title: Information Assurance in B2C e-Commerce Transactions: A Process-Oriented Framework and Empirical Analysis of Websites using Semantic Networks.

abstract: What aspects of information assurance can be identified in Business-to-Consumer (B-toC) online transactions? The purpose of this research is to build a theoretical framework for studying information assurance based on a detailed analysis of academic literature for online exchanges in B-to-C electronic commerce. Further, a semantic network content analysis is conducted to analyze the representations of information assurance in B-to-C electronic commerce in the real online market place (transaction Web sites of selected Fortune 500 firms). The results show that the transaction websites focus on some perspectives and not on others. For example, we see an emphasis on the importance of technological and consumer behavioral elements of information assurance such as issues of online security and privacy. Further corporate practitioners place most emphasis on transaction-related information assurance issues. Interestingly, the product and institutional dimension of information assurance in online transaction websites are only


title: Can One Language Bootstrap the Other: A Case Study on Event Extraction

abstract: This paper proposes a new bootstrapping framework using cross-lingual information projection. We demonstrate that this framework is particularly effective for a challenging NLP task which is situated at the end of a pipeline and thus suffers from the errors propagated from upstream processing and has low-performance baseline. Using Chinese event extraction as a case study and bitexts as a new source of information, we present three bootstrapping techniques. We first conclude that the standard mono-lingual bootstrapping approach is not so effective. Then we exploit a second approach that potentially benefits from the extra information captured by an English event extraction system and projected into Chinese. Such a cross-lingual scheme produces significant performance gain. Finally we show that the combination of mono-lingual and cross-lingual information in bootstrapping can further enhance the performance. Ultimately this new framework obtained 10.1% relative improvement in trigger labeling (F-measure) and 9.5% relative improvement in argument-labeling.


title: Application of Machine Learning for Optimization of 3-D Integrated Circuits and Systems

abstract: The 3-D integration helps improve performance and density of electronic systems. However, since electrical and thermal performance for 3-D integration is related to each other, their codesign is required. Machine learning, a promising approach in artificial intelligence, has recently shown promise for addressing engineering optimization problems. In this paper, we apply machine learning for the optimization of 3-D integrated systems where the electrical performance and thermal performance need to be analyzed together for maximizing performance. In such systems, modeling can be challenging due to the multiscale geometries involved, which increases computation time per iteration. In this paper, we show that machine learning can be applied to such systems where multiple parameters can be optimized to achieve the desired performance using the minimum number of iterations. These results have been compared with other promising optimization methods in this paper. The results show that on an average, 4.4%, 31.1%, and 6.9% improvement in temperature gradient, CPU time, and skew are possible using machine learning, as compared with other methods.


title: Towards simulating star formation in turbulent high-z galaxies with mechanical supernova feedback

abstract: Feedback from supernovae is essential to understanding the self-regulation of star formation in galaxies. However, the efficacy of the process in a cosmological context remains unclear due to excessive radiative losses during the shock propagation. To better understand the impact of SN explosions on the evolution of galaxies, we perform a suite of high-resolution (12 pc), zoom-in cosmological simulations of a Milky Way-like galaxy at z=3 with adaptive mesh refinement. We find that SN explosions can efficiently regulate star formation, leading to the stellar mass and metallicity consistent with the observed mass-metallicity relation and stellar mass-halo mass relation at z~3. This is achieved by making three important changes to the classical feedback scheme: i) the different phases of SN blast waves are modelled directly by injecting radial momentum expected at each stage, ii) the realistic time delay of SNe, commencing at as early as 3 Myr, is required to disperse very dense gas before a runaway collapse sets in at the galaxy centre via mergers of gas clumps, and iii) a non-uniform density distribution of the ISM is taken into account below the computational grid scale for the cell in which SN explodes. The last condition is motivated by the fact that our simulations still do not resolve the detailed structure of a turbulent ISM in which the fast outflows can propagate along low-density channels. The simulated galaxy with the SN feedback model shows strong outflows, which carry approximately ten times larger mass than star formation rate, as well as smoothly rising circular velocity. Other feedback models that do not meet the three conditions form too many stars, producing a peaked rotation curve. Our results suggest that understanding the structure of the turbulent ISM may be crucial to assess the role of SN and other feedback processes in galaxy formation theory.


title: Optimised KD-trees for fast image descriptor matching

abstract: In this paper, we look at improving the KD-tree for a specific usage: indexing a large number of SIFT and other types of image descriptors. We have extended priority search, to priority search among multiple trees. By creating multiple KD-trees from the same data set and simultaneously searching among these trees, we have improved the KD-treepsilas search performance significantly.We have also exploited the structure in SIFT descriptors (or structure in any data set) to reduce the time spent in backtracking. By using Principal Component Analysis to align the principal axes of the data with the coordinate axes, we have further increased the KD-treepsilas search performance.


title: Complementary and alternative medicine use amongst Palestinian diabetic patients

abstract: Objectives: To measure the frequency of herbal medicine use among patients with diabetes mellitus in Palestine; to determine demographic characteristics that may increase the likelihood of Complementary and Alternative Medicine (CAM) use and to find out how benefits, if any, were perceived by patients. Method: Cross-sectional survey of patients attending the outpatient diabetes departments at 7 Governmental Hospitals. The method was based on semi-structured questionnaires. Results: A total of 1883 patients with diabetes were interviewed. Of the participants, 51.9% (n ¼ 977) reported taking herbs primarily bought from Palestine (98%) and used in crude form mainly as decoctions (44.1%). The five most common herbal products used were: Trigonella berythea (Fabaceae) (n ¼ 191, 19.6%), Rosmarinus officinalis (Lamiaceae) (n ¼ 132, 13.5%), Olea europaea (Oleaceae) (n ¼ 131, 13.4%), Teucrium capitatum (Lamiaceae) (n ¼ 111, 11.4%), and Cinnamomum zeylanicum (Lauraceae) (n ¼ 105, 10.8%). Most CAM users were above 40 years old 79.6% (n ¼ 778), predominantly female (53.2%) and residents of refugee camps and rural areas (59.3, and 53.5, respectively). The recommendations of a family member or friend was the main factor prompting the use of CAM (40.2 and 37.1%). Most CAM users (71.7%) were satisfied with the perceived effects. Interestingly, 68% of patients recruited in the study did not disclose CAM use to their physicians or pharmacists. Conclusion: Use of herbal therapies in diabetes is highly prevalent in Palestine. More than 70% of those using CAM (977, 51.9%) reported positive benefits including a feeling of slowing down disease progression, symptom relief, disease resolution or a reduction in the side effects of allopathic medication. Use of CAM should be explored with patients before clinical decisions are made. There is a need for health education relating to herbal use in conjunction with conventional medicines in diabetes.


title: Contemporary Review of Injectable Facial Fillers

abstract: Perhaps the most significant change in facial rejuvenation in the last 10 years has been the introduction of nonsurgical treatments for the relaxation of facial wrinkles and for the restoration of lost volume. Fillers such as paraffin and silicone have been used in the past for volume restoration, but only recently have new fillers been developed whose safety and efficacy have been supported by clinical research. The introduction of hyaluronic acid (HA) fillers in 2003 began the filler revolution and paved the way for development of biostimulatory and permanent materials. There is an abundance of high-level evidence-based studies comparing the HA fillers, calcium hydroxylapatite, and poly(methyl methacrylate) with collagen and other HA formulations, but there is only limited high-level data evaluating poly-L-lactic acid.


Optimization by Simmulated Annealing


title: Why do beliefs about intelligence influence learning success? A social cognitive neuroscience model

abstract: Students’ beliefs and goals can powerfully influence their learning success. Those who believe intelligence is a fixed entity (entity theorists) tend to emphasize ’performance goals,’ leaving them vulnerable to negative feedback and likely to disengage from challenging learning opportunities. In contrast, students who believe intelligence is malleable (incremental theorists) tend to emphasize ’learning goals’ and rebound better from occasional failures. Guided by cognitive neuroscience models of top–down, goal-directed behavior, we use event-related potentials (ERPs) to understand how these beliefs influence attention to information associated with successful error correction. Focusing on waveforms associated with conflict detection and error correction in a test of general knowledge, we found evidence indicating that entity theorists oriented differently toward negative performance feedback, as indicated by an enhanced anterior frontal P3 that was also positively correlated with concerns about proving ability relative to others. Yet, following negative feedback, entity theorists demonstrated less sustained memory-related activity (left temporal negativity) to corrective information, suggesting reduced effortful conceptual encoding of this material–a strategic approach that may have contributed to their reduced error correction on a subsequent surprise retest. These results suggest that beliefs can influence learning success through top–down biasing of attention and conceptual processing toward goal-congruent information.


title: Impact of Trust on Internet Banking Adoption: A Literature Review

abstract: Banks spend a great amount of money on providing the Internet banking service to customers. Hence, the lack of adoption of Internet banking can bring negative consequences for the banks. As a result, banks continuously try to adapt their business strategies to increase customer satisfaction. This paper provides an overview of the extant research into the relationships between trust and Internet banking adoption from the customers' perspective. The literature review covers empirical studies on trust and online trust, the factors of trust and the adoption of Internet banking. To facilitate the review and analysis of the studies reported in the literature, we propose a framework of customer factors and website factors which affect trust and organize the impact of trust on Internet banking adoption under the proposed framework. The findings indicate that trust has a positive effect on the adoption of Internet banking. Moreover, the results show that there are many factors related to trust in electronic services. The majority of the research has been conducted in the context of Internet banking. Drawing on these studies, the review presented in this paper offers an overview of the relevant variables that may affect people's trust in Internet banking.


title: Excited State Properties of Hybrid Perovskites

abstract: ConspectusMetal halide perovskites have come to the attention of the scientific community for the progress achieved in solar light conversion. Energy sustainability is one of the priorities of our society, and materials advancements resulting in low-cost but efficient solar cells and large-area lighting devices represent a major goal for applied research. From a basic point of view, perovskites are an exotic class of hybrid materials combining some merits of organic and inorganic semiconductors: large optical absorption, large mobilities, and tunable band gap together with the possibility to be processed in solution. When a novel class of promising semiconductors comes into the limelight, lively discussions ensue on the photophysics of band-edge excitations, because just the states close to the band edge are entailed in energy/charge transport and light emission. This was the case several decades ago for III–V semiconductors, it has been up to 10 years ago for organics, and it is currently the case for pe...


The (too Many) Problems of Analogical Reasoning with Word Vectors


Need ful fi llment and experiences on social media : A case on Facebook and


title: Dynamic Terahertz Spoof Surface Plasmon–Polariton Switch Based on Resonance and Absorption

abstract: In this brief, a terahertz (THz) switch consisting of perfect conductor metamaterials is demonstrated both theoretically and numerically. Specifically, we build a THz logic block based on waveguide-cavity-waveguide, thus providing a strong electromagnetic field accumulation inside a small cavity. Furthermore, this brief shows that the subwavelength metallic cavity can confine light for a long time within a very small area. Therefore, an arbitrarily designed cavity with the high quality factor Q and the small effective area Aeff can be easily utilized for an efficient THz switch. These promising results can be used to provide new switch types and open up new vistas in the area of THz subwavelength optics.


title: A 25.6 W 13.56 MHz wireless power transfer system with a 94% efficiency GaN Class-E power amplifier

abstract: In this work, we propose the design and implementation of a 13.56 MHz GaN Class-E power amplifier, which takes into account transistor parasitic effects. The design uses the parasitic capacitance of the transistor to replace the charging capacitance, simplifying the circuit structure and obtaining a 93.6% efficiency at output power of 26.8 W. In addition, a wireless power transfer system using the proposed Class-E amplifier is demonstrated, achieving a 73.4% system efficiency when the power delivered to the load is 25.6 W.


title: Augmented Reality behind the wheel - Human Interactive Assistance by Mobile Robots

abstract: Novel approaches have taken Augmented Reality (AR) beyond traditional body-worn or hand-held displays, leading to the creation of a new branch of AR: Spatial Augmented Reality (SAR) providing additional application areas. SAR is a rapidly emerging field that uses digital projectors to render virtual objects onto 3D objects in the real space. When mounting digital projectors on robots, this collaboration paves the way for unique Human-Robot Interactions (HRI) that otherwise would not be possible. Adding to robots the capability of projecting interactive Augmented Reality content enables new forms of interactions between humans, robots, and virtual objects, enabling new applications. In this work it is investigated the use of SAR techniques on mobile robots for better enabling this to interact in the future with elderly or injured people during rehabilitation, or with children in the pediatric ward of a hospital.


title: This robot stinks! Differences between perceived mistreatment of robot and computer partners

abstract: Robots (and computers) are increasingly being used in scenarios where they interact socially with people. How people react to these agents is telling about the perceived animacy of such agents. Mistreatment of robots (or computers) by co-workers might provoke such telling reactions. The purpose of this study was to discover if people perceived mistreatment directed towards a robot any differently than toward a computer. This will provide some understanding of how people perceive robots in collaborative social settings. We conducted a between-subjects study with 80 participants. Participants worked cooperatively with either a robot or a computer which acted as the"recorder"for the group. A confederate either acted aggressively or neutrally towards the"recorder."We hypothesized that people would not socially accept mistreatment towards an agent that they felt was intelligent and similar to themselves; that participants would perceive the robot as more similar in appearance and emotional capability to themselves than a computer; and would observe more mistreatment. The final results supported our hypothesis; the participants observed mistreatment in the robot, but not the computer. Participants felt significantly more sympathetic towards the robot and also believed that it was much more emotionally capable.


title: Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality

abstract: We characterize the sum capacity of the vector Gaussian broadcast channel by showing that the existing inner bound of Marton and the existing upper bound of Sato are tight for this channel. We exploit an intimate four-way connection between the vector broadcast channel, the corresponding point-to-point channel (where the receivers can cooperate), the multiple-access channel (MAC) (where the role of transmitters and receivers are reversed), and the corresponding point-to-point channel (where the transmitters can cooperate).


title: Modeling Spatial Variations of Urban Growth Patterns in Chinese Cities: The Case of Nanjing

abstract: Revealing spatially varying relationships between urban growth patterns and underlying determinants is important for better understanding local dimensions of urban development. Through a case study of Nanjing, China, we employ both global and local logistic regressions to model the probability of urban land expansion against a set of spatial variables. We found that compared with other fast growing coastal cities, Nanjing remains a relatively compact city. The orthodox logistic regression found the significance of proximity, neighborhood conditions, and urban agglomeration in urban land change. The logistic GWR significantly improves the global logistic regression model in terms of better model goodness-of-fit and lower level of spatial autocorrelation of residuals. More importantly, the local estimates of parameters of spatial variables enable us to investigate spatial variations of the influences of spatial variables on urban growth. We have found distinctive local patterns and effects of urban growth in Nanjing, shaped by local urban spatial and institutional structures. A probability surface of urban growth, which is generated from raster calculations among the parameter and variable surfaces, provides a clear scenario of urban growth patterns and can be useful for decision making. This study also shows the importance of policy studies and fieldwork in the interpretation of results generated from statistical and GIS modeling.


title: Eleven Deaths of Mr. K.—Contributing Factors to Suicide in Narcissistic Personalities

abstract: The objective of this paper is to discuss and illuminate the problem of abrupt suicide in relatively well-functioning individuals without a major DSM-IV mental illness. A case of a man diagnosed with narcissistic personality, who first allegedly staged a suicide attempt and later, without overt warning, killed himself in the context of financial losses and divorce, will be discussed. The paper addresses how and why a life event can generate an internal subjective experience that evokes a sudden deadly self-attack. Discussion of eleven explanatory hypotheses serves to further the understanding of these seemingly inexplicable events. We conclude that additional studies are necessary, especially of the interconnected interaction between life event, psychological functioning, and neurobiological correlates to expand understanding and develop proactive treatment strategies. Language: en


title: An Access Control Framework for Cloud-Enabled Wearable Internet of Things

abstract: Internet of Things (IoT) has become a pervasive and diverse concept in recent years. IoT applications and services have given rise to a number of sub-fields in the IoT space. Wearable technology, with its particular set of characteristics and application domains, has formed a rapidly growing sub-field of IoT, viz., Wearable Internet of Things (WIoT). While numerous wearable devices are available in the market today, security and privacy are key factors for wide adoption of WIoT. Wearable devices are resource constrained by nature with limited storage, power, and computation. A Cloud-Enabled IoT (CEIoT) architecture, a dominant paradigm currently shaping the industry and suggested by many researchers, needs to be adopted for WIoT. In this paper, we develop an access control framework for cloud-enabled WIoT (CEWIoT) based on the Access Control Oriented (ACO) architecture recently developed for CEIoT in general. We first enhance the ACO architecture from the perspective of WIoT by adding an Object Abstraction Layer, and then develop our framework based on interactions between different layers of this enhanced ACO architecture. We present a general classification and taxonomy of IoT devices, along with brief introduction to various application domains of IoT and WIoT. We then present a remote health and fitness monitoring use case to illustrate different access control aspects of our framework and outline its possible enforcement in a commercial CEIoT platform, viz., AWS IoT. Finally, we discuss the objectives of our access control framework and relevant open problems.


title: Leveraging User Interaction Signals for Web Image Search

abstract: User interfaces for web image search engine results differ significantly from interfaces for traditional (text) web search results, supporting a richer interaction. In particular, users can see an enlarged image preview by hovering over a result image, and an `image preview' page allows users to browse further enlarged versions of the results, and to click-through to the referral page where the image is embedded. No existing work investigates the utility of these interactions as implicit relevance feedback for improving search ranking, beyond using clicks on images displayed in the search results page. In this paper we propose a number of implicit relevance feedback features based on these additional interactions: hover-through rate, 'converted-hover' rate, referral page click through, and a number of dwell time features. Also, since images are never self-contained, but always embedded in a referral page, we posit that clicks on other images that are embedded on the same referral webpage as a given image can carry useful relevance information about that image. We also posit that query-independent versions of implicit feedback features, while not expected to capture topical relevance, will carry feedback about the quality or attractiveness of images, an important dimension of relevance for web image search. In an extensive set of ranking experiments in a learning to rank framework, using a large annotated corpus, the proposed features give statistically significant gains of over 2% compared to a state of the art baseline that uses standard click features.


title: CRADLE: An Online Plan Recognition Algorithm for Exploratory Domains

abstract: In exploratory domains, agents’ behaviors include switching between activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users’ activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE (Cumulative Recognition of Activities and Decreasing Load of Explanations) that uses this grammar to recognize agents’ interactions in exploratory domains. We compare the performance of CRADLE with state-of-the-art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-the-art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-time support to users in a growing class of interesting and challenging domains.


Understanding the Characteristics of


title: Digital games for physical therapy: fulfilling the need for calibration and adaptation

abstract: With the advent of computer games involving the movement of the player's whole body or body parts, an opportunity arises to develop games for people with motor disabilities. In this paper we present four minigames developed for people suffering from spasticity and loss of motor control. We thereby focus on the input devices, sensor signal processing and mapping of players' actions on events in the game. In order to adapt the game to the player's motor skills and goals, specific attention should be paid to calibration procedures and adjustable parameters. We illustrate how this can be done and simultaneously, we demonstrate the feasibility for the development of digital games for physical therapy with currently available commercial input devices.


title: Nicotinamide Mononucleotide, a Key NAD+ Intermediate, Treats the Pathophysiology of Diet- and Age-Induced Diabetes in Mice

abstract: Summary Type 2 diabetes (T2D) has become epidemic in our modern lifestyle, likely due to calorie-rich diets overwhelming our adaptive metabolic pathways. One such pathway is mediated by nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in mammalian NAD + biosynthesis, and the NAD + -dependent protein deacetylase SIRT1. Here, we show that NAMPT-mediated NAD + biosynthesis is severely compromised in metabolic organs by high-fat diet (HFD). Strikingly, nicotinamide mononucleotide (NMN), a product of the NAMPT reaction and a key NAD + intermediate, ameliorates glucose intolerance by restoring NAD + levels in HFD-induced T2D mice. NMN also enhances hepatic insulin sensitivity and restores gene expression related to oxidative stress, inflammatory response, and circadian rhythm, partly through SIRT1 activation. Furthermore, NAD + and NAMPT levels show significant decreases in multiple organs during aging, and NMN improves glucose intolerance and lipid profiles in age-induced T2D mice. These findings provide critical insights into a potential nutriceutical intervention against diet- and age-induced T2D.


title: PowerLine Positioning: A Practical Sub-Room-Level Indoor Location System for Domestic Use

abstract: Using existing communications infrastructure, such as 802.11 and GSM, researchers have demonstrated effective indoor localization. Inspired by these previous approaches, and recognizing some limitations of relying on infrastructure users do not control, we present an indoor location system that uses an even more ubiquitous domestic infrastructure—the residential powerline. PowerLine Positioning (PLP) is an inexpensive technique that uses fingerprinting of multiple tones transmitted along the powerline to achieve sub-room-level localization. We describe the basics behind PLP and demonstrate how it compares favorably to other fingerprinting techniques.


title: Profiling Grounded Theory Approaches in Information Systems Research

abstract: Studies claiming to use the Grounded theory methodology (GTM) have been quite prevalent in information systems (IS) literature. A cursory review of this literature reveals conflict in the understanding of GTM, with a variety of grounded theory approaches apparent. The purpose of this investigation was to establish what alternative grounded theory approaches have been employed in IS, and to what extent each has been used. In order to accomplish this goal, a comprehensive set of IS articles that claimed to have followed a grounded theory approach were reviewed. The articles chosen were those published in the widely acknowledged top eight IS-centric journals, since these journals most closely represent exemplar IS research. Articles for the period 1985-2008 were examined. The analysis revealed four main grounded theory approaches in use, namely (1) the classic grounded theory approach, (2) the evolved grounded theory approach, (3) the use of the grounded theory approach as part of a mixed methodology, and (4) the application of grounded theory techniques, typically for data analysis purposes. The latter has been the most common approach in IS research. The classic approach was the least often employed, with many studies opting for an evolved or mixed method approach. These and other findings are discussed and implications drawn.


Motherhood in the Face of Trauma


title: Going Deeper: Autonomous Steering with Neural Memory Networks

abstract: Although autonomous driving is an area which has been extensively explored in computer vision, current deep learning based methods such as direct image to action mapping approaches are not able to generate accurate results, making their application questionable. This is largely due to the lack of capacity of the current state-of-the-art architectures to capture long term dependencies which can model different human preferences and their behaviour under different contexts. Our work explores a new paradigm in deep autonomous driving where the model incorporates both visual input as well as the steering wheel trajectory and attains a long term planning capacity via neural memory networks. Furthermore, this work investigates optimal feature fusion techniques to combine these multimodal information sources, without discarding the vital information that they offer. The effectiveness of the proposed architecture is illustrated using two publicly available datasets where in both cases the proposed model demonstrates human like behaviour under challenging situations including illumination variations, discontinuous shoulder lines, lane merges, and divided highways, outperforming the current state-of-the-art.


title: Communication-Constrained Mobile Edge Computing Systems for Wireless Virtual Reality: Scheduling and Tradeoff

abstract: Mobile edge computing (MEC) is expected to be an effective solution to deliver virtual reality (VR) videos over wireless networks. In contrast to previous computation-constrained MEC, which reduces the computation-resource consumption at the mobile device by increasing the communication-resource consumption, we develop a communications-constrained MEC framework to reduce communication-resource consumption by fully exploiting the computation and caching resources at the mobile VR device in this paper. Specifically, according to a task modularization, the MEC server only delivers the components which have not been stored in the VR device, and then the VR device uses the received components and other cached components to construct the task, yielding low communication cost but high delay. The MEC server also computes the task by itself to reduce the delay, however, it consumes more communication-resource due to the delivery of entire task. Therefore, we propose a task scheduling strategy to decide which computation model should the MEC server operates to minimize the communication-resource consumption under the delay constraint. Finally, the tradeoffs among communications, computing, and caching are also discussed, and we analytically find that given a target communication-resource consumption, the transmission rate is inversely proportional to the computing ability of mobile VR device.


title: The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations

abstract: This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al.. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genre-matched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domain-independent representations for sentence meaning.


title: IRIT at e-Risk

abstract: In this paper, we present the method we developed when participating to the e-Risk pilot task. We use machine learning in order to solve the problem of early detection of depressive users in social media relying on various features that we detail in this paper. We submitted 4 models which differences are also detailed in this paper. Best results were obtained when using a combination of lexical and statistical features.


title: “ Little Brothers Watching You : ” Raising Awareness of Data Leaks on Smartphones

abstract: Today's smartphone applications expect users to make decisions about what information they are willing to share, but fail to provide sufficient feedback about which privacy-sensitive information is leaving the phone, as well as how frequently and with which entities it is being shared. Such feedback can improve users' understanding of potential privacy leakages through apps that collect information about them in an unexpected way. Through a qualitative lab study with 19 participants, we first discuss misconceptions that smartphone users currently have with respect to two popular game applications that frequently collect the phone's current location and share it with multiple third parties. To measure the gap between users' understanding and actual privacy leakages, we use two types of interfaces that we developed: just-in-time notifications that appear the moment data is shared and a visualization that summarizes the shared data. We then report on participants' perceived benefits and concerns regarding data sharing with smartphone applications after experiencing notifications and having viewed the visualization. We conclude with a discussion on how heightened awareness of users and usable controls can mitigate some of these concerns.


title: Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems

abstract: Abstract With the rapid development of cloud computing, how to reduce energy consumption as well as maintain high computation capacity has become a timely and important challenge. Existing Virtual Machines (VMs) scheduling schemes have mainly focused on enhancing the cluster resource utilization and reducing power consumption by improving the legacy “bin-packing” algorithm. However, different resource-intensive applications running on VMs in realistic scenarios have significant effects on the system performance and energy consumption. Furthermore, instantaneous peak loads may lead to a scheduling error, which can significantly impede the energy efficiency of scheduling algorithms. In this paper, we propose a new scheduling approach named P r e A n t P o l i c y that consists of a prediction model based on fractal mathematics and a scheduler on the basis of an improved ant colony algorithm. The prediction model determines whether to trigger the execution of the scheduler by virtue of load trend prediction, and the scheduler is responsible for resource scheduling while minimizing energy consumption under the premise of guaranteeing the Quality-of-Service (QoS). Through extensive analysis and simulation experiments using real workload traces from the compute clusters of Google, the performance results demonstrate that the proposed approach exhibits excellent energy efficiency and resource utilization. Moreover, this approach offers an effective dynamic capacity provisioning model for resource-intensive applications in a heterogeneous computing environment and can reduce the consumption of system resources and energy when scheduling is triggered by instantaneous peak loads.


title: Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos

abstract: Recent advances in media generation techniques have made it easier for attackers to create forged images and videos. State-of-the-art methods enable the real-time creation of a forged version of a single video obtained from a social network. Although numerous methods have been developed for detecting forged images and videos, they are generally targeted at certain domains and quickly become obsolete as new kinds of attacks appear. The method introduced in this paper uses a capsule network to detect various kinds of spoofs, from replay attacks using printed images or recorded videos to computer-generated videos using deep convolutional neural networks. It extends the application of capsule networks beyond their original intention to the solving of inverse graphics problems.


Neural Shift-Reduce CCG Semantic Parsing


title: Color image segmentation based on homogram thresholding and region merging

abstract: In this paper, a color image segmentation approach based on homogram thresholding and region merging is presented. The homogram considers both the occurrence of the gray levels and the neighboring homogeneity value among pixels. Therefore, it employs both the local and global information. Fuzzy entropy is utilized as a tool to perform homogram analysis for finding all major homogeneous regions at the first stage. Then region merging process is carried out based on color similarity among these regions to avoid oversegmentation. The proposed homogram-based approach (HOB) is compared with the histogram-based approach (HIB). The experimental results demonstrate that the HOB can find homogeneous regions more effectively than HIB does, and can solve the problem of discriminating shading in color images to some extent.


Chapter 2 Communicating Scientific Findings in Court


title: Deep Learning Advances in Computer Vision with 3D Data: A Survey

abstract: Deep learning has recently gained popularity achieving state-of-the-art performance in tasks involving text, sound, or image processing. Due to its outstanding performance, there have been efforts to apply it in more challenging scenarios, for example, 3D data processing. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required.


title: Semi-dense Visual Odometry for a Monocular Camera

abstract: We propose a fundamentally novel approach to real-time visual odometry for a monocular camera. It allows to benefit from the simplicity and accuracy of dense tracking - which does not depend on visual features - while running in real-time on a CPU. The key idea is to continuously estimate a semi-dense inverse depth map for the current frame, which in turn is used to track the motion of the camera using dense image alignment. More specifically, we estimate the depth of all pixels which have a non-negligible image gradient. Each estimate is represented as a Gaussian probability distribution over the inverse depth. We propagate this information over time, and update it with new measurements as new images arrive. In terms of tracking accuracy and computational speed, the proposed method compares favorably to both state-of-the-art dense and feature-based visual odometry and SLAM algorithms. As our method runs in real-time on a CPU, it is of large practical value for robotics and augmented reality applications.


title: The Internet Gaming Disorder Scale

abstract: Recently, the American Psychiatric Association included Internet gaming disorder (IGD) in the appendix of the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–5). The main aim of the current study was to test the reliability and validity of 4 survey instruments to measure IGD on the basis of the 9 criteria from the DSM–5: a long (27-item) and short (9-item) polytomous scale and a long (27-item) and short (9-item) dichotomous scale. The psychometric properties of these scales were tested among a representative sample of 2,444 Dutch adolescents and adults, ages 13–40 years. Confirmatory factor analyses demonstrated that the structural validity (i.e., the dimensional structure) of all scales was satisfactory. Both types of assessment (polytomous and dichotomous) were also reliable (i.e., internally consistent) and showed good criterion-related validity, as indicated by positive correlations with time spent playing games, loneliness, and aggression and negative correlations with self-esteem, prosocial behavior, and life satisfaction. The dichotomous 9-item IGD scale showed solid psychometric properties and was the most practical scale for diagnostic purposes. Latent class analysis of this dichotomous scale indicated that 3 groups could be discerned: normal gamers, risky gamers, and disordered gamers. On the basis of the number of people in this last group, the prevalence of IGD among 13- through 40-year-olds in the Netherlands is approximately 4%. If the DSM–5 threshold for diagnosis (experiencing 5 or more criteria) is applied, the prevalence of disordered gamers is more than 5%.


title: A survey of hard real-time scheduling for multiprocessor systems

abstract: This survey covers hard real-time scheduling algorithms and schedulability analysis techniques for homogeneous multiprocessor systems. It reviews the key results in this field from its origins in the late 1960s to the latest research published in late 2009. The survey outlines fundamental results about multiprocessor real-time scheduling that hold independent of the scheduling algorithms employed. It provides a taxonomy of the different scheduling methods, and considers the various performance metrics that can be used for comparison purposes. A detailed review is provided covering partitioned, global, and hybrid scheduling algorithms, approaches to resource sharing, and the latest results from empirical investigations. The survey identifies open issues, key research challenges, and likely productive research directions.


title: Region of Interest Autoencoders with an Application to Pedestrian Detection

abstract: We present the Region of Interest Autoencoder (ROIAE), a combined supervised and reconstruction model for the automatic visual detection of objects. More specifically, we augment the detection loss function with a reconstruction loss that targets only foreground examples. This allows us to exploit more effectively the information available in the sparsely populated foreground training data used in common detection problems. Using this training strategy we improve the accuracy of deep learning detection models. We carry out experiments on the Caltech-USA pedestrian detection dataset and demonstrate improvements over two supervised baselines. Our first experiment extends Fast R-CNN and achieves a 4% relative improvement in test accuracy over its purely supervised baseline. Our second experiment extends Region Proposal Networks, achieving a 14% relative improvement in test accuracy.


title: Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization

abstract: Fine-grained visual categorization is an emerging research area and has been attracting growing attention recently. Due to the large inter-class similarity and intra-class variance, it is extremely challenging to recognize objects in fine-grained domains. A traditional spatial pyramid matching model could obtain desirable results for the basic-level category classification by weak alignment, but may easily fail in fine-grained domains, since the discriminative features are extremely localized. This paper proposes a new framework for fine-grained visual categorization. First, an efficient part localization method incorporates semantic prior into geometric alignment. It detects the less deformable parts, such as the head of birds with a template-based model, and localizes other highly deformable parts with simple geometric alignment. Second, we learn one-vs-all features, which are simple and transplantable. The learned mid-level features are dimension friendly and more robust to outlier instances. Furthermore, in view that some subcategories are too similar to tell them apart easily, we fuse the subcategories iteratively according to their similarities, and learn fused one-vs-all features. Experimental results show the superior performance of our algorithms over the existing methods.


title: What makes things fun to learn? heuristics for designing instructional computer games

abstract: In this paper, I will describe my intuitions about what makes computer games fun. More detailed descriptions of the experiments and the theory on which this paper is based are given by Malone (1980a, 1980b). My primary goal here is to provide a set of heuristics or guidelines for designers of instructional computer games. I have articulated and organized common sense principles to spark the creativity of instructional designers (see Banet, 1979, for an unstructured list of similar principles). To demonstrate the usefulness of these principles, I have included several applications to actual or proposed instructional games. Throughout the paper I emphasize games with educational uses, but I focus on what makes the games fun, not on what makes them educational. Though I will not emphasize the point in this paper, these same ideas can be applied to other educational environments and life situations. In a sense, the categories I will describe constitute a general taxonomy of intrinsic motivation —of what makes an activity fun or rewarding for its own sake rather than for the sake of some external reward (See Lepper and Greene, 1979). I think the essential characteristics of good computer games and other intrinsically enjoyable situations can be organized into three categories: challenge, fantasy, and curiosity .


1 “Design of High Gain Folded-Cascode Operational Amplifier Using 1.25 um CMOS


title: Transfer metric learning for action similarity using high-level semantics

abstract: We analyze the benefits of using high-level semantics for transfer metric learning.We present a hierarchical model based on the embedded structure in attribute space.We propose a novel decorrelated normalized space (DNS) for transfer metric learning.We test using a challenging scheme where the target is more diverse than the source.Representation provides superior performance and DNS improves transfer effectiveness. The goal of transfer learning is to exploit previous experiences and knowledge in order to improve learning in a novel domain. This is especially beneficial for the challenging task of learning classifiers that generalize well when only few training examples are available. In such a case, knowledge transfer methods can help to compensate for the lack of data. The performance and robustness against negative transfer of these approaches is influenced by the interdependence between knowledge representation and transfer type. However, this important point is usually neglected in the literature; instead the focus lies on either of the two aspects. In contrast, we study in this work the effect of various high-level semantic knowledge representations on different transfer types in a novel generic transfer metric learning framework. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the semantic attribute space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach compared to state-of-the-art.


title: A Novel Approach for Addressing Wandering Off Elderly Using Low Cost Passive RFID Tags

abstract: Wandering (e.g. elopement) by elderly persons at acute hospitals and nursing homes poses a significant problem to providing patient care as such incidents can lead to injury and even accidental morbidity. These problems are particularly serious given aging populations around the world. While various technologies exit (such as door alarms), they are expensive and the reliability of such systems have not been evaluated in the past. In this article we propose two novel methods for a very low cost solution to address the problem of wandering off patients using body worn low cost passive Radio Frequency Identification (RFID) tags using phase based measurements. Our approach requires no modification to the air interface protocols, firmware or hardware. Results from extensive experiments show that: (i) the proposed algorithms can accurately identify whether a person is moving into or out of, for example, a room; and (ii) it can be implemented in real-time to develop a low cost wandering off alarm.


title: Towards an Information Extraction System Based on Ontology to Match Resumes and Jobs

abstract: While Internet takes up by far the most significant part of our daily lives, finding jobs/employees on the Internet has started to play a crucial role for job seekers and employers. Online recruitment websites and human resources consultancy and recruitment companies enable job seekers to create their resume, a brief written formal document including job seeker's basic information such as personal information, educational information, work experience and qualifications in order to find and apply for desirable jobs, whereas they enable companies to find qualified employees they are looking for. However resumes may be written in many ways that make it difficult for online recruitment companies to keep these data in their relational databases. In this study, a project that Kariyer.net (largest online recruitment website in Turkey) and TUBITAK (The Scientific and Technological Research Council of Turkey) have been jointly working is proposed. In this mentioned project, a system enables free structured format of resumes to transform into an ontological structure model. The produced system based on ontological structure model and called Ontology based Resume Parser (ORP) will be tested on a number of Turkish and English resumes. The proposed system will be kept in Semantic Web approach that provides companies to find expert finding in an efficient way.


title: Network Function Virtualization: State-of-the-art and Research Challenges

abstract: Network function virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling network functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in operating expenses (OPEX) and capital expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of software defined networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.


title: The Impact of Inrush Currents on the Mechanical Stress of High-Voltage Power Transformer Coils

abstract: From failure experience on power transformers, it was very often suspected that inrush currents, occurring when energizing unloaded transformers, were reason for damage. In this paper, it was investigated how mechanical forces within the transformer coils build up under inrush compared to those occurring at short circuit. Two-dimensional and three-dimensional computer modeling for a real 268 MVA, 525/17.75 kV three-legged step up transformer was employed. The results show that inrush current peaks of 70% of the rated short circuit current cause local forces in the same order of magnitude as those at short circuit. The resulting force summed up over the high voltage coil is even three times higher. Although inrush currents normally are smaller, the forces can have similar amplitudes as those at short circuit however with longer exposure time. Therefore, care has to be taken to avoid such high inrush currents. Today controlled switching offers an elegant and practical solution.


title: A Survey of the State of Cloud Computing in Healthcare

abstract: Analysts, researchers and organizations alike seem to agree that cloud computing will be a defining trend in the coming decade impacting wide range of businesses and how those businesses are practiced. Large technology companies are already investing millions of dollars in building infrastructure, services, tools and applications to facilitate cloud computing for consumers, organizations and businesses to use and take advantage. It remains to be seen how cloud computing will impact the healthcare business since it is very diverse, complex and unique and presents several challenges such as protecting members health records in addition to following HIPAA guidelines set by federal compliance regulations. In addition to these the rising cost of healthcare solutions is another major concern. Efforts are being made to reduce these costs for consumers and IT will play a big role in achieving it and also improving clinical and quality outcomes for patients. It will be very interesting to see how cloud computing will address and contribute towards these issues in the healthcare industry. The purpose of this paper is to explore the current state and trends of cloud computing in healthcare.


title: Potential Test of Papaya Leaf and Seed Extract (Carica Papaya) as Larvicides against Anopheles Mosquito Larvae Mortality. Sp in Jayapura, Papua Indonesia

abstract: Anopheles mosquitoes, sp is the main vector of malaria disease that is widespread in many parts of the world including in Papua Province. There are four speciesof Anopheles mosquitoes, sp, in Papua namely: An.farauti, An.koliensis, An. subpictus, and An.punctulatus. Larviciding synthetic cause resistance. This study aims to analyze the potential of papaya leaf and seeds extracts (Carica papaya) as larvicides against the mosquitoes Anopheles sp. The experiment was conducted at the Laboratory of Health Research and Development in Jayapura Papua province. The method used is an experimental post - only control group design. Sampling was done randomly on the larvae of Anopheles sp of breeding places in Kampung Kehiran Jayapura Sentani District, 1,500 larvae. Analysis of data using statistical analysis to test the log - probit mortality regression dosage, Kruskall - Wallis and Mann - Whitney. The results showed that papaya leaf extract effective in killing larvae of Anopheles sp, value Lethal Concentration (LC50) were 422.311 ppm, 1399.577 ppm LC90, Lethal Time (LT50) 13.579 hours, LT90 23.478 hours. Papaya seed extract is effective in killing mosquito larvae Anopheles sp, with 21.983 ppm LC50, LC90 ppm 137.862, 13.269 hours LT50, LT90 26.885 hours. Papaya seed extract is more effective in killing larvae of Anopheles sp. The mixture of papaya leaf extract and seeds are effective in killing mosquito larvae Anopheles sp, indicated by the percentage of larval mortality, the observation hours to 12, the highest larval mortality in comparison 0,05:0,1 extract, 52%, ratio 0.1 : 0.1 by 48 %, on a 24 hour observation, larval mortality in both groups reached 100 %.


title: Design of High Performance Permanent-Magnet Synchronous Wind Generators

abstract: This paper is devoted to the analysis and design of high performance permanent-magnet synchronous wind generators (PSWGs). A systematic and sequential methodology for the design of PMSGs is proposed with a high performance wind generator as a design model. Aiming at high induced voltage, low harmonic distortion as well as high generator efficiency, optimal generator parameters such as pole-arc to pole-pitch ratio and stator-slot-shoes dimension, etc. are determined with the proposed technique using Maxwell 2-D, Matlab software and the Taguchi method. The proposed double three-phase and six-phase winding configurations, which consist of six windings in the stator, can provide evenly distributed current for versatile applications regarding the voltage and current demands for practical consideration. Specifically, windings are connected in series to increase the output voltage at low wind speed, and in parallel during high wind speed to generate electricity even when either one winding fails, thereby enhancing the reliability as well. A PMSG is designed and implemented based on the proposed method. When the simulation is performed with a 6 Ω load, the output power for the double three-phase winding and six-phase winding are correspondingly 10.64 and 11.13 kW. In addition, 24 Ω load experiments show that the efficiencies of double three-phase winding and six-phase winding are 96.56% and 98.54%, respectively, verifying the proposed high performance operation.


title: A physics based model for twisted and coiled actuator

abstract: This paper presents the static and dynamic modeling for a recently discovered artificial muscle-twisted and coiled actuator (TCA). This actuator can generate large force and displacement; moreover, it is low-cost, easy to fabricate, and customizable. Since the discovery of TCA, it has been widely adopted for various robotic applications. Nevertheless, theoretical models to describe the static performance and dynamic response are underexplored. In this paper, we aim to model the statics and dynamics for TCA from physics perspective. Specifically, the developed model utilizes parameters related to the working principle and material properties of the actuator. Experiments are conducted to verify the proposed model, and the results demonstrate that the proposed model can predict the static performance and dynamic response for the actuator. Given the wide applications of TCA in robotics, the developed model will enable closed-loop control of robotic systems with TCAs to achieve precise motion.


title: Behavior recognition via sparse spatio-temporal features

abstract: A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio-temporal case. For this purpose, we show that the direct 3D counterparts to commonly used 2D interest point detectors are inadequate, and we propose an alternative. Anchoring off of these interest points, we devise a recognition algorithm based on spatio-temporally windowed data. We present recognition results on a variety of datasets including both human and rodent behavior.


title: Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

abstract: Background: Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data. Objective: To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations. Methods: We have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types. Results: Our results show that the SVM model achieved the best average F1-score of 89.1% on test data, outperforming the long short-term memory (LSTM) model with attention (F1-score of 65.72%) as well as the rule induction baseline system (F1-score of 7.47%) by a large margin. The bidirectional LSTM model with attention achieved the best performance among different RNN models. With the inclusion of additional features in the LSTM model, its performance can be boosted to an average F1-score of 77.35%. Conclusions: It shows that classical learning models (SVM) remains advantageous over deep learning models (RNN variants) for clinical relation identification, especially for long-distance intersentential relations. However, RNNs demonstrate a great potential of significant improvement if more training data become available. Our work is an important step toward mining EHRs to improve the efficacy of drug safety surveillance. Most importantly, the annotated data used in this study will be made publicly available, which will further promote drug safety research in the community. [JMIR Public Health Surveill 2018;4(2):e29]


title: doi:10.1093/comjnl/bxm041 Restoration of Bayer-sampled Image Sequences

abstract: Spatial resolution of digital images are limited due to optical/sensor blurring and sensor site density. In single-chip digital cameras, the resolution is further degraded because such devices use a color filter array to capture only one spectral component at a pixel location. The process of estimating the missing two color values at each pixel location is known as demosaicking. Demosaicking methods usually exploit the correlation among color channels. When there are multiple images, it is possible not only to have better estimates of the missing color values but also to improve the spatial resolution further (using super-resolution reconstruction). In this paper, we propose a multi-frame spatial resolution enhancement algorithm based on the projections onto convex sets technique.


Bridging the ‘two communities’: how an emerging primary healthcare global research consortium can help achieve universal health coverage in low and middle-income countries


title: Scheduling algorithms and operating systems support for real-time systems

abstract: This paper summarizes the state of the real-time field in the areas of scheduling and operating system kernels. Given the vast amount of work that has been done by both the operations research and computer science communities in the scheduling area, we discuss four paradigms underlying the scheduling approaches and present several exemplars of each. The four paradigms are: static table-driven scheduling, static priority preemptive scheduling, dynamic planning-based scheduling, and dynamic best effort scheduling. In the operating system context, we argue that most of the proprietary commercial kernels as well as real-time extensions to time-sharing operating system kernels do not fit the needs of predictable realtime systems. We discuss several research kernels that are currently being built to explicitly meet the needs of real-time applications. >


title: Fast Online Upper Body Pose Estimation from Video.

abstract: Estimation of human body poses from video is an important problem in computer vision with many applications. Most existing methods for video pose estimation are offline in nature, where all frames in the video are used in the process to estimate the body pose in each frame. In this work, we describe a fast online video upper body pose estimation method (CDBN-MODEC) that is based on a conditional dynamic Bayesian network model, which predicts upper body pose in a frame without using information from future frames. Our method combines fast single image based pose estimation methods with the temporal correlation of poses between frames. We collect a new high frame rate upper body pose dataset that better reflects practical scenarios calling for fast online video pose estimation. When evaluated on this dataset and the VideoPose2 benchmark dataset, CDBN-MODEC achieves improvements in both performance and running efficiency over several state-of-art online video pose estimation methods.


title: Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm

abstract: A robot manipulator sharing its workspace with humans should be able to quickly detect collisions and safely react for limiting injuries due to physical contacts. In the absence of external sensing, relative motions between robot and human are not predictable and unexpected collisions may occur at any location along the robot arm. Based on physical quantities such as total energy and generalized momentum of the robot manipulator, we present an efficient collision detection method that uses only proprioceptive robot sensors and provides also directional information for a safe robot reaction after collision. The approach is first developed for rigid robot arms and then extended to the case of robots with elastic joints, proposing different reaction strategies. Experimental results on collisions with the DLR-III lightweight manipulator are reported.


title: Precise ellipse estimation without contour point extraction

abstract: This paper presents a simple linear operator that accurately estimates the parameters of ellipse features. Based on the dual conic model, the operator directly exploits the raw gradient information in the neighborhood of an ellipse’s boundary, thus avoiding the intermediate stage of precisely extracting individual edge points. Moreover, under the dual representation, the dual conic can easily be constrained to a dual ellipse when minimizing the algebraic distance. The new operator is compared to other estimation approaches, including those limited to the center position, in simulation as well as in real situation experiments.


title: SecMANO: Towards Network Functions Virtualization (NFV) Based Security MANagement and Orchestration

abstract: Network Functions Virtualization (NFV) has recently emerged as one of the major technological driving forces that significantly accelerate the evolution of today's computer and communication networks. Despite the advantages of NFV, e.g., saving investment cost, optimizing resource consumption, improving operational efficiency, simplifying network service lifecycle management, lots of novel security threats and vulnerabilities will be introduced, thereby impeding its further development and deployment in practice. In this paper, we briefly report our threat analysis in the context of NFV, and identify the corresponding security requirements. The purpose is to establish a comprehensive threat taxonomy and provide a guideline to develop effective security countermeasures. Furthermore, a conceptual design framework for NFV based security management and service orchestration is presented, with an objective to dynamically and adaptively deploy and manage security functions on the demands of users and customers. A use case about NFV based access control is also developed, illustrating the feasibility and advantages of implementing NFV based security management and orchestration.


title: On Higher-order Singular Value Decomposition from Incomplete Data

abstract: Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values, one can first impute the missing entries through a certain tensor completion method and then perform HOSVD to the reconstructed data. However, the two-step procedure can be inefficient and does not make reliable decomposition. In this paper, we formulate an incomplete HOSVD problem and combine the two steps into solving a single optimization problem, which simultaneously achieves imputation of missing values and also tensor decomposition. We also present two algorithms for solving the problem based on block coordinate update. Global convergence of both algorithms is shown under mild assumptions. The convergence of the second algorithm implies that of the popular higher-order orthogonality iteration (HOOI) method, and thus we, for the first time, give global convergence of HOOI. In addition, we compare the proposed methods to state-of-the-art ones for solving incomplete HOSVD and also low-rank tensor completion problems and demonstrate the superior performance of our methods over other compared ones. Furthermore, we apply them to face recognition and MRI image reconstruction to show their practical performance.


title: Resource allocation across multiple cloud data centres

abstract: Web applications with rich AJAX-driven user interfaces make asynchronous server-side calls to switch application state. To provide the best user experience, the response time of these calls must be as low as possible. Since response time is bounded by network delay, it can be minimised by placing application components closest to the network location of the majority of anticipated users. However, with a limited budget for hosting applications, developers need to select data centre locations strategically. In practice, the best choice is difficult to achieve manually due to dynamic client workloads and effects such as flash crowds. In this paper, we propose a cloud management middleware that automatically adjusts the placement of web application components across multiple cloud data centres. Based on observations and predictions of client request rates, it migrates application components between data centres. Our evaluation with two data centres and globally distributed clients on PlanetLab shows that our approach can decrease median client response times by 21% for a realistic multi-tier web application.


title: Development of robot hand with suction mechanism for robust and dexterous grasping

abstract: In this study, we propose a robot hand referred to as iGRIPP 4 (Integrated Gripper for Power and Precision Grasp 4), which has suction mechanism at each fingertip. This robot hand can grasp various objects steadily and achieve dexterous manipulations with a simple mechanism. The iGRIPP 4 has three fingers and two servomotors which are designed to grasp objects in power grasp and precision grasp. The suction mechanism at the fingertips enhances grasp stability, and enables the hand to hold large objects. In addition, the combination of the grasp mechanism and the suction system make it possible to perform some dexterous manipulations. Examples of the manipulations include the picking up a thin object and the packaging an object in a bag. These manipulations generally require hands with many degrees of freedom and intricate control. In this paper, the mechanism of the iGRIPP 4 is presented, and the two dexterous manipulations that can be done with this robot hand are described.


title: Video Propagation Networks

abstract: We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a Video Propagation Network that processes video frames in an adaptive manner. The model is applied online: it propagates information forward without the need to access future frames. In particular we combine two components, a temporal bilateral network for dense and video adaptive filtering, followed by a spatial network to refine features and increased flexibility. We present experiments on video object segmentation and semantic video segmentation and show increased performance comparing to the best previous task-specific methods, while having favorable runtime. Additionally we demonstrate our approach on an example regression task of color propagation in a grayscale video.


title: Generalized dictionary for multitask learning with boosting

abstract: While multitask learning has been extensively studied, most existing methods rely on linear models (e.g. linear regression, logistic regression), which may fail in dealing with more general (nonlinear) problems. In this paper, we present a new approach that combines dictionary learning with gradient boosting to achieve multitask learning with general (nonlinear) basis functions. Specifically, for each task we learn a sparse representation in a nonlinear dictionary that is shared across the set of tasks. Each atom of the dictionary is a nonlinear feature mapping of the original input space, learned in function space by gradient boosting. The resulting model is a hierarchical ensemble where the top layer of the hierarchy is the task-specific sparse coefficients and the bottom layer is the boosted models common to all tasks. The proposed method takes the advantages of both dictionary learning and boosting for multitask learning: knowledge across tasks can be shared via the dictionary, and flexibility and generalization performance are guaranteed by boosting. More important, this general framework can be used to adapt any learning algorithm to (nonlinear) multitask learning. Experimental results on both synthetic and benchmark real-world datasets confirm the effectiveness of the proposed approach for multitask learning.


title: Context-Aware Synthesis and Placement of Object Instances

abstract: Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem. Solving it requires (a) determining a location to place an object in the scene and (b) determining its appearance at the location. Such an object insertion model can potentially facilitate numerous image editing and scene parsing applications. In this paper, we propose an end-to-end trainable neural network for the task of inserting an object instance mask of a specified class into the semantic label map of an image. Our network consists of two generative modules where one determines where the inserted object mask should be (i.e., location and scale) and the other determines what the object mask shape (and pose) should look like. The two modules are connected together via a spatial transformation network and jointly trained. We devise a learning procedure that leverage both supervised and unsupervised data and show our model can insert an object at diverse locations with various appearances. We conduct extensive experimental validations with comparisons to strong baselines to verify the effectiveness of the proposed network.


title: A Modular Architecture for Hybrid Planning with Theories

abstract: Planning technology has made huge strides, alongside other combinatorial optimisation solving technologies, over the past decade. Automated planning systems now exist for temporal and metric problems, including management of continuous time and concurrency, continuous numeric resources and action costs [3,1,2,12,7,8,11,9]. There is an increasing interest in combining planners with specialised solvers, such as optimisation alogorithms, to achieve a hybrid form of planning. In this context, the relationship between planning and model-checking, planning and constraint-solving and planning and control are all being clarified.


title: Disentangling Motion, Foreground and Background Features in Videos

abstract: This paper instroduces an unsupervised framework to ::: extract semantically rich features for video representation. ::: Inspired by how the human visual system groups objects ::: based on motion cues, we propose a deep convolutional ::: neural network that disentangles motion, foreground and ::: background information. The proposed architecture consists ::: of a 3D convolutional feature encoder for blocks of 16 ::: frames, which is trained for reconstruction tasks over the ::: first and last frames of the sequence. The model is trained ::: with a fraction of videos from the UCF-101 dataset taking as ::: ground truth the bounding boxes around the activity regions. ::: Qualitative results indicate that the network can successfully ::: update the foreground appearance based on pure-motion ::: features. The benefits of these learned features are shown ::: in a discriminative classification task when compared with ::: a random initialization of the network weights, providing a ::: gain of accuracy above the 10%.


title: FPGA-Based Speed Control IC for PMSM Drive With Adaptive Fuzzy Control

abstract: The new generation of field programmable gate array (FPGA) technologies enables an embedded processor intellectual property (IP) and an application IP to be integrated into a system-on-a-programmable-chip (SoPC) developing environment. Therefore, this study presents a speed control integrated circuit (IC) for permanent magnet synchronous motor (PMSM) drive under this SoPC environment. First, the mathematic model of PMSM is defined and the vector control used in the current loop of PMSM drive is explained. Then, an adaptive fuzzy controller adopted to cope with the dynamic uncertainty and external load effect in the speed loop of PMSM drive is proposed. After that, an FPGA-based speed control IC is designed to realize the controllers. The proposed speed control IC has two IPs, a Nios II embedded processor IP and an application IP. The Nios II processor is used to develop the adaptive fuzzy controller in software due to the complicated control algorithm and low sampling frequency control (speed control: 2 kHz). The designed application IP is utilized to implement the current vector controller in hardware owing to the requirement for high sampling frequency control (current loop: 16 kHz, pulsewidth modulation circuit: 4-8 MHz) but simple computation. Finally, an experimental system is set up and some experimental results are demonstrated.


title: Estimating User Location in Social Media with Stacked Denoising Auto-encoders

abstract: Only very few users disclose their physical locations, which may be valuable and useful in applications such as marketing and security monitoring; in order to automatically detect their locations, many approaches have been proposed using various types of information, including the tweets posted by the users. It is not easy to infer the original locations from textual data, because text tends to be noisy, particularly in social media. Recently, deep learning techniques have been shown to reduce the error rate of many machine learning tasks, due to their ability to learn meaningful representations of input data. We investigate the potential of building a deep-learning architecture to infer the location of Twitter users based merely on their tweets. We find that stacked denoising auto-encoders are well suited for this task, with results comparable to state-of-the-art models.


title: Automatic Driver Monitoring Using Electrical Impedance Measurement on Steering Wheel

abstract: This article deals with the analysis of dependence among the driver's bioelectrical impedance status and surrounding effects, which can negatively influence the driver's attention and thus lead to decrease the driver's safety while driving. Bioelectrical impedance was measured using AD5933 circuit and two conductive silicon electrodes placed on the steering wheel. The measurement was performed on several drivers of various age at a different daytime and in a different grade of traffic. Hereafter acquired data were statistically analyzed and significant values, which affect the measured driver's impedance with given probability were identified. Based on results of the analysis, we proposed an algorithm of software for detection the events that could possibly decrease the driver's safety. The driver's impedance data acquired from the steering wheel is used as a control variable for the detection algorithm.


title: Software Quality Issues when choosing a Programming Language

abstract: For high quality software, an important part of the project is the choice of the programming language and compiler to be used. This paper examines some of the issues affecting software quality that should be considered when making this choice, including the extent to which the programming language encourages the writing of good quality programs; the degree to which the precise effect of the programs written in the language have been formally defined; and the degree of confidence that can be placed in the compiler implementation. As an important part of this, the paper will include reference to the role of formal definitions of programming languages, programming language standards and compiler validation techniques.


title: An exploratory study of backtracking strategies used by developers

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


title: Confidence through Attention

abstract: Attention distributions of the generated translations are a useful bi-product of attention-based recurrent neural network translation models and can be treated as soft alignments between the input and output tokens. In this work, we use attention distributions as a confidence metric for output translations. We present two strategies of using the attention distributions: filtering out bad translations from a large back-translated corpus, and selecting the best translation in a hybrid setup of two different translation systems. While manual evaluation indicated only a weak correlation between our confidence score and human judgments, the use-cases showed improvements of up to 2.22 BLEU points for filtering and 0.99 points for hybrid translation, tested on English German and English Latvian translation.


title: Analytic problems for elliptic curves

abstract: We consider some problems of analytic number theory for elliptic curves which can be considered as analogues of classical questions around the distribution of primes in arithmetic progressions to large moduli, and of the question of twin primes. This leads to some local results on the distribution of the group structures of elliptic curves defined over a prime finite field, exhibiting an interesting dichotomy for the occurence of the possible groups. (Note : This paper was initially written in 2000/01, but after a four year wait for a referee report, it is now withdrawn and deposited in the arXiv).


title: Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT

abstract: Parental separation can be psychologically distressing for families-particularly when conflict between parents is high. In Australia, reforms to Family Law legislation have introduced mandatory mediation, which has increased the need for community-based family dispute resolution (FDR) services. Providers have responded to these needs with innovative solutions, including online computer-assisted mediation technologies. We evaluated the unified theory of acceptance and use of technology (UTAUT) within the context of an Australian online family dispute resolution (OFDR) system. The model was extended to include trust and personal web innovativeness, which we proposed would be implicated in the intentions of staff to use a family mediation service. A cross-sectional web-survey methodology was employed to collect data from 127 staff in the implementation organization. The measurement and structural models were evaluated with partial least squares (PLS) modeling and the results provided some support for our hypotheses. Specifically, the core UTAUT model was partially validated in this context. Moreover, effort expectancy mediated the effects of trust in technology on behavioral intention. Contrary to expectations, trust in the organization and innovativeness did not produce significant effects on intention. Practically, these results suggest that pre-contemplation technology acceptance research can have utility for change management and system design.


title: Aiding Autonomous Vehicles with Fault-tolerant V2V Communication

abstract: Vehicle-to-vehicle (V2V) communication is a key component of the future autonomous driving systems. V2V can provide an improved awareness of the surrounding environment, and the knowledge about the future actions of nearby vehicles. However, V2V communication is subject to different kind of failures and delays, so a distributed fault-tolerant approach is required for safe and efficient transportation. This work considers fully autonomous vehicles that operates using local sensory information, and aided with fault-tolerant V2V communication. The sensors provide all basic functionality, but are overridden by V2V whenever is possible to increase the efficiency. As an example scenario, we consider intersection crossing (IC) with autonomous vehicles that cooperate via V2V communication, and propose a fully distributed and a fault-tolerant algorithm for this problem. According to our numerical results, based on a real data set, we show the crossing delay is only slightly increased in the presence of a burst of V2V failures, and that V2V can be successfully used in most scenarios.


title: Efficient RF energy harvesting by using multiband microstrip antenna arrays with multistage rectifiers

abstract: This paper covers the design, implementation and usage of a matched multiband microstrip antenna intended to collect RF energy efficiently in commonly used portions of the selected cellular frequency spectrum including the ISM bands. Multistage Schottky rectifier antenna (rectenna) array circuit is proposed for high energy conversion efficiency to improve battery life and portability. Design details for the multiband microstrip antenna prototype is presented.


title: Systems & Control for the future of humanity, research agenda: Current and future roles, impact and grand challenges

abstract: Following in the footsteps of the renowned report “Control in an Information Rich World,” Report of the Panel on “Future Directions in Control, Dynamics, and Systems” chaired by Richard Murray (2002), this paper aims to demonstrate that Systems & Control is at the heart of the Information and Communication Technologies to most application domains. As such, Systems & Control should be acknowledged as a priority by funding agencies and supported at the levels necessary to enable technologies addressing critical societal challenges. A second intention of this paper is to present to the industrials and the young research generation, a global picture of the societal and research challenges where the discipline of Systems & Control will play a key role. Throughout, this paper demonstrates the extremely rich, current and future, cross-fertilization between five critical societal challenges and seven key research and innovation Systems & Control scientific challenges. This paper is authored by members of the IFAC Task Road Map Committee, established following the 19th IFAC World Congress in Cape Town. Other experts who authored specific parts are listed below.


title: Scene-Based Movie Summarization Via Role-Community Networks

abstract: Video summarization techniques aim at condensing a full-length video to a significantly shortened version that still preserves the major semantic content of the original video. Movie summarization, being a special class of video summarization, is particularly challenging since a large variety of movie scenarios and film styles complicate the problem. In this paper, we propose a two-stage scene-based movie summarization method based on mining the relationship between role-communities since the role-communities in earlier scenes are usually used to develop the role relationship in later scenes. In the analysis stage, we construct a social network to characterize the interactions between role-communities. As a result, the social power of each role-community is evaluated by the community's centrality value and the role communities are clustered into relevant groups based on the centrality values. In the summarization stage, a set of feasible summary combinations of scenes is identified and an information-rich summary is selected from these candidates based on social power preservation. Our evaluation results show that in at most test cases the proposed method achieves better subjective performance than attention-based and role-based summarization methods in terms of semantic content preservation for a movie summary.


title: Updating freeze: Aligning animal and human research

abstract: Abstract Freezing is widely used as the main outcome measure for fear in animal studies. Freezing is also getting attention more frequently in human stress research, as it is considered to play an important role in the development of psychopathology. Human models on defense behavior are largely based on animal models. Unfortunately, direct translations between animal and human studies are hampered by differences in definitions and methods. The present review therefore aims to clarify the conceptualization of freezing. Neurophysiological and neuroanatomical correlates are discussed and a translational model is proposed. We review the upcoming research on freezing in humans that aims to match animal studies by using physiological indicators of freezing (bradycardia and objective reduction in movement). Finally, we set the agenda for future research in order to optimize mutual animal–human translations and stimulate consistency and systematization in future empirical research on the freezing phenomenon.


title: The thing about the quotes: “Music Technology” degrees in Britain

abstract: This is a paper about the educational landscape of Music Technology in Britain. Betweening is a project funded by Palatine (Higher Education Academy). Its aim is to investigate and collate information about existing models for running degree courses related to the interdisciplinary ::: field of music technology in higher education institutions in Britain. The gathering of this information will result in the provision of data for representing the educational landscape of music technology in Britain and will provide an oversight of the different models used in different higher education institutions.


title: An adaptive write buffer management scheme for flash-based SSDs

abstract: Solid State Drives (SSD's) have shown promise to be a candidate to replace traditional hard disk drives. The benefits of SSD's over HDD's include better durability, higher performance, and lower power consumption, but due to certain physical characteristics of NAND flash, which comprise SSD's, there are some challenging areas of improvement and further research. We focus on the layout and management of the small amount of RAM that serves as a cache between the SSD and the system that uses it. Of the techniques that have previously been proposed to manage this cache, we identify several sources of inefficient cache space management due to the way pages are clustered in blocks and the limited replacement policy. We find that in many traces hot pages reside in otherwise cold blocks, and that the spatial locality of most clusters can be fully exploited in a limited time period, so we develop a hybrid page/block architecture along with an advanced replacement policy, called BPAC, or Block-Page Adaptive Cache, to exploit both temporal and spatial locality. Our technique involves adaptively partitioning the SSD on-disk cache to separately hold pages with high temporal locality in a page list and clusters of pages with low temporal but high spatial locality in a block list. In addition, we have developed a novel mechanism for flash-based SSD's to characterize the spatial locality of the disk I/O workload and an approach to dynamically identify the set of low spatial locality clusters. We run trace-driven simulations to verify our design and find that it outperforms other popular flash-aware cache schemes under different workloads. For instance, compared to a popular flash aware cache algorithm BPLRU, BPAC reduces the number of cache evictions by up to 79.6% and 34% on average.


title: Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction

abstract: We propose a novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word similarity. The improved vectors significantly outperform standard models and distinguish antonyms from synonyms with an average precision of 0.66-0.76 across word classes (adjectives, nouns, verbs). Moreover, we integrate the lexical contrast vectors into the objective function of a skip-gram model. The novel embedding outperforms state-of-the-art models on predicting word similarities in SimLex-999, and on distinguishing antonyms from synonyms.


title: Performance evaluation of the UFMC scheme under various transmission impairments

abstract: The next generation mobile network (fifth generation or 5G) considers the novel multi-carrier (MC) technique dubbed as Universal Filtered Multi-Carrier (UFMC) for implementation. It maintains the advantages of Orthogonal Frequency Division Multiplexing (OFDM) from the fourth generation while avoiding its drawbacks; mainly, the requirements for a strict synchronization between transmitter and receiver. This convenience, however, increases computational complexity as UFMC employs filters to achieve this effect. While the performance of OFDM and UFMC against frequency synchronization error is investigated thoroughly, their performance to delay spread (aka multipath) and timing error has little attention. This paper contains the results of a simulation of UFMC against these communication impediments using MATLAB/Simulink™. In both schemes, the bit error rate is measured when the same number of bits modulated, transmitted, equalized, and demodulated. It is shown that UFMC outshines OFDM under both time and frequency synchronization errors and lags in performance under delay spread when delay spread is below a certain value. This is expected as OFDM preserves orthogonality due to the use of a cyclic prefix (CP). Also investigated in this study is the effect of increasing the filter length of UFMC in its performance. An increase in performance is evident for increasing lengths, but this convenience requires an increase in the number of sub-bands which complicates the system.


Cache Missing for Fun and Profit


Critical Success Factors for ERP Implementation : A Classification


title: Time course of changes in performance and inflammatory responses after acute plyometric exercise.

abstract: The objectives of the present investigation were to study the inflammatory and performance responses after an acute bout of intense plyometric exercise during a prolonged recovery period. Participants were randomly assigned to either an experimental group (P, n = 12) that performed intense plyometric exercises or a control group (C, n = 12) that rested. The delayed onset of muscle soreness (DOMS), knee range of motion (KROM), creatine kinase (CK) and lactate dehydrogenase (LDH) activities, white blood cell count, C reactive protein (CRP), uric acid (UA), cortisol, testosterone, IL-6, IL-1b strength (isometric and isokinetic), and countermovement (CMJ) and static (SJ) jumping performance were measured at rest, immediately postexercise and at 24, 48, 72, 96, and 120 hours of recovery. Lactate was measured at rest and postexercise. Strength remained unchanged throughout recovery, but CMJ and SJ declined (p < 0.05) by 8-20%. P induced a marked rise in DOMS, CK, and LDH (peaked 24-48 hours postexercise) and a KROM decline. An acute-phase inflammatory response consisting of leukocytosis (postexercise and at 24 hours), an IL-6, IL-1b, CRP, and cortisol elevation (during the first 24 hours of recovery) and a delayed increase of UA (peaked at 48 hours) and testosterone (peaked at 72 hours) was observed in P. The results of this investigation indicate that performing an acute bout of intense plyometric exercise may induce a short-term muscle damage and marked but transient inflammatory responses. Jumping performance seems to deteriorate for as long as 72 hours postexercise, whereas strength appears to remain unchanged. The acute-phase inflammatory response after a plyometric exercise protocol appears to follow the same pattern as in other exercise models. These results clearly indicate the need of sufficient recovery between successive plyometric exercise training sessions.