title: OBSTACLE AVOIDANCE PROCEDURE FOR MOBILE ROBOTS

abstract: The common set of procedures provided by most of indoor mobile robots consists of Mapping Building, Auto- localization, and Navigation (Path Planning and Obstacle Avoidance). Considering that real indoor environments are commonly dynamic where known obstacles (e.g. chairs, tables, sofas, etc.) and unknown obstacles (persons walking close to the robot, unmapped environment characteristics, etc.) are constantly changing their positions, it is necessary to use sensor data to provide the robot a detailed real-time environmental view. Most of these obstacles may be located on a priori planned path and the robot controller must execute a skilful manoeuvre that avoids a collision and directs the robot to its goal position. Undoubtedly, it is essential that both navigation procedures (path planning and obstacle avoidance) work together and harmoniously, trying to solve possible conflicts between their two different behaviours (go to the goal position as soon as possible and avoid potential collisions during the path). In this article we describe the development of a new obstacle avoidance procedure based on the previous Obstacle Velocity approach. This procedure was denominated as BOA and was tested in a mobile robot in wide and narrow environments. The robot has been equipped with an intelligent control and navigation system and two SICK two-dimensional (2-D) laser range finder LMS 200.


Intrusion Detection Systems & Multisensor Data Fusion: Creating Cyberspace Situational Awareness


title: Effective Slot Filling Based on Shallow Distant Supervision Methods

abstract: Spoken Language Systems at Saarland University (LSV) participated this year with 5 runs at the TAC KBP English slot filling track. Effective algorithms for all parts of the pipeline, from document retrieval to relation prediction and response post-processing, are bundled in a modular end-to-end relation extraction system called RelationFactory. The main run solely focuses on shallow techniques and achieved significant improvements over LSV's last year's system, while using the same training data and patterns. Improvements mainly have been obtained by a feature representation focusing on surface skip n-grams and improved scoring for extracted distant supervision patterns. Important factors for effective extraction are the training and tuning scheme for distant supervision classifiers, and the query expansion by a translation model based on Wikipedia links. In the TAC KBP 2013 English Slotfilling evaluation, the submitted main run of the LSV RelationFactory system achieved the top-ranked F1-score of 37.3%.


title: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

abstract: Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.


title: Potential Negative Effects of Mobile Learning on Students' Learning Achievement and Cognitive Load—A Format Assessment Perspective

abstract: Owing to the advancement of mobile and wireless communication technologies, an increasing number of mobile learning studies have been conducted in recent years. In a mobile learning environment, students are able to learn indoors and outdoors with access to online resources at any time. However, whether or not new learning scenarios that combine both real-world contexts and digital-world resources are beneficial to the students has been questioned. Moreover, it is also interesting to probe whether the existing e-learning strategies are effective when situated in those mobile learning scenarios. In this study, an in-field activity on an indigenous culture course of an elementary school with a formative assessment-based learning strategy was conducted to investigate the possible negative effects of mobile learning by analyzing the students’ cognitive load and learning achievement. It is interesting to find that, without proper treatment, the performance of students using those existing online learning strategies, known to be “effective,” might be disappointing or may even negatively affect the students’ learning achievements. Furthermore, the negative effects could be due to the heavy cognitive load caused by an improper learning design. Such findings offer good references for those who intend to design and conduct mobile learning activities.


title: Controlling the tip-sample force of contact mode atomic force microscopes using PI-like fuzzy control technique

abstract: In this study, a PI-like fuzzy logic controller is designed to accommodate the nonlinear behaviors in the constant tip-deflection systems for contact mode AFM. By exploiting the fuzzy logic structure of the controller, heuristic knowledge is incorporated and results in a non-linear controller with improved transient performance over traditional PI controllers. Using the proposed controller allows the cantilever tip to track sample surface rapidly and accurately. The rapid tracking response facilitates us to observe high aspect ratio micro structure accurately and quickly. Additionally, continuously manual gain tuning by trial and error in commercial AFMs is alleviated. In final, the proposed PI-like fuzzy controller shows better performance than traditional PI controllers.


title: Resilient wireless sensor networks for cyber-physical systems

abstract: Owing to their low deployment costs, wireless sensor networks (WSN) may act as a key enabling technology for a variety of spatially distributed cyber-physical system (CPS) applications, ranging from intelligent traffic control to smart grids. However, besides providing tremendous benefits in terms of deployment costs, they also open up new possibilities for malicious attackers, who aim to cause financial losses or physical damage. Since perfectly securing these spatially distributed systems is either impossible or financially unattainable, we need to design them to be resilient to attacks: even if some parts of the system are compromised or unavailable due to the actions of an attacker, the system as a whole must continue to operate with minimal losses. In a CPS, control decisions affecting the physical process depend on the observed data from the sensor network. Any malicious activity in the sensor network can therefore severely impact the physical process, and consequently the overall CPS operations. These factors necessitate a deeper probe into the domain of resilient WSN for CPS. In this chapter, we provide an overview of various dimensions in this field, including objectives of WSN in CPS, attack scenarios and vulnerabilities, the notion of attack resilience in WSN for CPS, and solution approaches toward attaining resilience. We also highlight major challenges, recent developments, and future directions in this area.


title: Robust portfolios: contributions from operations research and finance

abstract: In this paper we provide a survey of recent contributions to robust portfolio strategies from operations research and finance to the theory of portfolio selection. Our survey covers results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estimation methods and Bayesian robust approaches.


title: Designing products with added emotional value : development and application of an approach for research through design

abstract: In this paper, a design approach is introduced for designing products with added emotional value. First, the approach was established, based on a theoretical framework and a non-verbal instrument to measure emotional responses. Second, the value of the design approach was assessed by applying it to the design of mobile telephones. Mobile telephones were found to be useful vehicles as people appear to have strong feelings about this product. A user study was conducted which resulted in the identification of two user groups (‘trend followers' and ‘security seekers') and for each group an emotional profile. Four telephones were designed which, for each group, either were intended to or were not intended to match the group's profile. Finally, these designs were evaluated with the non-verbal instrument. The results indicate that the approach is appropriate for designing products with added emotional value. The advantages and disadvantages of the approach are discussed and further research directions indicated.


title: Occlusion detection using horizontally segmented windows for vehicle tracking

abstract: This paper proposes an efficient algorithm for detecting occlusions in a video sequences of ground vehicles using color information. The proposed method uses a rectangular window to track a target vehicle, and the window is horizontally divided into several sub-regions of equal width. Each region is determined to be occluded or not based on the color histogram similarity to the corresponding region of the target. The occlusion detection results are used in likelihood computation of the conventional tracking algorithm based on particle filtering. Experimental results in real scenes show that the proposed method finds the occluded region successfully and improves the performance of the conventional trackers.


title: Motion kinematics analysis of wheeled–legged rover over 3D surface with posture adaptation

abstract: This paper proposes a general formulation of the kinetostatic model of articulated wheeled rovers that move on rough terrains. Differential kinematic model is used to control the generalized trajectory of the robot, composed of position and posture parameters. These posture parameters have been optimized in order to provide high stability and traction performance, during motion on irregular ground surface. Numerical simulation and experimental results, carried out on a hybrid wheeled-legged robot, show the validity of the approach presented in this paper.


IN VITRO STUDY OF SELF EMULSIFYING DRUG DELIVERY SYSTEM OF POORLY WATER SOLUBLE DRUG SPIRONOLACTONE


title: HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications

abstract: Body area networks, including smart sensors, are widely reshaping health applications in the new era of smart cities. To meet increasing security and privacy requirements, physiological signal-based biometric human identification is gaining tremendous attention. This paper focuses on two major impediments: the signal processing technique is usually both complicated and data-dependent and the feature engineering is time-consuming and can fit only specific datasets. To enable a data-independent and highly generalizable signal processing and feature learning process, a novel wavelet domain multiresolution convolutional neural network is proposed. Specifically, it allows for blindly selecting a physiological signal segment for identification purpose, avoiding the complicated signal fiducial characteristics extraction process. To enrich the data representation, the random chosen signal segment is then transformed to the wavelet domain, where multiresolution time-frequency representation is achieved. An auto-correlation operation is applied to the transformed data to remove the phase difference as the result of the blind segmentation operation. Afterward, a multiresolution 1-D-convolutional neural network (1-D-CNN) is introduced to automatically learn the intrinsic hierarchical features from the wavelet domain raw data without data-dependent and heavy feature engineering, and perform the user identification task. The effectiveness of the proposed algorithm is thoroughly evaluated on eight electrocardiogram datasets with diverse behaviors, such as with or without severe heart diseases, and with different sensor placement methods. Our evaluation is much more extensive than the state-of-the-art works, and an average identification rate of 93.5% is achieved. The proposed multiresolution 1-D-CNN algorithm can effectively identify human subjects, even from randomly selected signal segments and without heavy feature engineering. This paper is expected to demonstrate the feasibility and effectiveness of applying the blind signal processing and deep learning techniques to biometric human identification, to enable a low algorithm engineering effort and also a high generalization ability.


title: Face recognition in a meeting room

abstract: We investigate the recognition of human faces in a meeting room. The major challenges of identifying human faces in this environment include low quality of input images, poor illumination, unrestricted head poses and continuously changing facial expressions and occlusion. In order to address these problems we propose a novel algorithm, dynamic space warping (DSW). The basic idea of the algorithm is to combine local features under certain spatial constraints. We compare DSW with the eigenface approach on data collected from various meetings. We have tested both front and profile face images and images with two stages of occlusion. The experimental results indicate that the DSW approach outperforms the eigenface approach in both cases.


title: KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare

abstract: The goal of diagnosis prediction task is to predict the future health information of patients from their historical Electronic Healthcare Records (EHR). The most important and challenging problem of diagnosis prediction is to design an accurate, robust and interpretable predictive model. Existing work solves this problem by employing recurrent neural networks (RNNs) with attention mechanisms, but these approaches suffer from the data sufficiency problem. To obtain good performance with insufficient data, graph-based attention models are proposed. However, when the training data are sufficient, they do not offer any improvement in performance compared with ordinary attention-based models. To address these issues, we propose KAME, an end-to-end, accurate and robust model for predicting patients' future health information. KAME not only learns reasonable embeddings for nodes in the knowledge graph, but also exploits general knowledge to improve the prediction accuracy with the proposed knowledge attention mechanism. With the learned attention weights, KAME allows us to interpret the importance of each piece of knowledge in the graph. Experimental results on three real world datasets show that the proposed KAME significantly improves the prediction performance compared with the state-of-the-art approaches, guarantees the robustness with both sufficient and insufficient data, and learns interpretable disease representations.


title: Managing the risk of IT outsourcing

abstract: While it can bring several benefits, IT outsourcing entails some risks. As is the case in other types of investments or business ventures, the risk associated with an IT outsourcing project must be evaluated and managed. This paper proposes a framework for the management of IT outsourcing risk and assesses the usefulness of the framework using data gathered about two cases of system development outsourcing. After providing a conceptual definition of risk and of risk exposure, the paper presents the proposed risk management framework. The two cases are then described, along with the evaluation of the level of risk exposure of each and the risk management mechanisms that were included in the contracts. The results of the study suggest that, by charting the various items that contribute to risk exposure and by specifically applying appropriate mechanisms that can target those elements with higher levels of risk exposure, outsourcing risk can be adequately managed.


title: An efficient protocol for realizing cooperative diversity in wireless networks

abstract: We develop two variants of an energy-efficient cooperative diversity protocol that combats fading induced by multipath propagation in wireless networks, The underlying techniques build upon the classical relay channel and related work and exploit space diversity available at distributed antennas through coordinated transmission and processing by cooperating radios. While applicable to any wireless setting, these protocols are particularly attractive in ad-hoc or peer-to-peer wireless networks, in which radios are typically constrained to employ a single antenna. Substantial energy-savings resulting from these protocols can lead to reduced battery drain, longer network lifetime, and improved network performance in terms of, e.g., capacity.


title: Time series classification from scratch with deep neural networks: A strong baseline

abstract: We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting. The proposed Fully Convolutional Network (FCN) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with the ResNet structure is also competitive. The global average pooling in our convolutional model enables the exploitation of the Class Activation Map (CAM) to find out the contributing region in the raw data for the specific labels. Our models provides a simple choice for the real world application and a good starting point for the future research. An overall analysis is provided to discuss the generalization capability of our models, learned features, network structures and the classification semantics.


title: Benefits of a Working Memory Training Program for Inattention in Daily Life: A Systematic Review and Meta-Analysis

abstract: BACKGROUND: Many common disorders across the lifespan feature impaired working memory (WM). Reported benefits of a WM training program include improving inattention in daily life, but this has not been evaluated in a meta-analysis. This study aimed to evaluate whether one WM training method has benefits for inattention in daily life by conducting a systematic review and meta-analysis. METHODS: We searched Medline and PsycINFO, relevant journals and contacted authors for studies with an intervention and control group reporting post-training estimates of inattention in daily life. To reduce the influence of different WM training methods on the findings, the review was restricted to trials evaluating the Cogmed method. A meta-analysis calculated the pooled standardised difference in means (SMD) between intervention and control groups. RESULTS: A total of 622 studies were identified and 12 studies with 13 group comparisons met inclusion criteria. The meta-analysis showed a significant training effect on inattention in daily life, SMD=-0.47, 95% CI -0.65, -0.29, pCONCLUSIONS: Benefits of a WM training program generalise to improvements in everyday functioning. Initial evidence shows that the Cogmed method has significant benefits for inattention in daily life with a clinically relevant effect size. Language: en


title: SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes

abstract: We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic segmentation is built on a deep autoencoder stack trained exclusively on synthetic depth data generated from our novel 3D scene library, SynthCam3D. Importantly, our network is able to segment real world scenes without any noise modelling. We present encouraging preliminary results.


title: Finding cut from the same cloth: Cross network link recommendation via joint matrix factorization

abstract: With the emergence of online forums associated with major diseases, such as diabetes mellitus, many patients are increasingly dependent on such disease-specific social networks to gain access to additional resources. Among these patients, it is common for them to stick to one disease-specific social network, although their desired resources might be spread over multiple social networks, such as patients with similar questions and concerns. Motivated by this application, in this paper, we focus on cross network link recommendation, which aims to identify similar users across multiple heterogeneous social networks. The problem setting is different from existing work on cross network link prediction, which either tries to link accounts of the same user from different social networks, or aims to match users with complementary expertise or interest. To approach the problem of cross network link recommendation, we propose to jointly decompose the user-keyword matrices from multiple social networks, while requiring them to share the same topics and user group-topic association matrices. This constraint comes from the fact that social networks dedicated to the same disease tend to share the same topics as well as the interests of users groups in certain topics. Based on this intuition, we construct a generic optimization framework, provide four instantiations and an iterative optimization algorithm with performance analysis. In the experiments, we demonstrate the superiority of the proposed algorithm over state-of-the-art techniques on various real-world data sets.


title: MOBA: a New Arena for Game AI

abstract: Games have always been popular testbeds for Artificial Intelligence (AI). In the last decade, we have seen the rise of the Multiple Online Battle Arena (MOBA) games, which are the most played games nowadays. In spite of this, there are few works that explore MOBA as a testbed for AI Research. In this paper we present and discuss the main features and opportunities offered by MOBA games to Game AI Research. We describe the various challenges faced along the game and also propose a discrete model that can be used to better understand and explore the game. With this, we aim to encourage the use of MOBA as a novel research platform for Game AI.


title: Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots

abstract: This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.


title: An Ultrahigh-Speed, Low Power Electrical Drive System

abstract: New emerging applications in the areas of portable power generation, small turbocompressors and spindles require the development of ultrahigh-speed, low power electrical drives. A 500 000 r/min, 100 W electrical drive system is presented. Because of the ultrahigh-speed requirements, standard machine design and power electronic topology choices no longer apply and the complete drive system has to be considered. A permanent magnet machine with a slotless litz-wire winding is used, which results in a low motor inductance and a high fundamental machine frequency. Three different combinations of power electronic topologies and commutation strategies have been experimentally investigated. A voltage source inverter with block commutation and an additional dc-dc converter is selected as the most optimal choice for the power electronics interface as it results in the lowest volume of the entire drive system due to lower switching losses, no heat sink cooling required, a small number of semiconductor devices, and relatively simple control implementation in a low cost digital signal processor.


title: Energy-efficient cache memories using a dual-Vt 4T SRAM cell with read-assist techniques

abstract: In order to improve the energy-efficiency of cache memories, this paper presents a static random access memory (SRAM) cell composed of four transistors using dual-Vt FinFET devices. The proposed 4T SRAM cell is designed by (i) removing pull-down transistors of the standard 6T SRAM, and (ii) using low-leakage high-Vt devices for pull-up transistors and fast low-Vt devices for access transistors. This dual-Vt design simultaneously improves hold and write characteristics, but results in a destructive read operation. Accordingly, read-assist techniques are employed to ensure a non-destructive and robust read operation. A selective row address decoder is also proposed to prevent the undesired write operation in half-selected cells. The 4T SRAM cell compared with the all-single-fin 6T counterpart has a 25% smaller layout area with an aspect ratio closer to one. Furthermore, using 7nm FinFET devices with a nominal supply voltage of 0.45V, the 4T SRAM cell achieves 3.5× lower cell leakage power. Because of these features, the energy consumption of a 32KB L1 (256KB L2) cache memory using 4T SRAM cell compared with its 6T counterpart is reduced by 18% (2×), with 35% (19%) higher cache access frequency.


SecureCoin: A Robust Secure and Efficient Protocol for Anonymous Bitcoin Ecosystem


title: A 0.18µm CMOS SoC for a 100m-range 10fps 200×96-pixel time-of-flight depth sensor

abstract: A number of potentially low-cost time-of-flight (ToF) 3D image sensors aiming at consumer electronics applications have recently appeared in CMOS. Diffused-light sensors taking advantage of SPAD pixels, conventional and pinned-photodiode lock-in pixels demonstrate centimeter-ranging performance in distances of typically up to 6m, and with the exception of, under low background light (BG) conditions. In those approaches, however, performance tends to rapidly deteriorate in severe BG conditions, such as outdoors, and long-distance ranges have yet to be reported. Another common limitation is their inability to cope with multi-echo target environments. Higher optical signal-to-background ratio (SBR), and hence better performance, is typically achieved by laser-scanning approaches, e.g. employing polygonal or MEMS mirrors. With the emerging need for high-resolution light detection and ranging (LIDAR) technologies in advanced driving-assistance systems (ADAS), we introduce an SoC that performs time-correlated single-photon counting (TCSPC) and complete DSP for a 100m-range ToF sensor. The chip provides the system-level electronics with a serial and low-bit-rate digital interface for: multi-echo distance, distance reliability, intensity, and BG-only intensity, thus mitigating system-level complexity and cost.


title: PHY layer performance evaluation of the IEEE 802.15.7 visible light communication standard

abstract: The IEEE 802.15.7 standard defines a PHY layer and medium access control (MAC) layer for visible light communication and promises data rates sufficient to support audio and video multimedia services. In this paper, we first provide an overview of different PHY types available in the IEEE 802.15.7 standard, then present an extensive comparative error rate performance evaluation of PHY I, II and III types through Monte Carlo simulations.


title: The Virtual Meditative Walk: Virtual Reality Therapy for Chronic Pain Management

abstract: Because the nature of chronic pain is complex, pharmacological analgesics are often not enough to achieve an ideal treatment plan. Virtual Reality (VR) technologies have emerged within medical research in recent years for treating acute pain, and proved to be an effective strategy based on pain distraction. This paper describes a VR system designed for chronic pain patients. The system incorporates biofeedback sensors, an immersive virtual environment, and stereoscopic sound titled the "Virtual Meditative Walk" (VMW). It was designed to enable chronic pain patients to learn Mindfulness-based stress reduction (MBSR), a form of meditation. By providing real-time visual and sonic feedback, VMW enables patients to learn how to manage their pain. A proof-of-concept user study was conducted to investigate the effectiveness of the VR system with chronic pain patients in clinical settings. Results show that the VMW was more effective in reducing perceived pain compared to the non-VR control condition.


title: Analysis on the Dropout Effect in Convolutional Neural Networks

abstract: Regularizing neural networks is an important task to reduce overfitting. Dropout [1] has been a widely-used regularization trick for neural networks. In convolutional neural networks (CNNs), dropout is usually applied to the fully connected layers. Meanwhile, the regularization effect of dropout in the convolutional layers has not been thoroughly analyzed in the literature. In this paper, we analyze the effect of dropout in the convolutional layers, which is indeed proved as a powerful generalization method. We observed that dropout in CNNs regularizes the networks by adding noise to the output feature maps of each layer, yielding robustness to variations of images. Based on this observation, we propose a stochastic dropout whose drop ratio varies for each iteration. Furthermore, we propose a new regularization method which is inspired by behaviors of image filters. Rather than randomly drop the activation, we selectively drop the activations which have high values across the feature map or across the channels. Experimental results validate the regularization performance of selective max-drop and stochastic dropout is competitive to the dropout or spatial dropout [2].


title: Automatic Identification and Classification of Misogynistic Language on Twitter

abstract: Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.


A Survey and Analysis on Classification and Regression Data Mining Techniques for Diseases Outbreak Prediction in Datasets


title: Monocular 3D pose estimation and tracking by detection

abstract: Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although current methods are able to recover 3D pose for a single person in controlled environments, they are severely challenged by real-world scenarios, such as crowded street scenes. To address this problem, we propose a three-stage process building on a number of recent advances. The first stage obtains an initial estimate of the 2D articulation and viewpoint of the person from single frames. The second stage allows early data association across frames based on tracking-by-detection. These two stages successfully accumulate the available 2D image evidence into robust estimates of 2D limb positions over short image sequences (= tracklets). The third and final stage uses those tracklet-based estimates as robust image observations to reliably recover 3D pose. We demonstrate state-of-the-art performance on the HumanEva II benchmark, and also show the applicability of our approach to articulated 3D tracking in realistic street conditions.


title: Planar Ultrawideband Modular Antenna (PUMA) arrays scalable to mm-waves

abstract: A new class of Planar Ultrawideband Modular Antenna (PUMA) arrays, the PUMAv3, is introduced. PUMAv3 is scalable to mm-wave frequencies (≈50 GHz) and improves bandwidth by 50% without the use of a matching network or external baluns. The enabling technical innovations include a new feeding mechanism that relies on capacitively loaded shorting vias and an alteration on the superstrate loading scheme. Infinite array full-wave simulations of a dual-polarized PUMAv3 suggest 10.6-47.6 GHz (4.5:1) operation with good VSWR levels out to θ = 45°, high port isolation and low cross-polarization.


title: Arthrodesis with Intramedular Fixation in Posttraumatic Arthrosis of the Midfoot: A Case Report

abstract: We present two middle-aged men with posttraumatic arthrosis of the midfoot. Both patients suffered from severe pain and their foot was unable to bear weight. Both were operated using new fusion bolt 6.5 mm and additional screws. In their case, arthrodesis was mandatory and effective intervention. After surgical treatment both patients were pain free and able to walk without crutches and return to daily work


title: An integrative framework of COBIT and TOGAF for designing IT governance in local government

abstract: To implement the good IT governance in local governance, we need an IT governance design for mature and appropriate models. There are some problems that appear in designing information technology governance in local government. The problem is how to control the information technology risk management at the operational level and designing information technology in mapping an integrated framework. So this led to the adoption of information technology in local government to be inefficient. This study proposes an integrated model framework of COBIT and TOGAF to design more comprehensive IT Governance to increase the efficiency of the application of IT in local government. An integrated model to solve the problem of IT risk management control are using the COBIT 5 framework. To solve the problem in designing an integrated IT governance is used TOGAF 9.1 framework. The result is the integration model combines three aspects: governance area, 6 principles and management area are used for the effective application of IT governance in local government.


T REE-STRUCTURED DECODING WITH DOUBLY-RECURRENT NEURAL NETWORKS


title: Circuit board defect detection using image processing and microcontroller

abstract: Errors are the inescapable quantities which must be resolved in order to achieve an efficient result and best quality. This paper suggests a basic and simplest method to quickly locate and detect the defects on printed circuit board which is termed as subtraction method. In this method, live image acquisition and inspection of circuit board is designed. The digital image preprocessing method is carried out on images. Subtraction method is used for comparison and matching of the reference image and defect image of a circuit board. Missing holes and missing paths can be detected and classified accordingly.


title: Maternal stress alters endocrine function of the feto-placental unit in rats

abstract: Prenatal stress (PS) can cause early and long-term developmental effects resulting in part from altered maternal and/or fetal glucocorticoid exposure. The aim of the present study was to assess the impact of chronic restraint stress during late gestation on feto-placental unit physiology and function in embryonic (E) day 21 male rat fetuses. Chronic stress decreased body weight gain and food intake of the dams and increased their adrenal weight. In the placenta of PS rats, the expression of glucose transporter type 1 (GLUT1) was decreased, whereas GLUT3 and GLUT4 were slightly increased. Moreover, placental expression and activity of the glucocorticoid “barrier” enzyme 11β-hydroxysteroid dehydrogenase type 2 was strongly reduced. At E21, PS fetuses exhibited decreased body, adrenal pancreas, and testis weights. These alterations were associated with reduced pancreatic β-cell mass, plasma levels of glucose, growth hormone, and ACTH, whereas corticosterone, insulin, IGF-1, and CBG levels were unaffected. Th...


title: Effectiveness of Mobile Applications in Vocabulary Teaching

abstract: Smartphones are increasingly becoming an ordinary part of our daily lives. With their remarkable capacity, applications used in these devices are extremely varied. In terms of language teaching, the use of these applications has opened new windows of opportunity, innovatively shaping the way instructors teach and students learn. This 4 week-long study aimed to investigate the effectiveness of a mobile application on teaching 40 figurative idioms from the Michigan Corpus of Academic Spoken English (MICASE) corpus compared to traditional activities. Quasi-experimental research design with pretest and posttest was employed to determine the differences between the scores of the control (n=25) and the experimental group (n=25) formed with convenience sampling. Results indicate that participants in the experimental group performed significantly better in the posttest, demonstrating the effectiveness of the mobile application used in this study on learning idioms. The study also provides recommendations towards the use of mobile applications in teaching vocabulary.


title: Personal Data: Thinking Inside the Box

abstract: We are in a 'personal data gold rush' driven by advertising being the primary revenue source for most online companies. These companies accumulate extensive personal data about individuals with minimal concern for us, the subjects of this process. This can cause many harms: privacy infringement, personal and professional embarrassment, restricted access to labour markets, restricted access to best value pricing, and many others. There is a critical need to provide technologies that enable alternative practices, so that individuals can participate in the collection, management and consumption of their personal data. In this paper we discuss the Databox, a personal networked device (and associated services) that collates and mediates access to personal data, allowing us to recover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data.


title: Semantic Indexing Using WordNet Senses

abstract: We describe in this paper a boolean Information Retrieval system that adds word semantics to the classic word based indexing. Two of the main tasks of our system, namely the indexing and retrieval components, are using a combined word-based and sense-based approach. The key to our system is a methodology for building semantic representations of open text, at word and collocation level. This new technique, called semantic indexing, shows improved effectiveness over the classic word based indexing techniques.


Printed in Great Britain AESTHETICS , VIDEO ART AND TELEVISION


title: Inertial Navigation System Aided by GPS and Selective Frequency Contents of Vector Measurements

abstract: This paper proposes an aiding technique to enhance error estimation in low-cost strapdown inertial navigation systems with application to Unmanned Air Vehicles (UAVs). The paper summarizes a high-accuracy, multi-rate integrated Global Positioning System/Inertial Navigation System (GPS/INS) using Extended Kalman Filter (EKF) for error compensation. A proposed new technique decomposes and optimally integrates the magnetic and gravitational observations in the EKF, taking into account the vehicle’s dynamics bandwidth information to properly trace inertial motion. In particular, the paper evidences that inertial misalignment errors and biases can be efiectively estimated resorting to the gravity vector low frequency information embodied in accelerometer measurements. The performance of the overall INS aiding architecture is assessed in simulation, and results obtained about UAV typical trajectories are presented and discussed.


title: Implementation and dual loop control of two phases interleaved boost converter for fuel cell applications

abstract: A fuel cell is usually a low-voltage with hybrid voltage/current source characteristics. So that their output voltage must be increased to approximately a few hundred volts to be suitable for vehicles power trains or for any industrial application. Therefore a dc-dc power converter is required to set up the fuel cell voltage to high voltage DC bus. In this paper, analysis, implementation and control of a dc-dc converter structure called two phases interleaved Boost converter (TBC) will be presented. This topology is widely used to reduce the input current ripples and reduce the size of passive component with high efficiency for fuel cell applications. The control of the IBC converter is designed by dual loop control that contains a voltage loop with a linear PI controller and a fast current loop with a non-linear sliding controller to ensure excellent performance results. The control loop has been validated by experimental results using small-scale test bench with dSPACE-1104 card.


title: Technological Application of Maltodextrins According to the Degree of Polymerization

abstract: Maltodextrin (MX) is an ingredient in high demand in the food industry, mainly for its useful physical properties which depend on the dextrose equivalent (DE). The DE has however been shown to be an inaccurate parameter for predicting the performance of the MXs in technological applications, hence commercial MXs were characterized by mass spectrometry (MS) to determine their molecular weight distribution (MWD) and degree of polymerization (DP). Samples were subjected to different water activities (aw). Water adsorption was similar at low aw, but radically increased with the DP at higher aw. The decomposition temperature (Td) showed some variations attributed to the thermal hydrolysis induced by the large amount of adsorbed water and the supplied heat. The glass transition temperature (Tg) linearly decreased with both, aw and DP. The microstructural analysis by X-ray diffraction showed that MXs did not crystallize with the adsorption of water, preserving their amorphous structure. The optical micrographs showed radical changes in the overall appearance of the MXs, indicating a transition from a glassy to a rubbery state. Based on these characterizations, different technological applications for the MXs were suggested.


title: Ontology-based access control model for security policy reasoning in cloud computing

abstract: There are many security issues in cloud computing service environments, including virtualization, distributed big-data processing, serviceability, traffic management, application security, access control, authentication, and cryptography, among others. In particular, data access using various resources requires an authentication and access control model for integrated management and control in cloud computing environments. Cloud computing services are differentiated according to security policies because of differences in the permitted access right between service providers and users. RBAC (Role-based access control) and C-RBAC (Context-aware RBAC) models do not suggest effective and practical solutions for managers and users based on dynamic access control methods, suggesting a need for a new model of dynamic access control that can address the limitations of cloud computing characteristics. This paper proposes Onto-ACM (ontology-based access control model), a semantic analysis model that can address the difference in the permitted access control between service providers and users. The proposed model is a model of intelligent context-aware access for proactively applying the access level of resource access based on ontology reasoning and semantic analysis method.


title: Knowlywood: Mining Activity Knowledge From Hollywood Narratives

abstract: Despite the success of large knowledge bases, one kind of knowledge that has not received attention so far is that of human activities. An example of such an activity is proposing to someone (to get married). For the computer, knowing that this involves two adults, often but not necessarily a woman and a man, that it often takes place in some romantic location, that it typically involves flowers or jewelry, and that it is usually followed by kissing, is a valuable asset for tasks like natural language dialog, scene understanding, or video search. This corresponds to the challenging task of acquiring semantic frames that capture human activities, their participating agents, and their typical spatio-temporal contexts. This paper presents a novel approach that taps into movie scripts and other narrative texts. We develop a pipeline for semantic parsing and knowledge distillation, to systematically compile semantically refined activity frames. The resulting knowledge base contains hundreds of thousands of activity frames, mined from about two million scenes of movies, TV series, and novels. A manual assessment study, with extensive sampling and statistical significance tests, shows that the frames and their attribute values have an accuracy of at least 80 percent. We also demonstrate the usefulness of activity knowledge by the extrinsic use case of movie scene search.


Real-time Multi-UAV Task Assignment in Dynamic and Uncertain Environments


Performance Managementand Performance Appraisal in the Public Sector 1


title: Athletes and novices are differently capable to recognize feint and non-feint actions

abstract: Fast motor reactions in sports often require the ability to predict the intended action of an opponent as early as possible. Therefore, the present paper investigates whether beach volleyball athletes are able to recognize different attack hits (i.e. smash vs. poke shot) at an earlier stage of the movement than novices. Beach volleyball athletes and novices took part in a response priming experiment (Experiment 1). Participants had to decide whether a presented target picture depicts a smash or a poke shot. Importantly, the preceding prime pictures were taken from different stages of the movements varying between the jump (beginning of the movements) and the hand-ball contact (end of the movements). Diverging response congruency effects was found for athletes and novices. Athletes were able to recognize at an earlier movement stage than novices which kind of attack hit was shown at the prime picture. It is suggested that athletes might implicitly read movement-related patterns in the depicted athlete’s body posture (e.g. the angle of the elbow). In contrast, novices might use information which is easier to access (e.g. hand-ball relation). In a second experiment, novice participants received a visual training to test for a potential perceptual source of the priming effects. Notably, participants did not improve their ability to differentiate the volleyball techniques, indicating that a better recognition performance in athletes is based on motor and not on perceptual expertise.


title: Enhanced step coverage by oblique angle physical vapor deposition

abstract: We present the use of an oblique angle physical vapor deposition (OAPVD) technique with substrate rotation to obtain conformal thin films with enhanced step coverage on patterned surfaces. We report the results of ruthenium (Ru) films sputter deposited on trench structures with aspect ratio ∼2 and show that OAPVD with an incidence angle less that 30° with respect to the substrate surface normal one can create a more conformal coating without overhangs and voids compared to that obtained by normal incidence deposition. A simple geometrical shadowing effect is presented to explain the results. The technique has the potential of extending the present PVD technique to future chip interconnect fabrication.


title: Miniaturized Substrate Integrated Waveguide Slot Antennas Based on Negative Order Resonance

abstract: Miniaturized waveguide slot antennas using negative order resonance are proposed and developed in this paper. The design of these novel resonant-type antennas is based on the study of a composite right/left-handed (CRLH) substrate integrated waveguide (SIW) starting from the dispersion diagram and the equivalent circuit. These proposed CRLH resonators are realized by etching the interdigital slot on the surface of the SIW. The slot acts as a series capacitor as well as a radiator leading to a CRLH antenna application. Two types of slot antennas, which are open-ended and short-ended, respectively, are proposed and discussed. The short-ended antenna, which represents a quasi-quarter-wavelength resonator, is characterized by a cavity-backed slot antenna thus providing a high gain. On the other hand, the open-ended antenna exhibits a small size owing to a quasi-half-wavelength operation in the left-handed (LH) region. Four antennas with one or two unit cells are fabricated and measured. Compared with the existing CRLH patch antennas and waveguide slot antennas, our antennas show advantages in terms of simplicity, low-profile, high efficiency, easy fabrication and integration with other circuits.


title: Scalable SPARQL Querying of Large RDF Graphs

abstract: The generation of RDF data has accelerated to the point where many data sets need to be partitioned across multiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, current solutions that allow the data to be partitioned across multiple machines are highly inecient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more ecient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query processing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster.


title: Semantic See-Through Rendering on Light Fields

abstract: We present a novel semantic light field (LF) refocusing technique that can achieve unprecedented see-through quality. Different from prior art, our semantic see-through (SST) differentiates rays in their semantic meaning and depth. Specifically, we combine deep learning and stereo matching to provide each ray a semantic label. We then design tailored weighting schemes for blending the rays. Although simple, our solution can effectively remove foreground residues when focusing on the background. At the same time, SST maintains smooth transitions in varying focal depths. Comprehensive experiments on synthetic and new real indoor and outdoor datasets demonstrate the effectiveness and usefulness of our technique.


title: End-to-End Quantum-like Language Models with Application to Question Answering

abstract: Language Modeling (LM) is a fundamental research topic ina range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models.


title: The Case for Holistic Data Integration

abstract: Current data integration approaches are mostly limited to few data sources, partly due to the use of binary match approaches between pairs of sources. We thus advocate for the development of more holistic, clustering-based data integration approaches that scale to many data sources. We outline different use cases and provide an overview of initial approaches for holistic schema/ontology integration and entity clustering. The discussion also considers open data repositories and so-called knowledge graphs.


title: A review on supply modulators for Envelope-Tracking Power Amplifiers

abstract: Envelope Tracking Power Amplifier (ET PA) is becoming prevalent because of its higher power efficiency (compared to its linear PAs) and higher linearity (compared to other high power-efficiency PAs such as Envelope Elimination and Restoration PAs and outphasing PAs). Of various building blocks in an ET PA, the supply modulator is the most critical block as the supply modulator has major effect on the overall performance of the ET PA, including bandwidth, linearity, power-efficiency, etc. In this paper, a comprehensive literature review on supply modulators for ET PAs is provided. Various supply modulator design architectures are discussed, and their advantages and drawbacks compared. The key design parameters and design trade-offs are also delineated. The reviews and discussions herein provide useful references and valuable insights to supply modulator designers.


Optimization Maze Robot Using A* and Flood Fill Algorithm


title: Learning Rates for Q-learning

abstract: In this paper we derive convergence rates for Q-learning. We show an interesting relationship between the convergence rate and the learning rate used in Q-learning. For a polynomial learning rate, one which is 1/tω at time t where ω∈(1/2,1), we show that the convergence rate is polynomial in 1/(1-γ), where γ is the discount factor. In contrast we show that for a linear learning rate, one which is 1/t at time t, the convergence rate has an exponential dependence on 1/(1-γ). In addition we show a simple example that proves this exponential behavior is inherent for linear learning rates.


Human-Robot Interaction: Recent Experiments and Future Work


Desired cell (K users) MODULATION, CODING AND SIGNAL P ROCESSING FOR WIRELESS C OMMUNICATIONS INTERFERENCE CANCELLATION FOR CELLULAR SYSTEMS: A CONTEMPORARY OVERVIEW Despite the enormous


Doped, Defect-Enriched Carbon Nanotubes as an Efficient Oxygen Reduction Catalyst for Anion Exchange Membrane Fuel Cells


title: Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

abstract: Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology. Remarkable progress has been made recently, and complex artificial neural sensory-motor systems can be built using this technology. Today, however, NE stands before a large conceptual challenge that must be met before there will be significant progress toward an age of genuinely intelligent neuromorphic machines. The challenge is to bridge the gap from reactive systems to ones that are cognitive in quality. In this paper, we describe recent advancements in NE, and present examples of neuromorphic circuits that can be used as tools to address this challenge. Specifically, we show how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilities.


WHAT IS A SYSTEMATIC LITERATURE REVIEW AND HOW DO I DO ONE


title: Supersaliency: Predicting Smooth Pursuit-Based Attention with Slicing CNNs Improves Fixation Prediction for Naturalistic Videos

abstract: Predicting attention is a popular topic at the intersection of human and computer vision, but video saliency prediction has only recently begun to benefit from deep learning-based approaches. Even though most of the available video-based saliency data sets and models claim to target human observers' fixations, they fail to differentiate them from smooth pursuit (SP), a major eye movement type that is unique to perception of dynamic scenes. In this work, we aim to make this distinction explicit, to which end we (i) use both algorithmic and manual annotations of SP traces and other eye movements for two well-established video saliency data sets, (ii) train Slicing Convolutional Neural Networks (S-CNN) for saliency prediction on either fixation- or SP-salient locations, and (iii) evaluate ours and over 20 popular published saliency models on the two annotated data sets for predicting both SP and fixations, as well as on another data set of human fixations. Our proposed model, trained on an independent set of videos, outperforms the state-of-the-art saliency models in the task of SP prediction on all considered data sets. Moreover, this model also demonstrates superior performance in the prediction of"classical"fixation-based saliency. Our results emphasize the importance of selectively approaching training set construction for attention modelling.


title: Enhancing Business Impact Analysis and Risk Assessment Applying a Risk-Aware Business Process Modeling and Simulation Methodology

abstract: Driven by the steadily growing number of natural disasters, the threat of terrorist and other criminal attacks as well as changed legislation and regulations, companies are increasingly forced to prepare against threats that endanger the survivability of crucial business activities. As a consequence, management has to pay more attention to business continuity issues including serious management commitment and more appropriate funding. Business impact analysis and risk assessment concepts enable adequate business continuity planning as they deliver essential information about the impact of resources' disruption on business. In this paper we present how these concepts can be enhanced through the application of the ROPE (Risk-Oriented Process Evaluation) methodology enabling risk-aware business process management and simulation. Moreover, we present essential extensions of the ROPE simulation capabilities leading to a more efficient and effective business continuity planning.


title: Constraint-Based Approaches for Balancing Bike Sharing Systems

abstract: In order to meet the users' demand, bike sharing systems must be regularly rebalanced. The problem of balancing bike sharing systems BBSS is concerned with designing optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS by means of Constraint Programming: first, we introduce two novel constraint models for the BBSS including a smart branching strategy that focusses on the most promising routes. Second, in order to speed-up the search process, we incorporate both models in a Large Neighborhood Search LNS approach that is adapted to the respective CP model. Third, we perform a computational evaluation on instances based on real-world data, where we see that the LNS approach outperforms the Branch & Bound approach and is competitive with other existing approaches.


title: Replication, statistical consistency, and publication bias

abstract: Abstract Scientific methods of investigation offer systematic ways to gather information about the world; and in the field of psychology application of such methods should lead to a better understanding of human behavior. Instead, recent reports in psychological science have used apparently scientific methods to report strong evidence for unbelievable claims such as precognition. To try to resolve the apparent conflict between unbelievable claims and the scientific method many researchers turn to empirical replication to reveal the truth. Such an approach relies on the belief that true phenomena can be successfully demonstrated in well-designed experiments, and the ability to reliably reproduce an experimental outcome is widely considered the gold standard of scientific investigations. Unfortunately, this view is incorrect; and misunderstandings about replication contribute to the conflicts in psychological science. Because experimental effects in psychology are measured by statistics, there should almost always be some variability in the reported outcomes. An absence of such variability actually indicates that experimental replications are invalid, perhaps because of a bias to suppress contrary findings or because the experiments were run improperly. Recent investigations have demonstrated how to identify evidence of such invalid experiment sets and noted its appearance for prominent findings in experimental psychology. The present manuscript explores those investigative methods by using computer simulations to demonstrate their properties and limitations. The methods are shown to be a check on the statistical consistency of a set of experiments by comparing the reported power of the experiments with the reported frequency of statistical significance. Overall, the methods are extremely conservative about reporting inconsistency when experiments are run properly and reported fully. The manuscript also considers how to improve scientific practice to avoid inconsistency, and discusses criticisms of the investigative method.


title: A Wideband Aperture Efficient 60-GHz Series-Fed E-Shaped Patch Antenna Array With Copolarized Parasitic Patches

abstract: A novel series-fed E-shaped patch antenna array with copolarized parasitic patches for enhanced aperture efficiency at 60-GHz unlicensed wideband applications is proposed. The additional radiation from the inserted copolarized parasitic patches between series-fed patches improves the gain flatness remarkably as well as the gain and aperture efficiency, because of the offset resonant frequency. The four-array antenna with parasitic patches presents 0.8-dB gain flatness over the whole 57–66 GHz unlicensed band, 14.5 dBi peak gain, and 63.6% aperture efficiency while that without parasitic patches shows 1.4-dB flatness, 13.4 dBi peak gain, and 49.2% aperture efficiency, without any change in the antenna size, $6.0\times 14.7\times 0.25$ mm2.


title: Automatic Segmentation and Yield Measurement of Fruit using Shape Analysis

abstract: requirements for the fruit harvesting system. In this paper, automatic segmentation and yield calculation of fruit based on shape analysis is presented. Color and shape analysis was utilized to segment the images of different fruits like apple, pomegranate, oranges, peach, litchi and plum obtained under different lighting conditions. First the input sectional tree image was converted from RGB colour space into the L*a*b colour space. The resultant image was then applied to the algorithm for fruit segmentation. The Edge detection and combination of a circular fitting algorithm was used for the automatic segmentation of fruit in the image. The resultant edge points were then used for fitting the approximate circular shape. The resultant fitted circles were used as a count of total number of fruits in an image. Hundred sectional tree images of different fruits were used for the segmentation and yield measurement. The results indicate that the proposed method can accurately segment the occluded fruits with the efficiency of 98% and the average yield measurement error was found as 31.4 %.


title: Asymmetrical Bell state analysis for photon-atoms hybrid system

abstract: Bell-state analysis (BSA) has great application in the quantum communication. To our best knowledge, the current works are devoted to the physical realization of symmetrical 2×2-dimensional or 2N × 2N-dimensional 2-qudit BSA, and there is no work focused on the physical realization of the asymmetrical high-dimensional (for example 3×4-dimensional 2-qudit) Bell-states complete analysis. In this paper, by using the nonlinear interaction between the atoms and photons, we propose a scheme to completely distinguish the asymmetrical 3×4-dimensional 2-qudit Bell states of a hybrid system. We use the quantum information splitting, which is exploited to resolve the degree-mismatch issue in the quantum state sharing schemes, as an example to show the application of the asymmetrical BSA. Finally, we discuss its possible realization with current experimental techniques. Our asymmetrical high-dimensional BSA protocol may pave a new way for high-capacity long-distance quantum communication.


title: A Method for Determining Customer and Utility Harmonic Contributions at the Point of Common Coupling

abstract: A new method is proposed in this paper to determine the harmonic contributions of a customer at the point of common coupling. The method can quantify customer and utility responsibilities for limit violations caused by either harmonic source changes or harmonic impedance changes. It can be implemented in current power quality monitors and digital revenue meters. The method makes it possible to develop fair and consistent billing schemes for harmonic distortion control.


title: A Fast Mechanical Switch for Medium-Voltage Hybrid DC and AC Circuit Breakers

abstract: The paper presents the design and experimental results of a Thomson coil based fast mechanical switch for hybrid AC and DC circuit breakers rated at 30 kV voltage and 630 A current. The compact design with optimized circuit parameters and geometric dimensions of components targets 2 mm travel within 1 ms when driven by a 2 mF capacitor bank pre-charged to 500 V. The use and design of a disc spring as the damping and holding mechanism is presented. Structural design of a complete switch assembly rather than just the actuator is given. Experimental results show that the switch can travel 1.3 mm in the first 1 ms, and 3.1 mm in the first 2 ms when driven by a 360 V 2 mF capacitor bank. Such fast mechanical switches facilitate hybrid circuit breaker interruptions within 2 or 3 milliseconds for ultra fast and highly efficient protections in 5–35 kV medium voltage DC as well as AC systems.


title: Augmented reality: Designing immersive experiences that maximize consumer engagement

abstract: Innovative marketers can now leverage augmented reality to craft immersive brand experiences, create more interactive advertising, and enable consumers to experience products and spaces in novel ways. Augmented reality (AR) is the practice of displaying digital information over people's real-time view of objects, people, or spaces in the physical world. While AR can play a valuable role in integrated marketing programs, little is known about the practice and how to execute effective AR programs in the marketplace. We address this gap by presenting a framework that describes the active and passive ingredients of augmented reality. We then describe the basic design decisions that marketers need to make when planning an augmented reality campaign. In addition, we explain how understanding and addressing the dynamics between various active and passive AR ingredients can help marketers to optimize their AR campaigns and enhance various types of consumer engagement: user-brand engagement, user-user engagement, and user-bystander engagement. Through our framework and analysis, we develop eight actionable recommendations—described with the acronym ENTANGLE—marketing managers can use to design immersive AR experiences that maximize consumer engagement.


title: Low-memory GEMM-based convolution algorithms for deep neural networks

abstract: Deep neural networks (DNNs) require very large amounts of computation both for training and for inference when deployed in the field. A common approach to implementing DNNs is to recast the most computationally expensive operations as general matrix multiplication (GEMM). However, as we demonstrate in this paper, there are a great many different ways to express DNN convolution operations using GEMM. Although different approaches all perform the same number of operations, the size of temporary data structures differs significantly. Convolution of an input matrix with dimensions $C \times H \times W$, requires $O(K^2CHW)$ additional space using the classical im2col approach. More recently memory-efficient approaches requiring just $O(KCHW)$ auxiliary space have been proposed. ::: We present two novel GEMM-based algorithms that require just $O(MHW)$ and $O(KW)$ additional space respectively, where $M$ is the number of channels in the result of the convolution. These algorithms dramatically reduce the space overhead of DNN convolution, making it much more suitable for memory-limited embedded systems. Experimental evaluation shows that our low-memory algorithms are just as fast as the best patch-building approaches despite requiring just a fraction of the amount of additional memory. Our low-memory algorithms have excellent data locality which gives them a further edge over patch-building algorithms when multiple cores are used. As a result, our low memory algorithms often outperform the best patch-building algorithms using multiple threads.


Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings.


title: Design of an anthropomorphic prosthetic hand driven by Shape Memory Alloy actuators

abstract: This paper presents the mechanical design of an ultra-lightweight, biomimetically actuated anthropomorphic hand for prosthetic purposes. The proposed design is based on an underactuated configuration of 16 joints and 7 active degrees of freedom. Shape Memory Alloy wires are used as motive elements of a specially designed actuation system installed within the envelope of the hand and forearm; due to their inherent contraction ability when heated, these innovative micro-actuators produce linear motion which is imparted to the fingers via a tendon transmission system. The overall design is completed with the integration of the necessary locking mechanisms for power saving. An experimental prototype of the suggested prosthesis has been fabricated using rapid prototyping techniques and it will be used as an evaluation platform for further research towards the development of a truly multifunctional, silent and cosmetically appealing hand for upper limb amputees.


title: Divide and Fuse: A Re-ranking Approach for Person Re-identification

abstract: As re-ranking is a necessary procedure to boost person re-identification (re-ID) performance on large-scale datasets, the diversity of feature becomes crucial to person reID for its importance both on designing pedestrian descriptions and re-ranking based on feature fusion. However, in many circumstances, only one type of pedestrian feature is available. In this paper, we propose a "Divide and use" re-ranking framework for person re-ID. It exploits the diversity from different parts of a high-dimensional feature vector for fusion-based re-ranking, while no other features are accessible. Specifically, given an image, the extracted feature is divided into sub-features. Then the contextual information of each sub-feature is iteratively encoded into a new feature. Finally, the new features from the same image are fused into one vector for re-ranking. Experimental results on two person re-ID benchmarks demonstrate the effectiveness of the proposed framework. Especially, our method outperforms the state-of-the-art on the Market-1501 dataset.


title: Centrality Measures In Text Mining: Prediction Of Noun Phrases That Appear In Abstracts

abstract: In this paper, we study different centrality measures being used in predicting noun phrases appearing in the abstracts of scientific articles. Our experimental results show that centrality measures improve the accuracy of the prediction in terms of both precision and recall. We also found that the method of constructing Noun Phrase Network significantly influences the accuracy when using the centrality heuristics itself, but is negligible when it is used together with other text features in decision trees.


title: Evaluation of Harmonic Content from a Tap Transformer Based Grid Connection System for Wind Power

abstract: Simulations done in MATLAB/Simulink together with experiments conducted at the Angstroms laboratory are used to evaluate and discuss the total harmonic distortion (THD) and total demand distortion (TDD) of a tap transformer based grid connection system. The grid connection topology can be used with different turbine and generator topologies and is here applied on a vertical axis wind turbine (VAWT) with a permanent magnet synchronous generator (PMSG) and its operational scheme. The full variable-speed wind conversion system consists of a diode rectifier, DC link, IGBT inverter, LCL-filter, and tap transformer. The full variable-speed operation is enabled by the use of the different step-up ratios of the tap transformer. In the laboratory study, a full experimental setup of the system was used, a clone of the on-site PMSG driven by a motor was used, and the grid was replaced with a resistive load. With a resistive load, grid harmonics and possible unbalances are removed. The results show a TDD and THD below 5% for the full operating range and harmonic values within the limits set up by IEEE-519. Furthermore, a change in tap, going to a lower step-up ratio, results in a reduction in both THD and TDD for the same output power.


title: STYX: a trusted and accelerated hierarchical SSL key management and distribution system for cloud based CDN application

abstract: Protecting the customer's SSL private key is the paramount issue to persuade the website owners to migrate their contents onto the cloud infrastructure, besides the advantages of cloud infrastructure in terms of flexibility, efficiency, scalability and elasticity. The emerging Keyless SSL solution retains on-premise custody of customers' SSL private keys on their own servers. However, it suffers from significant performance degradation and limited scalability, caused by the long distance connection to Key Server for each new coming end-user request. The performance improvements using persistent session and key caching onto cloud will degrade the key invulnerability and discourage the website owners because of the cloud's security bugs. In this paper, the challenges of secured key protection and distribution are addressed in philosophy of "Storing the trusted DATA on untrusted platform and transmitting through untrusted channel". To this end, a three-phase hierarchical key management scheme, called STYX1 is proposed to provide the secured key protection together with hardware assisted service acceleration for cloud-based content delivery network (CCDN) applications. The STYX is implemented based on Intel Software Guard Extensions (SGX), Intel QuickAssist Technology (QAT) and SIGMA (SIGn-and-MAc) protocol. STYX can provide the tight key security guarantee by SGX based key distribution with a light overhead, and it can further significantly enhance the system performance with QAT based acceleration. The comprehensive evaluations show that the STYX not only guarantees the absolute security but also outperforms the direct HTTPS server deployed CDN without QAT by up to 5x throughput with significant latency reduction at the same time.


title: Fast Fibonacci Encoding Algorithm

abstract: Data compression has been widely applied in many data pro- cessing areas. Compression methods use variable-length codes with the shorter codes assigned to symbols or groups of symbols that appear in the data frequently. Fibonacci code, as a representative of these codes, is often utilized for the compression of small numbers. Time consump- tion of encoding as well as decoding algorithms is important for some applications in the data processing area. In this case, eciency of these algorithms is extremely important. There are some works related to the fast decoding of variable-length codes. In this paper, we introduce the Fast Fibonacci encoding algorithm; our approach is up-to 4.6◊ more ecient than the conventional bit-oriented algorithm.


title: Possible complications of erythropoietin therapy in patients with chronic renal failure

abstract: Introduction There have been many publications in the past 20 years about positive effects of human recombinant erythropoietin, which is used in treatment of anemia, especially in patients on dialysis. Complications The most important complications in patients treated with erythropoietin include: hypertensive reactions; thrombosis of AV fistula in patients on hemodialysis and appearance of severe anemia as a part of Pure Red Cell Aplasia (PRCA). The first two complications were managed quite easily with adequate erythropoietin dosage, and slower establishment of normal hemoglobin kevel, hematocrit level and red blood cell count, (our "target" Hb varied between 100 and 110 g/dl). Pure Red Cell Aplasia (PRCA) Pure Red Cell Aplasia is a progressive, marked anemia with sudden appearance of signifacnt loss or complete absence of erythrocyte precursor cells in normal bone marrow. In patients with end stage renal disease treated with erythropoietin PRCA appears in acute form as a consequence of production of neutralizing antibodies to erythropoietin. Time period between the beginning of erythropoietin therapy and appearance of PRCA is from 3 weeks to approximately 9 months. Symptoms and signs PRCA is characterized by sudden appearance of anemia in patients who had a satisfactory response to erythropoietin therapy till that moment. In PRCA, anemia is normocytic, normochromic with normal survival of red blood cells, without deficit in components such as iron, folic acid or vitamin B12, low reticulocyte count, decrease in Hg and normal platelet count. Diagnosis Diagnosis is based on clinical data (marked anemia), bone marrow biopsy, which shows a lower number of precursor red blood cells and presence of antibodies against erythropoietin. Before PRCA is diagnosed, all other causes for erythropoietin resistance must be excluded. Therapy Therapy of PRCA is based on cessation of erythropoietin therapy (all kinds), and correction of anemia with blood transfusions. Incidence PRCA is very rare and occurs in less than 1:10.000 patient-years in patients treated with erythropoietin, not lethal by itself and generally reversible. Till December 31, 2002. PRCA has been diagnosed in 142 patients world wide. In Serbia and Montenegro till this moment there hasn't been a single case of this syndrome. Preventive measures Continuous follow-up of reticulocyte count is the first step. Although this is a very rare disease, most of European Societies of Nephrology made protocols that recommend only intravenous application of -epoetin. Considering this new situation, Nephrology Society of Serbia and Montenegro recommends that -epoetin should be given to patients on hemodialysis only intravenously, while subcutaneous application of -epoetin is recommended in patients before beginning the dialysis treatment and in patients on hemodialysis, or who had undergone kidney transplantation.


title: Index Modulation with PAPR and Beamforming for 5G MIMO-OFDM

abstract: Although key techniques for next-generation wireless communication have been explored separately, relatively little work has been done to investigate their potential cooperation for performance optimization. To address this problem, we propose a holistic framework for robust 5G communication based on multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM). More specifically, we design a new framework that supports: 1) index modulation based on OFDM (OFDM–M) [1]; 2) sub-band beamforming and channel estimation to achieve massive path gains by exploiting multiple antenna arrays [2]; and 3) sub-band pre-distortion for peak-to-average-power-ratio (PAPR) reduction [3] to significantly decrease the PAPR and communication errors in OFDM-IM by supporting a linear behavior of the power amplifier in the modem. The performance of the proposed framework is evaluated against the state-of-the-art QPSK, OFDM-IM [1] and QPSK-spatiotemporal QPSK-ST [2] schemes. The results show that our framework reduces the bit error rate (BER), mean square error (MSE) and PAPR compared to the baselines by approximately 6–13dB, 8–13dB, and 50%, respectively.


title: Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms

abstract: The interaction of an autonomous mobile robot with the real world critically depends on the robots morphology and on its environment. Building a model of these aspects is extremely complex, making simulation insufficient for accurate validation of control algorithms.


title: Using user generated online photos to estimate and monitor air pollution in major cities

abstract: With the rapid development of economy in China over the past decade, air pollution has become an increasingly serious problem in major cities and caused grave public health concerns in China. Recently, a number of studies have dealt with air quality and air pollution. Among them, some attempt to predict and monitor the air quality from different sources of information, ranging from deployed physical sensors to social media. These methods are either too expensive or unreliable, prompting us to search for a novel and effective way to sense the air quality. In this study, we propose to employ the state of the art in computer vision techniques to analyze photos that can be easily acquired from online social media. Next, we establish the correlation between the haze level computed directly from photos with the official PM 2.5 record of the taken city at the taken time. Our experiments based on both synthetic and real photos have shown the promise of this image-based approach to estimating and monitoring air pollution.


title: Automatic detection of circular objects by ellipse growing

abstract: We present a new method for the automatic detection of circular objects in images: we detect an osculating circle to an elliptic arc using a Hough transform, iteratively deforming it into an ellipse, removing outlier pixels, and searching for a separate edge. The voting space for the Hough transform is restricted to one and two dimensions for efficiency, and special weighting schemes are introduced to enhance the accuracy. We demonstrate the effectiveness of our method using real images. Finally, we apply our method to the calibration of a turntable for 3D object shape reconstruction.


title: ArchJava: connecting software architecture to implementation

abstract: Software architecture describes the structure of a system, enabling more effective design, program understanding, and formal analysis. However, existing approaches decouple implementation code from architecture, allowing inconsistencies, causing confusion, violating architectural properties, and inhibiting software evolution. ArchJava is an extension to Java that seamlessly unifies software architecture with implementation, ensuring that the implementation conforms to architectural constraints. A case study applying ArchJava to a circuit-design application suggests that ArchJava can express architectural structure effectively within an implementation, and that it can aid in program understanding and software evolution.


title: AVX Acceleration of DD Arithmetic Between a Sparse Matrix and Vector

abstract: High precision arithmetic can improve the convergence of Krylov subspace methods; however, it is very costly. One system of high precision arithmetic is double-double (DD) arithmetic, which uses more than 20 double precision operations for one DD operation. We accelerated DD arithmetic using AVX SIMD instructions. The performances of vector operations in 4 threads are 51–59 % of peak performance in a cache and bounded by the memory access speed out of the cache. For SpMV, we used a double precision sparse matrix A and DD vector x to reduce memory access and achieved performances of 17–41 % of peak performance using padding in execution. We also achieved performances that were 9–33 % of peak performance for a transposed SpMV. For these cases, the performances were not bounded by memory access.


title: Deep neural network for manufacturing quality prediction

abstract: Expected product quality is affected by multi-parameter in complex manufacturing processes. Product quality prediction can offer the possibility of designing better system parameters at the early production stage. Many existing approaches fail at providing favorable results duo to shallow architecture in prediction model that can not learn multi-parameter's features insufficiently. To address this issue, a deep neural network (DNN), consisting of a deep belief network (DBN) in the bottom and a regression layer on the top, is proposed in this paper. The DBN uses a greedy algorithm for unsupervised feature learning. It could learn effective features for manufacturing quality prediction in an unsupervised pattern which has been proven to be effective for many fields. Then the learned features are inputted into the regression tool, and the quality predictions are obtained. One type of manufacturing system with multi-parameter is investigated by the proposed DNN model. The experiments show that the DNN has good performance of the deep architecture, and overwhelms the peer shallow models. It is recommended from this study that the deep learning technique is more promising in manufacturing quality prediction.


title: Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements

abstract: Abstract Different approaches have been proposed to select features and channels for pattern recognition classification of myoelectric upper-limb prostheses. The goal of this work is to use deterministic methods to select the feature-channels pairs that best classify the hand postures at different limb positions. Two selection methods were tried. One is a distance-based feature selection (DFSS) that determines a separability index using the Mahalanobis distance between classes. The second method is a correlation-based feature selection (CFSS) that measures the amount of mutual information between features and classes. To evaluate the performance of these selection methods, EMG data from 10 able-bodied subjects were acquired when performing 5 hand postures at 9 different arm positions and 10 time-domain and frequency-domain features were extracted. Classification accuracy using both methods was always higher than including all the features and channels and showed slight improvement over classification using the state-of-art TD features when evaluated against limb variation. The CFSS method always used less feature-channel pairs compared to the DFSS method. Using both methods, selection of channels placed on the posterior side of the forearm was significantly higher than anterior side. Such methods could be used as fast screening filters to select features and channels that best classify different hand postures at different arm positions.


title: Stack-based genetic programming

abstract: Some recent work in the field of genetic programming (GP) has been concerned with finding optimum representations for evolvable and efficient computer programs. This paper describes a new GP system in which target programs run on a stack-based virtual machine. The system is shown to have certain advantages in terms of efficiency and simplicity of implementation, and for certain problems, its effectiveness is shown to be comparable or superior to current methods. >


title: COVAREP — A collaborative voice analysis repository for speech technologies

abstract: Speech processing algorithms are often developed demonstrating improvements over the state-of-the-art, but sometimes at the cost of high complexity. This makes algorithm reimplementations based on literature difficult, and thus reliable comparisons between published results and current work are hard to achieve. This paper presents a new collaborative and freely available repository for speech processing algorithms called COVAREP, which aims at fast and easy access to new speech processing algorithms and thus facilitating research in the field. We envisage that COVAREP will allow more reproducible research by strengthening complex implementations through shared contributions and openly available code which can be discussed, commented on and corrected by the community. Presently COVAREP contains contributions from five distinct laboratories and we encourage contributions from across the speech processing research field. In this paper, we provide an overview of the current offerings of COVAREP and also include a demonstration of the algorithms through an emotion classification experiment.


title: IDEOLOGY AND POLITICAL DISCOURSE: A CRITICAL DISCOURSE ANALYSIS OF ERDOGAN'S POLITICAL SPEECH

abstract: The way we perceive language is the foundation of our social construction and individual or group relationships, and studies in sociolinguistics have tried to explain this relationship between the use of language and the importance of perceptions. A particular discourse, spoken or written, can stem from different sources such as power, cultural or social background, region or social status. This paper aims to discuss the realisation of identity and background by means of language use in a political discourse, which is mainly grounded in Norman Fairclough‟s assumptions in critical discourse analysis. For this aim, the discursive strategies of the Turkish Prime Minister Recep Tayyip Erdogan during a debate in the World Economic Forum in Davos in January 2009 will be examined within the context of his ideological, cultural and language background.


title: Multi-View Learning With Incomplete Views

abstract: One underlying assumption of the conventional multi-view learning algorithms is that all examples can be successfully observed on all the views. However, due to various failures or faults in collecting and pre-processing the data on different views, we are more likely to be faced with an incomplete-view setting, where an example could be missing its representation on one view (i.e., missing view) or could be only partially observed on that view (i.e., missing variables). Low-rank assumption used to be effective for recovering the random missing variables of features, but it is disabled by concentrated missing variables and has no effect on missing views. This paper suggests that the key to handling the incomplete-view problem is to exploit the connections between multiple views, enabling the incomplete views to be restored with the help of the complete views. We propose an effective algorithm to accomplish multi-view learning with incomplete views by assuming that different views are generated from a shared subspace. To handle the large-scale problem and obtain fast convergence, we investigate a successive over-relaxation method to solve the objective function. Convergence of the optimization technique is theoretically analyzed. The experimental results on toy data and real-world data sets suggest that studying the incomplete-view problem in multi-view learning is significant and that the proposed algorithm can effectively handle the incomplete views in different applications.


title: Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems

abstract: This paper describes a statistically motivated framework for performing real-time dialogue state updates and policy learning in a spoken dialogue system. The framework is based on the partially observable Markov decision process (POMDP), which provides a well-founded, statistical model of spoken dialogue management. However, exact belief state updates in a POMDP model are computationally intractable so approximate methods must be used. This paper presents a tractable method based on the loopy belief propagation algorithm. Various simplifications are made, which improve the efficiency significantly compared to the original algorithm as well as compared to other POMDP-based dialogue state updating approaches. A second contribution of this paper is a method for learning in spoken dialogue systems which uses a component-based policy with the episodic Natural Actor Critic algorithm. The framework proposed in this paper was tested on both simulations and in a user trial. Both indicated that using Bayesian updates of the dialogue state significantly outperforms traditional definitions of the dialogue state. Policy learning worked effectively and the learned policy outperformed all others on simulations. In user trials the learned policy was also competitive, although its optimality was less conclusive. Overall, the Bayesian update of dialogue state framework was shown to be a feasible and effective approach to building real-world POMDP-based dialogue systems.


title: Rethink energy accounting with cooperative game theory

abstract: Energy accounting determines how much a software principal contributes to the total system energy consumption. It is the foundation for evaluating software and for operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track all hardware usage by software in a heterogeneous multicore system like modern smartphones and tablets. In this work, we argue that energy accounting should be formulated as a cooperative game and that the Shapley value provides the ultimate ground truth for energy accounting policies. We reveal the important flaws of existing energy accounting policies based on the Shapley value theory and provide Shapley value-based energy accounting, a practical approximation of the Shapley value, for battery-powered mobile systems. We evaluate this approximation against existing energy accounting policies in two ways: (i) how well they identify the top energy consuming applications, and (ii) how effective they are in system energy management. Using a prototype based on Texas Instruments Pandaboard and smartphone workload, we experimentally demonstrate existing energy accounting policies can deviate by 400% in attributing energy consumption to running applications and can be up to 25% less effective in system energy management when compared to Shapley value-based energy accounting.


title: MicMac – a free, open-source solution for photogrammetry

abstract: The publication familiarizes the reader with MicMac - a free, open-source photogrammetric software for 3D reconstruction. A brief history of the tool, its organisation and unique features vis-à-vis other software tools are in the highlight. The essential algorithmic aspects of the structure from motion and image dense matching problems are discussed from the implementation and the user’s viewpoints.


title: More Than a Game: Sports-Themed Video Games and Player

abstract: This article considers the social importance of sports-themed video games, and more specifically, discusses their use and role in the construction of gaming and wider social narratives. Here, building on our own and wider sociological and video games studies, we advocate adopting an audience research perspective that allows for consideration of not only narratives within games but also how these narratives are used and located within the everyday lives of gamers. In particular, we argue that sports-themed games provide an illustrative example of how media texts are used in identity construction, performances, and social narratives.


title: A Search/Crawl Framework for Automatically Acquiring Scientific Documents

abstract: Despite the advancements in search engine features, ranking methods, technologies, and the availability of programmable APIs, current-day open-access digital libraries still rely on crawl-based approaches for acquiring their underlying document collections. In this paper, we propose a novel search-driven framework for acquiring documents for scientific portals. Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine. Next, research papers and sources of research papers are identified from the search results using accurate classification modules. Our experiments highlight not only the performance of our individual classifiers but also the effectiveness of our overall Search/Crawl framework. Indeed, we were able to obtain approximately 0.665 million research documents through our fully-automated framework using about 0.076 million queries. These prolific results position Web search as an effective alternative to crawl methods for acquiring both the actual documents and seed URLs for future crawls.


title: Toward Fast and Accurate Neural Discourse Segmentation

abstract: Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.