title: Autogeneration and Autotuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

abstract: This paper develops and evaluates search and optimization techniques for autotuning 3D stencil (nearest neighbor) computations on GPUs. Observations indicate that parameter tuning is necessary for heterogeneous GPUs to achieve optimal performance with respect to a search space. Our proposed framework takes a most concise specification of stencil behavior from the user as a single formula, autogenerates tunable code from it, systematically searches for the best configuration and generates the code with optimal parameter configurations for different GPUs. This autotuning approach guarantees adaptive performance for different generations of GPUs while greatly enhancing programmer productivity. Experimental results show that the delivered floating point performance is very close to previous handcrafted work and outperforms other autotuned stencil codes by a large margin. Furthermore, heterogeneous GPU clusters are shown to exhibit the highest performance for dissimilar tuning parameters leveraging proportional partitioning relative to single-GPU performance.


title: GeoCrowd: enabling query answering with spatial crowdsourcing

abstract: With the ubiquity of mobile devices, spatial crowdsourcing is emerging as a new platform, enabling spatial tasks (i.e., tasks related to a location) assigned to and performed by human workers. In this paper, for the first time we introduce a taxonomy for spatial crowdsourcing. Subsequently, we focus on one class of this taxonomy, in which workers send their locations to a centralized server and thereafter the server assigns to every worker his nearby tasks with the objective of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (or MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space. Finally, our experimental evaluations on both real-world and synthetic data verify the applicability of our proposed approaches and compare them by measuring both the number of assigned tasks and the travel cost of the workers.


title: Metacognition and Open Learner Models

abstract: This paper considers some of the similarities between the goals of supporting and encouraging metacognition in intelligent tutoring systems and learning in general, and the benefits of opening the learner model to the learner and to the teacher. We identify the purposes for an open learner model and provide examples of two important classes of open learner models: those within a particular teaching system and those that are first-class citizens with value independently of a teaching system. We conclude with a discussion of the links between metacognition and the many purposes of making a learner model open.


title: A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis

abstract: Post-traumatic stress disorder (PTSD) is a traumatic-stressor related disorder developed by exposure to a traumatic or adverse environmental event that caused serious harm or injury. Structured interview is the only widely accepted clinical practice for PTSD diagnosis but suffers from several limitations including the stigma associated with the disease. Diagnosis of PTSD patients by analyzing speech signals has been investigated as an alternative since recent years, where speech signals are processed to extract frequency features and these features are then fed into a classification model for PTSD diagnosis. In this paper, we developed a deep belief network (DBN) model combined with a transfer learning (TL) strategy for PTSD diagnosis. We computed three categories of speech features and utilized the DBN model to fuse these features. The TL strategy was utilized to transfer knowledge learned from a large speech recognition database, TIMIT, for PTSD detection where PTSD patient data is difficult to collect. We evaluated the proposed methods on two PTSD speech databases, each of which consists of audio recordings from 26 patients. We compared the proposed methods with other popular methods and showed that the state-of-the-art support vector machine (SVM) classifier only achieved an accuracy of 57.68%, and TL strategy boosted the performance of the DBN from 61.53% to 74.99%. Altogether, our method provides a pragmatic and promising tool for PTSD diagnosis.


title: Will Preoperative Atrophy and Fatty Degeneration of the Shoulder Muscles Improve after Rotator Cuff Repair in Patients with Massive Rotator Cuff Tears?

abstract: Recently, retear rate after repair for massive cuff tear have been improved through devised suture techniques. However, reported retear rate is relevant to preoperative atrophy and fatty degeneration. The purpose of this study was to investigate whether preoperative atrophy and fatty degeneration of rotator cuff muscles improve by successful repair. Twenty-four patients with massive rotator cuff tear were evaluated on the recovery of atrophy and fatty degeneration of supraspinatus and infraspinatus muscle after surgery. Atrophy was classified by the occupation ratio and fatty degeneration by modified Goutallier's classification. Both were assessed on magnetic resonance imaging (MRI) before and after the operation. When the cuff was well repaired, improvement of the atrophy and fatty degeneration were observed in a half and a one-fourth of the cases, respectively. In retear cases, however, atrophy and fatty degeneration became worse. Improvement of atrophy and fatty degeneration of the rotator cuff muscles may be expected in the cases with successful achievement of rotator cuff repair for large and massive tear.


title: Online Dictionary Learning for Kernel LMS

abstract: Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have demonstrated superiority over their linear counterpart for nonlinear system identification. Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data. This dramatically increases the computational burden and memory requirement. A variety of strategies based on dictionary learning have been proposed to overcome this severe drawback. In the literature, there is no theoretical work that strictly analyzes the problem of updating the dictionary in a time-varying environment. In this paper, we present an analytical study of the convergence behavior of the Gaussian least-mean-square algorithm in the case where the statistics of the dictionary elements only partially match the statistics of the input data. This theoretical analysis highlights the need for updating the dictionary in an online way, by discarding the obsolete elements and adding appropriate ones. We introduce a kernel least-mean-square algorithm with $\ell_1$ -norm regularization to automatically perform this task. The stability in the mean of this method is analyzed, and the improvement of performance due to this dictionary adaptation is confirmed by simulations.


title: Exactly Sparse Extended Information Filters for Feature-based SLAM

abstract: Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM scalability problem for arbitrarily large environments. One such estimator that has received due attention is the Sparse Extended Information Filter (SEIF) proposed by Thrun et al., which is reported to be nearly constant time, irrespective of the size of the map. The key to the SEIF's scalability is to prune weak links in what is a dense information (inverse covariance) matrix to achieve a sparse approximation that allows for efficient, scalable SLAM. We demonstrate that the SEIF sparsification strategy yields error estimates that are overconfident when expressed in the global reference frame, while empirical results show that relative map consistency is maintained. ::: ::: In this paper, we propose an alternative scalable estimator based on an information form that maintains sparsity while preserving consistency. The paper describes a method for controlling the population of the information matrix, whereby we track a modified version of the SLAM posterior, essentially by ignoring a small fraction of temporal measurements. In this manner, the Exactly Sparse Extended Information Filter (ESEIF) performs inference over a model that is conservative relative to the standard Gaussian distribution. We compare our algorithm to the SEIF and standard EKF both in simulation as well as on two nonlinear datasets. The results convincingly show that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the EKF.


title: A review of Yorùbá Automatic Speech Recognition

abstract: Automatic Speech Recognition (ASR) has recorded appreciable progress both in technology and application. Despite this progress, there still exist wide performance gap between human speech recognition (HSR) and ASR which has inhibited its full adoption in real life situation. A brief review of research progress on Yoruba Automatic Speech Recognition (ASR) is presented in this paper focusing of variability as factor contributing to performance gap between HSR and ASR with a view of x-raying the advances recorded, major obstacles, and chart a way forward for development of ASR for Yoruba that is comparable to those of other tone languages and of developed nations. This is done through extensive surveys of literatures on ASR with focus on Yoruba. Though appreciable progress has been recorded in advancement of ASR in the developed world, reverse is the case for most of the developing nations especially those of Africa. Yoruba like most of languages in Africa lacks both human and materials resources needed for the development of functional ASR system much less taking advantage of its potentials benefits. Results reveal that attaining an ultimate goal of ASR performance comparable to human level requires deep understanding of variability factors.


title: Mobile and ubiquitous learning in higher education settings. A systematic review of empirical studies

abstract: Mobile and ubiquitous learning are increasingly attracting academic and public interest, especially in relation to their application in higher education settings.The systematic analysis of 36 empirical papers supports the view that knowledge gains from instructionist learning designs are facilitated by distributed and more frequent learning activities enabled by push mechanisms. They also lend themselves to the activation of learners during classroom lectures. In addition, and as a particular advantage of mobile technology, "hybrid" designs, where learners create multimodal representations outside the classroom and then discussed their substantiated experiences with peers and educators, helped to connect learning in formal and more informal and personalized learning environments.Generally, empirical evidence that would favour the broad application of mobile and ubiquitous learning in higher education settings is limited and because mobile learning projects predominantly take instructionist approaches, they are non-transformatory in nature. However, by harnessing the increasing access to digital mobile media, a number of unprecedented educational affordances can be operationalised to enrich and extend more traditional forms of higher education. Systematic review to study mobile and ubiquitous learning in higher education.Positive outcomes associated mainly with instructionist and hybrid designs.Instructionist benefits caused by more frequent learning activities.Hybridisation links formal education with informal and personalized learning.Limited evidence to legitimize broad application of mobile learning in HE.


title: Vision based localization of a small UAV for generating a large mosaic image

abstract: This paper describes a way to generate mosaic image using a small lightweight UAV for effective remote sensing. Payload limits are the source of most error in small lightweight UAVs to generate accurate mosaic images. We have overcome inherent small lightweight UAV drawbacks by providing them with integration of a GPS receiver, inertial sensors, and an image sensor to generate a large mosaic image. Position and attitude data of a UAV is estimated using onboard sensors and image correlation from SIFT features between overlapped images. A nonlinear optimization algorithm is used to optimize the position and attitude of the UAV. We demonstrated that our proposed mosaic image effectively enables low-cost remote sensing using a small lightweight UAV.


title: Better together: Simultaneous presentation of speech and gesture in math instruction supports generalization and retention

abstract: Abstract When teachers gesture during instruction, children retain and generalize what they are taught (Goldin-Meadow, 2014). But why does gesture have such a powerful effect on learning? Previous research shows that children learn most from a math lesson when teachers present one problem-solving strategy in speech while simultaneously presenting a different, but complementary, strategy in gesture (Singer & Goldin-Meadow, 2005). One possibility is that gesture is powerful in this context because it presents information simultaneously with speech. Alternatively, gesture may be effective simply because it involves the body, in which case the timing of information presented in speech and gesture may be less important for learning. Here we find evidence for the importance of simultaneity: 3rd grade children retain and generalize what they learn from a math lesson better when given instruction containing simultaneous speech and gesture than when given instruction containing sequential speech and gesture. Interpreting these results in the context of theories of multimodal learning, we find that gesture capitalizes on its synchrony with speech to promote learning that lasts and can be generalized.


title: Screening for cancer-related distress: Summary of evidence from tools to programmes.

abstract: AbstractIntroduction. A number of studies have addressed the development and testing of tools for measuring cancer-related distress. Except for studies of diagnostic validity, knowledge on the effect of screening for psychological distress on psychological well-being is limited. We aimed to describe and critically discuss the findings of randomized trials of the effect of screening and to identify components necessary for future studies of the effectiveness of screening programmes. Methods. A search was made of the Embase/Medline and Web of Knowledge abstract databases from inception to September 2010. Our inclusion criterion was randomized controlled trials concerning the effect of screening for psychological distress on psychological outcomes. We compared the randomized trials on the following aspects: design and methods, setting and sample, screening and intervention, effects on psychological distress, staff utilization of screening results, possible confounding factors and other methodological limitat...


title: HyViDE: a framework for virtual data center network embedding

abstract: Cloud computing technologies have matured enough that the service providers are compelled to migrate their services to virtualized infrastructure in cloud data centers. However, moving the computation and network to shared physical infrastructure poses a multitude of questions, both for service providers and for data center owners. In this work, we propose HyViDE - a framework for optimal placement of multiple virtual data center networks on a physical data center network. HyViDE preselects a subset of virtual data center network requests and uses a hybrid strategy for embedding them on the physical data center. Coordinated static and dynamic embedding algorithms are used in this hybrid framework to minimize the rejection of requests and fulfill QoS demands of the embedded networks. HyViDE can employ suitable static and dynamic strategies to meet the objectives of data center owners and customers. Experimental evaluation of our algorithms on HyViDE shows that, the acceptance rate is high with faster servicing of requests.


title: Large-scale audio feature extraction and SVM for acoustic scene classification

abstract: This work describes a system for acoustic scene classification using large-scale audio feature extraction. It is our contribution to the Scene Classification track of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (D-CASE). The system classifies 30 second long recordings of 10 different acoustic scenes. From the highly variable recordings, a large number of spectral, cepstral, energy and voicing-related audio features are extracted. Using a sliding window approach, classification is performed on short windows. SVM are used to classify these short segments, and a majority voting scheme is employed to get a decision for longer recordings. On the official development set of the challenge, an accuracy of 73 % is achieved. SVM are compared with a nearest neighbour classifier and an approach called Latent Perceptual Indexing, whereby SVM achieve the best results. A feature analysis using the t-statistic shows that mainly Mel spectra are the most relevant features.


title: Genetic variation, classification and 'race'

abstract: New genetic data has enabled scientists to re-examine the relationship between human genetic variation and 'race'. We review the results of genetic analyses that show that human genetic variation is geographically structured, in accord with historical patterns of gene flow and genetic drift. Analysis of many loci now yields reasonably accurate estimates of genetic similarity among individuals, rather than populations. Clustering of individuals is correlated with geographic origin or ancestry. These clusters are also correlated with some traditional concepts of race, but the correlations are imperfect because genetic variation tends to be distributed in a continuous, overlapping fashion among populations. Therefore, ancestry, or even race, may in some cases prove useful in the biomedical setting, but direct assessment of disease-related genetic variation will ultimately yield more accurate and beneficial information.


Dependency Parsing Features for Semantic Parsing


title: Schenkerian Analysis by Computer: A Proof of Concept

abstract: Abstract A system for automatically deriving a Schenkerian reduction of an extract of tonal music is described. Schenkerian theory is formalized in a quasi-grammatical manner, expressing a reduction as a binary-tree structure. Computer software which operates in the manner of a chart parser using this grammar has been implemented, capable of deriving a matrix of reduction possibilities, in polynomial time, from a representation of the score. A full reduction of the extract can be discovered by selecting a tree from this matrix. The number of possible valid reductions for even short extracts is found to be extremely large, so criteria are required to distinguish good reductions from bad ones. To find such criteria, themes from five Mozart piano sonatas are analysed and samples of ‘good’ reductions (defined by reference to pre-existing analyses of these themes) are compared with randomly sampled reductions. Nine criteria are thereby derived, which can be applied in the process of parsing and selecting a red...


title: Place recognition using keypoint voting in large 3D lidar datasets

abstract: In developing autonomous solutions for mapping and localization, one problem that often needs to be dealt with is determining when an area is revisited despite having poor or no prior information on the relative alignment error. There are well-formulated approaches for recognizing such matches using the rich information in camera data; however, it is a much more challenging problem using lidar sensors alone. Most existing approaches employ a pairwise place comparison of place descriptors and thus finding matches requires linear time per place. We instead propose the use of a keypoint voting approach to achieve sub-linear matching times. A constant number of nearest neighbor votes per keypoint are queried from a database of local descriptors and aggregated to determine likely place matches. It becomes critical to analyze the distributions of vote scores such that a suitable threshold for matching scores can be determined a priori, so that the system is not overwhelmed by false positives nor starved for true matches. We have empirically determined that the vote scores follow a log-normal distribution, and we are able to fit a parametric model of its hyper-parameters based on the number of neighbors, the number of keypoints in a place, and the total number of keypoints in the database. We demonstrate the performance of our system in a variety of large scale 3D lidar datasets using data collected from a continually scanning handheld lidar sensor, and also on two publicly available lidar datasets.


title: Neural Lattice Language Models

abstract: In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions - including polysemy and existence of multi-word lexical items - into our language model. Experiments on multiple language modeling tasks show that English neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95% relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94% relative to a character-level baseline.


title: A Planar MICS Band Antenna Combined With a Body Channel Communication Electrode for Body Sensor Network

abstract: A 2.5 times 1.8 cm2 medical implant communication service band antenna is combined with an electrode for body channel communication. The proposed design enables a body sensor network controller to communicate with health-care devices located on and inside a patient's body. The spiral microstrip antenna with its radiating body and ground plane placed side-by-side has the thickness of 2 mm and can be attached to human skin conveniently. The propagation loss of the body channel is measured when the proposed antenna is used as the skin interface for BCC in the 10-70-MHz band, and the results are compared with the cases of Ag/AgCl and circular dry electrodes. The equivalent-circuit model of the antenna as the electrode is also derived from the measured impedance characteristics. The LC resonance structure to drive the on-body antenna with its capacitance increased due to the skin contact reduces the power consumption of the TX buffer by >50%. TheS 11-parameter of the on-body antenna, its radiation pattern, and the signal loss inside the human body are investigated.


title: Data Loss Prevention Based on Text Classification in Controlled Environments

abstract: Loss of sensitive data is a common problem with potentially severe consequences. By categorizing documents according to their sensitivity, security controls can be performed based on this classification. However, errors in the classification process may effectively result in information leakage. While automated classification techniques can be used to mitigate this risk, little work has been done to evaluate the effectiveness of such techniques when sensitive content has been transformed (e.g., a document can be summarized, rewritten, or have paragraphs copy-pasted into a new one). To better handle these more difficult data leaks, this paper proposes the use of controlled environments to detect misclassification. By monitoring the incoming information flow, the documents imported into a controlled environment can be used to better determine the sensitivity of the document(s) created within the same environment. Our evaluation results show that this approach, using techniques from machine learning and information retrieval, provides improved detection of incorrectly classified documents that have been subject to more complex data transformations.


title: Identifying Informational vs. Conversational Questions on Community Question Answering Archives

abstract: Questions on community question answering websites usually reflect one of two intents: learning information or starting a conversation. In this paper, we revisit this fundamental classification task of informational versus conversational questions, which was originally introduced and studied in 2009. We use a substantially larger dataset of archived questions from Yahoo Answers, which includes the question»s title, description, answers, and votes. We replicate the original experiments over this dataset, point out the common and different from the original results, and present a broad set of characteristics that distinguish the two question types. We also develop new classifiers that make use of additional data types, advanced machine learning, and a large dataset of unlabeled data, which achieve enhanced performance.


title: Catalyst: triggering collective action with thresholds

abstract: The web is a catalyst for drawing people together around shared goals, but many groups never reach critical mass. It can thus be risky to commit time or effort to a goal: participants show up only to discover that nobody else did, and organizers devote significant effort to causes that never get off the ground. Crowdfunding has lessened some of this risk by only calling in donations when an effort reaches a collective monetary goal. However, it leaves unsolved the harder problem of mobilizing effort, time and participation. We generalize the concept into activation thresholds, commitments that are conditioned on others' participation. With activation thresholds, supporters only need to show up for an event if enough other people commit as well. Catalyst is a platform that introduces activation thresholds for on-demand events. For more complex coordination needs, Catalyst also provides thresholds based on time or role (e.g., a bake sale requiring commitments for bakers, decorators, and sellers). In a multi-month field deployment, Catalyst helped users organize events including food bank volunteering, on-demand study groups, and mass participation events like a human chess game. Our results suggest that activation thresholds can indeed catalyze a large class of new collective efforts.


title: A Taxonomy for learning, teaching, and assessing Digital Logic Design

abstract: It is not uncommon for new faculty members to be overwhelmed by course development. Oddly enough, most of what is developed by new faculty members has been developed before, and frequently, at a higher quality. Both new and old faculty could benefit from a mechanism to exchange materials and ideas. Such a mechanism exists and is widely known - the popular Bloom's taxonomy. In 2001 an update to the original Bloom's taxonomy was published that added an extra dimension to the original taxonomy and emphasized the requirement for alignment of objectives with instructional methods and assessment techniques. Conforming to this latter specification, this paper summarizes this taxonomy and outlines how it might be utilized to develop a Taxonomy for learning, teaching, and assessing a single course, "digital logic design." The taxon is offered as a lesson-based mechanism for packaging learning objectives, instructional methods, and assessment techniques into a single, taxonomy-aligned element that can be exchanged among educators. These taxons provide a framework for the exchange of pedagogical techniques such as active learning within specific lesson contexts. It is the intention of this paper to offer the foundation for further development of a medium of interchange for these elements.


title: Multimedia Security: Open Problems and Solutions

abstract: In this paper we introduce and develop a framework for visual datahiding technologies that aim at resolving emerging problems of modern multimedia networking. First, we present the main open issues of multimedia security and secure communications. Secondly, we formulate multimedia data-hiding as communications with side information and advocate an appropriate information-theoretic framework for the analysis of different data-hiding methods in various applications. Finally, we discuss data-hiding-based solutions to some multimedia security related problems.


title: Advanced features for enterprise-wide role-based access control

abstract: The administration of users and access rights in large enterprises is a complex and challenging task. Roles are a powerful concept for simplifying access control, but their implementation is normally restricted to single systems and applications. In this article we define enterprise roles capable of spanning all IT systems in an organisation. We show how the enterprise role-based access control (ERBAC) model exploits the RBAC model outlined in the NIST standard draft and describe its extensions. We have implemented ERBAC as a basic concept of SAM Jupiter, a commercial security administration tool. Based on practical experience with the deployment of Enterprise Roles during SAM implementation projects in large organisations, we have enhanced the ERBAC model by including different ways of parametrising the roles. We show that using parameters can significantly reduce the number of roles needed in an enterprise and simplify the role structure, thereby reducing the administration effort considerably. The enhanced ERBAC features are illustrated by real-life examples.


title: Influence of Life Stress on Depression: Moderation by a Polymorphism in the 5-HTT Gene

abstract: In a prospective-longitudinal study of a representative birth cohort, we tested why stressful experiences lead to depression in some people but not in others. A functional polymorphism in the promoter region of the serotonin transporter (5-HTT) gene was found to moderate the influence of stressful life events on depression. Individuals with one or two copies of the short allele of the 5-HTT promoter polymorphism exhibited more depressive symptoms, diagnosable depression, and suicidality in relation to stressful life events than individuals homozygous for the long allele. This epidemiological study thus provides evidence of a gene-by-environment interaction, in which an individual’s response to environmental insults is moderated by his or her genetic makeup.


title: Reputation and Feedback Systems in Online Platform Markets

abstract: Online marketplaces have become ubiquitous, as sites such as eBay, Taobao, Uber, and Airbnb are frequented by billions of users. The success of these marketplaces is attributed to not only the ease in which buyers can find sellers, but also the trust that these marketplaces help facilitate through reputation and feedback systems. I begin by briefly describing the basic ideas surrounding the role of reputation in facilitating trust and trade, and offer an overview of how feedback and reputation systems work in online marketplaces. I then describe the literature that explores the effects of reputation and feedback systems on online marketplaces and highlight some of the problems of bias in feedback and reputation systems as they appear today. I discuss ways to address these problems to improve the practical design of online marketplaces and suggest some directions for future research.


title: String Similarity Metrics for Ontology Alignment

abstract: Ontology alignment is an important part of enabling the semantic web to reach its full potential. The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention. In this work we evaluate a wide range of such metrics, along with string pre-processing strategies such as removing stop words and considering synonyms, on different types of ontologies. We also present a set of guidelines on when to use which metric. We furthermore show that if optimal string similarity metrics are chosen, those alone can produce alignments that are competitive with the state of the art in ontology alignment systems. Finally, we examine the improvements possible to an existing ontology alignment system using an automated string metric selection strategy based upon the characteristics of the ontologies to be aligned.


A Shadow Detection and Removal from a Single Image Using LAB Color Space


title: Acquiring reflectance and shape from continuous spherical harmonic illumination

abstract: We present a novel technique for acquiring the geometry and spatially-varying reflectance properties of 3D objects by observing them under continuous spherical harmonic illumination conditions. The technique is general enough to characterize either entirely specular or entirely diffuse materials, or any varying combination across the surface of the object. We employ a novel computational illumination setup consisting of a rotating arc of controllable LEDs which sweep out programmable spheres of incident illumination during 1-second exposures. We illuminate the object with a succession of spherical harmonic illumination conditions, as well as photographed environmental lighting for validation. From the response of the object to the harmonics, we can separate diffuse and specular reflections, estimate world-space diffuse and specular normals, and compute anisotropic roughness parameters for each view of the object. We then use the maps of both diffuse and specular reflectance to form correspondences in a multiview stereo algorithm, which allows even highly specular surfaces to be corresponded across views. The algorithm yields a complete 3D model and a set of merged reflectance maps. We use this technique to digitize the shape and reflectance of a variety of objects difficult to acquire with other techniques and present validation renderings which match well to photographs in similar lighting.


title: Foot landmarking for footwear customization

abstract: As consumers are becoming increasingly selective of what they wear on their feet, manufacturers are experiencing problems developing and fitting the right footwear. Literature suggests that shoes with a shape similar to feet may be comfortable because they attempt to maintain the feet in a neutral posture. The objective of this paper is to develop a metric to quantify mismatches between feet and lasts and also to be able to generate the two-dimensional outline of the foot using the minimum number of landmarks. Fifty Hong Kong Chinese were participants in the experiment. In addition to subject weight, height, foot length and foot width, the left foot outlines were drawn and 18 landmarks were marked on each of the two-dimensional foot outlines. A step-wise procedure was used to reduce the chosen 18 landmarks to eight, such that the mean absolute negative error (an indicator of ‘tightness’) between the foot outline and the modelled curve was 1.3 mm. These eight landmarks seem to show an improvement over those proposed by other researchers, thus showing the importance of choosing the right landmarks for modelling the foot. The positive and negative absolute errors were on average 1.8 mm and 1.3 mm respectively. Moreover, the mean errors for the toe region and for the rest of the foot were 1.7 mm and 1.6 mm respectively. The results indicate that the foot outline, an important component for footwear functionality and fitting, may be modelled using eight critical landmarks.


title: Comparison of Machine Learning Techniques for Psychophysiological Stress Detection

abstract: Previous research has indicated that physiological signals can be used to detect mental stress. There is however no consensus on the optimal algorithm for this detection. The aim of this study is to compare different machine learning techniques for the measurement of stress based on physiological responses in a controlled environment. Electrocardiogram (ECG), galvanic skin response (GSR), temperature and respiration were measured during a laboratory stress test. Six machine learning techniques were investigated using a general and personal approach. The results show that personalized dynamic Bayesian networks and generalized support vector machines render the best average classification results with 84.6 % and 82.7 % respectively.


title: Hidden topic Markov models

abstract: Algorithms such as Latent Dirichlet Allocation (LDA) have achieved significant progress in modeling word document relationships. These algorithms assume each word in the document was generated by a hidden topic and explicitly model the word distribution of each topic as well as the prior distribution over topics in the document. Given these parameters, the topics of all words in the same document are assumed to be independent. In this paper, we propose modeling the topics of words in the document as a Markov chain. Specifically, we assume that all words in the same sentence have the same topic, and successive sentences are more likely to have the same topics. Since the topics are hidden, this leads to using the well-known tools of Hidden Markov Models for learning and inference. We show that incorporating this dependency allows us to learn better topics and to disambiguate words that can belong to different topics. Quantitatively, we show that we obtain better perplexity in modeling documents with only a modest increase in learning and inference complexity.


title: Neglected Infections of Poverty in the United States of America

abstract: In the United States, there is a largely hidden burden of diseases caused by a group of chronic and debilitating parasitic, bacterial, and congenital infections known as the neglected infections of poverty. Like their neglected tropical disease counterparts in developing countries, the neglected infections of poverty in the US disproportionately affect impoverished and under-represented minority populations. The major neglected infections include the helminth infections, toxocariasis, strongyloidiasis, ascariasis, and cysticercosis; the intestinal protozoan infection trichomoniasis; some zoonotic bacterial infections, including leptospirosis; the vector-borne infections Chagas disease, leishmaniasis, trench fever, and dengue fever; and the congenital infections cytomegalovirus (CMV), toxoplasmosis, and syphilis. These diseases occur predominantly in people of color living in the Mississippi Delta and elsewhere in the American South, in disadvantaged urban areas, and in the US–Mexico borderlands, as well as in certain immigrant populations and disadvantaged white populations living in Appalachia. Preliminary disease burden estimates of the neglected infections of poverty indicate that tens of thousands, or in some cases, hundreds of thousands of poor Americans harbor these chronic infections, which represent some of the greatest health disparities in the United States. Specific policy recommendations include active surveillance (including newborn screening) to ascertain accurate population-based estimates of disease burden; epidemiological studies to determine the extent of autochthonous transmission of Chagas disease and other infections; mass or targeted treatments; vector control; and research and development for new control tools including improved diagnostics and accelerated development of a vaccine to prevent congenital CMV infection and congenital toxoplasmosis.


title: Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression

abstract: While the research on convolutional neural networks (CNNs) is progressing quickly, the real-world deployment of these models is often limited by computing resources and memory constraints. In this paper, we address this issue by proposing a novel filter pruning method to compress and accelerate CNNs. Our work is based on the linear relationship identified in different feature map subspaces via visualization of feature maps. Such linear relationship implies that the information in CNNs is redundant. Our method eliminates the redundancy in convolutional filters by applying subspace clustering to feature maps. In this way, most of the representative information in the network can be retained in each cluster. Therefore, our method provides an effective solution to filter pruning for which most existing methods directly remove filters based on simple heuristics. The proposed method is independent of the network structure, thus it can be adopted by any off-the-shelf deep learning libraries. Experiments on different networks and tasks show that our method outperforms existing techniques before fine-tuning, and achieves the state-of-the-art results after fine-tuning.


title: Wearable, wireless brain computer interfaces in augmented reality environments

abstract: After Pierre Gloor and Hans Berger discovered the human electroencephalogram or EEG produced in the brain in 1969, brain computer interfaces (BCIs) became a reality. However, for more than a couple of decades, besides the common social fascination with such devices, they have not yet been considered as a feasible alternative interface for common daily activities. This is attributed to issues such as response time, costs and long initial user training periods. We define and outlines the current BCI technologies and reviews the current status of BCIs in the context of wearable computers. The use of augmented reality environments and the integration of wireless technologies such as Bluetooth are proposed as potential catalysts in the process of incorporating BCIs into daily life.


title: Tunable and flexible Barium Strontium Titanate (BST) varactors on Liquid Crystal Polymer (LCP) substrates

abstract: Tunable inter-digital capacitors (IDCs) are presented on flexible Liquid Crystal Polymer (LCP) substrates for the first time. Barium Strontium Titanate (BST) ferroelectric material is used as the tuning mechanism. A unique transfer process has been implemented to realize the BST thin film on the LCP substrate. In addition, due to the monolithic integration process, there is no wire bonding or packaging. This has allowed for successful demonstration of tunable and flexible IDCs up to 50 GHz. Tunability of 22–28% and Q factors of 80–7 have been measured in the frequency range of 0.01–50 GHz. To our knowledge, this is the first demonstration of a tunable ferroelectric device on 100% flexible materials without the use of any rigid components. This technology will have a wide range of applications in new generation of agile, high-frequency, and flexible electronics.


title: jMonkeyEngine 3.0 Game Development: A Practical Guide

abstract: Are you interested in developing cutting-edge 3D games? Do you want to master jMonkey 3.0's fully-featured graphics and game technologies? If so, then this book is for you! jMonkeyEngine 3.0 is a high level Open Source game development environment that supports advanced 3D graphics, techniques, and hardware. Built on Java, it is integrated with OpenGL and its SDK is built on Netbeans. The jMonkey platform allows developers to build sophisticated high-end 3D graphics and game environments to create games with the feel and appearance that top developers demand. Who this book is for: This book is for developers who are familiar with Java and interested in learning how to develop games and graphic applications using JME3. Readers who are interested in understanding both the mechanics of 3D graphics and game design will find this a useful and rewarding book. In this book: Learn how to develop 3D games using Java and JME3 Discover how to create and manipulate scene graphs Learn how to use lighting and materials to create gripping graphics Create first-class landscapes and models Explore JME's physics engine to develop realistic worlds Learn how to support multiple players and embed AI techniques Develop a space-based game that illustrates the techniques presented across the book Masses of Code. Download it for Free.


Pokémon Go: cardiovascular benefit or injury risk?


title: Relation Networks for Object Detection

abstract: Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. ::: This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.


title: Measuring the Diversity of Automatic Image Descriptions

abstract: Automatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of generic descriptions as a lack of diversity in the output, which we quantify using established metrics and two new metrics that frame image description as a word recall task. This framing allows us to evaluate system performance on the head of the vocabulary, as well as on the long tail, where system performance degrades. We use these metrics to examine the diversity of the sentences generated by nine state-of-the-art systems on the MS COCO data set. We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives. However, the adversarially-trained models only produce more types from the head of the vocabulary and not the tail. Besides vocabulary-based methods, we also look at the compositional capacity of the systems, specifically their ability to create compound nouns and prepositional phrases of different lengths. We conclude that there is still much room for improvement, and offer a toolkit to measure progress towards the goal of generating more diverse image descriptions.


A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification


title: Pin it! Improving Android network security at runtime

abstract: Smartphones are increasingly used worldwide and are now an essential tool for our everyday tasks. These tasks are supported by smartphone applications (apps) which commonly rely on network communication to provide a certain utility such as online banking. From a security and privacy point of view a properly secured (encrypted) communication channel is important in order to protect sensitive information against passive and active attacks. Previous research outlined that developers often fail to implement proper certificate validation in their custom SSL/TLS implementations and thus fail to secure the network communication. Previous research however proposed solutions for developers and not for the affected users. This global growth introduced drastic changes to the network utilization. In this paper we discuss this issue on the basis of Android apps. We analyzed over 50,000 Android apps, collected during two consecutive years, regarding the correct use of SSL/TLS protocols. Furthermore, we discuss the current situation. We propose dynamic certificate pinning, a device-based solution that overcomes the problem of broken SSL/TLS implementations in Android apps. To the best of our knowledge, we are the first to solve this problem by combining established techniques such as certificate pinning with dynamic instrumentation techniques to tackle one of the major security challenges in the network communication of smartphone applications.


title: A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction

abstract: Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether “similar” dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients’ health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological “state” of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, $p=0.001$ , $p=0.006$ from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone ( $p=0.005$ and $p=0.045$ ). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.


Issues , Challenges and Best Practices of Software Testing Activity


title: Smartphone-Based Online and Offline Speech Recognition System for ROS-Based Robots

abstract: Speech recognition is one of the fundamental requirements for fully autonomous robotic systems nowadays. The objective of the presented work is to offer a smartphone based speech recognition system for ROS (Robot Operating System) based autonomous robotic systems. The proposed recognition process consists of three steps, namely acquisition , preprocessing , and result extraction . In the contribution we also present a comparison by applying the proposed recognition process using Online and Offline speech servers. The Online method uses Google speech server whereas the Offline uses Simon speech server. The speech recognition and the robotic controlling system work under a server-client architecture. For the evaluation of the Online , we used an Android smartphone, a Pioneer-3DX robot, and Google speech server. For the Offline recognition evaluation, an Android smartphone, a Pioneer-3AT and a standalone Simon speech server are used. The applications running on the smartphone act like clients and communicate with both speech server and robotic system. The contribution provided in this paper is two fold: One using smartphone with Offline speech recognition server Simon recognition feature with robotic navigation on ROS platform. DOI: http://dx.doi.org/10.5755/j01.itc.43.4.5980


title: Wearable EOG goggles: Seamless sensing and context-awareness in everyday environments

abstract: In this article we introduce the analysis of eye motion as a new input modality for activity recognition, context-awareness and mobile HCI applications. We describe a novel embedded eye tracker that, in contrast to common systems using video cameras, relies on Electrooculography (EOG). This self-contained wearable device consists of goggles with dry electrodes integrated into the frame and a small pocket-worn component with a DSP for real-time EOG signal processing. It can store data locally for long-term recordings or stream processed EOG signals to a remote device over Bluetooth. We show how challenges associated with wearability, eye motion analysis and signal artefacts caused by physical activity can be addressed with a combination of a special mechanical design, optimised algorithms for eye movement detection and adaptive signal processing. In two case studies, we demonstrate that EOG is a suitable measurement technique for the recognition of reading activity and eye-based human-computer interaction. Eventually, wearable EOG goggles may pave the way for seamless eye movement analysis in everyday environments and new forms of context-awareness not possible today.


title: Natural AD-Like Neuropathology in Octodon degus: Impaired Burrowing and Neuroinflammation

abstract: Research was conducted under permit No. 5193 issued by the Servicio Agricola y Ganadero, Chile, with the supervision of the Ethics Committee of the Faculty of Sciences, Universidad de Chile.


title: QROCK: A quick version of the ROCK algorithm for clustering of categorical data

abstract: The ROCK algorithm is an agglomerative hierarchical clustering algorithm for clustering categorical data [Guha S., Rastogi, R., Shim, K., 1999. ROCK: A robust clustering algorithm for categorical attributes. In: Proc. IEEE Internat. Conf. Data Engineering, Sydney, March 1999]. In this paper we prove that under certain conditions, the final clusters obtained by the algorithm are nothing but the connected components of a certain graph with the input data-points as vertices. We propose a new algorithm QROCK which computes the clusters by determining the connected components of the graph. This leads to a very efficient method of obtaining the clusters giving a drastic reduction of the computing time of the ROCK algorithm. We also justify that it is more practical for specifying the similarity threshold rather than specifying the desired number of clusters a priori. The QROCK algorithm also detects the outliers in this process. We also discuss a new similarity measure for categorical attributes.


title: Penile Duplication in Newborn with Multiple Anomalies

abstract: We present a case of male, premature born child with multiple anomalies. The physical status was dominated by penile duplication, with additional, abortion scrotum in the median line, without palpable content and omphalocele with semi septum of the anus. By ultrasound examination of internal organs, we found a complex congenital heart anomaly, multiple anomalies in abdomen and micro calcifications in the central nervous system. General condition of the child required a prolonged mechanical ventilation and intensive care, so with clinical picture of liver failure at the age of 45 days occurred fetal outcome.


title: A personality-based adaptive system for visualizing classical music performances

abstract: To enhance the experience of listening to classical orchestra music, either in the concert hall or at home, we present a personalized system that integrates three visualization/interaction concepts: Score Follower (points to the current position in the score), Orchestra Layout (illustrates instruments that are currently playing and their dynamics), and Structure Visualization (visualizes structural elements such as themes or motifs). Motivated by previous literature that found evidence for connections between personality and music consumption and preference, we first assessed in a user study to which extent personality traits and music visualization preferences correlate. Measuring preference via pragmatic quality and personality traits according to the Big Five Inventory (BFI) questionnaire, we found substantial interconnections between them. These translate into rules relating certain personality traits (e.g., extraversion or agreeableness) to preference rankings of the visualizations. In the proposed personality-based system, users are grouped into four clusters according to their answers to the most significant personality questions determined in the study. The order of the visualizations for a given user is adapted with respect to the ranking preferred by other users in the same cluster. Evaluation of the system was carried out by a second user study that showed a significantly higher normalized discounted cumulative gain (NDCG) for the personalized system in comparison to a system with randomized order of the visualizations.


title: Hardware/Software Co-Design of an Accelerator for FV Homomorphic Encryption Scheme Using Karatsuba Algorithm

abstract: Somewhat Homomorphic Encryption (SHE) schemes allow to carry out operations on data in the cipher domain. In a cloud computing scenario, personal information can be processed secretly, inferring a high level of confidentiality. For many years, practical parameters of SHE schemes were overestimated, leading to only consider the FFT algorithm to accelerate SHE in hardware. Nevertheless, recent work demonstrates that parameters can be lowered without compromising the security [1] . Following this trend, this work investigates the benefits of using Karatsuba algorithm instead of FFT for the Fan-Vercauteren (FV) Homomorphic Encryption scheme. The proposed accelerator relies on an hardware/software co-design approach, and is designed to perform fast arithmetic operations on degree 2,560 polynomials with 135 bits coefficients, allowing to compute small algorithms homomorphically. Compared to a functionally equivalent design using FFT, our accelerator performs an homomorphic multiplication in 11.9 ms instead of 15.46 ms, and halves the size of logic utilization and registers on the FPGA.


title: An Uncrewed Aerial Vehicle Attack Scenario and Trustworthy Repair Architecture

abstract: With the growing ubiquity of uncrewed aerial vehicles (UAVs), mitigating emergent threats in such systems has become increasingly important. In this short paper, we discuss an indicative class of UAVs and a potential attack scenario in which a benign UAV completing a mission can be compromised by a malicious attacker with an antenna and a commodity computer with open-source ground station software. We attest to the relevance of such a scenario for both enterprise and defense applications. We describe a system architecture for resiliency and trustworthiness in the face of these attacks. Our system is based on the quantitative assessment of trust from domain-specific telemetry data and the application of program repair techniques to UAV flight plans. We conclude with a discussion of restoring trust in post-repair UAV mission integrity.


title: Cross-Entropy for Monte-Carlo Tree Search

abstract: Recently, Monte-Carlo Tree Search (MCTS) has become a popular approach for intelligent play in games. Amongst others, it is successfully used in most state-of-the-art Go programs. To improve the playing strength of these Go programs any further, many parameters dealing with MCTS should be fine-tuned. In this paper, we propose to apply the Cross-Entropy Method (CEM) for this task. The method is comparable to Estimation-of-Distribution Algorithms (EDAs), a new area of evolutionary computation. We tested CEM by tuning various types of parameters in our Go program MANGO. The experiments were performed in matches against the open-source program GNU GO. They revealed that a program with the CEM-tuned parameters played better than without. Moreover, MANGO plus CEM outperformed the regular MANGO for various time settings and board sizes. From the results we may conclude that parameter tuning by CEM genuinely improved the playing strength of MANGO, for various time settings. This result may be generalized to other game engines using MCTS.


title: Optimized magnetic design for inductive power transfer coils

abstract: Inductive Power Transfer (IPT) is well-established for applications with biomedical implants and radio-frequency identification systems. Recently, also systems for the charging of the batteries of consumer electronic devices and of electric and hybrid electric vehicles have been developed. The efficiency η of the power transfer of IPT systems is given by the inductor quality factor Q and the magnetic coupling k of the transmission coils. In this paper, the influence of the transmission frequency on the inductor quality factor and the efficiency is analyzed taking also the admissible field emissions as limited by standards into account. Aspects of an optimization of the magnetic design with respect to a high magnetic coupling and a high quality factor are discussed for IPT at any power level. It is shown that the magnetic coupling mainly depends on the area enclosed by the coils and that their exact shape has only a minor influence. The results are verified with an experimental prototype.


title: Anonymizing NYC Taxi Data: Does It Matter?

abstract: The widespread use of location-based services has led to an increasing availability of trajectory data from urban environments. These data carry rich information that are useful for improving cities through traffic management and city planning. Yet, it also contains information about individuals which can jeopardize their privacy. In this study, we work with the New York City (NYC) taxi trips data set publicly released by the Taxi and Limousine Commission (TLC). This data set contains information about every taxi cab ride that happened in NYC. A bad hashing of the medallion numbers (the ID corresponding to a taxi) allowed the recovery of all the medallion numbers and led to a privacy breach for the drivers, whose income could be easily extracted. In this work, we initiate a study to evaluate whether "perfect" anonymity is possible and if such an identity disclosure can be avoided given the availability of diverse sets of external data sets through which the hidden information can be recovered. This is accomplished through a spatio-temporal join based attack which matches the taxi data with an external medallion data that can be easily gathered by an adversary. Using a simulation of the medallion data, we show that our attack can re-identify over 91% of the taxis that ply in NYC even when using a perfect pseudonymization of medallion numbers. We also explore the effectiveness of trajectory anonymization strategies and demonstrate that our attack can still identify a significant fraction of the taxis in NYC. Given the restrictions in publishing the taxi data by TLC, our results indicate that unless the utility of the data set is significantly compromised, it will not be possible to maintain the privacy of taxi medallion owners and drivers.


title: ULTRA WIDEBAND POWER DIVIDER USING TAPERED LINE

abstract: A power divider with ultra-wideband (UWB) performance has been designed. The quarter-wave transformer in the conventional Wilkinson power divider is replaced by an exponentially tapered microstrip line. Since the tapered line provides a consistent impedance transformation across all frequencies, very low amplitude ripple of 0.2dB peak-to-peak in the transmission coe-cient and superior input return loss better than 15dB are achieved over an ultra-wide bandwidth. Two additional resistors are added along the tapered line to improve the output return loss and isolation. Simulation performed using CST Microwave Studio and measured results conflrm the good performance of the proposed circuit. The return loss and the isolation between the output ports are better than 15dB across the band 2{ 10.2GHz. Standard ofi-the-shelf resistance values can be selected by optimizing the physical locations to mount the resistors. Better performance can be achieved with more isolation resistors added. Hence, the number of isolation resistors to be used may be selected based on the desired bandwidth and level of isolation and return loss speciflcations.


A Propagation Measurement Set-Up for MIMO System Channel Characterization and Preliminary Results


title: Hyaluronic Acid Injections for Correction of the Tear Trough Deformity

abstract: Background: The tear trough, though small in physical dimensions, sits at the perceptual epicenter of the face. Because small changes here make large changes in how other people interpret one’s state of being, it would be beneficial to have a simple way of addressing the area. Despite the complexities of cause, the solution is to level the hollow and smooth the junction between the lid and the cheek. Methods: Hyaluronic acid injections are used to address the tear trough deformity. Very small quantities, 0.2 to 0.4 cc, are used per side, and the placement is in the orbicularis oculi muscle or at the periosteum. The key is to inject deep to the dermis and to massage the injected area smooth. Results: All patients were improved roughly in proportion to their skin quality. The best patients for the treatment are those with young, thick skin and a definite hollow. The duration of the effect is often more than 1 year at this location. Minor bruising and irregularities were self-limiting or treated with light massage. Large overfills or irregularities are correctable with injection of hyaluronidase. Conclusions: Tear trough injection with hyaluronic acid products is relatively simple, effective, and safe, and can offer significant improvement in this difficult area. It can be performed for primary deformities or to improve a surgical result.


Overview of the Second Social Media Mining for Health (SMM4H) Shared Tasks at AMIA 2017


title: Deterministic quantum teleportation of atomic qubits

abstract: Quantum teleportation1 provides a means to transport quantum information efficiently from one location to another, without the physical transfer of the associated quantum-information carrier. This is achieved by using the non-local correlations of previously distributed, entangled quantum bits (qubits). Teleportation is expected to play an integral role in quantum communication2 and quantum computation3. Previous experimental demonstrations have been implemented with optical systems that used both discrete and continuous variables4,5,6,7,8,9, and with liquid-state nuclear magnetic resonance10. Here we report unconditional teleportation5 of massive particle qubits using atomic (9Be+) ions confined in a segmented ion trap, which aids individual qubit addressing. We achieve an average fidelity of 78 per cent, which exceeds the fidelity of any protocol that does not use entanglement11. This demonstration is also important because it incorporates most of the techniques necessary for scalable quantum information processing in an ion-trap system12,13.


title: Physics-based fast single image fog removal

abstract: Imaging in poor weather is often severely degraded by scattering due to suspended particles in the atmosphere such as haze, fog and mist. Poor visibility becomes a major problem for most outdoor vision applications. In this paper, we propose a novel fast defogging method from a single image of a scene based on a fast bilateral filtering approach. The complexity of our method is only a linear function of the number of input image pixels and this thus allows a very fast implementation. Results on a variety of outdoor foggy images demonstrate that our method achieves good restoration for contrast and color fidelity, resulting in a large improvement in image visibility.


title: EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial

abstract: Neurofeedback is attracting renewed interest as a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behaviour. It promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of EEG neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.


title: High Performance Table-Based Algorithm for Pipelined CRC Calculation

abstract: In this paper, we present a fast cyclic redun- dancy check (CRC) algorithm that performs CRC compu- tation for an arbitrary length of message in parallel. For a given message with any length, the algorithm first chunks the message into blocks, each of which has a fixed size equal to the degree of the generator polynomial. Then it computes CRC for the chunked blocks in parallel using lookup tables, and the results are combined together with XOR operations. In the traditional implementation, it is the feedback that makes pipelining problematic. In the proposed algorithm, we solve this problem by taking advantage of CRC's properties and pipelining the feedback loop. The short pipeline latency of our algorithm enables a faster clock frequency than previous approaches, and it also allows easy scaling of the parallelism while only slightly affecting timing. We build a Verilog implementation our pipelined CRC. The simulation results show that our approach is faster and more space- efficient than previous CRC implementations; with 256 bits input each clock cycle, our approach achieves about 36% throughput improvement with about 12% area reduction. Index Terms—Keywords: CRC, lookup table, pipelining


title: Real-time bidding for online advertising: measurement and analysis

abstract: The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.


title: Learning Tree Augmented Naive Bayes for Ranking

abstract: Naive Bayes has been widely used in data mining as a simple and effective classification algorithm. Since its conditional independence assumption is rarely true, numerous algorithms have been proposed to improve naive Bayes, among which tree augmented naive Bayes (TAN) [3] achieves a significant improvement in term of classification accuracy, while maintaining efficiency and model simplicity. In many real-world data mining applications, however, an accurate ranking is more desirable than a classification. Thus it is interesting whether TAN also achieves significant improvement in term of ranking, measured by AUC(the area under the Receiver Operating Characteristics curve) [8,1]. Unfortunately, our experiments show that TAN performs even worse than naive Bayes in ranking. Responding to this fact, we present a novel learning algorithm, called forest augmented naive Bayes (FAN), by modifying the traditional TAN learning algorithm. We experimentally test our algorithm on all the 36 data sets recommended by Weka [12], and compare it to naive Bayes, SBC [6], TAN [3], and C4.4 [10], in terms of AUC. The experimental results show that our algorithm outperforms all the other algorithms significantly in yielding accurate rankings. Our work provides an effective and efficient data mining algorithm for applications in which an accurate ranking is required.


title: A Three-Way Model for Collective Learning on Multi-Relational Data

abstract: Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is significantly faster to compute.


title: Physical matching of CD-SEM: noise analysis and verification in FAB environment

abstract: CD-SEMs fleet matching is a widely discussed subject and various approaches and procedures to determine it were ::: described in the literature [1,2,4-6]. The different approaches for matching are all based on statistical treatment of CD ::: measurements that are performed on dedicated test structures. The test structures are a limited finite set of features, thus ::: the matching results should be treated as valid only for the specific defined set of test features. The credibility of the ::: matching should be in question for different layers and specifically production layers. Since matching is crucial for ::: reliable process monitoring by a fleet of CD-SEMs, the current matching approaches must be extended so that the ::: matching will be only tool dependent and reproducible on all layers regardless their specific material or topographic ::: characteristics. In our previous work [1] the new approach named "Physical Matching" was introduced and a new ::: matching procedure based on the direct estimation of tool physical parameters was described. This approach extends the ::: conventional matching methods to enable significant improvement of the matching between CD-SEM tools in ::: production environment. ::: In this work we present results of applying the physical matching method in FAB environment by using the physical ::: parameters of the brightness and SNR, extend it to noise frequency domain characteristics monitoring, and enhanced ::: collection uniformity. Improving the collection uniformity is also demonstrated and proved to be a significant factor. ::: The advantage of the physical matching with noise spectra analysis approach for a case study is demonstrated. This ::: method will enable detection of specific reasons for mismatching between the tools, based on analysis of specific ::: frequencies that are resulted from known mechanical/electrical noise. The proposed procedure allows tool problems ::: fixing before CD measurements are affected. In order to get a reliable visualization of the difference between two ::: systems, new automatic and manual tool finger print methods were developed. The application of the proposed approach ::: to vendor to vendor matching problem is considered.


title: Integration of image quality and motion cues for face anti-spoofing

abstract: A multi-cues integration framework is proposed using a hierarchical neural network.Bottleneck representations are effective in multi-cues feature fusion.Shearlet is utilized to perform face image quality assessment.Motion-based face liveness features are automatically learned using autoencoders. Many trait-specific countermeasures to face spoofing attacks have been developed for security of face authentication. However, there is no superior face anti-spoofing technique to deal with every kind of spoofing attack in varying scenarios. In order to improve the generalization ability of face anti-spoofing approaches, an extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection. Shearlet is utilized to develop an image quality-based liveness feature. Dense optical flow is utilized to extract motion-based liveness features. A bottleneck feature fusion strategy can integrate different liveness features effectively. The proposed approach was evaluated on three public face anti-spoofing databases. A half total error rate (HTER) of 0% and an equal error rate (EER) of 0% were achieved on both REPLAY-ATTACK database and 3D-MAD database. An EER of 5.83% was achieved on CASIA-FASD database.


title: A Semantic Graph-Based Approach for Mining Common Topics from Multiple Asynchronous Text Streams

abstract: In the age of Web 2.0, a substantial amount of unstructured content are distributed through multiple text streams in an asynchronous fashion, which makes it increasingly difficult to glean and distill useful information. An effective way to explore the information in text streams is topic modelling, which can further facilitate other applications such as search, information browsing, and pattern mining. In this paper, we propose a semantic graph based topic modelling approach for structuring asynchronous text streams. Our model integrates topic mining and time synchronization, two core modules for addressing the problem, into a unified model. Specifically, for handling the lexical gap issues, we use global semantic graphs of each timestamp for capturing the hidden interaction among entities from all the text streams. For dealing with the sources asynchronism problem, local semantic graphs are employed to discover similar topics of different entities that can be potentially separated by time gaps. Our experiment on two real-world datasets shows that the proposed model significantly outperforms the existing ones.


title: A high efficiency flyback micro-inverter with a new adaptive snubber for photovoltaic applications

abstract: Based on the hybrid operation of interleaved flyback micro-inverter in discontinuous and boundary conduction modes (DCM and BCM), a novel adaptive snubber is proposed in this paper. The proposed snubber limits the drain-to-source voltage overshoot of the flyback's main switch during the turn-off process, enabling the use of lower voltage MOSFETs. It also recovers the stored energy in the leakage inductance of the flyback transformer and provides soft switching for the main flyback switch by limiting the rising slope of the MOSFET voltage during the turn-off process resulting in higher efficiency. Exploiting the natural resonant of the flyback converter in BCM, the adopted controller provides ZVS and ZCS for the main switch during the BCM operation. The operation of the flyback micro-inverter with associated controllers is analytically studied, and considerations for an optimum design aiming to higher efficiency are presented. Performance of the flyback micro-inverter with the proposed adaptive snubber and the corresponding controllers is experimentally verified based on a 250W interleaved flyback micro-inverter hardware setup.


title: On the saddle point problem for non-convex optimization

abstract: A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it is often thought that a main source of difficulty for the ability of these local methods to find the global minimum is the proliferation of local minima with much higher error than the global minimum. Here we argue, based on results from statistical physics, random matrix theory, and neural network theory, that a deeper and more profound difficulty originates from the proliferation of saddle points, not local minima, especially in high dimensional problems of practical interest. Such saddle points are surrounded by high error plateaus that can dramatically slow down learning, and give the illusory impression of the existence of a local minimum. Motivated by these arguments, we propose a new algorithm, the saddle-free Newton method, that can rapidly escape high dimensional saddle points, unlike gradient descent and quasi-Newton methods. We apply this algorithm to deep neural network training, and provide preliminary numerical evidence for its superior performance.


title: Realization of Three-Qubit Quantum Error Correction with Superconducting Circuits

abstract: A controlled-controlled NOT, or Toffoli, gate is used to develop a fast, high-fidelity, three-qubit error correction protocol with the potential to correct arbitrary single-qubit errors.


Aripiprazole as an adjunctive treatment for refractory major depression


title: Contextual Semibandits via Supervised Learning Oracles

abstract: We study an online decision making problem where on each round a learner chooses a list of items based on some side information, receives a scalar feedback value for each individual item, and a reward that is linearly related to this feedback. These problems, known as contextual semibandits, arise in crowdsourcing, recommendation, and many other domains. This paper reduces contextual semibandits to supervised learning, allowing us to leverage powerful supervised learning methods in this partial-feedback setting. Our first reduction applies when the mapping from feedback to reward is known and leads to a computationally efficient algorithm with near-optimal regret. We show that this algorithm outperforms state-of-the-art approaches on real-world learning-to-rank datasets, demonstrating the advantage of oracle-based algorithms. Our second reduction applies to the previously unstudied setting when the linear mapping from feedback to reward is unknown. Our regret guarantees are superior to prior techniques that ignore the feedback.


title: Heuristic evaluation for games: usability principles for video game design

abstract: Most video games require constant interaction, so game designers must pay careful attention to usability issues. However, there are few formal methods for evaluating the usability of game interfaces. In this paper, we introduce a new set of heuristics that can be used to carry out usability inspections of video games. The heuristics were developed to help identify usability problems in both early and functional game prototypes. We developed the heuristics by analyzing PC game reviews from a popular gaming website, and the review set covered 108 different games and included 18 from each of 6 major game genres. We analyzed the reviews and identified twelve common classes of usability problems seen in games. We developed ten usability heuristics based on the problem categories, and they describe how common game usability problems can be avoided. A preliminary evaluation of the heuristics suggests that they help identify game-specific usability problems that can easily be overlooked otherwise.


title: Goal-Oriented Requirements Engineering: A Guided Tour

abstract: Goals capture, at different levels of abstraction, the various objectives the system under consideration should achieve. Goal-oriented requirements engineering is concerned with the use of goals for eliciting, elaborating, structuring, specifying, analyzing, negotiating, documenting, and modifying requirements. This area has received increasing attention. The paper reviews various research efforts undertaken along this line of research. The arguments in favor of goal orientation are first briefly discussed. The paper then compares the main approaches to goal modeling, goal specification and goal-based reasoning in the many activities of the requirements engineering process. To make the discussion more concrete, a real case study is used to suggest what a goal-oriented requirements engineering method may look like. Experience, with such approaches and tool support are briefly discussed as well.


title: What is an emerging technology?

abstract: Despite the growing interest around the emergence of novel technologies, especially from the policy-making perspective, there is still no consensus on what classifies a technology as ’emergent’. The present paper aims to fill this gap by developing a definition of ’emerging technologies’ and a framework for their detection and analysis. The definition is developed by combining a basic understanding of the term and in particular the concept of ’emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The conceptual effort is then used to develop a framework for the operationalisation of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.


title: Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

abstract: User modeling for individual users on the Social Web plays an important role and is a fundamental step for personalization as well as recommendations. Recent studies have proposed different user modeling strategies considering various dimensions such as temporal dynamics and semantics of user interests. Although previous work proposed different user modeling strategies considering the temporal dynamics of user interests, there is a lack of comparative studies on those methods and therefore the comparative performance over each other is unknown. In terms of semantics of user interests, background knowledge from DBpedia has been explored to enrich user interest profiles so as to reveal more information about users. However, it is still unclear to what extent different types of information from DBpedia contribute to the enrichment of user interest profiles. In this paper, we propose user modeling strategies which use Concept Frequency - Inverse Document Frequency (CF-IDF) as a weighting scheme and incorporate either or both of the dynamics and semantics of user interests. To this end, we first provide a comparative study on different user modeling strategies considering the dynamics of user interests in previous literature to present their comparative performance. In addition, we investigate different types of information (i.e., categories, classes and connected entities via various properties) for entities from DBpedia and the combination of them for extending user interest profiles. Finally, we build our user modeling strategies incorporating either or both of the best-performing methods in each dimension. Results show that our strategies outperform two baseline strategies significantly in the context of link recommendations on Twitter.


title: Kinect with ROS, interact with Oculus: Towards Dynamic User Interfaces for robotic teleoperation

abstract: Teleoperation remains an important aspect for robotic systems especially when deployed in unstructured environments. While a range of research strives for robots that are completely autonomous, many robotic applications still require some level of human-in-the-loop control. In any situation where teleoperation is required an effective User Interface (UI) remains a key component within the systems design. Current advancements in Virtual Reality (VR) software and hardware such as the Oculus Rift, HTC Vive and Google Cardboard combined with greater transparency to robotic systems afforded by middleware such as the Robot Operating System (ROS) provides an opportunity to rapidly improve traditional teleoperation interfaces. This paper uses a System of System (SoS) approach to present the concept of a Virtual Reality Dynamic User Interface (VRDUI) for the teleoperation of heterogeneous robots. Different geometric virtual workspaces are discussed and a cylindrical workspace aligned with interactive displays is presented as a virtual control room. A presentation mode within the proposed VRDUI is also detailed, this shows how point cloud information obtained from the Microsoft Kinect can be incorporated within the proposed virtual workspace. This point cloud data is successfully processed into an OctoMap utilizing the octree data structure to create a voxelized representation of the 3D scanned environment. The resulting OctoMap is then displayed to an operator as a 3D point cloud using the Oculus Rift Head Mounted Display (HMD).


title: Application of BCI systems in neurorehabilitation: a scoping review

abstract: AbstractPurpose: To review various types of electroencephalographic activities of the brain and present an overview of brain–computer interface (BCI) systems’ history and their applications in rehabilitation. Methods: A scoping review of published English literature on BCI application in the field of rehabilitation was undertaken. IEEE Xplore, ScienceDirect, Google Scholar and Scopus databases were searched since inception up to August 2012. All experimental studies published in English and discussed complete cycle of the BCI process was included in the review. Results and discussion: In total, 90 articles met the inclusion criteria and were reviewed. Various approaches that improve the accuracy and performance of BCI systems were discussed. Based on BCI’s clinical application, reviewed articles were categorized into three groups: motion rehabilitation, speech rehabilitation and virtual reality control (VRC). Almost half of the reviewed papers (48%) concentrated on VRC. Speech rehabilitation and motion re...


title: Brief mindfulness meditation training alters psychological and neuroendocrine responses to social evaluative stress

abstract: Summary Objective: To test whether a brief mindfulness meditation training intervention buffers selfreported psychological and neuroendocrine responses to the Trier Social Stress Test (TSST) in young adult volunteers. A second objective evaluates whether pre-existing levels of dispositional mindfulness moderate the effects of brief mindfulness meditation training on stress reactivity. Methods: Sixty-six (N = 66) participants were randomly assigned to either a brief 3-day (25-min per day) mindfulness meditation training or an analytic cognitive training control program. All participants completed a standardized laboratory social-evaluative stress challenge task (the TSST) following the third mindfulness meditation or cognitive training session. Measures of psychological (stress perceptions) and biological (salivary cortisol, blood pressure) stress reactivity were collected during the social evaluative stress-challenge session. Results: Brief mindfulness meditation training reduced self-reported psychological stress reactivity but increased salivary cortisol reactivity to the TSST, relative to the cognitive training comparison program. Participants who were low in pre-existing levels of dispositional mindfulness and then received mindfulness meditation training had the greatest cortisol reactivity to the TSST. No significant main or interactive effects were observed for systolic or diastolic blood pressure reactivity to the TSST.


title: A Typology of Technology‐Enhanced Tourism Experiences

abstract: Experiences constitute the essence of the tourism industry. While literature has recognised the recent impact of technology on experiences, its empirical exploration remains scarce. This study addresses the gap by empirically exploring five leading industry cases to generate a holistic understanding of technology enhanced tourism experiences. The main contribution of this paper lies in the development of a nine-field experience typology matrix based on the increasing intensity of cocreation and technology implementation. The final contribution of this study is the development of an experience hierarchy and discussing its relevance for experience enhancement in tourism research and practice.


title: Deep self-taught learning for facial beauty prediction

abstract: Most modern research of facial beauty prediction focuses on geometric features by traditional machine learning methods. Geometric features may easily lose much feature information characterizing facial beauty, rely heavily on accurate manual landmark localization of facial features and impose strict restrictions on training samples. Deep architectures have been recently demonstrated to be a promising area of research in statistical machine learning. In this paper, deep self-taught learning is utilized to obtain hierarchical representations, learn the concept of facial beauty and produce human-like predictor. Deep learning is helpful to recognize a broad range of visual concept effectively characterizing facial beauty. Through deep learning, reasonable apparent features of face images are extracted without depending completely on artificial feature selection. Self-taught learning, which has the ability of automatically improving network systems to understand the characteristics of data distribution and making recognition significantly easier and cheaper, is used to relax strict restrictions of training samples. Moreover, in order to choose a more appropriate method for mapping high-level representations into beauty ratings efficiently, we compare the performance of five regression methods and prove that support vector machine (SVM) regression is better. In addition, novel applications of deep self-taught learning on local binary pattern (LBP) and Gabor filters are presented, and the improvements on facial beauty prediction are shown by deep self-taught learning combined with LBP. Finally, human-like performance is obtained with learning features in full-sized and high-resolution images.


Robust Control Over a Packet-based Network


title: A review of features and functional building blocks of social media

abstract: In recent years, social network sites have been revolutionised, giving rise to the new term “social media”. Social media are tools that facilitate dialogue and interaction among users using the internet. However, social media users have not yet fully understood how social media can be used because they have limited knowledge on the functionalities of its features. In particular, research indicates that users often ignore social media structures and make little effort to understand the characteristics of social media. This problem raises fundamental and important questions, including: What should users know about the application of social media in the new era of computing technology? How should they combine the features of social media with their skills and preferences in order to use social media more effectively? Knowledge of the features and functionalities of social media may help to improve users' skills and preferences in using social media applications. In this paper, we review the features and functional building blocks of social media sites with a particular focus on its use in the marketing, education and government domains. The findings from the review enable organisations to understand the social media ecology, their consumers and their consumers' engagement needs.


title: NoSQL databases: a step to database scalability in web environment

abstract: Purpose – The paper aims to focus on so‐called NoSQL databases in the context of cloud computing.Design/methodology/approach – Architectures and basic features of these databases are studied, particularly their horizontal scalability and concurrency model, that is mostly weaker than ACID transactions in relational SQL‐like database systems.Findings – Some characteristics like a data model and querying capabilities of NoSQL databases are discussed in more detail.Originality/value – The paper shows vary different data models and query possibilities in a common terminology enabling comparison and categorization of NoSQL databases.


A Report on the Analysis of Metrics and Measures on Software Quality Factors – A Literature Study


title: Developments of power system protection and control

abstract: Synchronized wide area communication has become a mature technology, which makes the real-time interaction between the substations and the wide area protection and control system possible. However, the present protection and control system to handle this real-time data has been recognized to be deficient. This paper begins by reviewing the development history of power system protection, with special attention paid to the recent development in the field of wide-area and integrated protections, in order to look into the future development of protection and control systems. Then the concept of integrated wide area protection and control is introduced, where it can be shown that a hierarchical protection and control system provides the protection and control for wide area or regional power substations/plants and their associated power networks. The system is mainly divided into three levels: the local, the substation/plant, and the wide area/regional. The integrated functions at each level are described in details with an aim to develop an optimal coordination mechanism between each level. The key element in the proposed system is the wide area real-time protection and control information platform, which not only enables the merger of three lines of defence for power system protection and control, but also provides a perfect tool for the application of cloud computing in substations and power networks.