Query: Topic-Relevance Map: Visualization for Improving Search Result Comprehension

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Designing for Exploratory Search on Touch Devices <=SEP=> Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices.

The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations <=SEP=> A useful starting point for designing advanced graphical user interfaces is the Visual InformationSeeking Mantra: Overview first, zoom and filter, then details-on-demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. This paper offers a task by data type taxonomy with seven data types (1-, 2-, 3-dimensional data, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extract).

TopicPanorama: A Full Picture of Relevant Topics <=SEP=> This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as well as distinctive topics from each source. Our approach models each textual corpus as a topic graph. These graphs are then matched using a consistent graph matching method. Next, we develop a level-of-detail (LOD) visualization that balances both readability and stability. Accordingly, the resulting visualization enhances the ability of users to understand and analyze the matched graph from multiple perspectives. By incorporating metric learning and feature selection into the graph matching algorithm, we allow users to interactively modify the graph matching result based on their information needs. We have applied our approach to various types of data, including news articles, tweets, and blog data. Quantitative evaluation and real-world case studies demonstrate the promise of our approach, especially in support of examining a topic-graph-based full picture at different levels of detail.

A user-centric evaluation framework for recommender systems <=SEP=> This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users' point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.

Interaction Design for Recommender Systems <=SEP=> 1 kirstens@sims.berkeley.edu, sinha@sims.berkeley.edu ABSTRACT Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user’s perspective. The goal of our research is to study users’ interactions with recommender systems in order to develop general design guidelines. We have studied users’ interactions with 11 online recommender systems. Our studies have highlighted the role of transparency (understanding of system logic), familiar recommendations, and information about recommended items in the user’s interaction with the system. Our results also indicate that there are multiple models for successful recommender systems.

Driving cycle-based design optimization of interior permanent magnet synchronous motor drives for electric vehicle application <=SEP=> The paper discusses the influence of driving cycles on the design optimization of permanent magnet synchronous machines. A bi-objective design optimization is presented for synchronous machines with V-shaped buried magnets. The machine length and the loss energy over a given driving cycle are defined as objectives. A total of 14 parameters defining geometry and winding layout are chosen as design parameters. Additionally some constraints like speed-torque-requirements and minimal stray field bridge widths are set. The optimization problem is solved using 2D finite element analysis and a high-performance differential evolution algorithm. The analyses are performed for the ARTEMIS driving cycle due to the more realistic driving behavior in comparison to the most commonly used New European Driving Cycle. Furthermore, a reference design optimization against the rated point loss energy is presented. The results show a much better performance of the driving cycle optimized machines in comparison to the rated point optimized machines in terms of the cycle-based loss energy. Loss savings depend strongly on the machine length and are approximately in between 15% and 45%.

Learning From Positive and Unlabeled Data: A Survey <=SEP=> Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

Machine Learning Techniques in ADAS: A Review <=SEP=> What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.

Radian: Visual Exploration of Traceroutes <=SEP=> Several projects deploy probes in the Internet. Probes are systems that continuously perform traceroutes and other networking measurements (e.g., ping) towards selected targets. Measurements can be stored and analyzed to gain knowledge on several aspects of the Internet, but making sense of such data requires suitable methods and tools for exploration and visualization. We present Radian, a tool that allows to visualize traceroute paths at different levels of detail and to animate their evolution during a selected time interval. We also describe extensive tests of the tool using traceroutes performed by RIPE Atlas Internet probes.

DifNet: Semantic Segmentation by Diffusion Networks <=SEP=> Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once, since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. Our DifNet is trained end-to-end without complex loss functions.

Any domain parsing: automatic domain adaptation for natural language parsing <=SEP=> of “Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing” by David McClosky , Ph.D., Brown University, May, 2010. Current efforts in syntactic parsing are largely data-driv en. These methods require labeled examples of syntactic structures to learn statistical patterns governing these structures. Labeled data typically requires expert annotators which makes it both time consuming and costly to p roduce. Furthermore, once training data has been created for one textual domain, portability to similar domains is limited. This domain-dependence has inspired a large body of work since syntactic parsing aims to capture syntactic patterns across an entire language rather than just a specific domain. The simplest approach to this task is to assume that the targe t domain is essentially the same as the source domain. No additional knowledge about the target domain is i cluded. A more realistic approach assumes that only raw text from the target domain is available. This a s umption lends itself well to semi-supervised learning methods since these utilize both labeled and unlab eled examples. This dissertation focuses on a family of semi-supervised me thods called self-training. Self-training creates semi-supervised learners from existing supervised learne rs with minimal effort. We first show results on self-training for constituency parsing within a single dom ain. While self-training has failed here in the past, we present a simple modification which allows it to succeed, p roducing state-of-the-art results for English constituency parsing. Next, we show how self-training is be neficial when parsing across domains and helps further when raw text is available from the target domain. On e of the remaining issues is that one must choose a training corpus appropriate for the target domain or perfo rmance may be severely impaired. Humans can do this in some situations, but this strategy becomes less prac tical as we approach larger data sets. We present a technique, Any Domain Parsing, which automatically detect s useful source domains and mixes them together to produce a customized parsing model. The resulting models perform almost as well as the best seen parsing models (oracle) for each target domain. As a result, we have a fully automatic syntactic constituency parser which can produce high-quality parses for all types of text, regardless of domain. Abstract of “Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing” by David McClosky , Ph.D., Brown University, May, 2010.of “Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing” by David McClosky , Ph.D., Brown University, May, 2010. Current efforts in syntactic parsing are largely data-driv en. These methods require labeled examples of syntactic structures to learn statistical patterns governing these structures. Labeled data typically requires expert annotators which makes it both time consuming and costly to p roduce. Furthermore, once training data has been created for one textual domain, portability to similar domains is limited. This domain-dependence has inspired a large body of work since syntactic parsing aims to capture syntactic patterns across an entire language rather than just a specific domain. The simplest approach to this task is to assume that the targe t domain is essentially the same as the source domain. No additional knowledge about the target domain is i cluded. A more realistic approach assumes that only raw text from the target domain is available. This a s umption lends itself well to semi-supervised learning methods since these utilize both labeled and unlab eled examples. This dissertation focuses on a family of semi-supervised me thods called self-training. Self-training creates semi-supervised learners from existing supervised learne rs with minimal effort. We first show results on self-training for constituency parsing within a single dom ain. While self-training has failed here in the past, we present a simple modification which allows it to succeed, p roducing state-of-the-art results for English constituency parsing. Next, we show how self-training is be neficial when parsing across domains and helps further when raw text is available from the target domain. On e of the remaining issues is that one must choose a training corpus appropriate for the target domain or perfo rmance may be severely impaired. Humans can do this in some situations, but this strategy becomes less prac tical as we approach larger data sets. We present a technique, Any Domain Parsing, which automatically detect s useful source domains and mixes them together to produce a customized parsing model. The resulting models perform almost as well as the best seen parsing models (oracle) for each target domain. As a result, we have a fully automatic syntactic constituency parser which can produce high-quality parses for all types of text, regardless of domain. Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing by David McClosky B. S., University of Rochester, 2004 Sc. M., Brown University, 2006 A dissertation submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in the Department of Computer Science at Brown University. Providence, Rhode Island May, 2010 c © Copyright 2010 by David McClosky This dissertation by David McClosky is accepted in its prese nt form by the Department of Computer Science as satisfying the disser tation requirement for the degree of Doctor of Philosophy. Date Eugene Charniak, Director Recommended to the Graduate Council Date Mark Johnson, Reader Cognitive and Linguistic Sciences Date Dan Klein, Reader University of California at Berkeley Approved by the Graduate Council Date Sheila Bond Dean of the Graduate School

Walking > Walking-in-Place > Flying, in Virtual Environments <=SEP=> A study by Slater, et al., [1995] indicated that naive subjects in an immersive virtual environment experience a higher subjective sense of presence when they locomote by walking-in-place (virtual walking) than when they push-button-fly (along the floor plane). We replicated their study, adding real walking as a third condition. Our study confirmed their findings. We also found that real walking is significantly better than both virtual walking and flying in ease (simplicity, straightforwardness, naturalness) as a mode of locomotion. The greatest difference in subjective presence was between flyers and both kinds of walkers. In addition, subjective presence was higher for real walkers than virtual walkers, but the difference was statistically significant only in some models. Follow-on studies show virtual walking can be substantially improved by detecting footfalls with a head accelerometer. As in the Slater study, subjective presence significantly correlated with subjects’ degree of association with their virtual bodies (avatars). This, our strongest statistical result, suggests that substantial potential presence gains can be had from tracking all limbs and customizing avatar appearance. An unexpected by-product was that real walking through our enhanced version of Slater’s visual-cliff virtual environment (Figure 1) yielded a strikingly compelling virtual experience—the strongest we and most of our visitors have yet experienced. The most needed system improvement is the substitution of wireless technology for all links to the user. CR

NL2KB: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval <=SEP=> Words to express relations in natural language (NL) statements may be different from those to represen t properties in knowledge bases (KB). The vocabulary g p becomes barriers for knowledge base construction and retrieval. With the demo system called NL2KB in this paper, users can browse which properties in KB side may be mapped to for a given relati onal pattern in NL side. Besides, they can retrieve the sets of relational patterns in NL side for a given property in KB side. We describe how the mapping is established in detail. Although the mined patterns are used for Chinese knowledge base applications, t he methodology can be extended to other languages.

Measuring User Influence in Twitter: The Million Follower Fallacy <=SEP=> Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user’s influence on others—a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.

Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl <=SEP=> We present DEPCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences from a web-scale crawl of the COMMON CRAWL project. The sentences are processed with a dependency parser and with a named entity tagger and contain provenance information, enabling various applications ranging from training syntax-based word embeddings to open information extraction and question answering. We built an index of all sentences and their linguistic meta-data enabling quick search across the corpus. We demonstrate the utility of this corpus on the verb similarity task by showing that a distributional model trained on our corpus yields better results than models trained on smaller corpora, like Wikipedia. This distributional model outperforms the state of art models of verb similarity trained on smaller corpora on the SimVerb3500 dataset.

Universal Blind Quantum Computation <=SEP=> We present a protocol which allows a client to have a server carry out a quantum computation for her such that the client's inputs, outputs and computation remain perfectly private, and where she does not require any quantum computational power or memory. The client only needs to be able to prepare single qubits randomly chosen from a finite set and send them to the server, who has the balance of the required quantum computational resources. Our protocol is interactive: after the initial preparation of quantum states, the client and server use two-way classical communication which enables the client to drive the computation, giving single-qubit measurement instructions to the server, depending on previous measurement outcomes. Our protocol works for inputs and outputs that are either classical or quantum. We give an authentication protocol that allows the client to detect an interfering server; our scheme can also be made fault-tolerant. We also generalize our result to the setting of a purely classical client who communicates classically with two non-communicating entangled servers, in order to perform a blind quantum computation. By incorporating the authentication protocol, we show that any problem in BQP has an entangled two-prover interactive proof with a purely classical verifier. Our protocol is the first universal scheme which detects a cheating server, as well as the first protocol which does not require any quantum computation whatsoever on the client's side. The novelty of our approach is in using the unique features of measurement-based quantum computing which allows us to clearly distinguish between the quantum and classical aspects of a quantum computation.

The evolution to 4G cellular systems: LTE-Advanced <=SEP=> This paper provides an in-depth view on the technologies being considered for Long Term Evolution-Advanced (LTE-Advanced). First, the evolution from third generation (3G) to fourth generation (4G) is described in terms of performance requirements and main characteristics. The new network architecture developed by the Third Generation Partnership Project (3GPP), which supports the integration of current and future radio access technologies, is highlighted. Then, the main technologies for LTE-Advanced are explained, together with possible improvements, their associated challenges, and some approaches that have been considered to tackle those challenges.

A closer look at GPUs <=SEP=> As the line between GPUs and CPUs begins to blur, it's important to understand what makes GPUs tick.

MRR: an unsupervised algorithm to rank reviews by relevance <=SEP=> The automatic detection of relevant reviews plays a major role in tasks such as opinion summarization, opinion-based recommendation, and opinion retrieval. Supervised approaches for ranking reviews by relevance rely on the existence of a significant, domain-dependent training data set. In this work, we propose MRR (Most Relevant Reviews), a new unsupervised algorithm that identifies relevant revisions based on the concept of graph centrality. The intuition behind MRR is that central reviews highlight aspects of a product that many other reviews frequently mention, with similar opinions, as expressed in terms of ratings. MRR constructs a graph where nodes represent reviews, which are connected by edges when a minimum similarity between a pair of reviews is observed, and then employs PageRank to compute the centrality. The minimum similarity is graph-specific, and takes into account how reviews are written in specific domains. The similarity function does not require extensive pre-processing, thus reducing the computational cost. Using reviews from books and electronics products, our approach has outperformed the two unsupervised baselines and shown a comparable performance with two supervised regression models in a specific setting. MRR has also achieved a significantly superior run-time performance in a comparison with the unsupervised baselines.

A generalized uncertainty principle and sparse representation in pairs of bases <=SEP=> An elementary proof of a basic uncertainty principle concerning pairs of representations of RN vectors in different orthonormal bases is provided. The result, slightly stronger than stated before, has a direct impact on the uniqueness property of the sparse representation of such vectors using pairs of orthonormal bases as overcomplete dictionaries. The main contribution in this paper is the improvement of an important result due to Donoho and Huo concerning the replacement of the l0 optimization problem by a linear programming minimization when searching for the unique sparse representation.

Happiness unpacked: positive emotions increase life satisfaction by building resilience. <=SEP=> Happiness-a composite of life satisfaction, coping resources, and positive emotions-predicts desirable life outcomes in many domains. The broaden-and-build theory suggests that this is because positive emotions help people build lasting resources. To test this hypothesis, the authors measured emotions daily for 1 month in a sample of students (N = 86) and assessed life satisfaction and trait resilience at the beginning and end of the month. Positive emotions predicted increases in both resilience and life satisfaction. Negative emotions had weak or null effects and did not interfere with the benefits of positive emotions. Positive emotions also mediated the relation between baseline and final resilience, but life satisfaction did not. This suggests that it is in-the-moment positive emotions, and not more general positive evaluations of one's life, that form the link between happiness and desirable life outcomes. Change in resilience mediated the relation between positive emotions and increased life satisfaction, suggesting that happy people become more satisfied not simply because they feel better but because they develop resources for living well.

Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention <=SEP=> Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-tointerpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here we explore the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network’s output (steering control). Our approach is two-stage. In the first stage, we use a visual attention model to train a convolution network endto- end from images to steering angle. The attention model highlights image regions that potentially influence the network’s output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network’s behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 hours of driving. We first show that training with attention does not degrade the performance of the end-to-end network. Then we show that the network causally cues on a variety of features that are used by humans while driving.

An exploratory study of backtracking strategies used by developers <=SEP=> Developers frequently backtrack while coding. They go back to an earlier state by removing inserted code or by restoring removed code for various reasons. However, little is known about when and how the developers backtrack, and modern IDEs do not provide much assistance for backtracking. As a first step towards gathering baseline knowledge about backtracking and designing more robust backtracking assistance tools in modern IDEs, we conducted an exploratory study with 12 professional developers and a follow-up online survey. Our study revealed several barriers they faced while backtracking. Subjects often manually commented and uncommented code, and often had difficulty finding relevant parts to backtrack. Backtracking was reported to be needed by 3/4 of the developers at least "sometimes".

Scientific and Pragmatic Challenges for Bridging Education and Neuroscience <=SEP=> Neuroscience has experienced rapid growth in recent years, spurred in part by the U.S. government’s designation of the 1990s as “The Decade of the Brain” (Jones & Mendell, 1999). The rapid development of functional neuroimaging techniques has given researchers unprecedented access to the behaving brains of healthy children and adults. The result has been a wave of new insights into thinking, emotion, motivation, learning, and development. As these insights suffuse the social sciences, they sometimes inspire reconsideration of existing explanations. This is most true in psychology, as marked by the births of cognitive neuroscience (Gazzaniga, Ivry, & Mangun, 2002), developmental neuroscience (Johnson, Munakata, & Gilmore, 2001), and social neuroscience (Cacioppo, Visser, & Pickett, 2005). It is increasingly true in economics, where the rapid rise of neuroeconomics (Camerer, Loewenstein, & Prelec, 2005) has caught the attention of the popular press (Cassidy, 2006). Other social sciences, including communication (Anderson et al., 2006), political science (McDermott, 2004), and sociology (Wexler, 2006), are just beginning to confront the question of whether their research can be informed by neuroscience. Education is somewhere between the two poles of early adopters and tentative newcomers. A decade ago, in this journal, Bruer (1997) forcefully considered the relevance of neuroscience to education. His conclusion—that neuroscience is “a bridge too far”—was noteworthy because Bruer was then director of the McDonnell Foundation, which was actively funding research in both disciplines. Although it was in his best interests to find connections between the disciplines, he found instead poorly drawn extrapolations that inflated neuroscience findings into educational neuromyths. Since Bruer’s cautionary evaluation, a number of commentators have considered the prospects for educational neuroscience. Many sound a more optimistic note (Ansari & Coch, 2006; Byrnes & Fox, 1998; Geake & Cooper, 2003; Goswami, 2006; Petitto & Dunbar, in press), and a textbook has even appeared (Blakemore & Frith, 2005). In this article, we negotiate the middle ground between the pessimism of Bruer and the optimism of those who followed. Table 1 summarizes eight concerns about connecting education and neuroscience. Some are drawn from Bruer (1997) and the ensuing commentaries. Others come from conversations with colleagues in both disciplines, and still others from our own experiences. These concerns do not seem to represent a blanket dismissal but rather a genuine curiosity (tempered by a healthy skepticism) about the implications of neuroscience for education. We begin by articulating the concerns along with some facts about neuroscience that make the concerns more concrete. We voice them in the strong tone in which we have heard them espoused. We then revisit the concerns, reinterpreting them as potential opportunities (also in Table 1). This approach permits us to review a selection of neuroscience studies relevant to content learning. We focus on recent functional magnetic resonance imaging (fMRI), or neuroimaging, studies for reasons of space and because these are the findings that have captured the most attention, both in the academy and in the popular press. Ideally, our review illustrates some elements of neuroscience so that education researchers can think more specifically about the prospects of educational neuroscience. We conclude with two reflections on moving from armchair arguments of a philosophical nature to scientific action on the ground. First, we argue that education and neuroscience can be bridged if (and only if) researchers collaborate across disciplinary lines on tractable problems of common interest. It is the success

Animal-assisted therapy and loneliness in nursing homes: use of robotic versus living dogs. <=SEP=> Loneliness is a common problem in long-term care facilities (LTCF) and previous work has shown that animal-assisted therapy (AAT) can to some degree reverse loneliness. Here, we compared the ability of a living dog (Dog) and a robotic dog (AIBO) to treat loneliness in elderly patients living in LTCF. In comparison with a control group not receiving AAT, both the Dog and AIBO groups had statistically significant improvements in their levels of loneliness. As measured by a modified Lexington Attachment to Pets Scale (MLAPS), residents showed high levels of attachment to both the dog and AIBO. Subscale analysis showed that the AIBO group scored lower than the living dog on "animal rights/animal welfare" but not on "general attachment" or "people substituting." However, MLAPS measures did not correlate with changes in loneliness, showing that attachment was not the mechanism by which AAT decreases loneliness. We conclude that interactive robotic dogs can reduce loneliness in residents of LTCF and that residents become attached to these robots. However, level of attachment does not explain the decrease in loneliness associated with AAT conducted with either a living or robotic dog.

Modelling and Simulation of Four Quadrant Operation of Three Phase Brushless DC Motor With Hysteresis Current <=SEP=> Brushless DC (BLDC) motor drives are becoming more popular in industrial, traction applications. This makes the control of BLDC motor in all the four quadrants very vitalThe motor is operated in four steady state operating modes of torque-speed plane. To control a BLDC machine it is generally required to measure the speed and position of rotor by using the sensor because the inverter phases, acting at any time, must be commutated depending on the rotor position. Simulation of the proposed model was done using MATLAB/ SIMULINK.

3D Soft Tissue Analysis – Part 1: Sagittal Parameters <=SEP=> The aim of this study was to develop a reliable threedimensional (3D) analysis of facial soft tissues. We determined the mean sagittal 3D values and relationships between sagittal skeletal parameters, and digitally recorded 3D soft tissue parameters. A total of 100 adult patients (nª = 53, nº = 47) of Caucasian ethnic origin were included in this study. Patients with syndromes, cleft lip and palate, noticeable asymmetry or anomalies in the number of teeth were excluded. Arithmetic means for seven sagittal 3D soft tissue parameters were calculated. The parameters were analyzed biometrically in terms of their reliability and gender-specific differences. The 3D soft tissue values were further analyzed for any correlations with sagittal cephalometric values. Reproducible 3D mean values were defined for 3D soft tissue parameters. We detected gender-specific differences among the parameters. Correlations between the sagittal 3D soft tissue and cephalometric measurements were statistically significant. 3D soft tissue analysis provides additional information on the sagittal position of the jaw bases and on intermaxillary sagittal relations. Further studies are needed to integrate 3D soft tissue analysis in future treatment planning and assessment as a supportive diagnostic tool. Ziel dieser Untersuchung war die Entwicklung einer reliablen dreidimensionalen (3D) Analyse der Gesichtsweichteile. Es sollten sagittale 3D-Durchschnittswerte bestimmt werden und Beziehungen zwischen sagittalen skelettalen Parametern und digital erfassten 3D-Weichteilparametern dargestellt werden. In die Studie eingeschlossen wurden 100 erwachsene Patienten (nª = 53, nº = 47) kaukasischer Herkunft. Ausgeschlossen wurden Patienten mit Syndromen, LKGSSpalten, auffälligen Asymmetrien oder Anomalien der Zahnzahl. Es wurden arithmetische Mittelwerte für sieben sagittale 3DWeichteilparameter ermittelt. Die Parameter wurden biometrisch hinsichtlich ihrer Reliabilität und geschlechtsspezifischer Unterschiede analysiert. Des Weiteren wurden die 3D-Weichteilwerte bezüglich bestehender Korrelationen zu sagittalen kephalometrischen Werten untersucht. Für die 3D-Weichteilparameter konnten reproduzierbare 3D-Durchschnittswerte definiert werden. Innerhalb der Parameter ließen sich geschlechtsspezifische Unterschiede feststellen. Die Korrelationen zwischen den sagittalen 3D-Weichteilmessungen und den kephalometrischen Messungen waren statistisch signifikant. Die 3D-Weichteilanalyse lässt Aussagen sowohl über den sagittalen Einbau der Kieferbasen als auch über die sagittalen intermaxillären Beziehungen zu. Weitere Untersuchungen sind wünschenswert, um die 3D-Weichteildiagnostik zukünftig als unterstützendes diagnostisches Instrument in die Behandlungsplanung und -bewertung integrieren zu können.

Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval <=SEP=> This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.

Making sense of social media streams through semantics: A survey <=SEP=> Using semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature. This paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches for mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.

A Document Skew Detection Method Using the Hough Transform <=SEP=> Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flatbed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realising a practical document reader. In this paper we describe a new algorithm for skew detection. We then compare the performance and results of this skew detection algorithm to other publidhed methods form O'Gorman, Hinds, Le, Baird, Posel and Akuyama. Finally, we discuss the theory of skew detection and the different apporaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to be extremenly fast, with run times averaging under 0.25 CPU seconds to calculate the angle on the DEC 5000/20 workstation.