based software engineering for designing an intrusion detection system. This paper also tells that before this component based software approach, other traditional approaches were used for designing intrusion detection system. But due to various drawbacks we proposed this component based approach for intrusion detection system. A comparison table is also illustrated in this paper on the basis of class diagram designed using UML language in a simulated environment. This comparison table will illustrate main difference between the traditional and component based approach and also shows that which parameters make this approach a better approach. Keywords—Component based software engineering, intrusion detection system, Classes etc.

Overview on electrostatic discharge protection designs for mixed-voltage I/O interfaces: design concept and circuit implementations <=SEP=> Electrostatic discharge (ESD) protection design for mixed-voltage I/O interfaces has been one of the key challenges of system-on-a-chip (SOC) implementation in nano-scale CMOS processes. The on-chip ESD protection circuit for mixed-voltage I/O interfaces should meet the gate-oxide reliability constraints and prevent the undesired leakage current paths. This paper presents an overview on the design concept and circuit implementations of the ESD protection designs for mixed-voltage I/O interfaces without using the additional thick gate-oxide process. The ESD design constraints in mixed-voltage I/O interfaces, the classification and analysis of ESD protection designs for mixed-voltage I/O interfaces, and the designs of high-voltage-tolerant power-rail ESD clamp circuit are presented and discussed.

Enterprise-wide Optimization : A New Frontier in Process Systems Engineering <=SEP=> Enterprise-wide optimization (EWO) is a new emerging area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to reduce costs and inventories. A major focus in EWO is the optimal operation of manufacturing facilities, which often requires the use of nonlinear process models. Major operational items include planning, scheduling, realtime optimization and inventory control. One of the key features of EWO is integration of the information and the decision-making among the various functions that comprise the supply chain of the company. This can be achieved with modern IT tools, which together with the internet, have promoted e-commerce. However, as will be discussed, to fully realize the potential of transactional IT tools, the development of sophisticated deterministic and stochastic linear/nonlinear optimization models and algorithms (analytical IT tools) is needed to explore and analyze alternatives of the supply chain to yield overall optimum economic performance, as well as high levels of customer satisfaction. An additional challenge is the integrated and coordinated decision-making across the various functions in a company (purchasing, manufacturing, distribution, sales), across various geographically distributed organizations (vendors, facilities and markets), and across various levels of decision-making (strategic, tactical and operational). © 2005 American Institute of Chemical Engineers AIChE J, 51: 1846–1857, 2005

Forecasting stock market movement direction with support vector machine <=SEP=> Support vector machine (SVM) is a very speci1c type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of 1nancial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classi1cation methods. Further, we propose a combining model by integrating SVM with the other classi1cation methods. The combining model performs best among all the forecasting methods. ? 2004 Elsevier Ltd. All rights reserved.

Classifying the Informative Behaviour of Emoji in Microblogs <=SEP=> Emoji are pictographs commonly used in microblogs as emotion markers, but they can also represent a much wider range of concepts. Additionally, they may occur in different positions within a message (e.g. a tweet), appear in sequences or act as word substitute. Emoji must be considered necessary elements in the analysis and processing of user generated content, since they can either provide fundamental syntactic information, emphasize what is already expressed in the text, or carry meaning that cannot be inferred from the words alone. We collected and annotated a corpus of 2475 tweets pairs with the aim of analyzing and then classifying emoji use with respect to redundancy. The best classification model achieved an F-score of 0.7. In this paper we shortly present the corpus, and we describe the classification experiments, explain the predictive features adopted, discuss the problematic aspects of our approach and suggest future improvements.

Der CDO als Komplement zum CIO <=SEP=> 

Sustainable Food Supply Chains : The Role of Collaboration and Sustainable Relationships <=SEP=> The life cycle approach is widely used in the analysis of sustainability. Its application to supply chains is necessary since the product flows, from processing of raw materials to the final customer, are considered. The role of the organizational aspects, expressed in terms of relationships between the supply chain agents, is little considered in the life cycle analysis approach. The aim of this paper is to extend the scope of the food chain life cycle analysis by adding the organizational dimension to the environmental, economic and social ones. Within this context, Collaboration and Sustainable Relationships concepts have been explored based on a literature survey. A theoretical framework, describing their role in assessing the organizational dimension in the life cycle analysis of the food supply chains, is defined. A hypothesis on their joint influence on the supply chains performances is formulated.

Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling <=SEP=> In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks’ execution latency and the MD’s energy consumption by jointly optimizing the task allocation decision and the MD’s central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks’ execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD’s flexible CPU range can have an impact on the task allocation.

Robustness beyond shallowness: incremental deep parsing <=SEP=> Robustness is a key issue for natural language processing in general and parsing in particular, and many approaches have been explored in the last decade for the design of robust parsing systems. Among those approaches is shallow or partial parsing, which produces minimal and incomplete syntactic structures, often in an incremental way. We argue that with a systematic incremental methodology one can go beyond shallow parsing to deeper language analysis, while preserving robustness. We describe a generic system based on such methodology and designed for building robust analyzers that tackle deeper linguistic phenomena than those traditionally handled by the now widespread shallow parsers. The rule formalism allows the recognition of n-ary linguistic relations between words or constituents on the basis of global or local structural, topological and/or lexical conditions. It offers the advantage of accepting various types of inputs, ranging from raw to chunked or constituent-marked texts, so for instance it can be used to process existing annotated corpora, or to perform a deeper analysis on the output of an existing shallow parser. It has been successfully used to build a deep functional dependency parser, as well as for the task of co-reference resolution, in a modular way.

From Single to Dual Robotic Therapy: A Review of the Development Process of iPAM <=SEP=> Stroke is a common condition in the UK with over 30,000 people per annum are left with significant disability. When severe arm paresis is occurs, functional recovery of the affected arm is often poor. This can have a major impact on physical independence. A contributing factor to the poor recovery relates to lack of intensity of appropriate arm exercise practice. Through the use of robotic technology it may be possible to augment the physical therapist's efforts and maximize the impact of the limited physical therapy resource. At Leeds University a prototype single robot system has been developed that is capable of moving a patient's arm through a set of movements prescribed by a physiotherapist. This system is robust to changes in the biomechanical and physiological properties through a large range of patients. Further development of the system has led to a dual robot system that provides a greater level of control over upper-arm joint co-ordination while offering additional support to the shoulder complex. In order to test the systems safely, two models of the human arm have been produced: 1) a 6 degree of freedom computational model simulating how the robot system could provide therapy to a patient. 2) A mechanical model (Derryck), representing the biomechanical properties of the shoulder and elbow. The prototype single robot system is shown to be capable of administering trajectory following (for Derryck) for a large range of arm parameters

Categorize the Quality of Cotton Seeds Based on the Different Germination of the Cotton Using Machine Knowledge Approach <=SEP=> A study was made in those region that expose major area of machine learning techniques which available in data mining. There are lots of Research work is analysized in agriculture field. It is a smart techniques is needed to find the resolution over the modern method. Cotton is an significant beneficial harvest and extensively traded commodity across the world. Its yield is insightful to weather, soil as well as organization practices. This study presents a concise idea of several of the broadly capitalized machine learning technique over agricultural domain. Among the algorithms like Navie bayes, j48 ,MLP,Randomforest,Random tree, I have founded J48 gives better result over the other algorithms using cotton seed quality accuracy is best.

MOOCs: So Many Learners, So Much Potential ... <=SEP=> Massive open online courses (MOOCs) have exploded onto the scene, promising to satisfy a worldwide thirst for a high-quality, personalized, and free education. This article explores where MOOCs fit within the e-learning and Artificial Intelligence in Education (AIED) landscape.

Facial Expression Analysis under Partial Occlusion: A Survey <=SEP=> Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.

A six degrees of freedom haptic interface for laparoscopic training <=SEP=> We present the novel kinematics, workspace characterization, functional prototype and impedance control of a six degrees of freedom haptic interface designed to train surgeons for laparoscopic procedures, through virtual reality simulations. The parallel kinematics of the device is constructed by connecting a 3RRP planar parallel mechanism to a linearly actuated modified delta mechanism with a connecting link. The configuration level forward and inverse kinematics of the device assume analytic solutions, while its workspace can be shaped to enable large end-effector translations and rotations, making it well-suited for laparoscopy operations. Furthermore, the haptic interface features a low apparent inertia with high structural stiffness, thanks to its parallel kinematics with grounded actuators. A model-based open-loop impedance controller with feed-forward gravity compensation has been implemented for the device and various virtual tissue/organ stiffness levels have been rendered.

Commercial DC-DC converters: A survey of isolated low-voltage very-high-current systems for transportation electrification <=SEP=> As the energy demand from low-voltage (LV) DC sources on vehicles keeps increasing, the need for DC-DC converters capable of handling very high current on the LV side is growing. A group of original equipment manufacturers (OEM) have set performance targets for such converters for the years 2015 and 2020. This paper presents an extensive survey of DC-DC converters that are: a) galvanically isolated, b) able to scale 12/24/48 V up to several hundred volts, and c) rated to carry several hundred amperes of current on the LV side. More than eighty converters from thirty manufacturers are analyzed. Characteristics such as voltage ranges, current capacity, power rating, efficiency, operating temperature, cooling types, weight, power density, volume, specific power, compliant codes, and standards are collected and presented in a graphical form. The current insights and trends are gathered and compared with the OEM targets. The results can serve as a technology indicator of this kind of converters in 2015 for researchers and practicing engineers of transportation electrification and others alike.