title: Computing the entropy of user navigation in the web

abstract: Navigation through the web, colloquially known as "surfing", is one of the main activities of users during web interaction. When users follow a navigation trail they often tend to get disoriented in terms of the goals of their original query and thus the discovery of typical user trails could be useful in providing navigation assistance. Herein, we give a theoretical underpinning of user navigation in terms of the entropy of an underlying Markov chain modelling the web topology. We present a novel method for online incremental computation of the entropy and a large deviation result regarding the length of a trail to realize the said entropy. We provide an error analysis for our estimation of the entropy in terms of the divergence between the empirical and actual probabilities. We then indicate applications of our algorithm in the area of web data mining. Finally, we present an extension of our technique to higher-order Markov chains by a suitable reduction of a higher-order Markov chain model to a first-order one.


WEIGHTED TEMPORAL PATTERN MINING WITH DIMENSIONALITY REDUCTION USING MODIFIED AFCM TECHNIQUE


title: External evaluation measures for subspace clustering

abstract: Knowledge discovery in databases requires not only development of novel mining techniques but also fair and comparable quality assessment based on objective evaluation measures. Especially in young research areas where no common measures are available, researchers are unable to provide a fair evaluation. Typically, publications glorify the high quality of one approach only justified by an arbitrary evaluation measure. However, such conclusions can only be drawn if the evaluation measures themselves are fully understood. In this paper, we provide the basis for systematic evaluation in the emerging research area of subspace clustering. We formalize general quality criteria for subspace clustering measures not yet addressed in the literature. We compare the existing external evaluation methods based on these criteria and pinpoint limitations. We propose a novel external evaluation measure which meets the requirements in form of quality properties. In thorough experiments we empirically show characteristic properties of evaluation measures. Overall, we provide a set of evaluation measures that fulfill the general quality criteria as recommendation for future evaluations. All measures and datasets are provided on our website and are integrated in our evaluation framework.


title: Michelangelo, the Last Judgment fresco, Saint Bartholomew and the Golden Ratio

abstract: Art and anatomy were particularly closely intertwined during the Renaissance period and numerous painters and sculptors expressed themselves in both fields. Among them was Michelangelo Buonarroti (1475–1564), who is renowned for having produced some of the most famous of all works of art, the frescoes on the ceiling and on the wall behind the altar of the Sistine Chapel in Rome. Recently, a unique association was discovered between one of Michelangelo's most celebrated works (The Creation of Adam fresco) and the Divine Proportion/Golden Ratio (GR) (1.6). The GR can be found not only in natural phenomena but also in a variety of human-made objects and works of art. Here, using Image-Pro Plus 6.0 software, we present mathematical evidence that Michelangelo also used the GR when he painted Saint Bartholomew in the fresco of The Last Judgment, which is on the wall behind the altar. This discovery will add a new dimension to understanding the great works of Michelangelo Buonarroti. Clin. Anat. 28:967–971, 2015. © 2015 Wiley Periodicals, Inc.


Modeling Hebb Learning Rule for Unsupervised Learning


title: Distributed Video Streaming with Forward Error Correction

abstract: With the explosive growth of video applications over the Internet, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. Many use techniques from source and channel coding, or implement transport protocols, or modify system architectures in order to deal with delay, loss, and time-varying nature of the Internet. In our previous work , we proposed a framework with a receiver driven protocol to coordinate simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. The receiver-driven protocol employs two algorithms: rate allocation and packet partition. The rate allocation algorithm determines the sending rate for each sender; the packet partition algorithm ensures no senders send the same packets, and at the same time, minimizes the probability of late packets. In this paper, we propose a novel rate allocation scheme to be used with Forward Error Correction (FEC) in order to minimize the probability of packet loss in bursty loss environments such as those caused by network congestion. Using both simulations and actual Internet experiments, we demonstrate the effectiveness of our rate allocation scheme in reducing packet loss, and hence, achieving higher visual quality for the streamed video.


title: SNF2 histone linker PHD RING helicase related Has_circ_0001649 as a diagnostic and prognostic biomarker in solid cancer

abstract: BACKGROUND ::: Dysregulated circular RNAs have been implicated in the pathogenesis of cancer. Recent studies indicate that has_circ_0001649 lowly expressed in multiple types of cancer. The purpose of this study is to investigate the roles of has_circ_0001649 as a diagnostic and prognostic marker for Chinese patients with cancer. ::: ::: ::: METHODS ::: Adhering to preferred reporting items for systematic reviews and meta-analyses guidelines, systematic literature searches were performed using Pubmed, Embase, and the web of Science to retrieve articles fulfilled all inclusion criteria. The significance of has_circ_0001649 in diagnosis and prognosis of cancer patients were evaluated. Meta-Disc 1. 4 and STATA 12. 0 were used to analyze the data from collected studies. ::: ::: ::: RESULTS ::: Eleven articles with 761 patients were included in present meta-analysis, of which 4 were about diagnosis, 5 were about prognosis, and 6 were about tumor differentiation grade. For the diagnostic value of has_circ_0001649, the pooled results for sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio with their 95% confidential intervals were 0. 78 (0. 73-0. 83), 0. 75 (0. 70-0. 80), 3. 17 (2. 56-3. 93), 0. 29 (0. 23-0. 36), and 11. 41 (7. 80-16. 7), respectively. The area under the curve of summary receiver operator characteristic was 0.8408 (Q = 0. 7725). Meanwhile, the result showed no obvious publication bias in this analysis for the P-value of Deeks' test was .489. For the prognostic value, the pooled hazard ratio for overall survival was 0.45 (0.324-0.626). Lower expression of has_circ_0001649 was also prone to lower tumor differentiation grade (odds ratio = 2.58, P < .0001). ::: ::: ::: CONCLUSIONS ::: Has_circ_0001649 could be used as a potential biomarker for diagnosis and prognosis in solid cancer. Further prospective studies are required to validate its clinical application.


title: Direct hand manipulation of constrained virtual objects

abstract: We propose a direct object manipulation system that a user can manipulate objects constrained to each other by his or her hands. Because there is no haptic feedback to a user, manipulation of constrained objects is much harder than that of a simple rigid body. In addition, many physics simulator usually suffers from instability in handling the constrained objects in a virtual system. We solve the problem by adding another micro simulator which enforce the constraint between the objects, and guarantees that the rendered image of an object and its physics counterpart always satisfy the given constraint. The experiment shows that the proposed method makes a user manipulate constrained objects with no difficulties in the way that he or she manipulates such objects in the real world.


title: Assisted behavior driven development using natural language processing

abstract: In Behavior Driven Development (BDD), acceptance tests provide the starting point for the software design flow and serve as a basis for the communication between designers and stakeholders. In this agile software development technique, acceptance tests are written in natural language in order to ensure a common understanding between all members of the project. As a consequence, mapping the sentences to actual source code is the first step of the design flow, which is usually done manually. ::: ::: However, the scenarios described by the acceptance tests provide enough information in order to automatize the extraction of both the structure of the implementation and the test cases. In this work, we propose an assisted flow for BDD where the user enters into a dialog with the computer which suggests code pieces extracted from the sentences. For this purpose, natural language processing techniques are exploited. This allows for a semi-automatic transformation from acceptance tests to source code stubs and thus provides a first step towards an automatization of BDD.


title: Electrostatic separation of metals and plastics from granular industrial wastes

abstract: The paper presents the main conclusions of authors' 15-year involvement in theoretical study, laboratory experimentation, equipment development and industry application of electrostatic separation technologies. The roll-type separator with combined corona-electrostatic field is the most advantageous technical solution when the purpose is to isolate conductive particles from nonconductive ones. The theoretical analysis and the laboratory tests of electrostatic separation point out the multitude of factors which influence the efficiency of this equipment: the configuration of the electric field, the size and the shape of the processed particles, the speed and the radius of the rotating roll electrode and the environmental conditions. The optimum electrode arrangement and operating parameters (high-voltage level, roll speed, feed rate) differ from one application to another. In-plant tests are recommended for the fine tuning of the electrostatic separation technology.


title: MICA - A new generation of versatile instruments in robotic surgery

abstract: Robotic surgery systems are highly complex and expensive pieces of equipment. Demands for lower cost of care can be met if these systems are employable in a flexible manner for a large variety of procedures. To protect the initial investment the capabilities of a robotic system need to be expandable as new tasks arise.


title: Hyaluronic acid: A key molecule in skin aging

abstract: Skin aging is a multifactorial process consisting of two distinct and independent mechanisms: intrinsic and extrinsic aging. Youthful skin retains its turgor, resilience and pliability, among others, due to its high content of water. Daily external injury, in addition to the normal process of aging, causes loss of moisture. The key molecule involved in skin moisture is hyaluronic acid (HA) that has unique capacity in retaining water. There are multiple sites for the control of HA synthesis, deposition, cell and protein association and degradation, reflecting the complexity of HA metabolism. The enzymes that synthesize or catabolize HA and HA receptors responsible for many of the functions of HA are all multigene families with distinct patterns of tissue expression. Understanding the metabolism of HA in the different layers of the skin and the interactions of HA with other skin components will facilitate the ability to modulate skin moisture in a rational manner.


title: TrBagg: A Simple Transfer Learning Method and its Application to Personalization in Collaborative Tagging

abstract: The aim of transfer learning is to improve prediction accuracy on a target task by exploiting the training examples for tasks that are related to the target one. Transfer learning has received more attention in recent years, because this technique is considered to be helpful in reducing the cost of labeling. In this paper, we propose a very simple approach to transfer learning: TrBagg, which is the extension of bagging. TrBagg is composed of two stages: Many weak classifiers are first generated as in standard bagging, and these classifiers are then filtered based on their usefulness for the target task. This simplicity makes it easy to work reasonably well without severe tuning of learning parameters. Further, our algorithm equips an algorithmic scheme to avoid negative transfer. We applied TrBagg to personalized tag prediction tasks for social bookmarks Our approach has several convenient characteristics for this task such as adaptation to multiple tasks with low computational cost.


title: Understanding the Great Recession Using Machine Learning Algorithms

abstract: Nyman and Ormerod (2017) show that the machine learning technique of random forests has the potential to give early warning of recessions. Applying the approach to a small set of financial variables and replicating as far as possible a genuine ex ante forecasting situation, over the period since 1990 the accuracy of the four-step ahead predictions is distinctly superior to those actually made by the professional forecasters. Here we extend the analysis by examining the contributions made to the Great Recession of the late 2000s by each of the explanatory variables. We disaggregate private sector debt into its household and non-financial corporate components. We find that both household and non-financial corporate debt were key determinants of the Great Recession. We find a considerable degree of non-linearity in the explanatory models. In contrast, the public sector debt to GDP ratio appears to have made very little contribution. It did rise sharply during the Great Recession, but this was as a consequence of the sharp fall in economic activity rather than it being a cause. We obtain similar results for both the United States and the United Kingdom.


title: Microscopy cell counting and detection with fully convolutional regression networks

abstract: This paper concerns automated cell counting and detection in microscopy images. The approach we take is to use convolutional neural networks (CNNs) to regress a cell spatial density map across the ...


title: Printflatables: Printing Human-Scale, Functional and Dynamic Inflatable Objects

abstract: Printflatables is a design and fabrication system for human-scale, functional and dynamic inflatable objects. We use inextensible thermoplastic fabric as the raw material with the key principle of introducing folds and thermal sealing. Upon inflation, the sealed object takes the expected three dimensional shape. The workflow begins with the user specifying an intended 3D model which is decomposed to two dimensional fabrication geometry. This forms the input for a numerically controlled thermal contact iron that seals layers of thermoplastic fabric. In this paper, we discuss the system design in detail, the pneumatic primitives that this technique enables and merits of being able to make large, functional and dynamic pneumatic artifacts. We demonstrate the design output through multiple objects which could motivate fabrication of inflatable media and pressure-based interfaces.


title: Frontomaxillary facial angle in trisomy 21 fetuses at 16–24 weeks of gestation

abstract: Objectives ::: ::: To establish a normal range for the frontomaxillary facial (FMF) angle by three-dimensional (3D) ultrasound imaging and to examine the FMF angle in trisomy 21 fetuses at 16–24 weeks of gestation. ::: ::: ::: ::: Methods ::: ::: We measured the FMF angle using 3D volumes of the fetal profile obtained with the transducer parallel to the long axis of the nose and at 45° to the palate, which had been acquired from 150 normal fetuses and 23 fetuses with trisomy 21. ::: ::: ::: ::: Results ::: ::: In the normal group there was no significant association between the FMF angle and gestational age; the mean FMF angle was 83.9° (range, 76.9–90.2°) and the 95th centile was 88.5°. In 15 (65.2%) of the fetuses with trisomy 21 the FMF angle was greater than 88.5°. Repeatability studies demonstrated that in 95% of cases the difference between two measurements of FMF angle by the same operator and different operators was less than 5°. ::: ::: ::: ::: Conclusions ::: ::: In the majority of second-trimester fetuses with trisomy 21 the FMF angle is increased. Copyright © 2008 ISUOG. Published by John Wiley & Sons, Ltd.


title: Cold pressor test using strain-gauge plethysmography

abstract: This laboratory activity is designed to teach students how to measure forearm muscle blood flow (FBF) to describe the mechanisms of peripheral blood flow thermal regulation in healthy subjects. The cold pressor test (CPT) is the clinical procedure used in the experiment to induce arterial vasoconstriction. Strain-gauge plethysmography is applied on the patient's forearm to noninvasive monitor vasoconstriction effects on local blood perfusion and physiological parameters such as blood pressure (BP) and heart rate (HR). Patients with an altered peripheral vascular resistance (e.g., in hypertension) have different responses to the CPT from healthy subjects. To date, experimental evidence remains unexplained, as we do not know if the BP and HR increase is caused by a decrease in flow rate or an increase in peripheral vascular resistance during the test. To clarify this situation, we have to quantify the parameter we assume is being conditioned by the regulatory physiological intervention, i.e., peripheral vascular resistance. Peripheral vascular resistance quantification can be calculated as the ratio between muscle flow and mean arterial pressure. Students will learn how to apply the instrumental procedure to collect and analyze data before, during, and after the CPT and to describe the physiological responses of the peripheral vascular system to external stressors. They will also learn how to distinguish healthy from pathological responses on the basis of how sympathetic nervous system reactions influence the biomechanics of peripheral vessels.


title: Building recognition from aerial images combining segmentation and shadow

abstract: We propose a novel building detection algorithm for processing high-resolution aerial images. Our algorithm exploits the building-shadow geometric relationship according to lighting models, making it suitable to detect buildings in a more general setting, possibly with irregular shapes. We use image segmentation to provide spatial support for both building and shadow detections. A novel confidence method is developed to label building and shadow segments by jointly reasoning: 1) the likelihood of shadows; 2) building-shadow configuration, and 3) building-building similarity. Our method is tested on a wide range of aerial images. Qualitative and quantitative results demonstrate its effectiveness on detecting and extracting buildings from background clutter.


DEL: Deep Embedding Learning for Efficient Image Segmentation


title: Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks

abstract: Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones. We propose a generative adversarial network (GAN) approach for predicting desirable 3D dose distributions that eschews the previous paradigms of site-specific feature engineering and predicting low-dimensional representations of the plan. Experiments on a dataset of oropharyngeal cancer patients show that our approach significantly outperforms previous methods on several clinical satisfaction criteria and similarity metrics.


title: Pedigree-ing Your Big Data: Data-Driven Big Data Privacy in Distributed Environments

abstract: This paper introduces a general framework for supporting data-driven privacy-preserving big data management in distributed environments, such as emerging Cloud settings. The proposed framework can be viewed as an alternative to classical approaches where the privacy of big data is ensured via security-inspired protocols that check several (protocol) layers in order to achieve the desired privacy. Unfortunately, this injects considerable computational overheads in the overall process, thus introducing relevant challenges to be considered. Our approach instead tries to recognize the "pedigree" of suitable summary data representatives computed on top of the target big data repositories, hence avoiding computational overheads due to protocol checking. We also provide a relevant realization of the framework above, the so-called Data-dRIven aggregate-PROvenance privacy-preserving big Multidimensional data (DRIPROM) framework, which specifically considers multidimensional data as the case of interest.


Iterative Residual Network for Deep Joint Image Demosaicking and Denoising.


Systematic study of the lowest energy states of Aun (n=1–4) using DFT


title: Does age or gender influence quality of life in children with atopic dermatitis?

abstract: Summary ::: ::: Background. Quality of life (QoL) is impaired in children with atopic dermatitis (AD) but the various aspects of QoL may not be equally affected. ::: ::: ::: ::: Aim. To evaluate if age and gender affect some aspects of QoL in children with AD. ::: ::: ::: ::: Method. The Children’s Dermatology Life Quality Index (CDLQI) was used for all children with AD seen at a paediatric dermatology clinic over a 3-year period. Disease severity was assessed using the SCORing Atopic Dermatitis (SCORAD) and Nottingham Eczema Severity Score (NESS) tools. ::: ::: ::: ::: Results. We reviewed CDLQI in 133 children (70 male and 63 female; age range 5–16 years) with AD. Itch, sleep disturbance, treatment and swimming/sports were the four aspects of QoL issues that were most commonly affected, in 50%, 47%, 38% and 29% of patients, respectively. Problems with interpersonal issues (friendship, school/holidays, and teasing/bullying) occurred in only a minority of children (≤ 10%). Girls had more problems with issues of clothes and shoes than did boys (OR = 2.86, 95% CI 1.05–8.00; P = 0.038). Significant itch and sleep disturbance affected both genders similarly but were generally more common in children ≤ 10 years (itch: OR = 2.31, 95% CI 1.04–5.14; P = 0.039; and sleep: OR = 2.31, 95% CI 1.05–5.13; P = 0.037). ::: ::: ::: ::: Conclusions. Not all aspects of QoL are affected equally in children with AD. The disease seems to affect personal domains of itch and sleep more than the interpersonal issues. Age and gender are relevant factors in QoL, with the issue of clothes/shoes being more troublesome for girls. itch and sleep disturbance seem to be a problem mainly in younger children.


title: Determining position of the inferior alveolar nerve via anatomical dissection and micro-computed tomography in preparation for dental implants.

abstract: BACKGROUND ::: The inferior alveolar nerve courses anteriorly within the mandibular canal, providing sensory nerve supply to the mandibular teeth, the buccal mucosa, the gingiva, and the soft tissues of the lower lip and chin. To avoid damage to this nerve and resulting sensory disturbances, its exact location must be known before placement of a dental implant. Imaging modalities currently used to visualize the position of the inferior alveolar nerve may be inaccurate. This study was undertaken to determine the accuracy of micro-computed tomography (micro-CT) for determining the position of this nerve. ::: ::: ::: METHODS ::: Micro-CT images of 16 cadaveric hemimandibles were acquired at a slice thickness of 154 µm. Each hemimandible was then sectioned at 6 predetermined locations between the ramus and the mental foramen, to yield 5 corticocancellous bone specimens. The superior, inferior, buccal and lingual distances for bone surrounding the mandibular canal were measured by direct digital caliper and compared with corresponding micro-CT measurements obtained by 3-dimensional visualization and modelling software. ::: ::: ::: RESULTS ::: There was substantial variability in the vertical position of the inferior alveolar nerve, depending on the length of time since tooth loss and the movement of the nerve from the lingual to buccal position as it courses from the posterior to the anterior aspect of the mandible. However, digital caliper and corresponding micro-CT measurements of the thickness of bone surrounding the inferior alveolar nerve were highly consistent, and no significant differences were detected between the two methods of measurement. ::: ::: ::: CLINICAL SIGNIFICANCE ::: The findings reported here confirm the accuracy of micro-CT in determining the location of the inferior alveolar nerve during planning for placement of dental implants in the human mandible.


title: Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"

abstract: Computer classification of facial expressions requires large amounts of data and this data needs to reflect the diversity of conditions seen in real applications. Public datasets help accelerate the progress of research by providing researchers with a benchmark resource. We present a comprehensively labeled dataset of ecologically valid spontaneous facial responses recorded in natural settings over the Internet. To collect the data, online viewers watched one of three intentionally amusing Super Bowl commercials and were simultaneously filmed using their webcam. They answered three self-report questions about their experience. A subset of viewers additionally gave consent for their data to be shared publicly with other researchers. This subset consists of 242 facial videos (168, 359 frames) recorded in real world conditions. The dataset is comprehensively labeled for the following: 1) frame-by-frame labels for the presence of 10 symmetrical FACS action units, 4 asymmetric (unilateral) FACS action units, 2 head movements, smile, general expressiveness, feature tracker fails and gender, 2) the location of 22 automatically detected landmark points, 3) self-report responses of familiarity with, liking of, and desire to watch again for the stimuli videos and 4) baseline performance of detection algorithms on this dataset. This data is available for distribution to researchers online, the EULA can be found at: http://www.affectiva.com/facial-expression-dataset-am-fed/.


title: Real-Time Autocalibration of MEMS Accelerometers

abstract: In this paper, the self-calibration of micromechanical acceleration sensors is considered, specifically, based solely on user-generated movement data without the support of laboratory equipment or external sources. The autocalibration algorithm itself uses the fact that under static conditions, the squared norm of the measured sensor signal should match the magnitude of the gravity vector. The resulting nonlinear optimization problem is solved using robust statistical linearization instead of the common analytical linearization for computing bias and scale factors of the accelerometer. To control the forgetting rate of the calibration algorithm, artificial process noise models are developed and compared with conventional ones. The calibration methodology is tested using arbitrarily captured acceleration profiles of the human daily routine and shows that the developed algorithm can significantly reject any misconfiguration of the acceleration sensor.


title: Face Alignment With Deep Regression

abstract: In this paper, we present a deep regression approach for face alignment. The deep regressor is a neural network that consists of a global layer and multistage local layers. The global layer estimates the initial face shape from the whole image, while the following local layers iteratively update the shape with local image observations. Combining standard derivations and numerical approximations, we make all layers able to backpropagate error differentials, so that we can apply the standard backpropagation to jointly learn the parameters from all layers. We show that the resulting deep regressor gradually and evenly approaches the true facial landmarks stage by stage, avoiding the tendency that often occurs in the cascaded regression methods and deteriorates the overall performance: yielding early stage regressors with high alignment accuracy gains but later stage regressors with low alignment accuracy gains. Experimental results on standard benchmarks demonstrate that our approach brings significant improvements over previous cascaded regression algorithms.


title: Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL

abstract: The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) 1 was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility.In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency.


title: Understanding the appeal of user‐generated media: a uses and gratification perspective

abstract: Purpose – User‐generated media (UGM) like YouTube, MySpace, and Wikipedia have become tremendously popular over the last few years. The purpose of this paper is to present an analytical framework for explaining the appeal of UGM.Design/methodology/approach – This paper is mainly theoretical due to a relative lack of empirical evidence. After an introduction on the emergence of UGM, this paper investigates in detail how and why people use UGM, and what factors make UGM particularly appealing, through a uses and gratifications perspective. Finally, the key elements of this study are summarized and the future research directions about UGM are discussed.Findings – This paper argues that individuals take with UGM in different ways for different purposes: they consume contents for fulfilling their information, entertainment, and mood management needs; they participate through interacting with the content as well as with other users for enhancing social connections and virtual communities; and they produce their...


title: Predictive safety based on track-before-detect for teleoperated driving through communication time delay

abstract: Teleoperated driving is known as a transient technology toward full autonomous driving in urban areas. However, this mobility concept suffers mainly from the communication time delay, which may result in safety hazards as well ::: as stop-and-go driving behavior in crowded inner-city areas. ::: This paper presents a novel active safety concept to assist the human operator of the teleoperated vehicle considering the communication time delay. The proposed system reacts not only to the actual driving hazards, but also to the upcoming hazards the human operator is not aware of because of time delay. ::: For this purpose, it predicts the future trajectories of dynamic objects in the vehicle surroundings using a stereo vision based track-before-detect approach and reacts autonomously to the predicted hazards through speed control. After each intervention, the human operator is informed about the autonomous intervention of the vehicle by a Human-Machine-Interface (HMI), having the ability to override this intervention. Results of the test drives show an overall increase of the safety by reduction of Time-To-Collision as well as an improvement of the acceptance of teleoperated driving through the reduction of the overall triggered deceleration during driving in urban areas.


title: FlowTime: Dynamic Scheduling of Deadline-Aware Workflows and Ad-Hoc Jobs

abstract: With rapidly increasing volumes of data to be processed in modern data analytics, it is commonplace to run multiple data processing jobs with inter-job dependencies in a datacenter cluster, typically as recurring data processing workloads. Such a group of inter-dependent data analytic jobs is referred to as a workflow, and may have a deadline due to its mission-critical nature. In contrast, non-recurring ad-hoc jobs are typically best-effort in nature, and rather than meeting deadlines, it is desirable to minimize their average job turnaround time. The state-of-the-art scheduling mechanisms focused on meeting deadlines for individual jobs only, and are oblivious to workflow deadlines. In this paper, we present FlowTime, a new system framework designed to make scheduling decisions for workflows so that their deadlines are met, while simultaneously optimizing the performance of ad-hoc jobs. To achieve this objective, we first adopt a divide-and-conquer strategy to transform the problem of workflow scheduling to a deadline-aware job scheduling problem, and then design an efficient algorithm that tackles the scheduling problem with both deadline-aware jobs and ad-hoc jobs by solving its corresponding optimization problem directly using a linear program solver. Our experimental results have clearly demonstrated that FlowTime achieves the lowest deadline-miss rates for deadline-aware workflows and 2-10 times shorter average job turnaround time, as compared to the state-of-the-art scheduling algorithms.


title: Funding, Management, and Individual-Level Factors: What Factors Matter in Predicting Perceived Organizational Effectiveness?

abstract: Scholars have examined organizational effectiveness in state and federal government agencies to determine which factors matter. Their findings revealed that public organizational effectiveness is affected by environmental, human resource management, organizational, and individual-level factors. However, what is conspicuously absent is the impact funding has on organizational effectiveness. To further previous literature, this article examined agency funding levels as well as management and individual-level factors to determine the relationship these factors had on organizational effectiveness. In doing so, a model was developed to predict perceived organizational effectiveness, and it was tested on 330 United States government workers in the state of New York. The findings from the analysis were clear: perceived organizational effectiveness was impacted by employee perceptions regarding the level of funding the agencies were allocated. Additionally, the level of role ambiguity and employee participation i...


title: Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

abstract: Moving object detection is an important and fundamental step for intelligent video surveillance systems because it provides a focus of attention for post-processing. A multilayer codebook-based background subtraction (MCBS) model is proposed for video sequences to detect moving objects. Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the nonstationary (dynamic) background and significantly increase the processing efficiency. Moreover, the pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. As a result, the proposed scheme can provide a high precision and efficient processing speed to meet the requirements of real-time moving object detection.


title: Multi-joint gripper - control of envelope gripping -

abstract: The study has proposed a multi-joint-gripper (MJG) that achieves envelope grasping for unknown shape objects. The mechanism mainly consists of a serially connected differential gear system (DGS) controlled by only two actuators, one is for driving all of the joints simultaneously and the other is for adjusting stiffness of every joints all together. We have developed the MJG with seven joints and succeeded the envelope grasping of various shape objects with no tactile or pressure sensor on contact surface between gripper and objects for the sake of being used in severe environments. The experiments also revealed necessity to control the stiffness of joints according to grasping conditions such as weight and elasticity of grasping objects. This paper proposes the way to control the joint stiffness followed by being elucidated with some simulation studies and experiments.


title: “Shake It Baby, Shake It”: Media Preferences, Sexual Attitudes and Gender Stereotypes Among Adolescents

abstract: In this study exposure to and preferences for three important youth media (TV, music styles/music TV, internet) were examined in relation to adolescents’ permissive sexual attitudes and gender stereotypes (i.e., views of men as sex-driven and tough, and of women as sex objects). Multivariate structural analysis of data from a school-based sample of 480 13 to 16-year-old Dutch students revealed that preferences, rather than exposure were associated with attitudes and stereotypes. For both girls and boys, preferences for hip-hop and hard-house music were associated positively with gender stereotypes and preference for classical music was negatively associated with gender stereotypes. Particularly for boys, using internet to find explicit sexual content emerged as a powerful indicator of all attitudes and stereotypes.


title: An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data

abstract: Human action recognition is an important task in computer vision. Extracting discriminative spatial and temporal features to model the spatial and temporal evolutions of different actions plays a key role in accomplishing this task. In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data. We build our model on top of the Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames. Furthermore, to ensure effective training of the network, we propose a regularized cross-entropy loss to drive the model learning process and develop a joint training strategy accordingly. Experimental results demonstrate the effectiveness of the proposed model,both on the small human action recognition data set of SBU and the currently largest NTU dataset.


title: A Comparative Study on Handwriting Digit Recognition Using Neural Networks

abstract: The handwritten digit recognition problem becomes one of the most famous problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the handwritten digit recognition problem. This paper focuses on Neural Network (NN) approaches. The most three famous NN approaches are deep neural network (DNN), deep belief network (DBN) and convolutional neural network (CNN). In this paper, the three NN approaches are compared and evaluated in terms of many factors such as accuracy and performance. Recognition accuracy rate and performance, however, is not the only criterion in the evaluation process, but there are interesting criteria such as execution time. Random and standard dataset of handwritten digit have been used for conducting the experiments. The results show that among the three NN approaches, DNN is the most accurate algorithm; it has 98.08% accuracy rate. However, the execution time of DNN is comparable with the other two algorithms. On the other hand, each algorithm has an error rate of 1-2% because of the similarity in digit shapes, specially, with the digits (1,7), (3,5), (3,8), (8,5) and (6,9).


title: A Study on Speech Recognition Control for a Surgical Robot

abstract: Speech recognition is common in electronic appliances and personal services, but its use for industrial and medical purposes is rare because of the presence of motion ambiguity. For minimally invasive surgical robotic assistants, this ambiguity arises because the robotic motion is not calibrated to the camera images. This paper presents a design for a speech recognition interface for an HIWIN robotic endoscope holder. A new intentional speech control is proposed to control movement over long distances. To decrease ambiguity, a method is proposed for voice-to-motion calibration that compares the degree of change in the endoscope image for a voice command. A speech recognition algorithm is implemented on Ubuntu OS, using CMU Sphinx. The control signal is sent to the robot controller using serial-port communication through a RS232 cable. The experimental results show that the proposed intentional speech control strategy has a navigation precision of up to 3.1° of angular displacement for the endoscope. The overall system processing time, including robotic motion, is 3.22 s for ∼1.8-s speech duration. The reference image navigation range is from 2.5 mm for ∼0.5-s speech duration up to 6 mm for ∼1.8-s speech duration, using a setup with camera tip that is located at a distance of 5 cm from the remote center of motion point.


title: Categorizing bugs with social networks: a case study on four open source software communities

abstract: Efficient bug triaging procedures are an important precondition for successful collaborative software engineering projects. Triaging bugs can become a laborious task particularly in open source software (OSS) projects with a large base of comparably inexperienced part-time contributors. In this paper, we propose an efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away. Our classification is based on nine measures to quantify the social embeddedness of bug reporters in the collaboration network. We demonstrate its applicability in a case study, using a comprehensive data set of more than 700,000 bug reports obtained from the Bugzilla installation of four major OSS communities, for a period of more than ten years. For those projects that exhibit the lowest fraction of valid bug reports, we find that the bug reporters' position in the collaboration network is a strong indicator for the quality of bug reports. Based on this finding, we develop an automated classification scheme that can easily be integrated into bug tracking platforms and analyze its performance in the considered OSS communities. A support vector machine (SVM) to identify valid bug reports based on the nine measures yields a precision of up to 90.3% with an associated recall of 38.9%. With this, we significantly improve the results obtained in previous case studies for an automated early identification of bugs that are eventually fixed. Furthermore, our study highlights the potential of using quantitative measures of social organization in collaborative software engineering. It also opens a broad perspective for the integration of social awareness in the design of support infrastructures.


title: Kernel methods, syntax and semantics for relational text categorization

abstract: Previous work on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntactic structures for both document retrieval and categorization. The main reason is the high reliability and effectiveness of language models, which are sufficient to accurately solve such retrieval tasks. However, when the latter involve the computation of relational semantics between text fragments simple statistical models may result ineffective. In this paper, we show that syntactic and semantic structures can be used to greatly improve complex categorization tasks such as determining if an answer correctly responds to a question. Given the high complexity of representing semantic/syntactic structures in learning algorithms, we applied kernel methods along with Support Vector Machines to better exploit the needed relational information. Our experiments on answer classification on Web and TREC data show that our models greatly improve on bag-of-words.


Challenges in Distributed Video Management and Delivery


Towards a Semantic Interoperability in an e-Government Application


title: Micro-rendering for scalable, parallel final gathering

abstract: Recent approaches to global illumination for dynamic scenes achieve interactive frame rates by using coarse approximations to geometry, lighting, or both, which limits scene complexity and rendering quality. High-quality global illumination renderings of complex scenes are still limited to methods based on ray tracing. While conceptually simple, these techniques are computationally expensive. We present an efficient and scalable method to compute global illumination solutions at interactive rates for complex and dynamic scenes. Our method is based on parallel final gathering running entirely on the GPU. At each final gathering location we perform micro-rendering: we traverse and rasterize a hierarchical point-based scene representation into an importance-warped micro-buffer, which allows for BRDF importance sampling. The final reflected radiance is computed at each gathering location using the micro-buffers and is then stored in image-space. We can trade quality for speed by reducing the sampling rate of the gathering locations in conjunction with bilateral upsampling. We demonstrate the applicability of our method to interactive global illumination, the simulation of multiple indirect bounces, and to final gathering from photon maps.


title: The Neurobehavioral Nature of Fishes and the Question of Awareness and Pain

abstract: This review examines the neurobehavioral nature of fishes and addresses the question of whether fishes are capable of experiencing pain and suffering. The detrimental effects of anthropomorphic thinking and the importance of an evolutionary perspective for understanding the neurobehavioral differences between fishes and humans are discussed. The differences in central nervous system structure that underlie basic neurobehavioral differences between fishes and humans are described. The literature on the neural basis of consciousness and of pain is reviewed, showing that: (1) behavioral responses to noxious stimuli are separate from the psychological experience of pain, (2) awareness of pain in humans depends on functions of specific regions of cerebral cortex, and (3) fishes lack these essential brain regions or any functional equivalent, making it untenable that they can experience pain. Because the experience of fear, similar to pain, depends on cerebral cortical structures that are absent from fish brains, it is concluded that awareness of fear is impossible for fishes. Although it is implausible that fishes can experience pain or emotions, they display robust, nonconscious, neuroendocrine, and physiological stress responses to noxious stimuli. Thus, avoidance of potentially injurious stress responses is an important issue in considerations about the welfare of fishes.


title: An integration method combining Rough Set Theory with formal concept analysis for personal investment portfolios

abstract: The classical Rough Set Theory (RST) always generates too many rules, making it difficult for decision makers to choose a suitable rule. In this study, we use two processes (pre process and post process) to select suitable rules and to explore the relationship among attributes. In pre process, we propose a pruning process to select suitable rules by setting up a threshold on the support object of decision rules, to thereby solve the problem of too many rules. The post process used the formal concept analysis from these suitable rules to explore the attribute relationship and the most important factors affecting decision making for choosing behaviours of personal investment portfolios. In this study, we explored the main concepts (characteristics) for the conservative portfolio: the stable job, less than 4 working years, and the gender is male; the moderate portfolio: high school education, the monthly salary between NT$30,001 (US$1000) and NT$80,000 (US$2667), the gender is male; and the aggressive portfolio: the monthly salary between NT$30,001 (US$1000) and NT$80,000 (US$2667), less than 4 working years, and a stable job. The study result successfully explored the most important factors affecting the personal investment portfolios and the suitable rules that can help decision makers.


title: Predicting NDUM Student's Academic Performance Using Data Mining Techniques

abstract: The ability to predict the students’ academic performance is very important in institution educational system. Recently some researchers have been proposed data mining techniques for higher education. In this paper, we compare two data mining techniques which are: Artificial Neural Network (ANN) and the combination of clustering and decision tree classification techniques for predicting and classifying students’ academic performance. The data set used in this research is the student data of Computer Science Department, Faculty of Science and Defence Technology, National Defence University of Malaysia (NDUM).


title: A Survey of Visual Analysis of Human Motion and Its Applications

abstract: This paper summarizes the recent progress in human motion analysis and its applications. In the beginning, we reviewed the motion capture systems and the representation model of human's motion data. Next, we sketched the advanced human motion data processing technologies, including motion data filtering, temporal alignment, and segmentation. The following parts overview the state-of-the-art approaches of action recognition and dynamics measuring since these two are the most active research areas in human motion analysis. The last part discusses some emerging applications of the human motion analysis in healthcare, human robot interaction, security surveillance, virtual reality and animation. The promising research topics of human motion analysis in the future is also summarized in the last part.


title: Learning interaction for collaborative tasks with probabilistic movement primitives

abstract: This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of imitation learning and its application to human-robot interaction using the concept of Interaction Primitives (IPs). We introduce the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. We evaluate our method on experiments using a lightweight arm interacting with a human partner and also using motion capture trajectories of two humans assembling a box. The advantages of ProMPs in relation to the original formulation for interaction are exposed and compared.


title: Posterior indirect reduction and pedicle screw fixation without laminectomy for Denis type B thoracolumbar burst fractures with incomplete neurologic deficit

abstract: The aim of this study is to evaluate the efficacy of posterior indirect reduction and pedicle screw fixation without laminectomy for the treatment of Denis type B thoracolumbar burst fractures with incomplete neurologic deficit. From March 2008 to May 2012, 36 consecutive patients of Denis type B thoracolumbar burst with incomplete neurologic deficit were enrolled. All of the patients accepted the treatments of posterior indirect reduction and pedicle screw fixation without laminectomy. Clinical and radiologic outcomes were assessed preoperatively and postoperatively. Operations were performed in a relatively short time without massive hemorrhage. Their neurologic functions were improved by at least one Frankel grade. The average score of American Spinal Injury Association (ASIA) motor increased from 25.4 ± 10.8 to 42.1 ± 10.5, and the recovery rate of the ASIA score was also increased. The pain level was relieved for all the patients. The local kyphosis angle was reduced from 25.9° ± 3.4° to 6.9° ± 2.2° (P <0.05) and remained 7.9° ± 2.0° (P > 0.05) at the latest follow-up. After the operation, the mean vertebral canal diameter increased from 5.5 ± 1.3 to 11.1 ± 2.2 mm (P < 0.05) and the mean canal stenosis index increased from 32.9 ± 7.8 to 84.8 ± 7.3 % (P < 0.05). There were no serious complications and fixation failures during follow-up. Denis type B thoracolumbar burst fractures with incomplete neurologic deficit can be effectively treated by posterior indirect reduction and pedicle screw fixation without laminectomy.


title: Generative Poisoning Attack Method Against Neural Networks

abstract: Poisoning attack is identified as a severe security threat to machine learning algorithms. In many applications, for example, deep neural network (DNN) models collect public data as the inputs to perform re-training, where the input data can be poisoned. Although poisoning attack against support vector machines (SVM) has been extensively studied before, there is still very limited knowledge about how such attack can be implemented on neural networks (NN), especially DNNs. In this work, we first examine the possibility of applying traditional gradient-based method (named as the direct gradient method) to generate poisoned data against NNs by leveraging the gradient of the target model w.r.t. the normal data. We then propose a generative method to accelerate the generation rate of the poisoned data: an auto-encoder (generator) used to generate poisoned data is updated by a reward function of the loss, and the target NN model (discriminator) receives the poisoned data to calculate the loss w.r.t. the normal data. Our experiment results show that the generative method can speed up the poisoned data generation rate by up to 239.38x compared with the direct gradient method, with slightly lower model accuracy degradation. A countermeasure is also designed to detect such poisoning attack methods by checking the loss of the target model.


title: Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition

abstract: Landmark-based human action recognition in videos is a challenging task in computer vision. One key step is to design a generic approach that generates discriminative features for the spatial structure and temporal dynamics. To this end, we regard the evolving landmark data as a high-dimensional path and apply non-linear path signature techniques to provide an expressive, robust, non-linear, and interpretable representation for the sequential events. We do not extract signature features from the raw path, rather we propose path disintegrations and path transformations as preprocessing steps. Path disintegrations turn a high-dimensional path linearly into a collection of lower-dimensional paths; some of these paths are in pose space while others are defined over a multiscale collection of temporal intervals. Path transformations decorate the paths with additional coordinates in standard ways to allow the truncated signatures of transformed paths to expose additional features. For spatial representation, we apply the signature transform to vectorize the paths that arise out of pose disintegration, and for temporal representation, we apply it again to describe this evolving vectorization. Finally, all the features are collected together to constitute the input vector of a linear single-hidden-layer fully-connected network for classification. Experimental results on four datasets demonstrated that the proposed feature set with only a linear shallow network and Dropconnect is effective and achieves comparable state-of-the-art results to the advanced deep networks, and meanwhile, is capable of interpretation.


title: Wideband Low-Loss Linear and Circular Polarization Transmit-Arrays in V-Band

abstract: Several linearly-polarized and circularly-polarized transmit-arrays are designed and demonstrated in the 60-GHz band. These arrays have a fairly simple structure with three metal layers and are fabricated with a standard printed-circuit board technology. The simulation method is based on an electromagnetic model of the focal source and the unit-cells, associated to an analytical modeling of the full structure. A theoretical analysis is presented for the optimization of the power budget with respect to the F/D ratio. Several prototypes are designed and characterized in V-band. The experimental results are in very good agreement with the simulations and demonstrate very satisfactory characteristics. Power efficiencies of 50-61% are reached with a 1-dB gain bandwidth up to 7%, and low cross-polarization level.


title: An Efficient Method for Document Categorization Based on Word2vec and Latent Semantic Analysis

abstract: Document categorization is the process of classifying documents from many mixed documents automatically, and the main problem is how to express document content in vector space completely. This paper proposes a new model named Latent Semantic Analysis (LSA) + word2vec to categorize documents. This is the first attempt of combining word2vec with LSA at document categorization and it can map document to vector space under the premise of keeping document contents fully. At first, we create a term by document matrix and the element of which is decided by Term Frequency-Inverse Document Frequency (TF-IDF) weighting and word vector trained by word2vec. This matrix is a 3-dimensional matrix and it can describe the meaning of every word and the content of every document exactly. Secondly, Singular Value Decomposition (SVD) is executed on the matrix and lower computational complexity is gained from this. The model is named LSA + word2vec. Then, document vector gained from the new model are put into Convolutional Neural Network (CNN) to train. CNN is an efficient deep learning algorithm, which improves the accuracy of classification greatly. We evaluate the performance based on the 20newsgroups corpus. The results show that our new model achieves better effects on document categorization tasks, and the accuracy made about 15% improvement than traditional methods, such as LSA and Vector Space Model (VSM).


title: Dopamine, cognitive function, and gamma oscillations: role of D4 receptors

abstract: Cognitive deficits in individuals with schizophrenia and close relatives are considered core symptoms of this disorder, and can manifest at the prodromal stage. Antipsychotics ameliorate positive symptoms but only modestly improve cognitive symptoms. The lack of treatments that improve cognitive abilities currently represents a major obstacle in developing more effective therapeutic strategies for this debilitating disorder. While D4 receptor-specific antagonists are ineffective in the treatment of positive symptoms, animal studies suggest that D4 receptor drugs can improve cognitive deficits. Moreover, recent work from our group suggests that D4 receptors synergize with the neuregulin/ErbB4 signaling pathway, genetically identified as risk factors for schizophrenia, in parvalbumin-expressing interneurons to modulate gamma oscillations. These high-frequency network oscillations correlate with attention and increase during cognitive tasks in healthy subjects but to a much lesser extent in affected individuals. This finding, along with other observations indicating impaired GABAergic function, has lead to the idea that abnormal neural activity in the prefrontal cortex in individuals with schizophrenia reflects a perturbation in excitation/inhibition balance. Here we review the current state of knowledge of D4 receptor functions in the prefrontal cortex and hippocampus, two major brain areas implicated in schizophrenia. Special emphasis is given to studies focusing on the potential role of D4 receptors in modulating GABAergic transmission and to an emerging concept of a close synergistic relationship between dopamine/D4R and neuregulin/ErbB4 signaling pathways that tunes the activity of PV interneurons to regulate gamma frequency network oscillations and potentially cognitive processes.


title: I want to answer; who has a question?: Yahoo! answers recommender system

abstract: Yahoo! Answers is currently one of the most popular question answering systems. We claim however that its user experience could be significantly improved if it could route the "right question" to the "right user." Indeed, while some users would rush answering a question such as "what should I wear at the prom?," others would be upset simply being exposed to it. We argue here that Community Question Answering sites in general and Yahoo! Answers in particular, need a mechanism that would expose users to questions they can relate to and possibly answer. We propose here to address this need via a multi-channel recommender system technology for associating questions with potential answerers on Yahoo! Answers. One novel aspect of our approach is exploiting a wide variety of content and social signals users regularly provide to the system and organizing them into channels. Content signals relate mostly to the text and categories of questions and associated answers, while social signals capture the various user interactions with questions, such as asking, answering, voting, etc. We fuse and generalize known recommendation approaches within a single symmetric framework, which incorporates and properly balances multiple types of signals according to channels. Tested on a large scale dataset, our model exhibits good performance, clearly outperforming standard baselines.


title: Rake-Pmi Automated Keyphrase Extraction: An unsupervised approach for automated extraction of keyphrases

abstract: Keyphrase extraction is a major step which is used in various applications such as document clustering, summarization. It can be solved using supervised as well as unsupervised approach. The unsupervised approach is based on the ranking of keyphrases and extracting the top few. For ranking, researchers either use statistical measures or language modeling. We propose a system called RAKE-PMI, which uses both approaches for ranking of phrases. PointWise Mutual Information(PMI) score of adjacent words in a phrase is calculated through the language modeling. For calculation of PMI, we extract the scores from keywords extracted from the entire corpora. This helps in analyzing the term collocation of words within a phrase. We compared proposed approach with one of the popular systems called RAKE and achieved a precision of .41 as compared to the precision of .36 achieved by RAKE for the given data set.


title: Relative localization and communication module for small-scale multi-robot systems

abstract: We characterize and improve an existing infrared relative localization/communication module used to find range and bearing between robots in small-scale multi-robot systems. Modifications to the algorithms of the original system are suggested which offer better performance. A mathematical model which accurately describes the system is presented and allows us to predict the performance of modules with augmented sensorial capabilities. Finally, the usefulness of the module is demonstrated in a multi-robot self-localization task using both a realistic robotic simulator and real robots, and the performance is analyzed


title: Hessian Matrix vs. Gauss-Newton Hessian Matrix

abstract: In this paper, we investigate how the Gauss-Newton Hessian matrix affects the basin of convergence in Newton-type methods. Although the Newton algorithm is theoretically superior to the Gauss-Newton algorithm and the Levenberg-Marquardt (LM) method as far as their asymptotic convergence rate is concerned, the LM method is often preferred in nonlinear least squares problems in practice. This paper presents a theoretical analysis of the advantage of the Gauss-Newton Hessian matrix. It is proved that the Gauss-Newton approximation function is the only nonnegative convex quadratic approximation that retains a critical property of the original objective function: taking the minimal value of zero on an $(n-1)$-dimensional manifold (or affine subspace). Due to this property, the Gauss-Newton approximation does not change the zero-on-$(n-1)$-D “structure” of the original problem, explaining the reason why the Gauss-Newton Hessian matrix is preferred for nonlinear least squares problems, especially when the initial point is far from the solution.


title: GaN based wideband T/R module for multi-function applications

abstract: The next generation of AESA antennas will be challenged with the need for enabling a combination of different operating modes within the same antenna front end, including radar, communication (data links), and jamming (electronic warfare, EW). This leads to enhanced demands especially with regard to the usable RF bandwidth. One main step to overcome this is the use of disruptive semiconductor materials for RF MMICs. As the RF section of today's T/R modules for AESA applications is typically based on GaAs technology, GaN and SiGe BiCMOS will challenge or even replace it, here. This paper will describe the design of a GaN-based T/R module using all-European MMICs and covering the X-band from 8 to 12 GHz and its realisation at Airbus Defence and Space in Ulm. It will show some measurement results and give an impression on achieved performance. Further this paper shall describe potential next steps and give an outlook towards future developments.


title: A Linear Approach to Face Shape and Texture Recovery using a 3D Morphable Model.

abstract: In this paper, we present a robust and efficient method to statistically recover the full 3D shape and texture of faces from single 2D images. We separate shape and texture recovery into two linear problems. For shape recovery, we learn empirically the generalization error of a 3D morphable model using out-of-sample data. We use this to predict the 2D variance associated with a sparse set of 2D feature points. This knowledge is incorporated into a parameter-free probabilistic framework which allows 3D shape recovery of a face in an arbitrary pose in a single step. Under the assumption of diffuseonly reflectance, we also show how photometric invariants can be used to recover texture parameters in an illumination insensitive manner. We present empirical results with comparison to the state-of-the-art analysis-by-synthesis methods and show an application of our approach to adjusting the pose of subjects in oil paintings.


title: Jointly learning heterogeneous features for RGB-D activity recognition

abstract: In this paper, we focus on heterogeneous features learning for RGB-D activity recognition. We find that features from different channels (RGB, depth) could share some similar hidden structures, and then propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogeneous multi-task learning. The proposed model formed in a unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to exploit latent shared features across different feature channels, 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces, and 3) transferring feature-specific intermediate transforms (i-transforms) for learning fusion of heterogeneous features across datasets. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by a simple inference model. Extensive experimental results on four activity datasets have demonstrated the efficacy of the proposed method. A new RGB-D activity dataset focusing on human-object interaction is further contributed, which presents more challenges for RGB-D activity benchmarking.


title: Probability Models for Customer-Base Analysis

abstract: As more firms begin to collect (and seek value from) richer customer-level datasets, a focus on the emerging concept of customer-base analysis is becoming increasingly common and critical. Such analyses include forward-looking projections ranging from aggregate-level sales trajectories to individual-level conditional expectations (which, in turn, can be used to derive estimates of customer lifetime value). We provide an overview of a class of parsimonious models (called probability models) that are well-suited to meet these rising challenges. We first present a taxonomy that captures some of the key distinctions across different kinds of business settings and customer relationships, and identify some of the unique modeling and measurement issues that arise across them. We then provide deeper coverage of these modeling issues, first for noncontractual settings (i.e., situations in which customer “death” is unobservable), then contractual ones (i.e., situations in which customer “death” can be observed). We review recent literature in these areas, highlighting substantive insights that arise from the research as well as the methods used to capture them. We focus on practical applications that use appropriately chosen data summaries (such as recency and frequency) and rely on commonly available software packages (such as Microsoft Excel).


title: The Influence of Mental Imagery Techniques on Sport Performance among Taekwondo Athletes

abstract: Imagery has been defined as “using all the senses to re-create or create an experience in the mind”. Imagery has both a cognitive and motivational function. The cognitive function of imagery is the use of mental imagery to experience specific sports skills and to plan strategies in advance for competitions. The motivational function of imagery is the use of imagery to experience goal attainment, effective coping and arousal management requirements. The mental practice literature provides evidence that imagery is an effective cognitive process for enhancing learning and performance of motor skills. The main aim of this study was to correlate the usage of imagery and sports performance among Taekwondo athletes. Data was collected from 78 Taekwondo athletes during a match among universities, using Imagery and Sports Performance questionnaire. One way ANOVA showed significant differences on the level of Imagery Usage among categories of Taekwondo athletes, F (3, 69) = 16.435, p<.01. Pearson correlation was used to determine the relationship between imagery and sports performance. The results indicated a positive correlation between internal imagery and sports performance, (r = 0.78; p<0.01), and external imagery and sports performance, (r = 0.79; p<0.01). Sports psychologists, sports counselors and coaches should use the present findings to recommend imagery to enhance athletes’ performance.


title: Towards a 99% efficient three-phase buck-type PFC rectifier for 400 V DC distribution systems

abstract: In telecom applications, the vision for a total power conversion efficiency from the mains to the output of PoL converters of 95% demands for an optimization of every conversion step, i.e. the PFC rectifier front-end should show an outstanding efficiency in the range of 99%. For recently discussed 400 V DC distribution bus voltages a buck-type PFC rectifier is a logical solution. In this paper, an efficiency-optimized, nearly 99% efficient, 5 kW three-phase buck-type PFC rectifier with 400 V output is presented. Methods for calculating losses of all components are described, and are used to optimize the converter design for efficiency at full load. Special attention is paid to semiconductor losses, which are shown to be dominant, with the parasitic device capacitance losses being a significant component. A prototype of the proposed rectifier is constructed which verifies the accuracy of the models used for loss calculation and optimization.


title: Ensemble of online neural networks for non-stationary and imbalanced data streams

abstract: Concept drift (non-stationarity) and class imbalance are two important challenges for supervised classifiers. ''Concept drift'' (or non-stationarity) refers to changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Research on classification of non-stationary and imbalanced data streams, mainly focuses on batch solutions, whereas online methods are more appropriate. Here, we propose an online ensemble of neural network (NN) classifiers. Ensemble models are the most frequent methods used for classifying non-stationary and imbalanced data streams. The main contribution is a two-layer approach for handling class imbalance and non-stationarity. In the first layer, cost-sensitive learning is embedded into the training phase of the NNs, and in the second layer a new method for weighting classifiers of the ensemble is proposed. The proposed method is evaluated on 3 synthetic and 8 real-world datasets. The results show statistically significant improvement compared to online ensemble methods with similar features.


title: Clitics in Arabic Language: A Statistical Study

abstract: Clitics in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic language. We tokenize large Arabic text using white-spaces and an automatic clitics tokenizer (AMIRA 2.0) and compare the unique-word count in both cases with English language. We also show the resulted distribution of clitics in Arabic and examine the performance of the used tokenizer. Using a 600 million words Arabic corpus, we report that the corresponding lexicon size could be reduced by 24.54% when applying clitics tokenization.


title: Experimental comparison between Wound Rotor and permanent magnet synchronous machine for Integrated Starter Generator applications

abstract: Various types of electric machines can be considered for an application to Integrated Starter Generator (ISG) in automotive applications. Nevertheless, high constraints impose optimal design and control to satisfy to the specification book. Due to the absence of permanent magnets on the rotor and one supplementary control variable (field current), Wound Rotor Synchronous Machine (WRSM) may allow a higher temperature and a higher power factor operations.


title: Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering

abstract: This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.


title: Why children are not vaccinated against measles: a cross-sectional study in two Nigerian States

abstract: BACKGROUND ::: Childhood vaccination rates in Nigeria are among the lowest in the world and this affects morbidity and mortality rates. A 2011 mixed methods study in two states in Nigeria examined coverage of measles vaccination and reasons for not vaccinating children. ::: ::: ::: METHODS ::: A household survey covered a stratified random cluster sample of 180 enumeration areas in Bauchi and Cross River States. Cluster-adjusted bivariate and then multivariate analysis examined associations between measles vaccination and potential determinants among children aged 12-23 months, including household socio-economic status, parental knowledge and attitudes about vaccination, and access to vaccination services. Focus groups of parents in the same sites subsequently discussed the survey findings and gave reasons for non-vaccination. A knowledge to action strategy shared findings with stakeholders, including state government, local governments and communities, to stimulate evidence-based actions to increase vaccination rates. ::: ::: ::: RESULTS ::: Interviewers collected data on 2,836 children aged 12-23 months in Cross River and 2,421 children in Bauchi. Mothers reported 81.8% of children in Cross River and 42.0% in Bauchi had received measles vaccine. In both states, children were more likely to receive measles vaccine if their mothers thought immunisation worthwhile, if immunisation was discussed in the home, if their mothers had more education, and if they had a birth certificate. In Bauchi, maternal awareness about immunization, mothers' involvement in deciding about immunization, and fathers' education increased the chances of vaccination. In Cross River, children from communities with a government immunisation facility were more likely to have received measles vaccine. Focus groups revealed lack of knowledge and negative attitudes about vaccination, and complaints about having to pay for vaccination. Health planners in both states used the findings to support efforts to increase vaccination rates. ::: ::: ::: CONCLUSION ::: Measles vaccination remains sub-optimal, particularly in Bauchi. Efforts to counter negative perceptions about vaccination and to ensure vaccinations are actually provided free may help to increase vaccination rates. Parents need to be made aware that vaccination should be free, including for children without a birth certificate, and vaccination could be an opportunity for issuing birth certificates. The study provides pointers for state level planning to increase vaccination rates.


Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining


title: A compact microstrip antenna with tapered peripheral slits for CubeSat RF Payloads at 436MHz: Miniaturization techniques, design & numerical results

abstract: We elaborate the design and simulation of a planar antenna that is suitable for CubeSat picosatellites. The antenna operates at 436 MHz and its main features are miniature size and the built-in capability to produce circular polarization. The miniaturization procedure is given in detail, and the electrical performance of this small antenna is documented. Two main miniaturization techniques have been applied, i.e. dielectric loading and distortion of the current path. We have added an extra degree of freedom to the latter. The radiator is integrated with the chassis of the picosatellite and, at the same time, operates at the lower end of the UHF spectrum. In terms of electrical size, the structure presented herein is one of the smallest antennas that have been proposed for small satellites. Despite its small electrical size, the antenna maintains acceptable efficiency and gain performance in the band of interest.


Contextual Recommendation with Path Constrained Random Walks ABSTRACT


title: Low Cost Smart Stick for Blind and Partially Sighted People

abstract: The paper represents a novel approach to overcome the major challenges face by blind and partially blind people to cross the road and read. The proposed voice stick used to cross the road and reach their destination without any support. It is introduced a new technique to read printed material using Android phone. Low cost able to serve as a smart blind stick being more efficient and helpful than the conventional one. This will assist the blind persons during the walk and provides an alarm if any hurdle is detected within the set range.


title: Learning Extractors from Unlabeled Text using Relevant Databases

abstract: Supervised machine learning algorithms for information extraction generally require large amounts of training data. In many cases where labeling training data is burdensome, there may, however, already exist an incomplete database relevant to the task at hand. Records from this database can be used to label text strings that express the same information. For tasks where text strings do not follow the same format or layout, and additionally may contain extra information, labeling the strings completely may be problematic. This paper presents a method for training extractors which fill in missing labels of a text sequence that is partially labeled using simple high-precision heuristics. Furthermore, we improve the algorithm by utilizing labeled fields from the database. In experiments with BibTeX records and research paper citation strings, we show a significant improvement in extraction accuracy over a baseline that only relies on the database for training data.


title: Cloud Forensics: A Meta-Study of Challenges, Approaches, and Open Problems

abstract: In recent years, cloud computing has become popular as a cost-effective and efficient computing paradigm. Unfortunately, today's cloud computing architectures are not designed for security and forensics. To date, very little research has been done to develop the theory and practice of cloud forensics. Many factors complicate forensic investigations in a cloud environment. First, the storage system is no longer local. Therefore, even with a subpoena, law enforcement agents cannot confiscate the suspect's computer and get access to the suspect's files. Second, each cloud server contains files from many users. Hence, it is not feasible to seize servers from a data center without violating the privacy of many other users. Third, even if the data belonging to a particular suspect is identified, separating it from other users' data is difficult. Moreover, other than the cloud provider's word, there is usually no evidence that links a given data file to a particular suspect. For such challenges, clouds cannot be used to store healthcare, business, or national security related data, which require audit and regulatory compliance. In this paper, we systematically examine the cloud forensics problem and explore the challenges and issues in cloud forensics. We then discuss existing research projects and finally, we highlight the open problems and future directions in cloud forensics research area. We posit that our systematic approach towards understanding the nature and challenges of cloud forensics will allow us to examine possible secure solution approaches, leading to increased trust on and adoption of cloud computing, especially in business, healthcare, and national security. This in turn will lead to lower cost and long-term benefit to our society as a whole.


title: Towards understanding cyberbullying behavior in a semi-anonymous social network

abstract: Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones to bully victims with offensive text, images, audio and video on a 24/7 basis. This paper studies negative user behavior in the Ask.fm social network, a popular new site that has led to many cases of cyberbullying, some leading to suicidal behavior. We examine the occurrence of negative words in Ask.fm's question+answer profiles along with the social network of "likes" of questions+answers. We also examine properties of users with "cutting" behavior in this social network.


Media Coverage of the Measles-Mumps-Rubella Vaccine and Autism Controversy and Its Relationship to MMR Immunization Rates in the United States


title: The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

abstract: Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-art methods allowing appropriate comparison of results across studies and potential integration and combination of extraction and recognition systems. In this paper we propose a basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis. In contrast to a large brute-force parameter set, we present a minimalistic set of voice parameters here. These were selected based on a) their potential to index affective physiological changes in voice production, b) their proven value in former studies as well as their automatic extractability, and c) their theoretical significance. The set is intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters. Our implementation is publicly available with the openSMILE toolkit. Comparative evaluations of the proposed feature set and large baseline feature sets of INTERSPEECH challenges show a high performance of the proposed set in relation to its size.


title: PID control system analysis, design, and technology

abstract: Designing and tuning a proportional-integral-derivative (PID) controller appears to be conceptually intuitive, but can be hard in practice, if multiple (and often conflicting) objectives such as short transient and high stability are to be achieved. Usually, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This stimulates the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating envelope. This development has further led to the incorporation of some advanced tuning algorithms into PID hardware modules. Corresponding to these developments, this paper presents a modern overview of functionalities and tuning methods in patents, software packages and commercial hardware modules. It is seen that many PID variants have been developed in order to improve transient performance, but standardising and modularising PID control are desired, although challenging. The inclusion of system identification and "intelligent" techniques in software based PID systems helps automate the entire design and tuning process to a useful degree. This should also assist future development of "plug-and-play" PID controllers that are widely applicable and can be set up easily and operate optimally for enhanced productivity, improved quality and reduced maintenance requirements.


title: Underwater Depth Estimation and Image Restoration Based on Single Images

abstract: In underwater environments, the scattering and absorption phenomena affect the propagation of light, degrading the quality of captured images. In this work, the authors present a method based on a physical model of light propagation that takes into account the most significant effects to image degradation: absorption, scattering, and backscattering. The proposed method uses statistical priors to restore the visual quality of the images acquired in typical underwater scenarios.


title: Media multitasking and psychological wellbeing in Chinese adolescents: Time management as a moderator

abstract: The present study examined the relationships among media multitasking, time management, and psychological wellbeing in Chinese adolescents. A total of 320 adolescents aged 11 to 18years old were recruited and asked to complete the Media Use Questionnaire, Chinese Adolescent Mental Health Inventory, and Adolescent Time Management Disposition scale. A structural equation model was used to assess possible relationships among media multitasking, time management, and psychological wellbeing. The results showed that the media multitasking index of the sample was 2.5, indicating that adolescents also had access to other 2.5 media tasks when performing the primary media task. Media multitasking was significantly negatively correlated with psychological wellbeing. Time management disposition was negatively correlated with Media multitasking and positively correlated with psychological wellbeing. Our findings indicate that time management disposition can moderate the relationship between Media multitasking and psychological wellbeing. The theoretical and practical implications of adolescent media use are discussed.


title: Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning

abstract: Deep reinforcement learning for multi-agent cooperation and competition has been a hot topic recently. This paper focuses on cooperative multi-agent problem based on actor-critic methods under local observations settings. Multi agent deep deterministic policy gradient obtained state of art results for some multi-agent games, whereas, it cannot scale well with growing amount of agents. In order to boost scalability, we propose a parameter sharing deterministic policy gradient method with three variants based on neural networks, including actor-critic sharing, actor sharing and actor sharing with partially shared critic. Benchmarks from rllab show that the proposed method has advantages in learning speed and memory efficiency, well scales with growing amount of agents, and moreover, it can make full use of reward sharing and exchangeability if possible.


title: Near-Optimal Access Partitioning for Memory Hierarchies with Multiple Heterogeneous Bandwidth Sources

abstract: The memory wall continues to be a major performance bottleneck. While small on-die caches have been effective so far in hiding this bottleneck, the ever-increasing footprint of modern applications renders such caches ineffective. Recent advances in memory technologies like embedded DRAM (eDRAM) and High Bandwidth Memory (HBM) have enabled the integration of large memories on the CPU package as an additional source of bandwidth other than the DDR main memory. Because of limited capacity, these memories are typically implemented as a memory-side cache. Driven by traditional wisdom, many of the optimizations that target improving system performance have been tried to maximize the hit rate of the memory-side cache. A higher hit rate enables better utilization of the cache, and is therefore believed to result in higher performance. In this paper, we challenge this traditional wisdom and present DAP, a Dynamic Access Partitioning algorithm that sacrifices cache hit rates to exploit under-utilized bandwidth available at main memory. DAP achieves a near-optimal bandwidth partitioning between the memory-side cache and main memory by using a light-weight learning mechanism that needs just sixteen bytes of additional hardware. Simulation results show a 13% average performance gain when DAP is implemented on top of a die-stacked memory-side DRAM cache. We also show that DAP delivers large performance benefits across different implementations, bandwidth points, and capacity points of the memory-side cache, making it a valuable addition to any current or future systems based on multiple heterogeneous bandwidth sources beyond the on-chip SRAM cache hierarchy.


title: Strategizing information systems-enabled organizational transformation: A transdisciplinary review and new directions

abstract: Twenty years after the promise of Information Systems enabling Organizational Transformation (IS-enabled OT), what have we learned? This paper reviews the literature in order to better understand this phenomenon. As specialists in IS, strategy and organizational studies, we analyze the discourse on OT found in the strategy, organizational theory and IS literature, and identify four structuring themes: organizational inertia, process, agency and performance. We apply the coding derived from these themes to a set of 62 empirical papers and discuss the results. Ten research avenues are then identified to show that IS-enabled OT is still a new frontier for strategic information systems research.


title: Modeling and Predicting Learning Behavior in MOOCs

abstract: Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, offer us an unprecedented opportunity to analyze students' learning behavior at a very fine granularity than ever before. Using dataset from xuetangX, one of the largest MOOCs from China, we analyze key factors that influence students' engagement in MOOCs and study to what extent we could infer a student's learning effectiveness. We observe significant behavioral heterogeneity in students' course selection as well as their learning patterns. For example, students who exert higher effort and ask more questions are not necessarily more likely to get certificates. Additionally, the probability that a student obtains the course certificate increases dramatically (3 x higher) when she has one or more "certificate friends". Moreover, we develop a unified model to predict students' learning effectiveness, by incorporating user demographics, forum activities, and learning behavior. We demonstrate that the proposed model significantly outperforms (+2.03-9.03% by F1-score) several alternative methods in predicting students' performance on assignments and course certificates. The model is flexible and can be applied to various settings. For example, we are deploying a new feature into xuetangX to help teachers dynamically optimize the teaching process.


title: Real-time factored ConvNets: Extracting the x factor in human parsing

abstract: © 2017. The copyright of this document resides with its authors. We propose a real-time and lightweight multi-task style ConvNet (termed a Factored ConvNet) for human body parsing in images or video. Factored ConvNets have isolated areas which perform known sub-tasks, such as object localization or edge detection. We call this area and sub-task pair an X factor. Unlike multi-task ConvNets which have independent tasks, the Factored ConvNet’s sub-task has direct effect on the main task outcome. In this paper we show how to isolate the X factor of foreground/background (f/b) subtraction from the main task of segmenting human body images into 31 different body part types. Knowledge of this X factor leads to a number of benefits for the Factored ConvNet: 1) Ease of network transfer to other image domains, 2) ability to personalize to humans in video and 3) easy model performance boosts. All achieved by either efficient network update or replacement of the X factor whilst avoiding catastrophic forgetting of previously learnt body part dependencies and structure. We show these benefits on a large dataset of images and also on YouTube videos.


title: Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering

abstract: Accurately answering a question about a given image requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge remains an algorithmic challenge. To advance research in this direction, a novel fact-based' visual question answering (FVQA) task has been introduced recently along with a large set of curated facts which link two entities, i.e., two possible answers, via a relation. Given a question-image pair, deep net techniques have been employed to successively reduce the large set of facts until one of the two entities of the final remaining fact is predicted as the answer. We observe that a successive process which considers one fact at a time to form a local decision is sub-optimal. Instead, we develop an entity graph and a graph convolutional net method toreason' about the correct answer by jointly considering all entities. We show on the challenging FVQA dataset that this leads to an improvement in accuracy of around 10% compared to the state-of-the-art.