Query: ISUALIZING A F RAMEWORK FOR T ANGIBILITY IN M ULTIMEDIA L EARNING FOR P RESCHOOLERS

Relevant passages:
Curlybot: Designing a New Class of Computational Toys <=SEP=> We introduce an educational toy, called curlybot, as the basis for a new class of toys aimed at children in their early stages of development — ages four and up. curlybot is an autonomous two-wheeled vehicle with embedded electronics that can record how it has been moved on any flat surface and then play back that motion accurately and repeatedly. Children can use curlybot to develop intuitions for advanced mathematical and computational concepts, like differential geometry, through play away from a traditional computer.
In our preliminary studies, we found that children learn to use curlybot quickly. They readily establish an affective and body syntonic connection with curlybot, because of its ability to remember all of the intricacies of their original gesture; every pause, acceleration, and even the shaking in their hand is recorded. Programming by example in this context makes the educational ideas implicit in the design of curlybot accessible to young children.

Is e-Learning the Solution for Individual Learning?. <=SEP=> Despite the fact that e-Learning exists for a relatively long time, it is still in its infancy. Current eLearning systems on the market are limited to technical gadgets and organizational aspects of teaching, instead of supporting the learning. As a result the learner has become deindividualized and demoted to a noncritical homogenous user. One way out of this drawback is the creation of individual e-Learning materials. For this purpose a flexible multidimensional data model and the generation of individual content are the solution. It is necessary to enable the interaction between the learners and the content in e-Learning systems in the same manner.

Human memory ; A proposed system and its control processes <=SEP=> 

Where the Action Is - The Foundations of Embodied Interaction <=SEP=> Most textbooks in HCI and CSCW do not offer a coherent and over–arching understanding of social and technological issues. They present a variety of techniques and technologies, and outline a little history, but offer little in terms of theory that addresses the complexity of collaborative systems’ structure and use. The majority of practitioners and researchers do not see theory as one of the things that they do. Immersed in their craft, focusing on technological innovation or ethnomethodological detail, they do not engage in theoretical abstraction. Technologists contentedly explore new tools and devices, with little heed to older disciplines or deeper discussion about the limits and assumptions inherent in their craft. The area of sociology most influential in CSCW, ethnomethodology, deliberately keeps theorising and generalisation at a distance, seeing abstraction as brutish and creativity as foreign. In our field, theory is like the public library. If asked, most of us would say that we are glad that it is around—but few of us actually go there. Most see it as a haven for the old, the unemployed and the eccentric. Paul Dourish is a card–carrying member, however. He reads avidly and widely, but is also a skilful system designer and developer. The result is a book that is deep, accessible and useful, which is a rare thing nowadays. In the introduction, Dourish lays out the structure of the book. He describes and reflects on two current trends in system design: tangible and social computing. By looking at the usually hidden assumptions in system design, he makes his later theoretical discussion more relevant and accessible to a computer science audience. He does not develop new philosophy or social theory, but draws upon established 20th century philosophy of language and phenomenology. Much of this material is likely to be unfamiliar to ‘the average programmer’ and, although Dourish presents it well, it may still be challenging to the reader. However, its use is one of the book’s main contributions. He uses it to ground a conceptual framework and a corresponding set of principles for system design practice. He aims to do justice to the sociality and heterogeneity of interactive media, and to avoid putting theory above practice—or vice versa. His ideal is balance: “the ability to develop systemsthat resonate with, rather than restrict (or, worse, refute), the social organization of action”. The term ‘tangible computing’ is used very broadly in this book. The chapter on this theme covers the designs of Hiroshi Ishii’s group at MIT’s Media Lab, but also ubiquitous computing work such as the badges, tabs and LiveBoards of Xerox PARC, and augmented reality systems such as Pierre Wellner’s DigitalDesk. Although ‘UbiComp’ is more fashionable nowadays, Dourish chooses a term that helps him focus more on our perception and “the ways we experience the everyday world” than on computation and technology. Much of this discussion centres on exemplary systems, and the way that an increasing number and variety of computational devices and sensors are distributed in our environment. This contrasts with older systems that used very few media and which were encapsulated in the beige box of the traditional PC. The following chapter is on social computing: “the application of sociological understanding to the design of interactive systems”. This stands in contrast to the more traditional tendency for designers to treat people as isolated system users, with little account taken of organisational and social context. Dourish draws upon some influential studies of existing technology such as that of an air traffic control room by Hughes et al., and the ethnography of a print shop by Bowers, Button and Sharrock. Compared to the previous chapter, exemplary systems are scarce. There are none of the previous chapter’s attractive images of exotic displays and devices, so beloved in undergraduate lectures and conference presentations, as this chapter addresses systems’ internal structure rather than external interaction. Dourish has developed more systems in the ‘social’ category than the ‘tangible’, and here he offers as examples Mansfield et al.’s Orbit and some of his own work: using a system design technique, computational reflection, to offer users an account of deep system structure. In describing this particular technique, Dourish touches on the essence of the entire book: What is radical is the relationship it proposes between technical design and social understandings. It argues that the most fruitful place to forge these relationships is at a foundational level, one that attempts to take sociological insights into the heart of the process and fabric of design. (p. 87)

Tangible Bits: Towards Seamless Interfaces between People, Bits and Atoms <=SEP=> This paper presents our vision of Human Computer Interaction (HCI): "Tangible Bits." Tangible Bits allows users to "grasp & manipulate" bits in the center of users’ attention by coupling the bits with everyday physical objects and architectural surfaces. Tangible Bits also enables users to be aware of background bits at the periphery of human perception using ambient display media such as light, sound, airflow, and water movement in an augmented space. The goal of Tangible Bits is to bridge the gaps between both cyberspace and the physical environment, as well as the foreground and background of human activities. This paper describes three key concepts of Tangible Bits: interactive surfaces; the coupling of bits with graspable physical objects; and ambient media for background awareness. We illustrate these concepts with three prototype systems – the metaDESK, transBOARD and ambientROOM – to identify underlying research issues.

Answering Location-Aware Graph Reachability Queries on GeoSocial Data <=SEP=> Thanks to the wide spread use of mobile and wearable devices, popular social networks, e.g., Facebook, prompts users to add spatial attributes to social entities, e.g., check-ins, traveling posts, and geotagged photos, leading to what is known as, The GeoSocial Graph. In such graph, usersmay issue a Reachability Query with Spatial Range Predicate (abbr. RangeReach). RangeReach finds whether an input vertex can reach any spatial vertex that lies within an input spatial range. The paper proposes GEOREACH, an approach that adds spatial data awareness to a graph database management system. GEOREACH allows efficient execution of RangeReach queries, yet without compromising a lot on the overall system scalability. Experiments based on system implementation inside Neo4j prove that GEOREACH exhibits up to two orders of magnitude better query performance and up to four times less storage than the state-of-the-art spatial and reachability indexing approaches.

Distributed Representation of Subgraphs <=SEP=> Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to nd network embeddings in order to exploit machine learning algorithms for mining tasks like node classication and edge prediction. However, most of the work focuses on nding distributed representations of nodes, which are inherently ill-suited to tasks such as community detection which are intuitively dependent on subgraphs. Here, we propose Sub2Vec, an unsupervised scalable algorithm to learn feature representations of arbitrary subgraphs. We provide means to characterize similarties between subgraphs and provide theoretical analysis of Sub2Vec and demonstrate that it preserves the so-called local proximity. We also highlight the usability of Sub2Vec by leveraging it for network mining tasks, like community detection. We show that Sub2Vec gets signicant gains over stateof-the-art methods and node-embedding methods. In particular, Sub2Vec oers an approach to generate a richer vocabulary of features of subgraphs to support representation and reasoning. ACM Reference format: Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan and B. Aditya Prakash. 2016. Distributed Representation of Subgraphs. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 9 pages. DOI: 10.1145/nnnnnnn.nnnnnnn

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks <=SEP=> Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled ’seed’ image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101).

Employee job attitudes and organizational characteristics as predictors of cyberloafing <=SEP=> Cyberloafing is the personal use of the Internet by employees while at work. The purpose of this study is to examine whether employee job attitudes, organizational characteristics, attitudes towards cyberloafing, and other non-Internet loafing behaviors serve as antecedents to cyberloafing behaviors. We hypothesize that the employee job attitudes of job involvement and intrinsic involvement are related to cyberloafing. In addition, we hypothesize that organizational characteristics including the perceived cyberloafing of one’s coworkers and managerial support for internet usage are related to cyberloafing. We also hypothesize that attitudes towards cyberloafing and the extent to which employees participate in non-Internet loafing behaviors (e.g., talking with coworkers, running personal errands) will both be related to cyberloafing. One hundred and forty-three working professional from a variety of industries were surveyed regarding their Internet usage at work. As hypothesized, the employee job attitudes of job involvement and intrinsic involvement were negatively related to cyberloafing. Also as predicted, the organizational characteristics of the perceived cyberloafing of one’s coworkers and managerial support for internet usage were positively related to cyberloafing. Finally, results showed that attitudes towards cyberloafing and participation in non-Internet loafing behaviors were positively related to cyberloafing. Implications for both organizations and employees are discussed. 2011 Elsevier Ltd. All rights reserved.

Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove <=SEP=> This paper presents a hand rehabilitation learning system, the SAFE Glove, a device that can be utilized to enhance the rehabilitation of subjects with disabilities. This system is able to learn fingertip motion and force for grasping different objects and then record and analyze the common movements of hand function including grip and release patterns. The glove is then able to reproduce these movement patterns in playback fashion to assist a weakened hand to accomplish these movements, or to modulate the assistive level based on the user's or therapist's intent for the purpose of hand rehabilitation therapy. Preliminary data have been collected from healthy hands. To demonstrate the glove's ability to manipulate the hand, the glove has been fitted on a wooden hand and the grasping of various objects was performed. To further prove that hands can be safely driven by this haptic mechanism, force sensor readings placed between each finger and the mechanism are plotted. These experimental results demonstrate the potential of the proposed system in rehabilitation therapy.

Mapping interference resolution across task domains: A shared control process in left inferior frontal gyrus <=SEP=> Work in functional neuroimaging has mapped interference resolution processing onto left inferior frontal regions for both verbal working memory and a variety of semantic processing tasks. The proximity of the identified regions from these different tasks suggests the existence of a common, domain-general interference resolution mechanism. The current research specifically tests this idea in a within-subject design using fMRI to assess the activation associated with variable selection requirements in a semantic retrieval task (verb generation) and a verbal working memory task with a trial-specific proactive interference manipulation (recent-probes). High interference trials on both tasks were associated with activity in the midventrolateral region of the left inferior frontal gyrus, and the regions activated in each task strongly overlapped. The results indicate that an elemental component of executive control associated with interference resolution during retrieval from working memory and from semantic memory can be mapped to a common portion of the left inferior frontal gyrus.

Transgender face recognition with off-the-shelf pre-trained CNNs: A comprehensive study <=SEP=> Face recognition has become a ubiquitous way of establishing identity in many applications. Gender transformation therapy induces changes to face on both for structural and textural features. A challenge for face recognition system is, therefore, to reliably identify the subjects after they undergo gender change while the enrolment images correspond to pre-change. In this work, we propose a new framework based on augmenting and fine-tuning deep Residual Network-50 (ResNet-50). We employ YouTube database with 37 subjects whose images are self-captured to evaluate the performance of state-of-the-schemes. Obtained results demonstrate the superiority of the proposed scheme over twelve different state-of-the-art schemes with an improved Rank — 1 recognition rate.

Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzziness <=SEP=> Since Internet has become an excellent source of consumer reviews, the area of sentiment analysis (also called sentiment extraction, opinion mining, opinion extraction, and sentiment mining) has seen a large increase in academic interest over the last few years. Sentiment analysis mines opinions at word, sentence, and document levels, and gives sentiment polarities and strengths of articles. As known, the opinions of consumers are expressed in sentiment Chinese phrases. But due to the fuzziness of Chinese characters, traditional machine learning techniques can not represent the opinion of articles very well. In this paper, we propose a multi-strategy sentiment analysis method with semantic fuzziness to solve the problem. The results show that this hybrid sentiment analysis method can achieve a good level of effectiveness.

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

Online shopping behavior model: A literature review and proposed model <=SEP=> In this study, we conducted extensive reviews of online shopping literatures and proposed a hierarchy model of online shopping behavior. We collected 47 studies and classified them by variables used. Some critical points were found that research framework, methodology, and lack of cross-cultural comparison, etc So we developed a cross-cultural model of online shopping including shopping value, attitudes to online retailer's attributes and online purchasing based on the integrated V-A-B model.

Spinal curvature determination from scoliosis X-Ray image using sum of squared difference template matching <=SEP=> Scoliosis is a disorder in which there is a sideways curve of the spine. Curve are often S-shaped or C-shaped. One of the methods to ascertain a patient with scoliosis is through using Cobb angle measurement. The importance of the automatic spinal curve detection system is to detect any spinal disorder quicker and faster. This study is intended as a first step based on computer-aided diagnosis. The spinal detection method that we propose is using template matching based on Sum of Squared Difference (SSD). This method is used to estimate the location of the vertebra. By using polynomial curve fitting, a spinal curvature estimation can be done. This paper discusses the performance of SSD method used to detect a variety of data sources of X-Ray from numerous patients. The results from the implementation indicate that the proposed algorithm can be used to detect all the X-ray images. The best result in this experiment has 96.30% accuracy using 9-subdivisions poly 5 algorithm, and the average accuracy is 86.01%.

A new weight initialization method for sigmoidal feedforward artificial neural networks <=SEP=> Initial weight choice has been recognized to be an important aspect of the training methodology for sigmoidal feedforward neural networks. In this paper, a new mechanism for weight initialization is proposed. The mechanism distributes the initial input to output weights in a manner that all weights (including thresholds) leading into a hidden layer are uniformly distributed in a region and the center of the region from which the weights are sampled are such that no region overlaps for two distinct hidden nodes. The proposed method is compared against random weight initialization routines on five function approximation tasks using the Resilient Backpropagation (RPROP) algorithm for training. The proposed method is shown to lead to about twice as fast convergence to a pre-specifled goal for training as compared to any of the random weight initialization methods. Moreover, it is shown that at least for these problems the networks reach a deeper minima of the error functional during training and generalizes better than the networks trained whose weights were initialized by random weight initialization methods.

Multitask Compressive Sensing <=SEP=> Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N << M this is performed under the assumption that u is sparse in the basis represented by the matrix Psi RMtimesM. It has been demonstrated that with appropriate design of the compressive measurements used to define v, the decompressive mapping vrarru may be performed with error ||u-u||2 2 having asymptotic properties analogous to those of the best adaptive transform-coding algorithm applied in the basis Psi. The mapping vrarru constitutes an inverse problem, often solved using l1 regularization or related techniques. In most previous research, if L > 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications the L ldquotasksrdquo defined by the mappings virarrui are not statistically independent, and it may be possible to improve the performance of the inversion if statistical interrelationships are exploited. In this paper, we address this problem within a multitask learning setting, wherein the mapping vrarru for each task corresponds to inferring the parameters (here, wavelet coefficients) associated with the desired signal vi, and a shared prior is placed across all of the L tasks. Under this hierarchical Bayesian modeling, data from all L tasks contribute toward inferring a posterior on the hyperparameters, and once the shared prior is thereby inferred, the data from each of the L individual tasks is then employed to estimate the task-dependent wavelet coefficients. An empirical Bayesian procedure for the estimation of hyperparameters is considered; two fast inference algorithms extending the relevance vector machine (RVM) are developed. Example results on several data sets demonstrate the effectiveness and robustness of the proposed algorithms.

Two cortical systems for memory-guided behaviour <=SEP=> Although the perirhinal cortex (PRC), parahippocampal cortex (PHC) and retrosplenial cortex (RSC) have an essential role in memory, the precise functions of these areas are poorly understood. Here, we review the anatomical and functional characteristics of these areas based on studies in humans, monkeys and rats. Our Review suggests that the PRC and PHC–RSC are core components of two separate large-scale cortical networks that are dissociable by neuroanatomy, susceptibility to disease and function. These networks not only support different types of memory but also appear to support different aspects of cognition.

User Based Aggregation for Biterm Topic Model <=SEP=> Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not distinguish background words from topical words. In this paper, we propose Twitter-BTM model to address those issues by considering user level personalization in BTM. Firstly, we use user based biterms aggregation to learn user specific topic distribution. Secondly, each user’s preference between background words and topical words is estimated by incorporating a background topic. Experiments on a large-scale real-world Twitter dataset show that Twitter-BTM outperforms several stateof-the-art baselines.

Sexual arousal patterns of bisexual men. <=SEP=> There has long been controversy about whether bisexual men are substantially sexually aroused by both sexes. We investigated genital and self-reported sexual arousal to male and female sexual stimuli in 30 heterosexual, 33 bisexual, and 38 homosexual men. In general, bisexual men did not have strong genital arousal to both male and female sexual stimuli. Rather, most bisexual men appeared homosexual with respect to genital arousal, although some appeared heterosexual. In contrast, their subjective sexual arousal did conform to a bisexual pattern. Male bisexuality appears primarily to represent a style of interpreting or reporting sexual arousal rather than a distinct pattern of genital sexual arousal.

Towards a Distributed Large-Scale Dynamic Graph Data Store <=SEP=> In many graph applications, the structure of the graph changes dynamically over time and may require real time analysis. However, constructing a large graph is expensive, and most studies for large graphs have not focused on a dynamic graph data structure, but rather a static one. To address this issue, we propose DegAwareRHH, a high performance dynamic graph data store designed for scaling out to store large, scale-free graphs by leveraging compact hash tables with high data locality. We extend DegAwareRHH for multiple processes and distributed memory, and perform dynamic graph construction on large scale-free graphs using emerging 'Big Data HPC' systems such as the Catalyst cluster at LLNL. We demonstrate that DegAwareRHH processes a request stream 206.5x faster than a state-of-the-art shared-memory dynamic graph processing framework, when both implementations use 24 threads/processes to construct a graph with 1 billion edge insertion requests and 54 million edge deletion requests. DegAwareRHH also achieves a processing rate of over 2 billion edge insertion requests per second using 128 compute nodes to construct a large-scale web graph, containing 128 billion edges, the largest open-source real graph dataset to our knowledge.

Automatic assessment of macular edema from color retinal images <=SEP=> Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.

Shoulder impingement revisited: evolution of diagnostic understanding in orthopedic surgery and physical therapy <=SEP=> “Impingement syndrome” is a common diagnostic label for patients presenting with shoulder pain. Historically, it was believed to be due to compression of the rotator cuff tendons beneath the acromion. It has become evident that “impingement syndrome” is not likely an isolated condition that can be easily diagnosed with clinical tests or most successfully treated surgically. Rather, it is likely a complex of conditions involving a combination of intrinsic and extrinsic factors. A mechanical impingement phenomenon as an etiologic mechanism of rotator cuff disease may be distinct from the broad diagnostic label of “impingement syndrome”. Acknowledging the concepts of mechanical impingement and movement-related impairments may better suit the diagnostic and interventional continuum as they support the existence of potentially modifiable impairments within the conservative treatment paradigm. Therefore, it is advocated that the clinical diagnosis of “impingement syndrome” be eliminated as it is no more informative than the diagnosis of “anterior shoulder pain”. While both terms are ambiguous, the latter is less likely to presume an anatomical tissue pathology that may be difficult to isolate either with a clinical examination or with diagnostic imaging and may prevent potentially inappropriate surgical interventions. We further recommend investigation of mechanical impingement and movement patterns as potential mechanisms for the development of shoulder pain, but clearly distinguished from a clinical diagnostic label of “impingement syndrome”. For shoulder researchers, we recommend investigations of homogenous patient groups with accurately defined specific pathologies, or with subgrouping or classification based on specific movement deviations. Diagnostic labels based on the movement system may allow more effective subgrouping of patients to guide treatment strategies.

Rapid Synthesis of Massive Face Sets for Improved Face Recognition <=SEP=> Recent work demonstrated that computer graphics techniques can be used to improve face recognition performances by synthesizing multiple new views of faces available in existing face collections. By so doing, more images and more appearance variations are available for training, thereby improving the deep models trained on these images. Similar rendering techniques were also applied at test time to align faces in 3D and reduce appearance variations when comparing faces. These previous results, however, did not consider the computational cost of rendering: At training, rendering millions of face images can be prohibitive; at test time, rendering can quickly become a bottleneck, particularly when multiple images represent a subject. This paper builds on a number of observations which, under certain circumstances, allow rendering new 3D views of faces at a computational cost which is equivalent to simple 2D image warping. We demonstrate this by showing that the run-time of an optimized OpenGL rendering engine is slower than the simple Python implementation we designed for the same purpose. The proposed rendering is used in a face recognition pipeline and tested on the challenging IJB-A and Janus CS2 benchmarks. Our results show that our rendering is not only fast, but improves recognition accuracy.

Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images <=SEP=> Recently, several machine learning methods for gender classification from frontal facial images have been proposed. Their variety suggests that there is not a unique or generic solution to this problem. In addition to the diversity of methods, there is also a diversity of benchmarks used to assess them. This gave us the motivation for our work: to select and compare in a concise but reliable way the main state-of-the-art methods used in automatic gender recognition. As expected, there is no overall winner. The winner, based on the accuracy of the classification, depends on the type of benchmarks used.

Efficient Support Vector Classifiers for Named Entity Recognition <=SEP=> NamedEntity (NE) recognitionis a task in which proper nouns and numerical information are extractedfrom documentsandareclassifiedinto categoriessuchas person,organization,and date. It is a key technologyof InformationExtractionand Open-DomainQuestionAnswering.First,weshow thatanNE recognizerbasedonSupportVectorMachines(SVMs)givesbetterscoresthanconventional systems.However, off-the-shelfSVM classifiersare too inefficient for this task.Therefore,we presenta methodthat makes the systemsubstantiallyfaster . This approachcan also be applied to other similar taskssuchaschunkingandpart-of-speechtagging. We alsopresentanSVM-basedfeatureselection methodandanefficient trainingmethod.

The Bad Boys of Cyberspace: Deviant Behavior in a Multimedia Chat Community <=SEP=> A wide variety of deviant behavior may arise as the population of an online multimedia community increases. That behavior can span the range from simple mischievous antics to more serious expressions of psychopathology, including depression, sociopathy, narcissism, dissociation, and borderline dynamics. In some cases the deviant behavior may be a process of pathological acting out—in others, a healthy attempt to work through. Several factors must be taken into consideration when explaining online deviance, such as social/cultural issues, the technical infrastructure of the environment, transference reactions, and the effects of the ambiguous, anonymous, and fantasy-driven atmosphere of cyberspace life. In what we may consider an "online community psychology," intervention strategies for deviant behavior can be explored along three dimensions: preventative versus remedial, user versus superuser based, and automated versus interpersonal.

Bayesian Optimization with Empirical Constraints ( PhD Proposal ) <=SEP=> This work is motivated by the experimental design problem of optimizing the power output of nano-enhanced microbial fuel cells. Microbial fuel cells (MFCs) (Bond and Lovley, 2003; Fan et al., 2007; Park and Zeikus, 2003; Reguera, 2005) use micro-organisms to break down organic matter and generate electricity. For a particular MFC design, it is critical to optimize the biological energetics and the microbial/electrode interface of the system, which research has shown to depend strongly on the surface properties of the anodes (Park and Zeikus, 2003; Reguera, 2005). This motivates the design of nano-enhanced anodes, where nano-structures (e.g. carbon nano-wire) are grown on the anode surface to improve the MFC’s power output. Unfortunately, there is little understanding of the interaction between various possible nano-enhancements and MFC capabilities for different micro-organisms. Thus, optimizing anode design for a particular application is largely guess work. Our goal is to develop algorithms to aid this process. Bayesian optimization (Jones, 2001a; Brochu et al., 2009) has been widely used for experimental design problems where the goal is to optimize an unknown function f(·), that is costly to evaluate. In general, we are interested in finding the point x∗ ∈ X d ⊂ Rd such that: x∗ = argmax x∈X d f(x), (1)

I know what you did on your smartphone: Inferring app usage over encrypted data traffic <=SEP=> Smartphones and tablets are now ubiquitous in many people's lives and are used throughout the day in many public places. They are often connected to a wireless local area network (IEEE 802.11 WLANs) and rely on encryption protocols to maintain their security and privacy. In this paper, we show that even in presence of encryption, an attacker without access to encryption keys is able to determine the users' behavior, in particular, their app usage. We perform this attack using packet-level traffic analysis in which we use side-channel information leaks to identify specific patterns in packets regardless of whether they are encrypted or not. We show that just by collecting and analyzing small amounts of wireless traffic, one can determine what apps each individual smartphone user in the vicinity is using. Furthermore, and more worrying, we show that by using these apps the privacy of the user is more at risk compared to using online services through browsers on mobile devices. This is due to the fact that apps generate more identifiable traffic patterns. Using random forests to classify the apps we show that we are able to identify individual apps, even in presence of noise, with great accuracy. Given that most online services now provide native apps that may be identified by this method, these attacks represent a serious threat to users' privacy.