title: Learning Everything about Anything: Webly-Supervised Visual Concept Learning

abstract: Recognition is graduating from labs to real-world applications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge to the vision researcher: scalability. How can we learn a model for any concept that exhaustively covers all its appearance variations, while requiring minimal or no human supervision for compiling the vocabulary of visual variance, gathering the training images and annotations, and learning the models? In this paper, we introduce a fully-automated approach for learning extensive models for a wide range of variations (e.g. actions, interactions, attributes and beyond) within any concept. Our approach leverages vast resources of online books to discover the vocabulary of variance, and intertwines the data collection and modeling steps to alleviate the need for explicit human supervision in training the models. Our approach organizes the visual knowledge about a concept in a convenient and useful way, enabling a variety of applications across vision and NLP. Our online system has been queried by users to learn models for several interesting concepts including breakfast, Gandhi, beautiful, etc. To date, our system has models available for over 50, 000 variations within 150 concepts, and has annotated more than 10 million images with bounding boxes.


title: Transcatheter embolization of a pseudoaneurysm of the inferior epigastric artery with N-butyl cyanoacrylate

abstract: A 41-year-old man presented with alcoholic liver cirrhosis with ascites and clotting abnormality. After therapeutic paracentesis, haemoperitoneum ensued without colour Doppler ultrasound or CT evidence of pseudoaneurysm or haematoma at the site of paracentesis. However, an angiogram of the inferior epigastric artery revealed an obvious small pseudoaneurysm arising from its small muscular branch, and this pseudoaneurysm was successfully treated by transcatheter embolization with N-butyl cyanoacrylate. Transcatheter embolization with N-butyl cyanoacrylate is useful treatment for pseudoaneurysms arising from the small muscular branch of the inferior epigastric artery, which cannot be catheterized superselectively close to the pseudoaneurysm.


title: P-MTI: physical-layer missing tag identification via compressive sensing

abstract: Radio frequency identification (RFID) systems are emerging platforms that support a variety of pervasive applications. RFID tags can be used to label items and enable item-level monitoring. The problem of identifying missing tags in RFID systems has attracted wide attention due to its practical importance (e.g., anti-theft). This paper presents P-MTI: a Physical-layer Missing Tag Identification scheme that effectively makes use of the lower-layer information and dramatically improves operational efficiency. Unlike conventional approaches, P-MTI looks into the aggregated tag responses instead of focusing on individual tag responses and extracts useful information from physical-layer collisions. P-MTI leverages the sparsity of missing tag events and reconstructs tag responses through compressive sensing. We prototype P-MTI using the USRP software defined radio and Intel WISP platform. We also evaluate the performance of P-MTI with extensive simulations and compare to previous approaches. The experiment results show the promising performance of P-MTI in identification accuracy, time efficiency, as well as robustness over noisy channels.


title: An Unequal Wilkinson Power Divider with Variable Dividing Ratio

abstract: In this paper, an unequal 1:N Wilkinson power divider with variable power dividing ratio is proposed. The proposed unequal power divider is composed of the conventional Wilkinson divider structure, rectangular-shaped defected ground structure (DGS), island in DGS, and varactor diodes of which capacitance is adjustable according to bias voltage. The high impedance value of microstrip line having DGS is going up and down by adjusting the bias voltage for varactor diodes. Output power dividing ratio (N) is adjusted from 2.59 to 10.4 for the unequal power divider with 2 diodes.


title: A pipeline for the segmentation and classification of 3D point clouds

abstract: This paper presents algorithms for fast segmentation of 3D point clouds and subsequent classification of the obtained 3D segments. The method jointly determines the ground surface and segments individual objects in 3D, including overhanging structures. When compared to six other terrain modelling techniques, this approach has minimal error between the sensed data and the representation; and is fast (processing a Velodyne scan in approximately 2 seconds). Applications include improved alignment of successive scans by enabling operations in sections (Velodyne scans are aligned 7% sharper compared to an approach using raw points) and more informed decision-making (paths move around overhangs). The use of segmentation to aid classification through 3D features, such as the Spin Image or the Spherical Harmonic Descriptor, is discussed and experimentally compared. Moreover, the segmentation facilitates a novel approach to 3D classification that bypasses feature extraction and directly compares 3D shapes via the ICP algorithm. This technique is shown to achieve accuracy on par with the best feature based classifier (92.1%) while being significantly faster and allowing a clearer understanding of the classifier’s behaviour.


title: An NLP-based cognitive system for disease status identification in electronic health records

abstract: This paper presents a natural language processing (NLP) based cognitive decision support system that automatically identifies the status of a disease from the clinical notes of a patient record. The system relies on IBM Watson Patient Record NLP analytics and supervised or semi-supervised learning techniques. It uses unstructured text in clinical notes, data from the structured part of a patient record, and disease control targets from the clinical guidelines. We evaluated the system using de-identified patient records of 414 hypertensive patients from a multi-specialty hospital system in the U.S. The experimental results show that, using supervised learning methods, our system can achieve an average 0.86 F1-score in identifying disease status passages and average accuracy of 0.77 in classifying the status as controlled or not. To the best of our knowledge, this is the first system to automatically identify disease control status from clinical notes.


title: DoSVis: Document Stance Visualization

abstract: Text visualization techniques often make use of automatic text classification methods. One of such methods is stance analysis, which is concerned with detecting various aspects of the writer's attitude towards utterances expressed in the text. Existing text visualization approaches for stance classification results are usually adapted to textual data consisting of individual utterances or short messages, and they are often designed for social media or debate monitoring tasks. In this paper, we propose a visualization approach called DoSVis (Document Stance Visualization) that focuses instead on individual text documents of a larger length. DoSVis provides an overview of multiple stance categories detected by our classifier at the utterance level as well as a detailed text view annotated with classification results, thus supporting both distant and close reading tasks. We describe our approach by discussing several application scenarios involving business reports and works of literature. (Less)


title: Sleep duration, wake/sleep symptoms, and academic performance in Hong Kong Secondary School Children

abstract: Background Sleep deprivation is common among teenagers. The aim of this study was to investigate the relationship between sleep duration, wake/sleep symptoms, and academic performance among Hong Kong students. Materials and methods The sleep habit questionnaires were distributed to all Year 11 students at an international school that catered to different ethnic groups in Hong Kong. Analysis of various parameters of academic performance and sleep habits and their relationships were undertaken. Results Fifty-nine students were recruited. The average sleep duration in this group was 7.23 h. The overall prevalence of excessive daytime sleepiness (defined as an Epworth Sleepiness Scale score of >10) was 25.4%. Eleven subjects had excessive class sleepiness, defined as high likelihood to fall asleep during at least one school session. Mathematics performance was positively correlated with sleep duration. Excessive sleepiness on rising was identified as a significant risk factor for poor performance in English and Mathematics. Sleepiness during the third and fourth lessons was identified as a significant risk factor for poor performance in Mathematics only. Conclusion Sleep deprivation was common in the studied cohort and it was associated with a decrease in Mathematics performance. Excessive sleepiness on rising and sleepiness during third and fourth lessons were associated with poorer grades in Mathematics and English. Excessive daytime sleepiness was reported in 25% of students. Bruxism and snoring were associated with excessive daytime sleepiness.


title: Customer Loyalty in e-Commerce Settings: An Empirical Study

abstract: The aim of this paper is to investigate the impact of individual dimensions of perceived service quality on customer satisfaction, trust and loyalty in e-commerce settings. Empirical results indicate that the perceived service quality has positive direct effects on both customer satisfaction and trust. The results also show that customer satisfaction appears to have a positive direct effect on trust, while both customer satisfaction and trust have direct positive effects on loyalty through word-of-mouth and intention. Thus, both satisfaction and trust play an imperative role in creating customer loyalty as suggested by many authors. Some theoretical and managerial implications and suggestions for future research are also provided.


title: Landmark recognition with compact BoW histogram and ensemble ELM

abstract: Along with the rapid development of mobile terminal devices, landmark recognition applications based on mobile devices have been widely researched in recent years. Due to the fast response time requirement of mobile users, an accurate and efficient landmark recognition system is thus urgent for mobile applications. In this paper, we propose a landmark recognition framework by employing a novel discriminative feature selection method and the improved extreme learning machine (ELM) algorithm. The scalable vocabulary tree (SVT) is first used to generate a set of preliminary codewords for landmark images. An efficient codebook learning algorithm derived from the word mutual information and Visual Rank technique is proposed to filter out those unimportant codewords. Then, the selected visual words, as the codebook for image encoding, are used to produce a compact Bag-of-Words (BoW) histogram. The fast ELM algorithm and the ensemble approach using the ELM classifier are utilized for landmark recognition. Experiments on the Nanyang Technological University campus's landmark database and the Fifteen Scene database are conducted to illustrate the advantages of the proposed framework.


title: Temporal Video Segmentation to Scenes Using High-Level Audiovisual Features

abstract: In this paper, a novel approach to video temporal decomposition into semantic units, termed scenes, is presented. In contrast to previous temporal segmentation approaches that employ mostly low-level visual or audiovisual features, we introduce a technique that jointly exploits low-level and high-level features automatically extracted from the visual and the auditory channel. This technique is built upon the well-known method of the scene transition graph (STG), first by introducing a new STG approximation that features reduced computational cost, and then by extending the unimodal STG-based temporal segmentation technique to a method for multimodal scene segmentation. The latter exploits, among others, the results of a large number of TRECVID-type trained visual concept detectors and audio event detectors, and is based on a probabilistic merging process that combines multiple individual STGs while at the same time diminishing the need for selecting and fine-tuning several STG construction parameters. The proposed approach is evaluated on three test datasets, comprising TRECVID documentary films, movies, and news-related videos, respectively. The experimental results demonstrate the improved performance of the proposed approach in comparison to other unimodal and multimodal techniques of the relevant literature and highlight the contribution of high-level audiovisual features toward improved video segmentation to scenes.


title: Internet gaming disorder in adolescence: Psychological characteristics of a clinical sample

abstract: Background and aims: ::: Internet gaming disorder (IGD) has become a topic of increasing research interest since its inclusion in Section 3 of the DSM-5. Given the lack of clinical studies concerning IGD, exploring the characteristics of clinical samples with IGD will help to delineate the gaming disorder construct and inform future treatment studies. ::: Methods: ::: Data collection consisted of clinical interviews comprising 31 male adolescents diagnosed with IGD. Alongside the clinical interviews, the participants were administered a battery of psychometric tests assessing the following: IGD, personality traits, comorbid symptomatology, emotional intelligence (EI), and family environment characteristics. ::: Results: ::: The results showed that the adolescents with IGD and their relatives reported a high number of hours per week and high presence of stressful life events in the majority of the sample. High scores on scales assessing depression, anxiety, and somatic disorders were found. However, the findings indicate the presence of several other comorbid disorders meaning that some of the adolescent sample with IGD had different clinical profiles. Several personality traits were found to be highly associated with IGD including introversion, inhibition, submissiveness, self-devaluation, interpersonal sensibility, obsessive–compulsive tendencies, phobic anxiety, and hostility, as well as paranoid and borderline personality traits. Other negative characteristics found in the present sample included a high level of social problems, low EI, and dysfunctional family relationships. ::: Discussion and conclusions: ::: The findings suggest a more global pattern of key psychological characteristics associated with Internet gaming disorder in adolescence. This may help in understanding the complexity of this proposed disorder and it may also help in designing more specialized interventions for adolescents with IGD. The findings have important implications for clinical practice and interventions.


title: Detecting suicidality on Twitter

abstract: This project was supported in part by funding from the NSW Mental ::: Health Commission and the NHMRC John Cade Fellowship 1056964. ::: PJB and ALC are supported by the NHMRC Early Career Fellowships ::: 1035262 and 1013199.


title: Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation

abstract: In this paper we tackle the problem of estimating the 3D pose of object instances, using convolutional neural networks. State of the art methods usually solve the challenging problem of regression in angle space indirectly, focusing on learning discriminative features that are later fed into a separate architecture for 3D pose estimation. In contrast, we propose an end-to-end learning framework for directly regressing object poses by exploiting Siamese Networks. For a given image pair, we enforce a similarity measure between the representation of the sample images in the feature and pose space respectively, that is shown to boost regression performance. Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art. Last, our feature learning formulation provides the ability of learning features that can perform under severe occlusions, demonstrating high performance on our novel hand-object dataset.


title: Characterization of receptors mediating AVP- and OT-induced glucagon release from the rat pancreas.

abstract: We characterized the receptors that mediate arginine vasopressin (AVP)- and oxytocin (OT)-induced glucagon release by use of a number of antagonists in the perfused rat pancreas and the fluorescence imaging of the receptors. AVP and OT (3 pM-3 nM) increased glucagon release in a concentration-dependent manner. The antagonist with potent V(1b) receptor-blocking activity, CL-4-84 (10 nM), abolished AVP (30 pM)-induced glucagon release but did not alter OT (30 pM)-induced glucagon release. d(CH(2))(5)[Tyr(Me)(2)]AVP (10 nM), a V(1a) receptor antagonist, and L-366,948 (10 nM), a highly specific OT-receptor antagonist, failed to inhibit AVP-induced glucagon release. In contrast, L-366,948 (10 nM) abolished OT (30 pM)-induced glucagon release but did not change the effect of AVP. Fluorescent microscopy of rat pancreatic sections showed that fluorescence-labeled AVP and OT bound to their receptors in the islets of Langerhans and that the bindings were inhibited by 1 microM of Cl-4-84 and L-366,948, respectively. Because AVP and OT at physiological concentrations (3-30 pM) increased glucagon release, we conclude that AVP and OT increase glucagon release under the physiological condition through the activation of V(1b) and OT receptors, respectively.


title: Safety and neutralizing rabies antibody in healthy subjects given a single dose of rabies immune globulin caprylate/chromatography purified

abstract: Background ::: Rabies immune globulin (RIG) and vaccination series are necessary for postexposure prophylaxis. A new formulation of RIG (human) purified by caprylate/chromatography (RIG-C) was evaluated. ::: ::: ::: Trial registration ::: ClinicalTrials.gov identifier: NCT02139657. ::: ::: ::: Materials and methods ::: This open-label, single-arm study in healthy subjects evaluated neutralizing rabies antibody concentrations produced from a single 20 IU/kg intramuscular (IM) dose of RIG-C as measured by rapid fluorescent focus inhibition test (50% neutralization endpoint) 1-hour postdose and on days 1, 2, 4, 6, 8, 10, 14, 18, and 21. ::: ::: ::: Results ::: Twelve subjects were enrolled into the study. No discontinuations, serious adverse events (AEs), or treatment-emergent clinically significant changes in laboratory parameters were observed. All AEs resolved and were mild except 1 moderate AE of oropharyngeal pain. Injection site pain (4 subjects) was most commonly reported. RIG-C produced a rapid increase in neutralizing rabies antibody: mean value 0.113 IU/mL at 24 hours after IM injection, peak on day 4 (0.132 IU/mL), persisting through day 21 (0.116 IU/mL). The mean reciprocal titer was 11.5 by day 2; the peak value of 12.1 was achieved on day 4; and a mean value ≥10.6 was maintained through day 21. ::: ::: ::: Conclusion ::: RIG-C was well tolerated and provided neutralizing rabies antibodies, which combined with vaccine series after rabies exposure, should result in effective prophylaxis per World Health Organization/Centers for Disease Control and Prevention guidelines.


ROS: an open-source Robot Operating System


title: Multiobjective Design Optimization of IGBT Power Modules Considering Power Cycling and Thermal Cycling

abstract: Insulated-gate bipolar transistor (IGBT) power modules find widespread use in numerous power conversion applications where their reliability is of significant concern. Standard IGBT modules are fabricated for general-purpose applications while little has been designed for bespoke applications. However, conventional design of IGBTs can be improved by the multiobjective optimization technique. This paper proposes a novel design method to consider die-attachment solder failures induced by short power cycling and baseplate solder fatigue induced by the thermal cycling which are among major failure mechanisms of IGBTs. Thermal resistance is calculated analytically and the plastic work design is obtained with a high-fidelity finite-element model, which has been validated experimentally. The objective of minimizing the plastic work and constrain functions is formulated by the surrogate model. The nondominated sorting genetic algorithm-II is used to search for the Pareto-optimal solutions and the best design. The result of this combination generates an effective approach to optimize the physical structure of power electronic modules, taking account of historical environmental and operational conditions in the field.


title: Face recognition by support vector machines

abstract: Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard eigenface approach using the nearest center classification (NCC) criterion.


title: OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning

abstract: The explosion of the Internet provides us with a tremendous resource of images shared online. It also confronts vision researchers the problem of finding effective methods to navigate the vast amount of visual information. Semantic image understanding plays a vital role towards solving this problem. One important task in image understanding is object recognition, in particular, generic object categorization. Critical to this problem are the issues of learning and dataset. Abundant data helps to train a robust recognition system, while a good object classifier can help to collect a large amount of images. This paper presents a novel object recognition algorithm that performs automatic dataset collecting and incremental model learning simultaneously. The goal of this work is to use the tremendous resources of the web to learn robust object category models for detecting and searching for objects in real-world cluttered scenes. Humans contiguously update the knowledge of objects when new examples are observed. Our framework emulates this human learning process by iteratively accumulating model knowledge and image examples. We adapt a non-parametric latent topic model and propose an incremental learning framework. Our algorithm is capable of automatically collecting much larger object category datasets for 22 randomly selected classes from the Caltech 101 dataset. Furthermore, our system offers not only more images in each object category but also a robust object category model and meaningful image annotation. Our experiments show that OPTIMOL is capable of collecting image datasets that are superior to the well known manually collected object datasets Caltech 101 and LabelMe.


Spatial Pyramid Matching


title: Grounding natural language instructions to semantic goal representations for abstraction and generalization

abstract: Language grounding is broadly defined as the problem of mapping natural language instructions to robot behavior. To truly be effective, these language grounding systems must be accurate in their selection of behavior, efficient in the robot’s realization of that selected behavior, and capable of generalizing beyond commands and environment configurations only seen at training time. One choice that is crucial to the success of a language grounding model is the choice of representation used to capture the objective specified by the input command. Prior work has been varied in its use of explicit goal representations, with some approaches lacking a representation altogether, resulting in models that infer whole sequences of robot actions, while other approaches map to carefully constructed logical form representations. While many of the models in either category are reasonably accurate, they fail to offer either efficient execution or any generalization without requiring a large amount of manual specification. In this work, we take a first step towards language grounding models that excel across accuracy, efficiency, and generalization through the construction of simple, semantic goal representations within Markov decision processes. We propose two related semantic goal representations that take advantage of the hierarchical structure of tasks and the compositional nature of language respectively, and present multiple grounding models for each. We validate these ideas empirically with results collected from following text instructions within a simulated mobile-manipulator domain, as well as demonstrations of a physical robot responding to spoken instructions in real time. Our grounding models tie abstraction in language commands to a hierarchical planner for the robot’s execution, enabling a response-time speed-up of several orders of magnitude over baseline planners within sufficiently large domains. Concurrently, our grounding models for generalization infer elements of the semantic representation that are subsequently combined to form a complete goal description, enabling the interpretation of commands involving novel combinations never seen during training. Taken together, our results show that the design of semantic goal representation has powerful implications for the accuracy, efficiency, and generalization capabilities of language grounding models.


title: Expressive Power of Abstract Meaning Representations

abstract: The syntax of abstract meaning representations AMRs can be defined recursively, and a systematic translation to first-order logic FOL can be specified, including a proper treatment of negation. AMRs without recurrent variables are in the decidable two-variable fragment of FOL. The current definition of AMRs has limited expressive power for universal quantification up to one universal quantifier per sentence. A simple extension of the AMR syntax and translation to FOL provides the means to represent projection and scope phenomena.


title: Driver Gaze Region Estimation without Use of Eye Movement

abstract: Automated estimation of the allocation of a driver's visual attention may be a critical component of future Advanced Driver Assistance Systems. In theory, vision-based tracking of the eye can provide a good estimate of gaze location. In practice, eye tracking from video is challenging because of sunglasses, eyeglass reflections, lighting conditions, occlusions, motion blur, and other factors. Estimation of head pose, on the other hand, is robust to many of these effects, but cannot provide as fine-grained of a resolution in localizing the gaze. However, for the purpose of keeping the driver safe, it is sufficient to partition gaze into regions. In this effort, we propose a system that extracts facial features and classifies their spatial configuration into six regions in real-time. Our proposed method achieves an average accuracy of 91.4% at an average decision rate of 11 Hz on a dataset of 50 drivers from an on-road study.


title: Personalized Interactive Narratives via Sequential Recommendation of Plot Points

abstract: In story-based games or other interactive systems, a drama manager (DM) is an omniscient agent that acts to bring about a particular sequence of plot points for the player to experience. Traditionally, the DM's narrative evaluation criteria are solely derived from a human designer. We present a DM that learns a model of the player's storytelling preferences and automatically recommends a narrative experience that is predicted to optimize the player's experience while conforming to the human designer's storytelling intentions. Our DM is also capable of manipulating the space of narrative trajectories such that the player is more likely to make choices that result in the recommended experience. Our DM uses a novel algorithm, called prefix-based collaborative filtering (PBCF), that solves the sequential recommendation problem to find a sequence of plot points that maximizes the player's rating of his or her experience. We evaluate our DM in an interactive storytelling environment based on choose-your-own-adventure novels. Our experiments show that our algorithms can improve the player's experience over the designer's storytelling intentions alone and can deliver more personalized experiences than other interactive narrative systems while preserving players' agency.


title: NoPhish App Evaluation: Lab and Retention Study

abstract: Phishing is a prevalent issue of today’s Internet. Previous approaches to counter phishing do not draw on a crucial factor to combat the threat - the users themselves. We believe user education about the dangers of the Internet is a further key strategy to combat phishing. For this reason, we developed an Android app, a game called –NoPhish–, which educates the user in the detection of phishing URLs. It is crucial to evaluate NoPhish with respect to its effectiveness and the users’ knowledge retention. Therefore, we conducted a lab study as well as a retention study (five months later). The outcomes of the studies show that NoPhish helps users make better decisions with regard to the legitimacy of URLs immediately after playing NoPhish as well as after some time has passed. The focus of this paper is on the description and the evaluation of both studies. This includes findings regarding those types of URLs that are most difficult to decide on as well as ideas to further improve NoPhish.


title: A COLOR SCIENTIST LOOKS AT VIDEO

abstract: This paper represents a critical review of some of the color processing in the consumer TV processing chain. As such, a default processing chain is assumed as a starting point. The flow of color image information through this chain is described and critiqued. That is followed by development and description of some “clean slate” theoretical approaches to video processing with color accuracy and quality as the highest priority. These two approaches are compared and contrasted to provide some practical insight into how color science could be used in a practical sense to improve consumer video processing. Additionally, some examples how color and image appearance models might be used in the development of consumer video systems are described. 1. COLORIMETRY OF IMAGING SYSTEMS Perhaps it is no coincidence that standardized methods of colorimetry have developed in parallel with various forms of commercially viable color imaging systems over the past century. As a reference point one could look at the establishment of the CIE 1931 Standard Colorimetric Observer only a few years before the invention of Kodachrome film. In fact, David Wright, one of the fathers of CIE colorimetry and an early researcher on color television, pointed out that had CIE colorimetry not existed prior to the development of television, it would have had to be invented.[1] Color measurement in imaging systems can be organized into a three-level hierarchy from devicedependent metrics through device-independent techniques to the viewing-conditions-independent methods of color appearance models.[2] Device-dependent metrics are those that define the amount of color signal in a given imaging device without defining the meaning of those signals outside the particular device being used (e.g., CMYK dye amounts or RGB digital counts). Device-independent metrics express the image information in terms of colorimetric coordinates such as XYZ or CIELAB or device coordinates such as RGB or CMYK combined with colorimetric definitions of the meaning of those device coordinates (i.e., a device definition, characterization, or profile). Viewing-condition-independent specifications recognize that the color appearance of any given scene or reproduction will depend on viewing conditions such as luminance level, surround, chromatic adaptation, etc. and attempt to specify final image appearance. In consumer video applications, the framework is present for some forms of device-independent color imaging, but actual implementations are not controlled to the degree necessary and, unfortunately, much of the consumer video world remains in the domain of devicedependency. 2. OBJECTIVES IN COLOR REPRODUCTION Hunt[3] defined six objectives in color reproduction and also reviewed them in his reference work on the reproduction of color.[4] These objectives are 1. spectral, 2. colorimetric, 3. exact, 4. equivalent, 5. corresponding, and 6. preferred color reproduction. While full description of these objectives is not possible in this short paper, they do provide the necessary theoretical framework for understanding the requirements for evolution of color reproduction from device-dependent to viewing-conditionsdependent systems. Consumer video does not presently achieve any of Hunt’s objectives, but it might be said that it is aiming for preferred color reproduction. However, as Hunt[3] pointed out, the first five objectives “provide a framework which is a necessary preliminary to any discussion of deliberate distortions of colour reproduction.” Fairchild[2] has taken a slightly different approach to this question by defining five levels of color reproduction that cast Hunt’s objectives into a different hierarchy. These levels are 1. color reproduction, 2. pleasing color reproduction, 3. colorimetric color reproduction, 4. color appearance reproduction, and 5. color preference reproduction. In this format, each successive level depends on the previous levels being achieved first. Consumer video systems are currently functioning at levels 1 and 2. The following sections attempt to describe some possibilities for advancing to a higher level. 3. DISPLAY-CENTRIC VIDEO Essentially from the moment of image capture, color in video is defined by some form of standardized display. For example, the encoded video signal (e.g. Y’CBCR) is defined by the display primaries, transfer function, and scaling.[5] Such a system can be sufficient to implement accurate device-independent color imaging for the displayed content. It could, but apparently is not, also be used for device-independent video capture if the cameras were colorimetrically characterized and the signal then encoded directly in display-centric characterized RGB (or a known transform thereof). However, the capture end of the system is rarely implemented in a colorimetrically-accurate method. Instead, the controls available to the videographer are used to capture “pleasing” images encoded for the chosen standard display rather than accurate measurements. For some color reproduction objectives this is perfectly adequate and appropriate, but for others accurate color information about the scene might well be lost at the very first step of the imaging chain. One is then left with processing and enhancing information for the display. This display-centric (sometimes called output-referred) bias of video systems is not necessarily a flaw, but it needs to be recognized that the processing of video color is focused on the display rather than the original scene (a scene-centric, or scene-referred approach). Once the display-centric nature of video systems is recognized, the task becomes one of properly interpreting the colorimetric meaning of the video signals in order to do appropriate video processing (e.g., differential processing of luminance and chromatic information or colorimetric transformations to displays that do not match the nominal standard display). Unfortunately, notations such as YCBCR are significantly overloaded and it is often difficult to determine just what the quantities represent (not to mention that there are other similar transformations with ill-defined names like YUV). Are the quantities linear or “gamma corrected”? If nonlinear, which transfer function was used? What primary set was used to define RGB? What coefficients were used to define the luminance transform? Is it even luminance? How are the quantities scaled and quantized? The questions seem endless and unless the image data encoding is accompanied with some definition of these variables, there is no chance for accurate colorimetry and meaningful perceptual processing of content. As Poynton[5] summarized, “the existence of several standard sets of primary chromaticities, the introduction of new coefficients, and continuing confusion between luminance and luma all beg for a notation to distinguish among the many possible combinations.” Given the potential for confusion (and the reality), how is it possible that acceptable results are obtained at all? The answer probably lies in the reality that very few, if any, displays match a given encoding standard, they are adjusted differently by users, and the viewing conditions have a significant impact on image appearance. Again, the best that can be done is to be aware of the potential for confusion, minimize it, and process the color information appropriately given what is available.


title: Visfer: Camera-based visual data transfer for cross-device visualization

abstract: Going beyond the desktop to leverage novel devices—such as smartphones, tablets, or large displays—for visual sensemaking typically requires supporting extraneous operations for device discovery, i...


title: Self-Discipline Gives Girls the Edge: Gender

abstract: Throughout elementary, middle, and high school, girls earn higher grades than boys in all major subjects. Girls, however, do not outperform boys on achievement or IQ tests. To date, explanations for the underprediction of girls' GPAs by standardized tests have focused on gender differences favoring boys on such tests. The authors' investigation suggests an additional explanation: Girls are more self-disciplined, and this advantage is more relevant to report card grades than to achievement or aptitude tests. Eighth-grade girls at an urban magnet school were more self-disciplined than their male counterparts according to delay of gratification measures and self-report, teacher, and parent ratings. Whereas girls earned higher grades in all courses, they did only marginally better on an achievement test and worse on an IQ test. Mediation analyses suggested girls earned higher GPAs at least in part because they were more self-disciplined.


title: Extreme entropy machines: robust information theoretic classification

abstract: Most existing classification methods are aimed at minimization of empirical risk (through some simple point-based error measured with loss function) with added regularization. We propose to approach the classification problem by applying entropy measures as a model objective function. We focus on quadratic Renyi's entropy and connected Cauchy---Schwarz Divergence which leads to the construction of extreme entropy machines (EEM). The main contribution of this paper is proposing a model based on the information theoretic concepts which on the one hand shows new, entropic perspective on known linear classifiers and on the other leads to a construction of very robust method competitive with the state of the art non-information theoretic ones (including Support Vector Machines and Extreme Learning Machines). Evaluation on numerous problems spanning from small, simple ones from UCI repository to the large (hundreds of thousands of samples) extremely unbalanced (up to 100:1 classes' ratios) datasets shows wide applicability of the EEM in real-life problems. Furthermore, it scales better than all considered competitive methods.


title: Geographic information extraction, disambiguation and ranking techniques

abstract: An important part of textual information around the world contains some kind of geographic features. User queries with geographic references are becoming very common and human expectations from a search engine are even higher. Although several works have been focused on this area, the interpretation of the geographic information in order to better satisfy the user needs continues being a challenge. This work proposes different techniques which are involved in the process of identifying and analyzing the geographic information in textual documents and queries in natural languages. A geographic ontology GeoNW has been built by combining GeoNames, WordNet and Wikipedia resources. Based on the information stored in GeoNW, geographic terms are identified and an algorithm for solving the toponym disambiguation problem is proposed. Once the geographic information is processed, we obtain a geographic ranking list of documents which is combined with a standard textual ranking list of documents for producing the final results. GeoCLEF test collection is used for evaluating the accuracy of the result.


title: A New Cascaded Multilevel Converter Topology with Reduced Number of Components

abstract: Nowadays the Internet has become an inevitable source of information for daily life activities. Internet has a collection of large amount of information hidden in the web pages. Webpage contains the noisy data like advertisements, page settings, navigation buttons and notices. The data is hidden in these type of images in the webpage. The data is hidden in the image will be extracted is still difficult. In this paper, the hidden objects(data) in image are extracted from the webpage. The Convolutional Neural Network(CNN) model is proposed to filter the image using the layers. After filtering the image, using Artificial Neural Network(ANN) the hidden data from the webpage will be extracted and stored in database. The data stored in the database is the structured data and it becomes more necessity. In existing system, the Back Propogation Neural Network (BPNN) model is used to filter the image, but it is inefficient. Compare to BPNN, the Convolutional Neural Network (CNN) model is the efficient method to filter the image.


title: Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems

abstract: Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art super...


title: Fonduer: Knowledge Base Construction from Richly Formatted Data

abstract: We focus on knowledge base construction (KBC) from richly formatted data. In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations conveyed jointly via textual, structural, tabular, and visual expressions. We introduce Fonduer, a machine-learning-based KBC system for richly formatted data. Fonduer presents a new data model that accounts for three challenging characteristics of richly formatted data: (1) prevalent document-level relations, (2) multimodality, and (3) data variety. Fonduer uses a new deep-learning model to automatically capture the representation (i.e., features) needed to learn how to extract relations from richly formatted data. Finally, Fonduer provides a new programming model that enables users to convert domain expertise, based on multiple modalities of information, to meaningful signals of supervision for training a KBC system. Fonduer-based KBC systems are in production for a range of use cases, including at a major online retailer. We compare Fonduer against state-of-the-art KBC approaches in four different domains. We show that Fonduer achieves an average improvement of 41 F1 points on the quality of the output knowledge base---and in some cases produces up to 1.87x the number of correct entries---compared to expert-curated public knowledge bases. We also conduct a user study to assess the usability of Fonduer's new programming model. We show that after using Fonduer for only 30 minutes, non-domain experts are able to design KBC systems that achieve on average 23 F1 points higher quality than traditional machine-learning-based KBC approaches.


title: Supervised Distributional Hypernym Discovery via Domain Adaptation

abstract: Comunicacio presentada a la Conference on Empirical Methods in Natural Language Processing celebrada els dies 1 a 5 de novembre de 2016 a Austin, Texas.


title: Mechanical Design of "Omni-Ball": Spherical Wheel for Holonomic Omnidirectional Motion

abstract: In this paper, mechanical design of a novel spherical wheel shape for a omni-directional mobile robot is presented. The wheel is used in a omnidirectional mobile robot realizing high step-climbing capability with its hemispherical wheel. Conventional Omniwheels can realize omnidirectional motion, however they have a poor step overcoming ability due to the sub-wheel small size. The proposed design solves this drawback by means of a 4 wheeled design. "Omni-Ball" is formed by two passive rotational hemispherical wheels and one active rotational axis. An actual prototype model has been developed to illustrate the concept and to perform preliminary motion experiments, through which the basic performance of the Omnidirectional vehicle with this proposed Omni-Ball mechanism was confirmed. An prototype has been developed to illustrate the concept. Motion experiments, with a test vehicle are also presented.


title: Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

abstract: Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.


title: A Coreless Electric Current Sensor With Circular Conductor Positioning Calibration

abstract: This paper presents a novel coreless current sensor unit for measuring electrical current of circular conductors. The proposed sensor unit consists of three magnetic field sensors and a microcontroller unit (MCU). The new sensor unit does not need a bulky magnetic core necessary for magnetic field concentration, and can be used in the same way as conventional current transformers (CTs). An algorithm is developed to calibrate the outputs of the magnetic field sensors (e.g., Hall effect integrated circuits (ICs) with respect to the relative position of the current-carrying circular conductor. The algorithm can be implemented on a low-cost MCU for automatic real-time operation. Unlike other coreless magnetic field sensors that need to be fixed onto the conductor for accurate current measurement, the proposed sensor unit affords a more flexible installation option. A hardware prototype is constructed for the proposed coreless current sensor unit. The performance evaluation results show that the accuracy of the proposed sensor unit is comparable to the conventional magnetic core CTs.


title: Principal component analysis for speed limit Traffic Sign Recognition

abstract: Traffic Sign Recognition has recently become a popular research field. The main applications include Autonomous Driving Systems, Driver Assistance Systems, road sign inventory, to name a few. In this paper, a speed limit traffic sign recognition system is proposed based on Principal Component Analysis (PCA) with preprocessing steps, that help on perspective and extreme luminance variation correction. The classification is performed by a feed-forward neural network, or Multi-Layer Perceptron (MLP). Experimental results present a classification accuracy similar to some state of the art systems, but with a more compact scheme.


title: Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization

abstract: Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain–computer interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limited if the data arise from different hardware systems, which often vary between application spaces. Currently, this creates a need to recalibrate classifiers, which negatively affects people's interest in using such systems. In this paper, we employ active weighted adaptation regularization (AwAR), which integrates weighted adaptation regularization (wAR) and active learning, to expedite the calibration process. wAR makes use of labeled data from the previous headset and handles class-imbalance, and active learning selects the most informative samples from the new headset to label. Experiments on single-trial event-related potential classification show that AwAR can significantly increase the classification accuracy, given the same number of labeled samples from the new headset. In other words, AwAR can effectively reduce the number of labeled samples required from the new headset, given a desired classification accuracy, suggesting value in collating data for use in wide scale transfer-learning applications.


Proposal Learning Semantics of WikiTables


title: 3-D Millimeter-Wave Statistical Channel Model for 5G Wireless System Design

abstract: This paper presents a 3-D statistical channel impulse response (IR) model for urban line of sight (LOS) and non-LOS channels developed from 28- and 73-GHz ultrawideband propagation measurements in New York City, useful in the design of 5G wireless systems that will operate in both the ultra-high frequency/microwave and millimeter-wave (mmWave) spectrum to increase channel capacities. A 3GPP-like stochastic IR channel model is developed from measured power delay profiles, angle of departure, and angle of arrival power spectra. The extracted statistics are used to implement a channel model and simulator capable of generating 3-D mmWave temporal and spatial channel parameters for arbitrary mmWave carrier frequency, signal bandwidth, and antenna beamwidth. The model presented here faithfully reproduces realistic IRs of measured urban channels, supporting air interface design of mmWave transceivers, filters, and multi-element antenna arrays.


title: Comparison of fundamental frequency and PWM methods applied on a hybrid cascaded multilevel inverter

abstract: This paper presents a hybrid cascaded multilevel inverter for electric vehicles (EV) / hybrid electric vehicles (HEV) and utility interface applications. The inverter consists of a standard 3-leg inverter (one leg for each phase) and H-bridge in series with each inverter leg. It can use only a single DC power source to supply a standard 3-leg inverter along with three full H-bridges supplied by capacitors or batteries. Both fundamental frequency and high switching frequency PWM methods are used for the hybrid multilevel inverter. An experimental 5 kW prototype inverter is built and tested. The above two switching control methods are validated and compared experimentally.


title: Next-best stereo: extending next best view optimisation for collaborative sensors

abstract: Most 3D reconstruction approaches passively optimise over all data, exhaustively matching pairs, rather than actively selecting data to process. This is costly both in terms of time and computer resources, and quickly becomes intractable for large datasets. This work proposes an approach to intelligently filter large amounts of data for 3D reconstructions of unknown scenes using monocular cameras. Our contributions are twofold: First, we present a novel approach to efficiently optimise the Next-Best View ( NBV ) in terms of accuracy and coverage using partial scene geometry. Second, we extend this to intelligently selecting stereo pairs by jointly optimising the baseline and vergence to find the NBV ’s best stereo pair to perform reconstruction. Both contributions are extremely efficient, taking 0.8ms and 0.3ms per pose, respectively. Experimental evaluation shows that the proposed method allows efficient selection of stereo pairs for reconstruction, such that a dense model can be obtained with only a small number of images. Once a complete model has been obtained, the remaining computational budget is used to intelligently refine areas of uncertainty, achieving results comparable to state-of-the-art batch approaches on the Middlebury dataset, using as little as 3.8% of the views.


title: Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

abstract: We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Our pipeline achieves state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D domain. We also evaluate on the LineMOD dataset where we can compete with other synthetically trained approaches. We further increase performance by correcting 3D orientation estimates to account for perspective errors when the object deviates from the image center and show extended results.


title: Learning Single-view 3D Reconstruction of Objects and Scenes

abstract: Author(s): Tulsiani, Shubham | Advisor(s): Malik, Jitendra | Abstract: We address the task of inferring the 3D structure underlying an image, in particular focusing on two questions -- how we can plausibly obtain supervisory signal for this task, and what forms of representation should we pursue. We first show that we can leverage image-based supervision to learn single-view 3D prediction, by using geometry as a bridge between the learning systems and the available indirect supervision. We demonstrate that this approach enables learning 3D structure across diverse setups e.g. learning deformable models, predctive models for volumetric 3D, or inferring textured meshes. We then advocate the case for inferring interpretable and compositional 3D representations. We present a method that discovers the coherent compositional structure across objects in a unsupervised manner by attempting to assemble shapes using volumetric primitives, and then demonstrate the advantages of predicting similar factored 3D representations for complex scenes.


title: Face Aging With Boundary Equilibrium Conditional Autoencoder

abstract: Since generative adversarial networks (GANs) were proposed in 2014, mode collapse has been a problem that affects many researchers when training GANs. With the reconstruction loss of an autoencoder, conditional adversarial autoencoder (CAAE) is free from mode collapse. However, its reconstruction loss will bring a saturation problem, in which the encoder maps every input image into just one latent variable. Combining the CAAE with a boundary equilibrium generative adversarial network, we propose a boundary equilibrium conditional autoencoder (BECAE) focusing on the face aging task. Our model is the first GANs that renders images through a discriminator. We also introduce some statistics to measure the level of the saturation problem. The results show that the BECAE has successfully solved the saturation problem and can generate face images of the same quality as the images generated by the CAAE.


title: Minimally Constrained Flight Simulation in Wind Tunnel

abstract: Experimental studies on aircraft stability and performance can be enhanced by using a rig in which the aircraft model support approximates free flight within a wind tunnel. Such multi-degree-of-fre...


title: Domination in bipolar fuzzy graphs

abstract: The concepts of cardinality, dominating set, independent set, total dominating number and independent dominating number of bipolar fuzzy graphs are introduced and investigated. The notion of an irredundance number of a bipolar fuzzy graph is also discussed.


title: Management Of Patients With Hepatitis B Virus Reactivation Post–DAA Treatment Of Chronic Hepatitis C Virus Infection In HCV–HBV Coinfected Patients With Pretreatment HBeAg Seroconversion And Early Degree Of Hepatic Fibrosis

abstract: Background and aim ::: Hepatitis C virus (HCV)-HBV coinfection is a significant health problem with rapid progression of liver disease without precise diagnosis and treatment. We aimed in this study to identify if there were any role of HBV antiviral therapy in patients with HBV reactivation after direct-acting antiviral therapy in HCV-HBV coinfected patients. ::: ::: ::: Methods ::: A prospective random study was carried out on 140 patients presenting with chronic HCV and chronic HBV coinfection. All patients had pretreatment HBeAg seroconversion, HBV DNA <2,000 IU/mL, normal liver enzymes, and F0/F1 hepatic fibrosis. They treated with sofosbuvir 400 mg and daklatasvir 60 mg once daily for 3 months. All patients underwent pretreatment hepatic fibrosis assessment using Fibro Scan and laboratory investigations: platelet count, liver-function tests, quantitative HCV PCR, HBsAg, HBc IgG, HBeAg, and HBeAb. All patients were followed up at 1, 3, 6, and 12 months from the start of HCV therapy. ::: ::: ::: Results ::: The study enrolled 140 HCV-HBV coinfected patients: 55% were F0 and the rest F1. All our patients had negative HCV PCR at 1 month posttreatment and had achieved sustained virologic response with negative HCV PCR 3 months after treatment end. Four patients showed HBV reactivation with raised HBV DNA PCR and liver enzymes. Their mean age was 23.7±2.7 years, and three were male. Regarding patients with HBV reactivation, at 12 months posttreatment they showed significant decreases in liver enzymes, bilirubin, and INR, with increased platelet count (P=0.001), each with undetectable HBV PCR (P=0.001). ::: ::: ::: Conclusion ::: HBV-HCV coinfected patients with no/mild hepatic fibrosis, HBeAg seroconversion, and HBV DNA <2,000 IU/mL can complete direct-acting antiviral therapy without HBV antiviral treatment with close monitoring.


title: Ride comfort evaluations on electric vehicle conversion and improvement using Magnetorheological semi active suspension system

abstract: This paper presents the development of 7 degrees of freedom of ride model of a passenger vehicle to study the ride comfort performance of passenger vehicle if converted into an electric vehicle. The data of Malaysian made passenger vehicle were used in this study by assuming that the vehicle is going to be converted into an electric vehicle. The simulation results from the developed simulation model are validated with the experimental results. Ride test known as pitch mode tests were conducted to validate the reliability of the developed simulation model. The validated simulation model was used to evaluate the vehicle ride comfort performance when converted into an electric vehicle. The validated simulation model was also integrated with the semi active suspension system in order to improve the EV conversion ride comfort performance. It was found that the EV conversion's ride comfort was not significantly affected from the modifications and the used of semi active suspension system can be an alternatives in further improving EV conversion ride comfort.


title: Communication synthesis techniques for multiprocessor systems

abstract: This paper presents an interactive communication synthesis approach for multiprocessor systems. The aim of the approach consists in mapping a high level specification into a modular and flexible target architecture. The input specification is composed of a set of finite state machines that communicate via a procedural call mechanism. If we assume that the communication critical part is done through a shared memory, this approach allows us to refine the communication structures (interfaces, controllers) in order to reach an operational model easily mappable onto the target architecture. While the choice of communication protocols influences what the best partition is, the target architecture is extendible in order to minimize the communication cost that can be added by the partitioning. The proposed approach is validated through the design of a communication controller.


title: Smart Elderly Home Monitoring System with an Android Phone

abstract: Falls, heart attack and stroke are among the leading causes of hospitalization for the elderly and illness individual. The chances of surviving a fall, heart attack or stroke are much greater if the senior gets help within an hour. In this project, a smart elderly home monitoring system (SEHMS) is designed and developed. An Android-based smart phone with 3-axial accelerometer is used as the telehealth device which could detect a fall of the carrier. The smart phone is then connected to the monitoring system by using the TCP/IP networking method via Wi-Fi. A graphical user interface (GUI) is developed as the monitoring system which exhibits the information gathered from the system. In addition, the concept of a remote panic button has been tested and implemented in this project by using the same android based smart phone. With the developed system, elderly and chronically ill patients could stay independently in their own home with care facilities and secure in the knowledge that they are being monitored.


title: First-class phantom types

abstract: Classical phantom types are datatypes in which type constraints are expressed using type variables that do not appear in the datatype cases themselves. They can be used to embed typed languages into Haskell or ML. However, while such encodings guarantee that only wellformed data can be constructed, they do not permit type-safe deconstruction without additional tagging and run-time checks. We introduce first-class phantom types, which make such constraints explicit via type equations. Examples of first-class phantom types include typed type representations and typed higher-order abstract syntax trees. These types can be used to support typed generic functions, dynamic typing, and staged compilation in higher-order, statically typed languages such as Haskell or Standard ML. In our system, type constraints can be equations between type constructors as well as type functions of higher-order kinds. We prove type soundness and decidability for a Haskell-like language extended by first-class phantom types.


Experimental and theoretical analysis of the electrical properties behavior with the temperature of organic thin-film transistors based on tetraphenyldibenzoperiflanthene deposited at different substrate temperatures


title: Coping and Well-Being in Parents of Children with Autism Spectrum Disorders (ASD)

abstract: This study examined psychological well-being and coping in parents of children with ASD and parents of typically developing children. 73 parents of children with ASD and 63 parents of typically developing children completed a survey. Parents of children with ASD reported significantly more parenting stress symptoms (i.e., negative parental self-views, lower satisfaction with parent–child bond, and experiences of difficult child behaviors), more depression symptoms, and more frequent use of Active Avoidance coping, than parents of typically developing children. Parents of children with ASD did not differ significantly in psychological well-being and coping when compared as according to child’s diagnosis. Study results reinforced the importance of addressing well-being and coping needs of parents of children with ASD.


title: GraphScape: A Model for Automated Reasoning about Visualization Similarity and Sequencing

abstract: We present GraphScape, a directed graph model of the vi- sualization design space that supports automated reasoning about visualization similarity and sequencing. Graph nodes represent grammar-based chart specifications and edges rep- resent edits that transform one chart to another. We weight edges with an estimated cost of the difficulty of interpreting a target visualization given a source visualization. We con- tribute (1) a method for deriving transition costs via a partial ordering of edit operations and the solution of a resulting lin- ear program, and (2) a global weighting term that rewards consistency across transition subsequences. In a controlled experiment, subjects rated visualization sequences covering a taxonomy of common transition types. In all but one case, GraphScape's highest-ranked suggestion aligns with subjects' top-rated sequences. Finally, we demonstrate applications of GraphScape to automatically sequence visualization presen- tations, elaborate transition paths between visualizations, and recommend design alternatives (e.g., to improve scalability while minimizing design changes).


title: MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics

abstract: The performance of a photovoltaic (PV) array is affected by temperature, solar insolation, shading, and array configuration. Often, the PV arrays get shadowed, completely or partially, by the passing clouds, neighboring buildings and towers, trees, and utility and telephone poles. The situation is of particular interest in case of large PV installations such as those used in distributed power generation schemes. Under partially shaded conditions, the PV characteristics get more complex with multiple peaks. Yet, it is very important to understand and predict them in order to extract the maximum possible power. This paper presents a MATLAB-based modeling and simulation scheme suitable for studying the I-V and P-V characteristics of a PV array under a nonuniform insolation due to partial shading. It can also be used for developing and evaluating new maximum power point tracking techniques, especially for partially shaded conditions. The proposed models conveniently interface with the models of power electronic converters, which is a very useful feature. It can also be used as a tool to study the effects of shading patterns on PV panels having different configurations. It is observed that, for a given number of PV modules, the array configuration (how many modules in series and how many in parallel) significantly affects the maximum available power under partially shaded conditions. This is another aspect to which the developed tool can be applied. The model has been experimentally validated and the usefulness of this research is highlighted with the help of several illustrations. The MATLAB code of the developed model is freely available for download.


title: Approximate max-flow min-(multi)cut theorems and their applications

abstract: Consider the multicommodity flow problem in which the object is to maximize the sum of commodities routed. We prove the following approximate max-flow min-multicut theorem: min multicut < max flow < min multicut, O(log k) where k is the number of commodities. Our proof is constructive; it enables us to find a multicut within O(logk) of the max flow (and hence also the optimal multicut). In addition, the proof technique provides a unified framework in which one can also analyse the case of flows with specified demands of Leighton and Rao and Klein et al. and thereby obtain an improved bound for the latter problem.


title: RaceTrack: efficient detection of data race conditions via adaptive tracking

abstract: Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult to find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of a program and reports a warning whenever a suspicious pattern of activity has been observed. RaceTrack uses a novel hybrid detection algorithm and employs an adaptive approach that automatically directs more effort to areas that are more suspicious, thus providing more accurate warnings for much less over-head. A post-processing step correlates warnings and ranks code segments based on how strongly they are implicated in potential data races. We implemented RaceTrack inside the virtual machine of Microsoft's Common Language Runtime (product version v1.1.4322) and monitored several major, real-world applications directly out-of-the-box,without any modification. Adaptive tracking resulted in a slowdown ratio of about 3x on memory-intensive programs and typically much less than 2x on other programs,and a memory ratio of typically less than 1.2x. Several serious data race bugs were revealed, some previously unknown.


title: A 32.55-kHz, 472-nW, 120ppm/°C, fully on-chip, variation tolerant CMOS relaxation oscillator for a real-time clock application

abstract: This paper proposes an ultra-low power fully on-chip CMOS relaxation oscillator (ROSC) for a real-time clock application. The circuit has a distinctive feature in compensation architecture of a comparator's non-idealities caused by offset voltage and delay time. The ROSC can generate a stable and higher oscillation clock frequency without increasing power by using a low reference voltage and employing a novel compensation architecture for comparators. Measurement results in a 0.18-μm CMOS process demonstrated that the circuit can generate a stable clock frequency of 32.55 kHz. The power dissipation was 472 nW. Measured line regulation and temperature coefficient were 1.1%/V and 120ppm/°C, respectively.


title: FogFlow: Easy Programming of IoT Services Over Cloud and Edges for Smart Cities

abstract: Smart city infrastructure is forming a large scale Internet of Things (IoT) system with widely deployed IoT devices, such as sensors and actuators that generate a huge volume of data. Given this large scale and geo-distributed nature of such IoT systems, fog computing has been considered as an affordable and sustainable computing paradigm to enable smart city IoT services. However, it is still a major challenge for developers to program their services to leverage benefits of fog computing. Developers have to figure out many details, such as how to dynamically configure and manage data processing tasks over cloud and edges and how to optimize task allocation for minimal latency and bandwidth consumption. In addition, most of the existing fog computing frameworks either lack service programming models or define a programming model only based on their own private data model and interfaces; therefore, as a smart city platform, they are quite limited in terms of openness and interoperability. To tackle these problems, we propose a standard-based approach to design and implement a new fog computing-based framework, namely FogFlow , for IoT smart city platforms. FogFlow’s programming model allows IoT service developers to program elastic IoT services easily over cloud and edges. Moreover, it supports standard interfaces to share and reuse contextual data across services. To showcase how smart city use cases can be realized with FogFlow, we describe three use cases and implement an example application for anomaly detection of energy consumption in smart cities. We also analyze FogFlow’s performance based on microbenchmarking results for message propagation latency, throughput, and scalability.


title: An airborne synthetic aperture radar (SAR) experiment to support RADARSAT-2 ground moving target indication (GMTI)

abstract: As part of the preparatory work for the RADARSAT-2 moving object detection experiment (MODEX special operating mode), an airborne experiment was conducted at Canadian Forces Base Petawawa in July 1999 to study synthetic aperture radar (SAR) along-track interferometry (ATI) and SAR displaced phase centre antenna (DPCA) as candidate measurement techniques. The CV 580 SAR system operated by Environment Canada was configured as a two-aperture temporal interferometer, and data were collected over an experiment site that contained coordinated, global positioning system (GPS) monitored, moving targets. A robust ATI phase probability density function was identified and used in conjunction with a moving-target matched filter to extract and measure target motion. Corner reflector and vehicle velocities were measured for target speeds between 9 and 100 km/h to within 5% accuracy. SAR ATI and SAR DPCA performed equally well over this range of velocities, but ATI appears to be the more robust processing algorithm.


title: An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks

abstract: We propose a method for semi-supervised training of structured-output neural networks. Inspired by the framework of Generative Adversarial Networks (GAN), we train a discriminator network to capture the notion of a quality of network output. To this end, we leverage the qualitative difference between outputs obtained on the labelled training data and unannotated data. We then use the discriminator as a source of error signal for unlabelled data. This effectively boosts the performance of a network on a held out test set. Initial experiments in image segmentation demonstrate that the proposed framework enables achieving the same network performance as in a fully supervised scenario, while using two times less annotations.


title: Theory and measurement of generalized problematic Internet use: A two-step approach

abstract: This study tested an updated cognitive-behavioral model of generalized problematic Internet use and reports results of a confirmatory analysis of the Generalized Problematic Internet Use Scale 2 (GPIUS2). Overall, the results indicated that a preference for online social interaction and use of the Internet for mood regulation, predict deficient self-regulation of Internet use (i.e., compulsive Internet use and a cognitive preoccupation with the Internet). In turn, deficient self-regulation was a significant predictor of the extent to which one's Internet use led to negative outcomes. Results indicated the model fit the data well and variables in the model accounted for 27% of the variance in mood regulation scores, 65% of variance in participants' deficient self-regulation scores, and 61% of variance in the negative outcome scores.


title: Adversarial Feature Learning

abstract: The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.


title: GenERRate: Generating Errors for Use in Grammatical Error Detection

abstract: This paper explores the issue of automatically generated ungrammatical data and its use in error detection, with a focus on the task of classifying a sentence as grammatical or ungrammatical. We present an error generation tool called GenERRate and show how GenERRate can be used to improve the performance of a classifier on learner data. We describe initial attempts to replicate Cambridge Learner Corpus errors using GenERRate.


title: Password sharing: implications for security design based on social practice

abstract: Current systems for banking authentication require that customers not reveal their access codes, even to members of the family. A study of banking and security in Australia shows that the practice of sharing passwords does not conform to this requirement. For married and de facto couples, password sharing is seen as a practical way of managing money and a demonstration of trust. Sharing Personal Identification Numbers (PINs) is a common practice among remote indigenous communities in Australia. In areas with poor banking access, this is the only way to access cash. People with certain disabilities have to share passwords with carers, and PIN numbers with retail clerks. In this paper we present the findings of a qualitative user study of banking and money management. We suggest design criteria for banking security systems, based on observed social and cultural practices of password and PIN number sharing.


title: Inverted files versus signature files for text indexing

abstract: Two well-known indexing methods are inverted files and signature files. We have undertaken a detailed comparison of these two approaches in the context of text indexing, paying particular attention to query evaluation speed and space requirements. We have examined their relative performance using both experimentation and a refined approach to modeling of signature files, and demonstrate that inverted files are distinctly superior to signature files. Not only can inverted files be used to evaluate typical queries in less time than can signature files, but inverted files require less space and provide greater functionality. Our results also show that a synthetic text database can provide a realistic indication of the behavior of an actual text database. The tools used to generate the synthetic database have been made publicly available


title: Feasibility of an innovative electronic mobile system to assist health workers to collect accurate, complete and timely data in a malaria control programme in a remote setting in Kenya

abstract: Background ::: The cornerstone of decision making aimed at improving health services is accurate and timely health information. The Ministry of Public Health and Sanitation in Kenya decided to pilot feasibility of Fionet, an innovation that integrates diagnostics, data capture and cloud services, in its malaria control programme to demonstrate usability and feasibility by primary level workers in a remote setting in Kenya.


title: RPL under mobility

abstract: This paper focuses on routing for vehicles getting access to infrastructure either directly or via multiple hops though other vehicles. We study Routing Protocol for Low power and lossy networks (RPL), a tree-based routing protocol designed for sensor networks. Many design elements from RPL are transferable to the vehicular environment. We provide a simulation performance study of RPL and RPL tuning in VANETs. More specifically, we seek to study the impact of RPL's various parameters and external factors (e.g., various timers and speeds) on its performance and obtain insights on RPL tuning for its use in VANETs.


title: Reliability and availability study on substation automation system based on IEC 61850

abstract: The introduction of IEC61850 standards for substation automation requires the specification of the reliability and availability performance parameter. Presently, the methodology to calculate and verify the parameters is not readily available. This paper proposed a methodology to calculate the parameters at device level, bay level and substation level. The paper will propose suitable monitoring and evaluation of those parameters for future improvement.


title: A correlative analysis process in a visual analytics environment

abstract: Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson's product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.


title: 3D Face Tracking and Texture Fusion in the Wild

abstract: We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos. With the use of a cascaded-regressor based face tracking and a 3D Morphable Face Model shape fitting, we obtain a semi-dense 3D face shape. We further use the texture information from multiple frames to build a holistic 3D face representation from the video frames. Our system is able to capture facial expressions and does not require any person-specific training. We demonstrate the robustness of our approach on the challenging 300 Videos in the Wild (300-VW) dataset. Our real-time fitting framework is available as an open source library at http://4dface.org.


title: Semi-Supervised Few-Shot Learning with Prototypical Networks

abstract: We consider the problem of semi-supervised few-shot classification (when the few labeled samples are accompanied with unlabeled data) and show how to adapt the Prototypical Networks to this problem. We first show that using larger and better regularized prototypical networks can improve the classification accuracy. We then show further improvements by making use of unlabeled data.


abstract: baxter p. & norman g. (2011) Self-assessment or self deception? A lack of association between nursing students’ self-assessment and performance. Journal of Advanced Nursing67(11), 2406–2413. ::: ::: Abstract ::: Aim. The aim of this study was to examine senior year nursing students’ ability to self-assess their performance when responding to simulated emergency situations. ::: ::: Background. Self-assessment is viewed as a critical skill in nursing and other health professional programmes. However, while students may spend considerable time completing self-assessments, there is little evidence that they actually acquire the skills to do so effectively. By contrast, a number of studies in medicine and elsewhere have cast doubt on the validity of self-assessment. ::: ::: Method. In 2007, a one-group pre-test, post-test design was used to answer the question, ‘How accurate are senior year nursing students in assessing their ability to respond to emergency situations in a simulated medical/surgical environment compared to observer assessment of their performance?’ A total of 27 fourth year nursing students from a university in Ontario were asked to complete a questionnaire before and after an objective structured clinical examination which assessed their ability to respond to emergency situations. Self-assessments were compared with observed performance. ::: ::: Findings. The experience of dealing with the simulated crisis situations significantly increased perceived confidence and perceived competence in dealing with emergency situations, although it did not affect self-perceived ability to communicate or collaborate. All but 1 of the 16 correlations between self-assessment and the objective structured clinical examination total scores were negative. Their self-assessment was also unrelated to several indices of experience in critical care settings. ::: ::: Conclusion. Self-assessment in nursing education to evaluate clinical competence and confidence requires serious reconsideration as our well-intentioned emphasis on this commonly used practice may be less than effective.


title: Evaluation of microRNAs-208 and 133a/b as differential biomarkers of acute cardiac and skeletal muscle toxicity in rats.

abstract: Conventional circulating biomarkers of cardiac and skeletal muscle (SKM) toxicity lack specificity and/or have a short half-life. MicroRNAs (miRNAs) are currently being assessed as biomarkers of tissue injury based on their long half-life in blood and selective expression in certain tissues. To assess the utility of miRNAs as biomarkers of cardiac and SKM injury, male Sprague-Dawley rats received a single dose of isoproterenol (ISO); metaproterenol (MET); allylamine (AAM); mitoxantrone (MIT); acetaminophen (APAP) or vehicle. Blood and tissues were collected from rats in each group at 4, 24 and 48h. ISO, MET, and AAM induced cardiac and SKM lesions and APAP induced liver specific lesions. There was no evidence of tissue injury with MIT by histopathology. Serum levels of candidate miRNAs were compared to conventional serum biomarkers of SKM/cardiac toxicity. Increases in heart specific miR-208 only occurred in rats with cardiac lesions alone and were increased for a longer duration than cardiac troponin and FABP3 (cardiac biomarkers). ISO, MET and AAM induced increases in MyL3 and skeletal muscle troponin (sTnl) (SKM biomarkers). MIT induced large increases in sTnl indicative of SKM toxicity, but sTnl levels were also increased in APAP-treated rats that lacked SKM toxicity. Serum levels of miR-133a/b (enriched in cardiac and SKM) increased following ISO, MET, AAM and MIT treatments but were absent in APAP-treated rats. Our results suggest that miR-133a/b are sensitive and specific markers of SKM and cardiac toxicity and that miR-208 used in combination with miR-133a/b can be used to differentiate cardiac from SKM toxicity.


title: MEDICAL EDUCATION QUARTET Medical education quartet Communication and information technology in medical education

abstract: Summary The past few years have seen rapid advances in communication and information technology (C&IT), and the pervasion of the worldwide web into everyday life has important implications for education. Most medical schools provide extensive computer networks for their students, and these are increasingly becoming a central component of the learning and teaching environment. Such advances bring new opportunities and challenges to medical education, and are having an impact on the way that we teach and on the way that students learn, and on the very design and delivery of the curriculum. The plethora of information available on the web is overwhelming, and both students and staff need to be taught how to manage it effectively. Medical schools must develop clear strategies to address the issues raised by these technologies. We describe how medical schools are rising to this challenge, look at some of the ways in which communication and information technology can be used to enhance the learning and teaching environment, and discuss the potential impact of future developments on medical education.


title: Perceptually Aware Image Retargeting for Mobile Devices

abstract: Retargeting aims at adapting an original high-resolution photograph/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile platforms (especially when retargeting videos). Typically, only low-level visual features are exploited, and human visual perception is not well encoded. In this paper, we propose a novel retargeting framework that rapidly shrinks a photograph/video by leveraging human gaze behavior. Specifically, we first derive a geometry-preserving graph ranking algorithm, which efficiently selects a few salient object patches to mimic the human gaze shifting path (GSP) when viewing a scene. Afterward, an aggregation-based CNN is developed to hierarchically learn the deep representation for each GSP. Based on this, a probabilistic model is developed to learn the priors of the training photographs that are marked as aesthetically pleasing by professional photographers. We utilize the learned priors to efficiently shrink the corresponding GSP of a retargeted photograph/video to maximize its similarity to those from the training photographs. Extensive experiments have demonstrated that: 1) our method requires less than 35 ms to retarget a photograph (or a video frame) on popular iOS/Android devices, which is orders of magnitude faster than the conventional retargeting algorithms; 2) the retargeted photographs/videos produced by our method significantly outperform those of its competitors based on a paired-comparison-based user study; and 3) the learned GSPs are highly indicative of human visual attention according to the human eye tracking experiments.


title: Deja Vu: Motion Prediction in Static Images

abstract: This paper proposes motion prediction in single still images by learning it from a set of videos. The building assumption is that similar motion is characterized by similar appearance. The proposed method learns local motion patterns given a specific appearance and adds the predicted motion in a number of applications. This work (i) introduces a novel method to predict motion from appearance in a single static image, (ii) to that end, extends of the Structured Random Forest with regression derived from first principles, and (iii) shows the value of adding motion predictions in different tasks such as: weak frame-proposals containing unexpected events, action recognition, motion saliency. Illustrative results indicate that motion prediction is not only feasible, but also provides valuable information for a number of applications.


title: Vapor transport deposition of antimony selenide thin film solar cells with 7.6% efficiency

abstract: Antimony selenide is an emerging promising thin film photovoltaic material thanks to its binary composition, suitable bandgap, high absorption coefficient, inert grain boundaries and earth-abundant constituents. However, current devices produced from rapid thermal evaporation strategy suffer from low-quality film and unsatisfactory performance. Herein, we develop a vapor transport deposition technique to fabricate antimony selenide films, a technique that enables continuous and low-cost manufacturing of cadmium telluride solar cells. We improve the crystallinity of antimony selenide films and then successfully produce superstrate cadmium sulfide/antimony selenide solar cells with a certified power conversion efficiency of 7.6%, a net 2% improvement over previous 5.6% record of the same device configuration. We analyze the deep defects in antimony selenide solar cells, and find that the density of the dominant deep defects is reduced by one order of magnitude using vapor transport deposition process. Antimony selenide possess several advantages for solar cell applications but state-of-the-art vapor transport deposition methods suffer from poor film quality. Here Wen et al. develop a fast and cheap method to reduce the defect density by 10 times and achieve a certified power conversion efficiency of 7.6%.


title: Large-Scale Video Retrieval Using Image Queries

abstract: Retrieving videos from large repositories using image queries is important for many applications, such as brand monitoring or content linking. We introduce a new retrieval architecture, in which the image query can be compared directly with database videos—significantly improving retrieval scalability compared with a baseline system that searches the database on a video frame level. Matching an image to a video is an inherently asymmetric problem. We propose an asymmetric comparison technique for Fisher vectors and systematically explore query or database items with varying amounts of clutter, showing the benefits of the proposed technique. We then propose novel video descriptors that can be compared directly with image descriptors. We start by constructing Fisher vectors for video segments, by exploring different aggregation techniques. For a database of lecture videos, such methods obtain a two orders of magnitude compression gain with respect to a frame-based scheme, with no loss in retrieval accuracy. Then, we consider the design of video descriptors, which combine Fisher embedding with hashing techniques, in a flexible framework based on Bloom filters. Large-scale experiments using three datasets show that this technique enables faster and more memory-efficient retrieval, compared with a frame-based method, with similar accuracy. The proposed techniques are further compared against pre-trained convolutional neural network features, outperforming them on three datasets by a substantial margin.


title: “Hands On” Visual Recognition for Visually Impaired Users

abstract: Blind or visually impaired (BVI) individuals are capable of identifying an object in their hands by combining the available visual cues (if available) with manipulation. It is harder for them to associate the object with a specific brand, a model, or a type. Starting from this observation, we propose a collaborative system designed to deliver visual feedback automatically and to help the user filling this semantic gap. Our visual recognition module is implemented by means of an image retrieval procedure that provides real-time feedback, performs the computation locally on the device, and is scalable to new categories and instances. We carry out a thorough experimental analysis of the visual recognition module, which includes a comparative analysis with the state of the art. We also present two different system implementations that we test with the help of BVI users to evaluate the technical soundness, the usability, and the effectiveness of the proposed concept.


title: Long short-term memory based operation log anomaly detection

abstract: Long short-term memory (LSTM) architecture is an important approach for capturing long-range temporal dependencies in sequences of arbitrary length. Moreover, stacked-LSTM (S-LSTM: formed by adding recurrent LSTM layer to the existing LSTM network in hidden layer) has capability to learn temporal behaviors quickly with sparse representations. To apply this to anomaly detection, we model the operation log samples of normal and anomalous events occurred in 1 minute time interval as time-series with the aim to detect and classify the events as either normal or anomalous. To select an appropriate LSTM network, experiments are conducted for various network parameters and network structures with the dataset provided by Cyber Security Data Mining Competition (CDMC2016). The experiments are run up to 1000 epochs with learning rate in the range [0.01-05]. S-LSTM network architecture has showed its strength by achieving the highest accuracy 0.996 with false positive rate 0.02 on the provided real-world test data by CDMC2016.


title: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

abstract: There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model - uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.


title: HDRF: Stream-Based Partitioning for Power-Law Graphs

abstract: Balanced graph partitioning is a fundamental problem that is receiving growing attention with the emergence of distributed graph-computing (DGC) frameworks. In these frameworks, the partitioning strategy plays an important role since it drives the communication cost and the workload balance among computing nodes, thereby affecting system performance. However, existing solutions only partially exploit a key characteristic of natural graphs commonly found in the real-world: their highly skewed power-law degree distributions. In this paper, we propose High-Degree (are) Replicated First (HDRF), a novel streaming vertex-cut graph partitioning algorithm that effectively exploits skewed degree distributions by explicitly taking into account vertex degree in the placement decision. We analytically and experimentally evaluate HDRF on both synthetic and real-world graphs and show that it outperforms all existing algorithms in partitioning quality.