title: 3D deep shape descriptor

abstract: Shape descriptor is a concise yet informative representation that provides a 3D object with an identification as a member of some category. We have developed a concise deep shape descriptor to address challenging issues from ever-growing 3D datasets in areas as diverse as engineering, medicine, and biology. Specifically, in this paper, we developed novel techniques to extract concise but geometrically informative shape descriptor and new methods of defining Eigen-shape descriptor and Fisher-shape descriptor to guide the training of a deep neural network. Our deep shape descriptor tends to maximize the inter-class margin while minimize the intra-class variance. Our new shape descriptor addresses the challenges posed by the high complexity of 3D model and data representation, and the structural variations and noise present in 3D models. Experimental results on 3D shape retrieval demonstrate the superior performance of deep shape descriptor over other state-of-the-art techniques in handling noise, incompleteness and structural variations.


title: Facebook bullying: An extension of battles in school

abstract: This study examines the phenomenon of cyberbullying on Facebook and how it is related to school bullying among secondary school students in Singapore, aged 13-17. We also focus on generic use of Facebook and risky Facebook behaviors as the predictors of cyberbullying and victimization on Facebook. 1676 secondary students, from two secondary schools, participated in a pen and paper survey. The findings show that the intensity of Facebook use and engagement in risky Facebook behaviors were related to Facebook victimization and Facebook bullying, respectively. Moderately strong positive relationships between school bullying and Facebook bullying, as well as between school victimization and Facebook victimization, were also uncovered.


A Diversity-Guided Particle Swarm Optimizer - the ARPSO


title: Functional analysis of a class I patatin gene SK24-1 in microtuber formation of transgenic potatoes

abstract: Expression of a class I patatin cDNA clone, SK24-1, in Escherichia coli revealed that the cDNA clone possessed lipid acyl hydrolase (LAH) activity. Transformed potato plants were obtained via Agrobacterium-mediated transformation using the chimeric constructs containing the sense and antisense cDNA under the control cauliflower mosaic virus 35S (CaMV 35S) promoter. In some sense transformed plants, both sense patatin RNA and LAH activity were increased and further resulted in a significant increase of percentage of plantlets that formed microtubers and numbers of microtubers per plantlet in vitro. All antisense plants displayed a reduction in LAH activity. Both sense and antisense RNA could be detected in antisense plants, but transcripts of antisense RNA resulted in a reduction of endogenous sense RNA. Moreover, expression of antisense cDNA in some antisense transformed plants led to a significant decrease in the number of microtubers formed. These results suggest that SK24-1 was involved in regulating m...


title: Feedback based Reputation on top of the Bitcoin Blockchain

abstract: The ability to assess the reputation of a member in a web community is a need addressed in many different ways according to the many different stages in which the nature of communities has evolved over time. In the case of reputation of goods/services suppliers, the solutions available to prevent the feedback abuse are generally reliable but centralized under the control of few big Internet companies. In this paper we show how a decentralized and distributed feedback management system can be built on top of the Bitcoin blockchain


title: Integrated Indoor Navigation System for Ground Vehicles With Automatic 3-D Alignment and Position Initialization

abstract: This paper introduces an autonomous integrated indoor navigation system for ground vehicles that fuses inertial sensors, light detection and ranging (LiDAR) sensors, received signal strength (RSS) observations in wireless local area networks (WLANs), odometry, and predefined occupancy floor maps. This paper proposes a solution for the problem of automatic self-alignment and position initialization indoors under the absence of an absolute navigation system such as Global Navigation Satellite Systems (GNSS). The initial tilt angles (roll and pitch) are estimated by an extended Kalman filter (EKF) that uses two horizontal accelerometers as measurements. The initial position and heading estimation is performed using a subimage matching algorithm based on normalized cross-correlation between projected 2-D LiDAR scans and an occupancy floor map of the environment. The ambiguities in position/heading initialization are resolved using RSS. The proposed position/heading estimation module is also utilized in navigation mode as a source of absolute position/heading updates to EKF for enhanced observability. The state predictor is an enhanced 3-D inertial navigation system that utilizes low-cost microelectromechanical system (MEMS)-based reduced inertial sensor set aided by vehicle odometry. In navigation mode, LiDAR scans are used to estimate the vehicle's relative motions using an inertial-aided iterative closest point algorithm. To fuse all available measurements, a multirate multimode EKF design is proposed to correct navigation states and estimate sensor biases. The developed system was tested under a real indoor office environment covered by an IEEE 802.11 WLAN on a mobile robot platform equipped with MEMS inertial sensors, a WLAN interface, a 2-D LiDAR scanner, and a quadrature encoder. Results demonstrated the capabilities of the self-alignment and initialization module and showed average submeter-level positioning accuracy.


title: A Dual Band CMOS Quadrature VCO for Low Power and Low Phase Noise Application

abstract: To design a multi standard and a multi band transceiver, a quadrature voltage controlled oscillator (VCO), which has a wide tuning range, is essential, while providing low phase noise with low current consumption. In this paper, a dual band quadrature VCO in 0.18 mum CMOS process is presented. To accomplish dual band operation, reconfigurable LC tank is adopted. Series coupling transistors are attached to generate quadrature phase signal with low power consumption and low phase noise. To improve phase noise performance, current sources are eliminated and a filter inductor at the common source node is added. The measured phase noises of low frequency mode and high frequency mode are -101.81 dBc/Hz and -100.59 dBc/Hz at 10 kHz offset frequency; and -123.83 dBc/Hz and -115.06 dBc/Hz at 1 MHz offset frequency and figure of merit (FoM) at 1 MHz offset frequency of -181.8 dB and -180.5 dB with 4 mA current consumption with a 1.7 V supply.


title: An automatic blocking mechanism for large-scale de-duplication tasks

abstract: De-duplication - identification of distinct records referring to the same real-world entity - is a well-known challenge in data integration. Since very large datasets prohibit the comparison of every pair of records, blocking has been identified as a technique of dividing the dataset for pairwise comparisons, thereby trading off recall of identified duplicates for efficiency. Traditional de-duplication tasks, while challenging, typically involved a fixed schema such as Census data or medical records. However, with the presence of large, diverse sets of structured data on the web and the need to organize it effectively on content portals, de-duplication systems need to scale in a new dimension to handle a large number of schemas, tasks and data sets, while handling ever larger problem sizes. In addition, when working in a map-reduce framework it is important that canopy formation be implemented as a hash function, making the canopy design problem more challenging. We present CBLOCK, a system that addresses these challenges. CBLOCK learns hash functions automatically from attribute domains and a labeled dataset consisting of duplicates. Subsequently, CBLOCK expresses blocking functions using a hierarchical tree structure composed of atomic hash functions. The application may guide the automated blocking process based on architectural constraints, such as by specifying a maximum size of each block (based on memory requirements), impose disjointness of blocks (in a grid environment), or specify a particular objective function trading off recall for efficiency. As a post-processing step to automatically generated blocks, CBLOCK rolls-up smaller blocks to increase recall. We present experimental results on two large-scale de-duplication datasets from a commercial search engine - consisting of over 140K movies and 40K restaurants respectively - and demonstrate the utility of CBLOCK.


title: Fatigue analysis of connecting rod of universal tractor through finite element method (ANSYS)

abstract: The connecting rod fatigue of universal tractor (U650) was investigated through the ANSYS software application and its lifespan was estimated. The reason for performing this research showed the connecting rod behavior affected by fatigue phenomenon due to the cyclic loadings and to consider the results for more savings in time and costs, as two very significant parameters relevant to manufacturing. The results indicate that with fully reverse loading, one can estimate longevity of a connecting rod and also find the critical points that more possibly the crack growth initiate from. Furthermore, the allowable number of load cycles and using fully reverse loading was gained 10 8 . It is suggested that the results obtained can be useful to bring about modifications in the process of connecting rod manufacturing.


title: Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data

abstract: Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (a#x2248;400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.


title: Antecedents and Consequences of Organizational Commitment Among Pakistani University Teachers

abstract: The purpose of this study was to determine if selected personal characteristics, facets of job satisfaction, and the two dimensions of organizational justice (distributive justice & procedural justice) significantly explained variance in the organizational commitment of Pakistani university teachers. In addition, the present study examined the influence of organizational commitment on two organizational outcomes—job performance and turnover intentions. Data were gathered from 125 full-time teachers from 33 universities in the three major cities of Pakistan: Lahore, Islamabad/Rawalpindi, and Peshawar. The results of the study indicate that the personal characteristics, facets of job satisfaction and two dimensions of organizational justice as a group were significantly related to organizational commitment of teachers. Individually, distributive justice and trust in management were found to be the strongest correlates of commitment. Moreover, commitment was found to be negatively related to turnover intentions (- .40) and positively related to a self-report measure of job performance (.32).


The relation of empathy to aggressive and externalizing/antisocial behavior.


title: Advanced mobile phone service: The cellular concept

abstract: This paper shows how a cellular system operating within a limited block of frequency spectrum can meet the objectives of a large-scale mobile-telephone service designed with attention to cost restraint. It explores the key elements of the cellular concept — frequency reuse and cell splitting — and describes certain mathematical properties of hexagonal cellular geometry. A description of the basic structure and features of amps shows how the cellular concept can be put into practice.


title: Mining data with random forests: A survey and results of new tests

abstract: Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets.


Discriminant Adaptive Nearest Neighbor Classification


Developing content-based recommender system using Hadoop Map Reduce


Anomaly-based network intrusion detection : Techniques , systems and challenges


Real-time detection and tracking of pedestrians in CCTV images using a deep convolutional neural network


title: Specific Recognition and Killing of Glioblastoma Multiforme by Interleukin 13-

abstract: The interleukin (IL) 13 receptor alpha2 (IL13Ralpha2) is a glioma-restricted cell-surface epitope not otherwise detected within the central nervous system. Here, we describe a novel approach for targeting glioblastoma multiforme (GBM) with IL13Ralpha2-specific cytolytic T cells (CTLs) by their genetic modification to express a membrane-tethered IL13 cytokine chimeric T-cell antigen receptor, or zetakine. Our prototype zetakine incorporates an IL13 E13Y mutein for selective binding to IL13Ralpha2. Human IL13-zetakine(+)CD8(+) CTL transfectants display IL13Ralpha2-specific antitumor effector function including tumor cell cytolysis, T(C)1 cytokine production, and zetakine-regulated autocrine proliferation. The E13Y amino acid substitution of the IL13 mutein of the zetakine endows CTL transfectants with the capacity to discriminate between IL13Ralpha2(+) GBM targets from targets expressing IL13Ralpha1. In vivo, the adoptive transfer of IL13-zetakine(+)CD8(+) CTL clones results in the regression of established human glioblastoma orthotopic xenografts. Pilot clinical trials have been initiated to evaluate the feasibility and safety of local-regional delivery of autologous IL13-zetakine redirected CTL clones in patients with recurrent GBM. Our IL13-zetakine is a prototype of a new class of chimeric immunoreceptors that signal through an engineered immune synapse composed of membrane-tethered cytokine muteins bound to cell-surface cytokine receptors on tumors.


title: A study on brushless PM slotless motor with toroidal winding

abstract: This paper presents a study on brushless permanent magnet (PM) slotless motor with toroidal winding (TWSL). Herein, slotted motor with conventional winding (i.e., CWS motor), which has already been applied to the industry, is compared to the TWSL motor. A finite-element analysis is used to comprehend the operation basis of the TWSL motor, including the linkage flux, back electromotive force, and torque. The proposed TWSL motor can generate the torque level as much as the CWS motor does. The proposed TWSL motor is manufactured and experimented upon to validate the finite-element analysis result.


title: Real-time topic-aware influence maximization using preprocessing

abstract: Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.


title: Boosting Optical Character Recognition: A Super-Resolution Approach

abstract: Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset (3) and the released Tesseract-OCR 3.02 system (1). We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images (78.80%). Index Terms—super resolution; optical character recogni- tion.


title: Dynamic modeling for resin self-alignment mechanism

abstract: Abstract A new self-alignment process using surface tension of resin material was developed to realize a low-temperature, fluxless, and cost-effective alternative alignment process for the future assembly of electronic and optoelectronic systems. In this new self-alignment process, we achieved self-alignment capability by using 3-dimensional pads to form the positioning boundary, and by mounting chips in the opposite direction. This process releases the force acted on liquid bumps, even when using resin material, which has been known not to possess self-alignment capability because of its low surface tension. As the viscosity of the resin material is considerably higher than that of the solders, the restoring force resulting from its low surface tension must be larger than the resistance force resulting from its high viscosity. In this paper, we propose a new passive alignment mechanism using the surface tension of a resin material and a comprehensive mathematical model in order to enhance understanding of the self-alignment behavior of resin. Moreover, we conducted a scaled-up experiment to calibrate and verify the model's accuracy. It has been proved that resin self-alignment behavior is totally different from the oscillatory motion of solder alignment. This shows that the motion is aperiodic, regardless of the initial conditions of the system, such as an overdamped system due to the low surface tension and high viscosity characteristics of resin material.


title: X-Band Robust AlGaN/GaN Receiver MMICs with over 41 dBm Power Handling

abstract: Gallium-Nitride technology is known for its high power density and power amplifier designs, but is also very well suited to realize robust receiver components. This paper presents the design and measurement of a robust AlGaN/GaN Low Noise Amplifier and Transmit/Receive Switch MMIC. Two versions of both MMICs have been designed in the Alcatel-Thales III-V lab AlGaN/GaN microstrip technology. One chipset version operates at X-band and the second also shows wideband performance. Input power handling of >46 dBm for the switch and >41 dBm for the LNA have been measured.


title: Scene text recognition and tracking to identify athletes in sport videos

abstract: We present an athlete identification module forming part of a system for the personalization of sport video broadcasts. The aim of this module is the localization of athletes in the scene, their identification through the reading of names or numbers printed on their uniforms, and the labelling of frames where athletes are visible. Building upon a previously published algorithm we extract text from individual frames and read these candidates by means of an optical character recognizer (OCR). The OCR-ed text is then compared to a known list of athletes' names (or numbers), to provide a presence score for each athlete. Text regions are tracked in subsequent frames using a template matching technique. In this way blurred or distorted text, normally unreadable by the OCR, is exploited to provide a denser labelling of the video sequences. Extensive experiments show that the method proposed is fast, robust and reliable, out-performing results of other systems in the literature.


title: Milligram-scale high-voltage power electronics for piezoelectric microrobots

abstract: Piezoelectric actuators can achieve high efficiency and power density in very small geometries, which shows promise for microrobotic applications, such as flapping-wing robotic insects. From the perspective of power electronics, such actuators present two challenges: high operating voltages, ranging from tens to thousands of volts, and a low electromechanical coupling factor, which necessitates the recovery of unused electrical energy. This paper explores the power electronics design problem by establishing the drive requirements of piezoelectric actuators, presenting circuit topologies and control methods suitable for driving different types of piezoelectric actuators in microrobotic applications, and demonstrating experimental realizations of sub-100mg power electronics circuits.


title: Quantifying the reflective DDoS attack capability of household IoT devices

abstract: Distributed Denial-of-Service (DDoS) attacks are increasing in frequency and volume on the Internet, and there is evidence that cyber-criminals are turning to Internet-of-Things (IoT) devices such as cameras and vending machines as easy launchpads for large-scale attacks. This paper quantifies the capability of consumer IoT devices to participate in reflective DDoS attacks. We first show that household devices can be exposed to Internet reflection even if they are secured behind home gateways. We then evaluate eight household devices available on the market today, including lightbulbs, webcams, and printers, and experimentally profile their reflective capability, amplification factor, duration, and intensity rate for TCP, SNMP, and SSDP based attacks. Lastly, we demonstrate reflection attacks in a real-world setting involving three IoT-equipped smart-homes, emphasising the imminent need to address this problem before it becomes widespread.


title: RSSI-Based Bluetooth Indoor Localization

abstract: The Global Positioning System (GPS) has been widely used to determine the location for a variety of different applications. However, it doesn't work well in indoor environments because it requires the line of sight to the satellites and therefore stops working when the line of sight is not available. High-precision indoor localization is critical to many personal and business applications. After Bluetooth Low Energy (BLE), an energy-efficient version of Bluetooth, is widely deployed, Bluetooth-based indoor localization turns out to be a practical method to locate Bluetooth-enabled devices due to its low battery cost. In this paper, we present two novel BLE-based localization schemes, Low-precision Indoor Localization (LIL) and High-precision Indoor Localization (HIL). Different than most of the existing localization methods that attempt to find the specific location of the object under investigation, LIL and HIL utilize the collected RSSI measurements to generate a small region in which the object is guaranteed to be found. Compared with LIL, HIL leads to smaller localization regions. However, HIL requires an extra data-training phase.


title: A New Evaluation Criterion with the Integration of Perplexity and Jensen-Shannon Divergence for Biterm Topic Model

abstract: P97, as a classical evaluation method for topic model, is good at predicting new documents. However, this method is not effective for Biterm Topic Model (BTM), especially short terms of web videos might leading to much more themes. In order to solve above problem, a new evaluation criterion is proposed with the integration of perplexity and Jensen-Shannon (JS) divergence. JS divergence focuses on differences and stability among themes for BTM model. Therefore, it can make up the deficiency of perplexity. The experiment demonstrates that our proposed method is more effective than several existing methods with a significant improvement.


Rectennas for microwave power transmission


title: Security and Privacy in Cloud Computing

abstract: Recent advances have given rise to the popularity and success of cloud computing. However, when outsourcing the data and business application to a third party causes the security and privacy issues to become a critical concern. Throughout the study at hand, the authors obtain a common goal to provide a comprehensive review of the existing security and privacy issues in cloud environments. We have identified five most representative security and privacy attributes (i.e., confidentiality, integrity, availability, accountability, and privacy-preservability). Beginning with these attributes, we present the relationships among them, the vulnerabilities that may be exploited by attackers, the threat models, as well as existing defense strategies in a cloud scenario. Future research directions are previously determined for each attribute.


title: Unsupervised Learning of Edges

abstract: Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries. Specifically, human annotators mark semantically meaningful edges which are subsequently used for training. Is this form of strong, high-level supervision actually necessary to learn to accurately detect edges? In this work we present a simple yet effective approach for training edge detectors without human supervision. To this end we utilize motion, and more specifically, the only input to our method is noisy semi-dense matches between frames. We begin with only a rudimentary knowledge of edges (in the form of image gradients), and alternate between improving motion estimation and edge detection in turn. Using a large corpus of video data, we show that edge detectors trained using our unsupervised scheme approach the performance of the same methods trained with full supervision (within 3-5%). Finally, we show that when using a deep network for the edge detector, our approach provides a novel pre-training scheme for object detection.


title: 3-D Reciprocal Collision Avoidance on Physical Quadrotor Helicopters with On-Board Sensing for Relative Positioning

abstract: In this paper, we present an implementation of 3-D reciprocal collision avoidance on real quadrotor helicopters where each quadrotor senses the relative position and velocity of other quadrotors using an on-board camera. We show that using our approach, quadrotors are able to successfully avoid pairwise collisions in GPS and motion-capture denied environments, without communication between the quadrotors, and even when human operators deliberately attempt to induce collisions. To our knowledge, this is the first time that reciprocal collision avoidance has been successfully implemented on real robots where each agent independently observes the others using on-board sensors. We theoretically analyze the response of the collision-avoidance algorithm to the violated assumptions by the use of real robots. We quantitatively analyze our experimental results. A particularly striking observation is that at times the quadrotors exhibit "reciprocal dance" behavior, which is also observed when humans move past each other in constrained environments. This seems to be the result of sensing uncertainty, which causes both robots involved to have a different belief about the relative positions and velocities and, as a result, choose the same side on which to pass.


title: The evolving theory of quality management: The role of Six Sigma

abstract: Abstract While Six Sigma is increasingly implemented in industry, little academic research has been done on Six Sigma and its influence on quality management theory and application. There is a criticism that Six Sigma simply puts traditional quality management practices in a new package. To investigate this issue and the role of Six Sigma in quality management, this study reviewed both the traditional quality management and Six Sigma literatures and identified three new practices that are critical for implementing Six Sigma's concept and method in an organization. These practices are referred to as: Six Sigma role structure, Six Sigma structured improvement procedure, and Six Sigma focus on metrics. A research model and survey instrument were developed to investigate how these Six Sigma practices integrate with seven traditional quality management practices to affect quality performance and business performance. Test results based on a sample of 226 US manufacturing plants revealed that the three Six Sigma practices are distinct practices from traditional quality management practices, and that they complement the traditional quality management practices in improving performance. The implications of the findings for researchers and practitioners are discussed and further research directions are offered.


title: OVERCOMING OBSTACLES TO IMPLEMENTING AN OUTCOME-BASED EDUCATION MODEL: TRADITIONAL VERSUS TRANSFORMATIONAL OBE

abstract: Attempts to introduce a new outcome- based curriculum in the Mechatronic Systems Engineering (MSE) Program at Simon Fraser University (SFU) have posed a range of challenges to teaching staff and students in terms of the most effective and efficient means for transitioning the program and achieving the expected improvements in educational outcomes. However, the mechanical process of pursuing outcomes without the deliberate revision of the pedagogy, attitudes and forms of assessments fails to attain the continuous improvement concept that OBE implies. This paper analyses MSE faculty interview responses to approaches they incorporate in their teaching practices and the effect these practices have on student learning. Class size, expectations of learner characteristics and reality, teaching practice and evaluation, and student motivation were the most commonly discussed challenges. Self- reported instructor characteristics and the perceived role of the instructor often contradicted the OBE model of learning. The results inform a critical discussion of the pedagogical aspects involved in reshaping existing curriculum to satisfy the needs of the 21st century learner. The process of transitioning from the content-driven to the outcome-based curriculum is revealing opportunities in terms of transformative teacher education as well as challenges that warrant further analysis.


title: Fast Efficient Object Detection Using Selective Attention

abstract: Retraction due to significant oversight


title: Induction of microbial secondary metabolism

abstract: Precursors often stimulate production of secondary metabolites either by increasing the amount of a limiting precursor, by inducing a biosynthetic enzyme (synthase) or both. These are usually amino acids but other small molecules also function as inducers. The most well-known are the auto-inducers which include γ-butyrolactones (butanolides) of the actinomycetes, N-acylhomoserine lactones of Gram-negative bacteria, oligopeptides of Gram-positive bacteria, and B-factor (3’-[1-butylphosphoryl] adenosine) of Amycolatopsis mediterranei. The actinomycete butanolides exert their effects via receptor proteins which normally repress chemical and morphological differentiation (secondary metabolism and differentiation into aerial mycelia and spores respectively) but, when complexed with the butanolide, can no longer function. Homoserine lactones of Gram-negative bacteria function at high cell density and are structurally related to the butanolides. They turn on plant and animal virulence, light emission, plasmid transfer, and production of pigments, cyanide and β-lactam antibiotics. They are made by enzymes homologous to Lux1, excreted by the cell, enter other cells at high density, bind to a LuxR homologue, the complex then binding to DNA upstream of genes controlled by “quorum sensing” and turning on their expression. Quorum sensing also operates in the case of the peptide pheromones of the Gram-positive bacteria. Here, secretion is accomplished by an ATP binding casette (ABC transporter), the secreted pheromone being recognized by a sensor component of a two-component signal transduction system. The pheromone often induces its own synthesis as well as those proteins involved in protein/peptide antibiotic (including bacteriocins and lantibiotics) production, virulence and genetic competence. The B-factor of A. mediterranei is an inducer of ansamycin (rifamycin) formation.


title: Novel SIW Cavity-Backed Antenna Array Without Using Individual Feeding Network

abstract: Substrate integrated waveguide (SIW) cavity-backed antenna arrays have advantages of low-profile, high-gain and low-cost fabrication. However, traditional SIW cavity-backed antenna arrays usually load with extra feeding networks, which make the whole arrays larger and more complex. A novel 4 × 4 SIW cavity-backed antenna array without using individual feeding network is presented in this letter. The proposed antenna array consists of sixteen SIW cavities connected by inductive windows as feeding network and wide slots on the surface of each cavity as radiating part. Without loading with extra feeding network, the array is compact.


title: Additive Quantization for Extreme Vector Compression

abstract: We introduce a new compression scheme for high-dimensional vectors that approximates the vectors using sums of M codewords coming from M different codebooks. We show that the proposed scheme permits efficient distance and scalar product computations between compressed and uncompressed vectors. We further suggest vector encoding and codebook learning algorithms that can minimize the coding error within the proposed scheme. In the experiments, we demonstrate that the proposed compression can be used instead of or together with product quantization. Compared to product quantization and its optimized versions, the proposed compression approach leads to lower coding approximation errors, higher accuracy of approximate nearest neighbor search in the datasets of visual descriptors, and lower image classification error, whenever the classifiers are learned on or applied to compressed vectors.


title: Thermal-Aware Task Scheduling for Energy Minimization in Heterogeneous Real-Time MPSoC Systems

abstract: With the continuous scaling of CMOS devices, the increase in power density and system integration level have not only resulted in huge energy consumption but also led to elevated chip temperature. Thus, energy efficient task scheduling with thermal consideration has become a pressing research issue in computing systems, especially for real-time embedded systems with limited cooling techniques. In this paper, we design a two-stage energy-efficient temperature-aware task scheduling scheme for heterogeneous real-time multiprocessor system-on-chip (MPSoC) systems. In the first stage, we analyze the energy optimality of assigning real-time tasks to multiple processors of an MPSoC system, and design a task assignment heuristic that minimizes the system dynamic energy consumption under the constraint of task deadlines. In the second stage, the optimality of minimizing the peak temperature of a processor is investigated, and a slack distribution heuristic is proposed to improve the temperature profile of each processor under the thermal constraint, thus the temperature-dependent system leakage energy consumption is reduced. Through the extensive efforts made in two stages, the system overall energy consumption is minimized. Experimental results have demonstrated the effectiveness of our scheme.


title: Facial expression recognition in peripheral versus central vision: role of the eyes and the mouth

abstract: This study investigated facial expression recognition in peripheral relative to central vision, and the factors accounting for the recognition advantage of some expressions in the visual periphery. Whole faces or only the eyes or the mouth regions were presented for 150 ms, either at fixation or extrafoveally (2.5° or 6°), followed by a backward mask and a probe word. Results indicated that (a) all the basic expressions were recognized above chance level, although performance in peripheral vision was less impaired for happy than for non-happy expressions, (b) the happy face advantage remained when only the mouth region was presented, and (c) the smiling mouth was the most visually salient and most distinctive facial feature of all expressions. This suggests that the saliency and the diagnostic value of the smile account for the advantage in happy face recognition in peripheral vision. Because of saliency, the smiling mouth accrues sensory gain and becomes resistant to visual degradation due to stimulus eccentricity, thus remaining accessible extrafoveally. Because of diagnostic value, the smile provides a distinctive single cue of facial happiness, thus bypassing integration of face parts and reducing susceptibility to breakdown of configural processing in peripheral vision.


title: Treatment of acrophobia in virtual reality: the role of immersion and presence.

abstract: In this study the effects of virtual reality exposure therapy (VRET) were investigated in patients with acrophobia. Feelings of presence in VRET were systematically varied by using either a head-mounted display (HMD) (low presence) or a computer automatic virtual environment (CAVE) (high presence). VRET in general was found to be more effective than no treatment. No differences were found in effectiveness between VRET using an HMD or CAVE. Results were maintained at 6 months follow-up. Results of VRET were comparable with those of exposure in vivo (Cyberpsychology and Behavior 4 (2001) 335). In treatment completers no relationship was found between presence and anxiety. Early drop-outs experienced less acrophobic complaints and psychopathology in general at pre-test. They also experienced less presence and anxiety in the virtual environment used in session one as compared to patients that completed VRET.


title: Work Related Stress, Burnout, Job Satisfaction and General Health of Nurses

abstract: Gaps in research focusing on work related stress, burnout, job satisfaction and general health of nurses is evident within developing contexts like South Africa. This study identified the relationship between work related stress, burnout, job satisfaction and general health of nurses. A total of 1200 nurses from four hospitals were invited to participate in this cross-sectional study (75% response rate). Participants completed five questionnaires and multiple linear regression analysis was used to determine significant relationships between variables. Staff issues are best associated with burnout as well as job satisfaction. Burnout explained the highest amount of variance in mental health of nurses. These are known to compromise productivity and performance, as well as affect the quality of patient care. Issues, such as security risks in the workplace, affect job satisfaction and health of nurses. Although this is more salient to developing contexts it is important in developing strategies and intervention programs towards improving nurse and patient related outcomes.


title: Video dehazing with spatial and temporal coherence

abstract: This paper describes a new framework for video dehazing, the process of restoring the visibility of the videos taken under foggy scenes. The framework builds upon techniques in single image dehazing, optical flow estimation and Markov random field. It aims at improving the temporal and spatial coherence of the dehazed video. In this framework, we first extract the transmission map frame-by-frame using guided filter, then estimate the forward and backward optical flow between two neighboring frames to find the matched pixels. The flow fields are used to help us building an MRF model on the transmission map to improve the spatial and temporal coherence of the transmission. The proposed algorithm is verified in both real and synthetic videos. The results demonstrate that our algorithm can preserve the spatial and temporal coherence well. With more coherent transmission map, we get better refocusing effect. We also apply our framework on improving the video coherence on the application of video denoising.


title: Visual Analysis and Dissemination of Scientific Literature Collections with SurVis

abstract: Bibliographic data such as collections of scientific articles and citation networks have been studied extensively in information visualization and visual analytics research. Powerful systems have been built to support various types of bibliographic analysis, but they require some training and cannot be used to disseminate the insights gained. In contrast, we focused on developing a more accessible visual analytics system, called SurVis, that is ready to disseminate a carefully surveyed literature collection. The authors of a survey may use our Web-based system to structure and analyze their literature database. Later, readers of the survey can obtain an overview, quickly retrieve specific publications, and reproduce or extend the original bibliographic analysis. Our system employs a set of selectors that enable users to filter and browse the literature collection as well as to control interactive visualizations. The versatile selector concept includes selectors for textual search, filtering by keywords and meta-information, selection and clustering of similar publications, and following citation links. Agreement to the selector is represented by word-sized sparkline visualizations seamlessly integrated into the user interface. Based on an analysis of the analytical reasoning process, we derived requirements for the system. We developed the system in a formative way involving other researchers writing literature surveys. A questionnaire study with 14 visual analytics experts confirms that SurVis meets the initially formulated requirements.


title: Evaluation of the low-frequency neutral-point voltage oscillations in the three-level inverter

abstract: The nearest vectors to the reference vector are commonly used in space-vector modulation (SVM) strategies. The main advantages of these modulation strategies are the low switching frequencies of the devices, the good output voltage spectra, and the low electromagnetic interference. However, when these techniques are applied to the three-level neutral-point (NP)-clamped inverter, low-frequency oscillations appear in the NP voltage for some operating conditions. As a result, the value of the dc-link capacitors must be increased in order to attenuate such oscillations. In this paper, these amplitudes are quantified for two modulation strategies that use nearest vectors to the reference vector. Owing to the nondimensional variables used in the analysis, the information provided will help for the calculation of the dc-link capacitors in a given specific application. Simulated and experimental examples are presented.


title: Bogor: an extensible and highly-modular software model checking framework

abstract: Model checking is emerging as a popular technology for reasoning about behavioral properties of a wide variety of software artifacts including: requirements models, architectural descriptions, designs, implementations, and process models. The complexity of model checking is well-known, yet cost-effective analyses have been achieved by exploiting, for example, naturally occurring abstractions and semantic properties of a target software artifact. semantic properties of target software artifacts. Adapting a model checking tool to exploit this kind of domain knowledge often requires in-depth knowledge of the tool's implementation.We believe that with appropriate tool support, domain experts will be able to develop efficient model checking-based analyses for a variety of software-related models. To explore this hypothesis, we have developed Bogor, a model checking framework with an extensible input language for defining domain-specific constructs and a modular interface design to ease the optimization of domain-specific state-space encodings, reductions and search algorithms. We present the pattern-oriented design of Bogor and discuss our experiences adapting it to efficiently model check Java programs and event-driven component-based designs.


Automatic Illustration of Short Texts via Web Images


title: Ultraino: An Open Phased-Array System for Narrowband Airborne Ultrasound Transmission

abstract: Modern ultrasonic phased-array controllers are electronic systems capable of delaying the transmitted or received signals of multiple transducers. Configurable transmit–receive array systems, capable of electronic steering and shaping of the beam in near real-time, are available commercially, for example, for medical imaging. However, emerging applications, such as ultrasonic haptics, parametric audio, or ultrasonic levitation, require only a small subset of the capabilities provided by the existing controllers. To meet this need, we present Ultraino, a modular, inexpensive, and open platform that provides hardware, software, and example applications specifically aimed at controlling the transmission of narrowband airborne ultrasound. Our system is composed of software, driver boards, and arrays that enable users to quickly and efficiently perform research in various emerging applications. The software can be used to define array geometries, simulate the acoustic field in real time, and control the connected driver boards. The driver board design is based on an Arduino Mega and can control 64 channels with a square wave of up to 17 Vpp and $\pi $ /5 phase resolution. Multiple boards can be chained together to increase the number of channels. The 40-kHz arrays with flat and spherical geometries are demonstrated for parametric audio generation, acoustic levitation, and haptic feedback.


Massive Open Online Courses: A Success of Cloud Computing in Education


title: Computational thinking in a game design course

abstract: As a part of an NSF-funded project to enhance computational thinking in undergraduate general education courses, activities and assessments were developed for a game design course taught at DePaul University. The focus of the course is on game analysis and design, but the course textbook uses an approach that is heavily grounded in computational thinking principles. We describe the course activities and assignments and discuss an initial assessment of those materials. Our results show that there is a gap in difficulty between several of the activities and indicate that the materials developed help students to better learn the computational thinking concepts in the course.


Contributions to the Theory of Optimal Control


title: Knowledge and Practice of Nursing Staff towards Infection Control Measures in the Palestinian Hospitals

abstract: Health care professionals are constantly exposed to microorganisms. Many of which can cause serious or even lethal infections. Nurses in particular are often exposed to various infections during the course of carrying out their nursing activities. Therefore nurses should have sound knowledge and strict adherence to infection control practice. Aim of the study: The study aimed to assess the level of knowledge and practice of infection control among nurses in governmental hospitals of Palestine Methods: A cross sectional study was conducted between November, 2014 and January, 2015 in governmental hospitals found in North West bank districts. Data were collected using pre tested questionnaire on 271 nurses selected by purposive sample. Collected data were checked, coded and transferred to SPSS version 20 for analysis. Frequency, Mean and other statistics were calculated. P-value less than 0.05 were set as statistically significant. Results: the current study revealed that, approximately half (53.9%) of the studied sample had fair knowledge level (>80%). However, the majority (91.1%) of the studied sample had Good practice (>80%). No significant statistical differences were found between mean knowledge scores towards age, years of experience, and training course (f=2.263, 1.607, 0.210) respectively at p < 0.05(0.082, 0.188, 0.647) respectively. Significant statistical differences were found between mean knowledge scores towards gender and qualification (F = 4.205, 4.531) respectively at p < 0.05 (0.041, 0.012 ) respectively. No significant statistical differences were found between mean practice scores towards age, years of experience, training course, and qualification(f=1.482, 2.625, 0.129, 0.727 ) respectively at p < 0.05(0.220, 0.051, 0.720, 0.484) respectively. Significant statistical differences were found in mean practice scores only in relation to gender, (F = 8.569) at p < 0.05 (0.004). Conclusion: Based on findings of the current study, it can be concluded that inspite of having good practice level regarding infection control, nurses had fair knowledge level. Recommendations: Updating knowledge and practice of nurses through continuing in-service educational programs; emphasizing the importance of following latest evidence-based practices of infection control in continuing education / training program; providing training programs for newly nurses about infection control and at regular intervals; and a replication of this study using observation checklist should be done to assess the level of practice. Keywords: Infection control, Knowledge, Practice, Nurse


title: Changes in hippocampal neurogenesis throughout early development

abstract: Adult hippocampal neurogenesis drastically diminishes with age but the underlying mechanisms remain unclear. Here, age-related influences on the hippocampal early neuroprogenitor cell (NPC) pool was examined by quantifying changes in Sox1-expressing cells in the dentate gyrus subgranular zone from early adulthood (3 months) to middle age (12 months). Proliferation of distinct NPC subpopulations (Sox1+, Nestin+, and Doublecortin+) and newborn cell survival were also investigated. Examination of total 5-bromodeoxyuridine (BrdU)+ and Doublecortin (DCX)± cells revealed an early and dramatic age-dependent decline of hippocampal neurogenesis. Increasing age from 3 to 12 months was primarily associated with reduced total proliferation, in vivo (-79% of BrdU+ cells) but not in vitro, and DCX+ cell numbers (-89%). When proliferative rates of individual NPC subpopulations were examined, a different picture emerged as proliferating Nestin+ neuroprogenitors (-95% at 9 months) and BrdU+/DCX+ neuroblasts and/or immature neurons (-83% at 12 months) declined the most, whereas proliferating Sox1+ NPCs only dropped by 53%. Remarkably, despite greatly reduced proliferative rates and recent reports of Nestin+ neuroprogenitor loss, total numbers of early Sox1+ NPCs were unaffected by age (at least up to middle age), and newborn cell survival within the dentate gyrus was increased. Neuronal differentiation was concomitantly reduced; however, thus suggesting age-associated changes in fate-choice determination.


title: code2vec: learning distributed representations of code

abstract: We present a neural model for representing snippets of code as continuous distributed vectors (``code embeddings''). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of the snippet. To this end, code is first decomposed to a collection of paths in its abstract syntax tree. Then, the network learns the atomic representation of each path while simultaneously learning how to aggregate a set of them. ::: ::: We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 12M methods. We show that code vectors trained on this dataset can predict method names from files that were unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. ::: ::: A comparison of our approach to previous techniques over the same dataset shows an improvement of more than 75%, making it the first to successfully predict method names based on a large, cross-project corpus. Our trained model, visualizations and vector similarities are available as an interactive online demo at http://code2vec.org. The code, data and trained models are available at https://github.com/tech-srl/code2vec.


title: Estimating Depth from RGB and Sparse Sensing

abstract: We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both indoor/outdoor scenes and produces state-of-the-art dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI datasets. We surpass the state-of-the-art for monocular depth estimation even with depth values for only 1 out of every \({\sim }10000\) image pixels, and we outperform other sparse-to-dense depth methods at all sparsity levels. With depth values for \(1{\slash }256\) of the image pixels, we achieve a mean error of less than \(1\%\) of actual depth on indoor scenes, comparable to the performance of consumer-grade depth sensor hardware. Our experiments demonstrate that it would indeed be possible to efficiently transform sparse depth measurements obtained using e.g. lower-power depth sensors or SLAM systems into high-quality dense depth maps.


title: Package-Aware Scheduling of FaaS Functions

abstract: We consider the problem of scheduling small cloud functions on serverless computing platforms. Fast deployment and execution of these functions is critical, for example, for microservices architectures. However, functions that require large packages or libraries are bloated and start slowly. A solution is to cache packages at the worker nodes instead of bundling them with the functions. However, existing FaaS schedulers are vanilla load balancers, agnostic of any packages that may have been cached in response to prior function executions, and cannot reap the benefits of package caching (other than by chance). To address this problem, we propose a package-aware scheduling algorithm that tries to assign functions that require the same package to the same worker node. Our algorithm increases the hit rate of the package cache and, as a result, reduces the latency of the cloud functions. At the same time, we consider the load sustained by the workers and actively seek to avoid imbalance beyond a configurable threshold. Our preliminary evaluation shows that, even with our limited exploration of the configuration space so-far, we can achieve 66% performance improvement at the cost of a (manageable) higher node imbalance.


title: Taxonomy Induction Using Hypernym Subsequences

abstract: We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimum-cost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms state-of-the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, this robustness has not been empirically proven in any previous approach.


title: The association of leadership styles and empowerment with nurses’ organizational commitment in an acute health care setting: a cross-sectional study

abstract: The current challenges facing healthcare systems, in relation to the shortage of health professionals, necessitates mangers and leaders to learn from different leadership styles and staff empowerment strategies, so as to create a work environment that encourages nursing staff commitment to patients and their organization. This study intends to measure the effects of nurses’ overall perception of the leadership style of their managers, and psychological empowerment on their organizational commitment in acute care units, in National Guard Health Affairs, Riyadh City, Saudi Arabia. This was a cross-sectional survey, where the data was obtained from nurses at King Abdulaziz Medical City. Hard copy questionnaires were distributed to 350 randomly selected nurses. Three hundred and thirty two (332) were completed, representing a response rate of 95 %. Three validated survey instruments were used to obtain the data: (1) The Multifactor Leadership Questionnaire (MLQ), formulated by Bass and Avolio (1997), (2) The Psychological Empowerment Scale developed by Spreitzer (1995) and (3) The Three-Component Model of Employee Commitment developed by Meyer and Allen (1997). A theoretical model that conceptually links leadership, empowerment, and organizational commitment was used. The SPSS program version 19 was employed to perform descriptive and inferential statistics including correlation and stepwise multiple regression analysis. Overall most nurses perceived their immediate nursing managers as not displaying the ideal level of transformational leadership (TFL) behaviors. Nurses’ commitment appeared to be negatively correlated with TFL style and perceived psychological empowerment. However, commitment was positively correlated with the Transactional Leadership (TAL) style. Analysis, also, showed that commitment is significantly associated with the nurse’s nationality by region: North American (P = 0.001) and Arab (p = 0.027). The other important predictors of commitment include TAL (P = 0.027), Laissez-faire Leadership (LFL (P = 0.012), and autonomy (P = 0.016). The linear combination of these predictors explained 20 % of the variability of the nurses’ commitment. The study findings suggest that leadership styles and employee empowerment could play an instrumental role in promoting organizational commitment of nurses working in acute health care settings, at least in the Saudi Arabian context.


title: Contemporary Hormonal Contraception and the Risk of Breast Cancer

abstract: BACKGROUND ::: Little is known about whether contemporary hormonal contraception is associated with an increased risk of breast cancer. ::: ::: ::: METHODS ::: We assessed associations between the use of hormonal contraception and the risk of invasive breast cancer in a nationwide prospective cohort study involving all women in Denmark between 15 and 49 years of age who had not had cancer or venous thromboembolism and who had not received treatment for infertility. Nationwide registries provided individually updated information about the use of hormonal contraception, breast-cancer diagnoses, and potential confounders. ::: ::: ::: RESULTS ::: Among 1.8 million women who were followed on average for 10.9 years (a total of 19.6 million person-years), 11,517 cases of breast cancer occurred. As compared with women who had never used hormonal contraception, the relative risk of breast cancer among all current and recent users of hormonal contraception was 1.20 (95% confidence interval [CI], 1.14 to 1.26). This risk increased from 1.09 (95% CI, 0.96 to 1.23) with less than 1 year of use to 1.38 (95% CI, 1.26 to 1.51) with more than 10 years of use (P=0.002). After discontinuation of hormonal contraception, the risk of breast cancer was still higher among the women who had used hormonal contraceptives for 5 years or more than among women who had not used hormonal contraceptives. Risk estimates associated with current or recent use of various oral combination (estrogen-progestin) contraceptives varied between 1.0 and 1.6. Women who currently or recently used the progestin-only intrauterine system also had a higher risk of breast cancer than women who had never used hormonal contraceptives (relative risk, 1.21; 95% CI, 1.11 to 1.33). The overall absolute increase in breast cancers diagnosed among current and recent users of any hormonal contraceptive was 13 (95% CI, 10 to 16) per 100,000 person-years, or approximately 1 extra breast cancer for every 7690 women using hormonal contraception for 1 year. ::: ::: ::: CONCLUSIONS ::: The risk of breast cancer was higher among women who currently or recently used contemporary hormonal contraceptives than among women who had never used hormonal contraceptives, and this risk increased with longer durations of use; however, absolute increases in risk were small. (Funded by the Novo Nordisk Foundation.).


title: Emotional disclosure and cognitive processing in couples coping with head and neck cancer

abstract: Head and neck cancer (HNC) patients and their spouses experience communication problems and high rates of emotional distress. Couple-based interventions that encourage emotional disclosure hold promise for improving cognitive processing and distress in this population, but more research needs to examine when and for whom emotional disclosure is an effective coping strategy. In this observational study, 125 HNC patients (83% male) and their spouses were videotaped discussing a cancer-related concern in the laboratory. Discussions were coded with the specific affect coding system. Actor–partner interdependence models showed that patient expression of negative emotions (i.e., disdain, contentiousness, distress) was not related to his/her own or the spouse’s cognitive processing (assessed as reaction times to cancer and noncancer words on a computerized cognitive task administered immediately following the discussion). When spouses expressed support (e.g., interest, validation), they had better cancer- (effect size r = − 0.21) and noncancer-related cognitive processing (r = − 0.17), but patients did not. However, when spouses expressed disdain (e.g., contempt) and contentiousness (e.g., criticism, domineering), patients had poorer cancer- (r = 0.20–0.22) and noncancer-related cognitive processing (r = 0.19–0.26). Findings suggest consideration of the valence of affective expression and which partner is disclosing/listening before unilaterally encouraging HNC couples to openly express emotions as a means of alleviating distress.


title: Blind Image Deblurring Using Dark Channel Prior

abstract: We present a simple and effective blind image deblurring method based on the dark channel prior. Our work is inspired by the interesting observation that the dark channel of blurred images is less sparse. While most image patches in the clean image contain some dark pixels, these pixels are not dark when averaged with neighboring highintensity pixels during the blur process. This change in the sparsity of the dark channel is an inherent property of the blur process, which we both prove mathematically and validate using training data. Therefore, enforcing the sparsity of the dark channel helps blind deblurring on various scenarios, including natural, face, text, and low-illumination images. However, sparsity of the dark channel introduces a non-convex non-linear optimization problem. We introduce a linear approximation of the min operator to compute the dark channel. Our look-up-table-based method converges fast in practice and can be directly extended to non-uniform deblurring. Extensive experiments show that our method achieves state-of-the-art results on deblurring natural images and compares favorably methods that are well-engineered for specific scenarios.


title: Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words

abstract: In this article, we present a comprehensive study aimed at computing semantic relatedness of word pairs. We analyze the performance of a large number of semantic relatedness measures proposed in the literature with respect to different experimental conditions, such as (i) the datasets employed, (ii) the language (English or German), (iii) the underlying knowledge source, and (iv) the evaluation task (computing scores of semantic relatedness, ranking word pairs, solving word choice problems). To our knowledge, this study is the first to systematically analyze semantic relatedness on a large number of datasets with different properties, while emphasizing the role of the knowledge source compiled either by the ‘wisdom of linguists’ (i.e., classical wordnets) or by the ‘wisdom of crowds’ (i.e., collaboratively constructed knowledge sources like Wikipedia). ::: ::: The article discusses benefits and drawbacks of different approaches to evaluating semantic relatedness. We show that results should be interpreted carefully to evaluate particular aspects of semantic relatedness. For the first time, we employ a vector based measure of semantic relatedness, relying on a concept space built from documents, to the first paragraph of Wikipedia articles, to English WordNet glosses, and to GermaNet based pseudo glosses. Contrary to previous research (Strube and Ponzetto 2006; Gabrilovich and Markovitch 2007; Zesch et al. 2007), we find that ‘wisdom of crowds’ based resources are not superior to ‘wisdom of linguists’ based resources. We also find that using the first paragraph of a Wikipedia article as opposed to the whole article leads to better precision, but decreases recall. Finally, we present two systems that were developed to aid the experiments presented herein and are freely available 1 for research purposes: (i) DEXTRACT, a software to semi-automatically construct corpus-driven semantic relatedness datasets, and (ii) JWPL, a Java-based high-performance Wikipedia Application Programming Interface (API) for building natural language processing (NLP) applications.


title: Text message corpus: Applying natural language processing to mobile device forensics

abstract: The average mobile device user sends a large quantity of text and other short messages. These text message data are of great value to law enforcement investigators who may be analyzing a suspect's mobile device or social media profile for evidence of criminal activity. Current tools and methodologies for analyzing text and other short message data generally only allow for simple keyword searches, which is often a time-consuming task for law enforcement investigators. In addition, there are limited corpora available containing text message data. An initial corpus of text message data for experimental purposes has been developed and made available to the research community. A simple methodology is proposed for feature extraction. The format of the data is given as well as basic statistics, suggestions for possible use, and future work.


title: User stories template for object-oriented applications

abstract: User stories present the mean of communication between customer and development team in agile methods; this paper provides a new user stories voice aimed to ease and automate analysis of objects and their action from the user story using OOADARE (Object-oriented Analysis and Design Approach for the Requirements Engineering) approach [1]. OOADARE approach was also missing a communication mean with the customer; user stories with the new template will do the job.


title: Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study

abstract: Deep neural networks (DNNs) have achieved unprecedented performance on a wide range of complex tasks, rapidly outpacing our understanding of the nature of their solutions. This has caused a recent surge of interest in methods for rendering modern neural systems more interpretable. In this work, we propose to address the interpretability problem in modern DNNs using the rich history of problem descriptions, theories and experimental methods developed by cognitive psychologists to study the human mind. To explore the potential value of these tools, we chose a well-established analysis from developmental psychology that explains how children learn word labels for objects, and applied that analysis to DNNs. Using datasets of stimuli inspired by the original cognitive psychology experiments, we find that state-of-the-art one shot learning models trained on ImageNet exhibit a similar bias to that observed in humans: they prefer to categorize objects according to shape rather than color. The magnitude of this shape bias varies greatly among architecturally identical, but differently seeded models, and even fluctuates within seeds throughout training, despite nearly equivalent classification performance. These results demonstrate the capability of tools from cognitive psychology for exposing hidden computational properties of DNNs, while concurrently providing us with a computational model for human word learning.


title: Edge computing in IoT context: Horizontal and vertical Linux container migration

abstract: In recent years, the development of the Internet of Things (IoT) has largely been supported by the parallel development of the Cloud computing capacities. Cloud computing offered elastic and on-demand computing power able to host and support crucial IoT functions. However, the proliferation of connected objects poses the problem of the confidentiality of the data produced. This proliferation also threatens the performances of the supporting networks. To tackle those problems, the research community has recently turned to Fog Computing. In this paper, we present CIoud4IoT, a platform able to perform horizontal (roaming) and vertical (offloading) migration of IoT functions. We implement our demonstration using a Kubernetes cIuster organised in three tiers: Cloud, Edge and IoT gateways. Our first use cases shows how IoT function roaming can be used in the context of health care data exploitation. The second use case exploits the IoT offloading capacity of our platform to optimize the remote diagnostic of mechanical engines.


title: An Experience-based Comparison of Unity and Unreal for a Stand-alone 3D Game Development Course

abstract: Students' interest in game development often leads departments to attempt to put together a stand-alone game development course. We have used both Unity and Unreal for just such a course. Unity and Unreal are discussed here in the context of how viable they are and how they compare in terms of usefulness for teaching game development. We also present them in context of high level overviews of other game engines and discuss which factors add to a more effective game engine for teaching game development. This paper is intended to help answer some of the questions asked by those without experience in the field who find themselves developing stand-alone game development courses.


title: Practical Black-Box Attacks against Machine Learning

abstract: Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24% of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder.


title: A Simple Ultrawideband Planar Rectangular Printed Antenna With Band Dispensation

abstract: A compact planar ultrawideband (UWB) antenna with band notched characteristics is presented. Modification in the shape of radiation element and ground plane with two symmetrical bevel slots on the lower edge of the radiation element and on the upper edge of the ground plane makes the antenna different from the rectangular printed monopole. These slots improve the input impedance bandwidth and the high frequency radiation characteristics. With this design, the reflection coefficient is lower than 10 dB in the 3.1-10.6 GHz frequency range and radiation pattern is similar to dipole antenna. With the inclusion of an additional small radiation patch, a frequency-notched antenna is also designed and good out of band performance from 5.0-6.0 GHz can be achieved. Measured results confirm that the antenna is suitable for UWB applications due to its compact size and high performance. Also an approximate empirical expression to calculate the lowest resonant frequency is proposed.


title: Toward understanding the causes of unanswered questions in software information sites: a case study of stack overflow

abstract: Stack Overflow is a highly successful question-answering website in the programming community, which not only provide quick solutions to programmers’ questions but also is considered as a large repository of valuable software engineering knowledge. However, despite having a very engaged and active user community, Stack Overflow currently has more than 300K unanswered questions. In this paper, we perform an initial investigation to understand why these questions remain unanswered by applying a combination of statistical and data mining techniques. Our preliminary results indicate that although there are some topics that were never answered, most questions remained unanswered because they apparently are of little interest to the user community.


title: Asexuality and Autochorissexualism (Identity-Less Sexuality)

abstract: I present evidence that target-oriented paraphilias may occur in some who report no sexual attraction for others or those who identify as asexual. One target-oriented paraphilia in some asexual people is a disconnection between their identity and a sexual target/object. I name this phenomenon autochorissexualism or “identity-less” sexuality.


title: Pseudomonas aeruginosa Isolated from Marine Environments in Tokyo Bay

abstract: Pseudomonas aeruginosa is a pathogenic bacterium that has been thoroughly investigated since the 19th century and is generally regarded as a freshwater or terrestrial organism. In 1995, it was reported that the OprP porin, an outer membrane protein corresponding to that of this bacterium, was widely distributed as a dissolved component in seawater. This finding led us to investigate the presence of P. aeruginosa in marine environments. Both culture-independent and -dependent methods were applied to seawater samples obtained in Tokyo Bay during four cruises. The DVC-FA (direct viable count–fluorescent antibody) technique showed that cells reactive to an antibody against P. aeruginosa were widely present in the bay, i.e., 103 to 104 cells/mL in the inner bay, and 102 to 103 cells/mL at the mouth. Bacterial cells isolated by selective medium were identified by three methods: the presence of oprI and oprL, two outer membrane lipoprotein genes specific to P. aeruginosa; the API20 NE kit; and 16S rDNA sequence analysis. The results confirmed that the majority of isolates from the bay were P. aeruginosa. Immuno-chemical analyses of the seawater results indicate that P. aeruginosa is commonly present in coastal marine environments and sheds OprP.


title: Empathic Robots for Long-term Interaction

abstract: As a great number of robotic products are entering people’s lives, the question of how can they behave in order to sustain long-term interactions with users becomes increasingly more relevant. In this paper, we present an empathic model for social robots that aim to interact with children for extended periods of time. The application of this model to a scenario where a social robot plays chess with children is described. To evaluate the proposed model, we conducted a long-term study in an elementary school and measured children’s perception of social presence, engagement and social support.


title: Privacy Protection Using a Rechargeable Battery for Energy Consumption in Smart Grids

abstract: Since the emergence of smart meters, they have played an important role in providing energy- efficient and economical service in smart grids, and they have been extensively deployed. Meanwhile, the incessant energy consumption data collected from smart meters also poses a serious threat to customer privacy. In this article, we present a systematic study of the state-of-the-art privacy- preserving schemes for smart meters. First, we introduce and formulate the technical challenges existing in practice. After that, we categorize and review several representative privacy-preserving schemes according to their respective design goals. Finally, we introduce three common quantitative evaluation metrics for privacy loss, and analyze the privacy leakage of the state-of-the-art schemes.


New York University 2016 System for KBP Event Nugget: A Deep Learning Approach


title: Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era

abstract: Astronomy is entering a new era of discovery, coincident with the establishment of new facilities for observation and simulation that will routinely generate petabytes of data. While an increasing reliance on automated data analysis is anticipated, a critical role will remain for visualization-based knowledge discovery. We have investigated scientific visualization applications in astronomy through an examination of the literature published during the last two decades. We identify the two most active fields for progress - visualization of large-N particle data and spectral data cubes - discuss open areas of research, and introduce a mapping between astronomical sources of data and data representations used in general purpose visualization tools. We discuss contributions using high performance computing architectures (e.g: distributed processing and GPUs), collaborative astronomy visualization, the use of workflow systems to store metadata about visualization parameters, and the use of advanced interaction devices. We examine a number of issues that may be limiting the spread of scientific visualization research in astronomy and identify six grand challenges for scientific visualization research in the Petascale Astronomy Era.


title: FROM POINT SAMPLES TO SURFACES-ON MESHING AND ALTERNATIVES

abstract: © 2018 International Society for Photogrammetry and Remote Sensing. All rights reserved. Terrestrial laser scanners deliver a dense point-wise sampling of an object's surface. For many applications a surface-like reconstruction is required. The most typical example is the visualization of the scanned data. Traditional approaches use meshing algorithms to reconstruct and triangulate the surface represented by the points. Especially in cultural heritage, where complex objects with delicate structures are recorded in highly detailed scans, this process is not without problems. Often long and tedious manual clean-up procedures are required to achieve satisfactory results. After summarizing our experience with current meshing technology we therefore explore alternative approaches for surface reconstruction. An alternative approach presented within this paper is point splatting. We have developed an algorithm to compute a suitable surfel representation directly from the raw laser scanner data. This results in a speedy and fully automated procedure for surface reconstruction. The properties of the different approaches for surface reconstruction are discussed considering a practical example from the field of cultural heritage. The Panagia Kera in Kritsa near Agios Nikolaos on the island of Crete was chosen as a suitable example.


title: Positive blood culture detection in time series data using a BiLSTM network

abstract: The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions. A computational model based on a bidirectional long short-term memory artificial neural network, is explored to assist doctors in the intensive care unit to predict whether examination of blood cultures of patients will return positive. As input it uses nine monitored clinical parameters, presented as time series data, collected from 2177 ICU admissions at the Ghent University Hospital. Our main goal is to determine if general machine learning methods and more specific, temporal models, can be used to create an early detection system. This preliminary research obtains an area of 71.95% under the precision recall curve, proving the potential of temporal neural networks in this context.


Economic Growth and Income Inequality


title: Exploring Serverless Computing for Neural Network Training

abstract: Serverless or functions as a service runtimes have shown significant benefits to efficiency and cost for event-driven cloud applications. Although serverless runtimes are limited to applications requiring lightweight computation and memory, such as machine learning prediction and inference, they have shown improvements on these applications beyond other cloud runtimes. Training deep learning can be both compute and memory intensive. We investigate the use of serverless runtimes while leveraging data parallelism for large models, show the challenges and limitations due to the tightly coupled nature of such models, and propose modifications to the underlying runtime implementations that would mitigate them. For hyperparameter optimization of smaller deep learning models, we show that serverless runtimes can provide significant benefit.


title: Enhance Machine Reading Comprehension on Multiple Sentence Questions with Gated and Dense Coreference Information

abstract: Machine reading comprehension (MC) is one of the most important problems in natural language processing. Most of the previous works rely heavily on features engineering and handcrafting techniques. Since the release of SQuAD, a large-scale MC dataset, many deep learning models have been proposed. However, these models are limited by the soft attention mechanism only relied on keywords that appears in a question. Therefore, the performance is always poor in a question that needs to infer an answer from multiple sentences, which cannot depend on keywords in a question. In this paper, we propose a deep learning model that incorporates coreference information to improve the prediction performance especially on multiple sentence question. We also propose the bi-directional answering technique that can help the model avoid a local maxima of the single directional answering method in a traditional model. The results have shown that our approach outperforms the baseline in terms of F1 and Exact Match (EM).


title: Minimum redundancy feature selection from microarray gene expression data

abstract: Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy - maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.


title: Context-Aware Time Series Anomaly Detection for Complex Systems

abstract: Systems with several components interacting to accomplish challenging tasks are ubiquitous; examples include large server clusters providing “cloud computing”, manufacturing plants, automobiles, etc. Our relentless efforts to improve the capabilities of these systems inevitably increase their complexity as we add more components or introduce more dependencies between existing ones. To tackle this surge in distributed system complexity, system operators collect continuous monitoring data from various sources including hardware and software-based sensors. A large amount of this data is either in the form of time-series or contained in logs, e.g., operators’ activity, system event, and error logs, etc. In this paper, we propose a framework for mining system operational intelligence from massive amount of monitoring data that combines the time series data with information extracted from text-based logs. Our work is aimed at systems where logs capture the context of a system’s operations and the time-series data record the state of different components. This category includes a wide variety of systems including IT systems (compute clouds, web services’ infrastructure, enterprise computing infrastructure, etc.) and complex physical systems such as manufacturing plants. We instantiate our framework for Hadoop. Our preliminary results using both real and synthetic datasets show that the proposed context-aware approach is more effective for detecting anomalies compared to a time series only approach.


title: A novel approach to OFDM radar processing

abstract: In this paper a novel approach for the calculation of the radar range profile in OFDM radar systems is presented. The proposed algorithm operates directly on the modulation symbols and overcomes the typical drawbacks of correlation based baseband signal processing. An example system configuration as well as simulation results from a dedicated MatLab model will be presented and discussed.


title: Im2Text and Text2Im: Associating Images and Texts for Cross-Modal Retrieval.

abstract: Building bilateral semantic associations between images and texts is among the fundamental problems in computer vision. In this paper, we study two complementary cross-modal prediction tasks: (i) predicting text(s) given an image (“Im2Text”), and (ii) predicting image(s) given a piece of text (“Text2Im”). We make no assumption on the specific form of text; i.e., it could be either a set of labels, phrases, or even captions. We pose both these tasks in a retrieval framework. For Im2Text, given a query image, our goal is to retrieve a ranked list of semantically relevant texts from an independent textcorpus (i.e., texts with no corresponding images). Similarly, for Text2Im, given a query text, we aim to retrieve a ranked list of semantically relevant images from a collection of unannotated images (i.e., images without any associated textual meta-data). We propose a novel Structural SVM based unified formulation for these two tasks. For both visual and textual data, two types of representations are investigated. These are based on: (1) unimodal probability distributions over topics learned using latent Dirichlet allocation, and (2) explicitly learned multi-modal correlations using canonical correlation analysis. Extensive experiments on three popular datasets (two medium and one web-scale) demonstrate that our framework gives promising results compared to existing models under various settings, thus confirming its efficacy for both the tasks.


title: GSLAM: Initialization-Robust Monocular Visual SLAM via Global Structure-from-Motion

abstract: Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank-1 matrix factorization technique which is more robust to the errors in map initialization. Second, we adopt a recent global SfM method for the pose-graph optimization, which leads to a multi-stage linear formulation and enables L1 optimization for better robustness to false loops. The combination of these two approaches generates more robust reconstruction and is significantly faster (4X) than recent state-of-the-art SLAM systems. We also present a new dataset recorded with ground truth camera motion in a Vicon motion capture room, and compare our method to prior systems on it and established benchmark datasets.


title: Identifying Node Role in Social Network Based on Multiple Indicators

abstract: It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.


title: How Open Source Projects Use Static Code Analysis Tools in Continuous Integration Pipelines

abstract: Static analysis tools are often used by software developers to entail early detection of potential faults, vulnerabilities, code smells, or to assess the source code adherence to coding standards and guidelines. Also, their adoption within Continuous Integration (CI) pipelines has been advocated by researchers and practitioners. This paper studies the usage of static analysis tools in 20 Java open source projects hosted on GitHub and using Travis CI as continuous integration infrastructure. Specifically, we investigate (i) which tools are being used and how they are configured for the CI, (ii) what types of issues make the build fail or raise warnings, and (iii) whether, how, and after how long are broken builds and warnings resolved. Results indicate that in the analyzed projects build breakages due to static analysis tools are mainly related to adherence to coding standards, and there is also some attention to missing licenses. Build failures related to tools identifying potential bugs or vulnerabilities occur less frequently, and in some cases such tools are activated in a "softer" mode, without making the build fail. Also, the study reveals that build breakages due to static analysis tools are quickly fixed by actually solving the problem, rather than by disabling the warning, and are often properly documented.


title: Modeling and simulation of Electrostatic Adhesion for Wall Climbing Robot

abstract: Electrostatic Adhesion (ESA) was chosen as the surface attachment mechanism for Wall Climbing Robot (WCR) for its qualitative advantages. ESA model was developed to account for both parallel-plane and fringing static fields. Such Electrostatic Pad model yields equations governing the ESA force and the geometrical conditions for maximum ESA pressure per pad area. This paper shows that the parameters due to applied voltage, and also geometrical properties, material properties and compliance to wall surface of the Electrostatic Pad do affect the ESA force. The simulation also shows that fringing fields may account up to above 10% of total generated ESA force.


Interpretable Neural Architectures for Attributing an Ad’s Performance to its Writing Style


title: Some Evidence for Unconscious Lie Detection

abstract: To maximize survival and reproductive success, primates evolved the tendency to tell lies and the ability to accurately detect them. Despite the obvious advantage of detecting lies accurately, conscious judgments of veracity are only slightly more accurate than chance. However, findings in forensic psychology, neuroscience, and primatology suggest that lies can be accurately detected when less-conscious mental processes (as opposed to more-conscious mental processes) are used. We predicted that observing someone tell a lie would automatically activate cognitive concepts associated with deception, and observing someone tell the truth would activate concepts associated with truth. In two experiments, we demonstrated that indirect measures of deception detection are significantly more accurate than direct measures. These findings provide a new lens through which to reconsider old questions and approach new investigations of human lie detection.


title: Design of 56 Gb/s PAM4 wire-line receiver with ring VCO based CDR in a 65 nm CMOS technology

abstract: This paper presents a 56 Gb/s 4-level pulse amplitude modulation (PAM4) wire-line receiver, which employs a quarter rate architecture. By employing a ring voltage control oscillator (VCO) based clock and data recovery (CDR) with separate proportional path, the complexity, power consumption and area can all be reduced. To reduce the noise of the detector and improve the stability of the CDR, both the major and minor transitions with the central crossover point are utilized to extract the phase error. The receiver is designed in a 65nm CMOS technology and supplied with 1.2V. The simulation results show that the proposed PAM4 receiver can work at 56 Gb/s with 76 mW consumption.


A Multi-Turbine Power Curve Approach


INVESTIGATING THE IMPACT OF E-CUSTOMER RELATIONSHIP MANAGEMENT ON HOTEL’S WEBSITE


title: Hemodynamic evoked response of the sensorimotor cortex measured noninvasively with near-infrared optical imaging

abstract: We have performed a noninvasive bilateral optical imaging study of the hemodynamic evoked response to unilateral finger opposition task, finger tactile, and electrical median nerve stimulation in the human sensorimotor cortex. This optical study shows the hemoglobin-evoked response to voluntary and nonvoluntary stimuli. We performed measurements on 10 healthy volunteers using block paradigms for motor, sensory, and electrical stimulations of the right and left hands separately. We analyzed the spatial/temporal features and the amplitude of the optical signal induced by cerebral activation during these three paradigms. We consistently found an increase (decrease) in the cerebral concentration of oxy-hemoglobin (deoxy-hemoglobin) at the cortical side contralateral to the stimulated side. We observed an optical response to activation that was larger in size and amplitude during voluntary motor task compared to the other two stimulations. The ipsilateral response was consistently smaller than the contralateral response, and even reversed (i.e., a decrease in oxy-hemoglobin, and an increase in deoxy-hemoglobin) in the case of the electrical stimulation. We observed a systemic contribution to the optical signal from the increase in the heart rate increase during stimulation, and we made a first attempt to subtract it from the evoked hemoglobin signal. Our findings based on optical imaging are in agreement with results in the literature obtained with positron emission tomography and functional magnetic resonance imaging.


title: Context Dependent Claim Detection

abstract: While discussing a concrete controversial topic, most humans will find it challenging to swiftly raise a diverse set of convincing and relevant claims that should set the basis of their arguments. Here, we formally define the challenging task of automatic claim detection in a given context and discuss its associated unique difficulties. Further, we outline a preliminary solution to this task, and assess its performance over annotated real world data, collected specifically for that purpose over hundreds of Wikipedia articles. We report promising results of a supervised learning approach, which is based on a cascade of classifiers designed to properly handle the skewed data which is inherent to the defined task. These results demonstrate the viability of the introduced task.