title: Evaluation of Complex-Valued Neural Networks on Real-Valued Classification Tasks

abstract: Complex-valued neural networks are not a new concept, however, the use of real-valued models has often been favoured over complex-valued models due to difficulties in training and performance. When comparing real-valued versus complex-valued neural networks, existing literature often ignores the number of parameters, resulting in comparisons of neural networks with vastly different sizes. We find that when real and complex neural networks of similar capacity are compared, complex models perform equal to or slightly worse than real-valued models for a range of real-valued classification tasks. The use of complex numbers allows neural networks to handle noise on the complex plane. When classifying real-valued data with a complex-valued neural network, the imaginary parts of the weights follow their real parts. This behaviour is indicative for a task that does not require a complex-valued model. We further investigated this in a synthetic classification task. We can transfer many activation functions from the real to the complex domain using different strategies. The weight initialisation of complex neural networks, however, remains a significant problem.


title: The effect of egocentric body movements on users' navigation performance and spatial memory in zoomable user interfaces

abstract: We present two experiments examining the impact of navigation techniques on users' navigation performance and spatial memory in a zoomable user interface (ZUI). The first experiment with 24 participants compared the effect of egocentric body movements with traditional multi-touch navigation. The results indicate a 47% decrease in path lengths and a 34% decrease in task time in favor of egocentric navigation, but no significant effect on users' spatial memory immediately after a navigation task. However, an additional second experiment with 8 participants revealed such a significant increase in performance of long-term spatial memory: The results of a recall task administered after a 15-minute distractor task indicate a significant advantage of 27% for egocentric body movements in spatial memory. Furthermore, a questionnaire about the subjects' workload revealed that the physical demand of the egocentric navigation was significantly higher but there was less mental demand.


title: State Mindfulness Scale (SMS): development and initial validation.

abstract: The goal of the present research was to develop and test a novel conceptual model and corresponding measure of state mindfulness-the State Mindfulness Scale (SMS). We developed the SMS to reflect traditional Buddhist and contemporary psychological science models of mindfulness not similarly reflected in extant published measures of the construct. Study 1 exploratory and confirmatory factor analyses supported a higher order 2-factor solution encompassing 1 second-order state mindfulness factor, and 2 first-order factors, one reflecting state mindfulness of bodily sensations and the other state mindfulness of mental events. Study 2 provided cross-sectional evidence of the convergent, discriminant, and incremental convergent validity of SMS scores with respect to other measures of state and trait mindfulness. Study 3, a randomized control experimental mindfulness intervention study, yielded a number of key findings with respect to SMS stability as a function of time and context, construct validity, incremental sensitivity to change in state mindfulness over time, and incremental predictive criterion-related validity. Findings are discussed with respect to the potential contribution of the SMS to the study of mindfulness as a statelike mental behavior, biopsychobehavioral research on the mechanisms of mindfulness, and clinical evaluation of mindfulness. (PsycINFO Database Record (c) 2013 APA, all rights reserved).


title: A Concept Analysis of Systems Thinking

abstract: PURPOSE ::: This concept analysis, written by the National Quality and Safety Education for Nurses (QSEN) RN-BSN Task Force, defines systems thinking in relation to healthcare delivery. ::: ::: METHODS ::: A review of the literature was conducted using five databases with the keywords “systems thinking” as well as “nursing education,” “nursing curriculum,” “online,” “capstone,” “practicum,” “RN-BSN/RN to BSN,” “healthcare organizations,” “hospitals,” and “clinical agencies.” Only articles that focused on systems thinking in health care were used. The authors identified defining attributes, antecedents, consequences, and empirical referents of systems thinking. ::: ::: FINDINGS ::: Systems thinking was defined as a process applied to individuals, teams, and organizations to impact cause and effect where solutions to complex problems are accomplished through collaborative effort according to personal ability with respect to improving components and the greater whole. Four primary attributes characterized systems thinking: dynamic system, holistic perspective, pattern identification, and transformation. ::: ::: CONCLUSION ::: Using the platform provided in this concept analysis, interprofessional practice has the ability to embrace planned efforts to improve critically needed quality and safety initiatives across patients’ lifespans and all healthcare settings.


title: Value scenarios: a technique for envisioning systemic effects of new technologies

abstract: In this paper we argue that there is a scarcity of methods which support critical, systemic, long-term thinking in current design practice, technology development and deployment. To address this need we introduce value scenarios, an extension of scenario-based design which can support envisioning the systemic effects of new technologies. We identify and describe five key elements of value scenarios; stakeholders, pervasiveness, time, systemic effects, and value implications. We provide two examples of value scenarios, which draw from our current work on urban simulation and human-robotic interaction . We conclude with suggestions for how value scenarios might be used by others.


title: AN INVENTORY-LOCATION MODEL: FORMULATION, SOLUTION ALGORITHM AND COMPUTATIONAL RESULTS

abstract: We introduce a distribution center (DC) location model that incorporates working inventory and safety stock inventory costs at the distribution centers. In addition, the model incorporates transport costs from the suppliers to the DCs that explicitly reflect economies of scale through the use of a fixed cost term. The model is formulated as a non-linear integer-programming problem. Model properties are outlined. A Lagrangian relaxation solution algorithm is proposed. By exploiting the structure of the problem we can find a low-order polynomial algorithm for the non-linear integer programming problem that must be solved in solving the Lagrangian relaxation subproblems. A number of heuristics are outlined for finding good feasible solutions. In addition, we describe two variable forcing rules that prove to be very effective at forcing candidate sites into and out of the solution. The algorithms are tested on problems with 88 and 150 retailers. Computation times are consistently below one minute and compare favorably with those of an earlier proposed set partitioning approach for this model (Shen, 2000; Shen, Coullard and Daskin, 2000). Finally, we discuss the sensitivity of the results to changes in key parameters including the fixed cost of placing orders. Significant reductions in these costs might be expected from e-commerce technologies. The model suggests that as these costs decrease it is optimal to locate additional facilities.


title: Adversarial Auto-encoders for Speech Based Emotion Recognition

abstract: Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition. They map the autoencoder's bottleneck layer output (termed as code vectors) to different noise Probability Distribution Functions (PDFs), that can be further regularized to cluster based on class information. In addition, they also allow a generation of synthetic samples by sampling the code vectors from the mapped PDFs. Inspired by these properties, we investigate the application of adversarial autoencoders to the domain of emotion recognition. Specifically, we conduct experiments on the following two aspects: (i) their ability to encode high dimensional feature vector representations for emotional utterances into a compressed space (with a minimal loss of emotion class discriminability in the compressed space), and (ii) their ability to regenerate synthetic samples in the original feature space, to be later used for purposes such as training emotion recognition classifiers. We demonstrate the promise of adversarial autoencoders with regards to these aspects on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus and present our analysis.


title: Adding network bandwidth resource management to Hadoop YARN

abstract: YARN (Yet Another Resource Negotiator) is an emerging resource management system in Hadoop platform which can process big data in the scale of petabytes/day. YARN, as a resource management system, needs manage all kinds of resources in the clusters, such as cpu, memory, disk io, network io and so on. At present, YARN mainly focuses on computing resource and storage resource (CPU and memory), while ignoring network management. But research shows that efficient network bandwidth resource management is crucial to optimize performance. In this paper we present a network bandwidth resource management mechanism based on YARN that takes into account the allocation and isolation of applications' network bandwidth. Experiments show that the proposed solution makes YARN run applications in a more predictable manner.


title: On detection of bitcoin mining redirection attacks

abstract: In this paper we study the question of centralisation in bitcoin digital currency. In theory bitcoin has been designed to be a totally decentralized distributed system. Satoshi Nakamoto has very clearly postulated that each node should be collecting recent transactions and trying to create new blocks (Satoshi08). In bitcoin transactions are aggregated in block in order to authenticate them and form an official ledger and history of bitcoin transactions. In practice as soon as expensive ASIC bitcoin miners have replaced general-purpose hardware, production of bitcoins and the validation of transactions has concentrated in the hands of a smaller group of people. Then at some moment in early 2012 an important decision was taken: the Stratum protocol was designed (Palatinus12) which took a deliberate decision to move the power of selecting which transactions are included in blocks from miners to pool managers. The growing difficulty of mining and large standard deviation in this process (Rosenfeld13; CourtoisBahack14) made that majority of miners naturally shifted to pooled mining. At this moment bitcoin ceased being a decentralized democratic system. In this paper we survey the question of a 51% attacks and show that there is a large variety of plausible attack scenarios. In particular we study one particularly subversive attack scenario which depends on non-trivial internal details of the bitcoin hashing process. How does it compare with the current mining practices? We have study the Stratum protocol in four popular real-life mining configurations. Our analysis shows that pools could very easily cheat the majority of people. However the most subversive versions of the attack are NOT facilitated and could potentially be detected.


title: COMPARATIVE STUDY OF ATTRIBUTE SELECTION USING GAIN RATIO AND CORRELATION BASED FEATURE SELECTION

abstract: Feature subset selection is of great importance in the field of data mining. The high dimension data makes testing and training of general classification methods difficult. In the present paper two filters approaches namely Gain ratio and Correlation based feature selection have been used to illustrate the significance of feature subset selection for classifying Pima Indian diabetic database (PIDD). The C4.5 tree uses gain ratio to determine the splits and to select the most important features. Genetic algorithm is used as search method with Correlation based feature selection as subset evaluating mechanism. The feature subset obtained is then tested using two supervised classification method namely, Back propagation neural network and Radial basis function network. Experimental results show that the feature subsets selected by CFS filter resulted in marginal improvement for both back propagation neural network and Radial basis function network classification accuracy when compared to feature subset selected by information gain filter.


title: Alveolar bone dimensional changes of post-extraction sockets in humans: a systematic review

abstract: Objective: To review the literature to assess the amount of change in height and width of the residual ridge after tooth extraction. Material and Methods: MEDLINE-PubMed and the Cochrane Central register of controlled trials (CENTRAL) were searched through up to March 2009. Appropriate studies which data reported concerning the dimensional changes in alveolar height and width after tooth extraction were included. Approximal height change, mid-buccal change, mid-crestal change, mid-lingual change, Alveolar width change and socket fill were selected as outcome variables. Mean values and if available standard deviations were extracted. Weighted mean changes were calculated. Results: Independent screening of the titles and abstracts of 1244 MEDLINE-PubMed and 106 Cochrane papers resulted in 12 publications that met the eligibility criteria. The reduction in width of the alveolar ridges was 3.87 mm. The mean clinical mid-buccal height loss was 1.67 mm. The mean crestal height change as assessed on the radiographs was 1.53 mm. Socket fill in height as measured relative to the original socket floor was on an average 2.57 mm. Conclusion: During the post-extraction healing period, the weighted mean changes as based on the data derived from the individual selected studies show the clinical loss in width to be greater than the loss in height, assessed both clinically as well as radiographically.


title: Deep Short Text Classification with Knowledge Powered Attention

abstract: Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention.


title: Local learning rules to attenuate forgetting in neural networks

abstract: Hebbian synaptic plasticity inevitably leads to interference and forgetting when different, overlapping memory patterns are sequentially stored in the same network. Recent work on artificial neural networks shows that an information-geometric approach can be used to protect important weights to slow down forgetting. This strategy however is biologically implausible as it requires knowledge of the history of previously learned patterns. In this work, we show that a purely local weight consolidation mechanism, based on estimating energy landscape curvatures from locally available statistics, prevents pattern interference. Exploring a local calculation of energy curvature in the sparse-coding limit, we demonstrate that curvature-aware learning rules reduce forgetting in the Hopfield network. We further show that this method connects information-geometric global learning rules based on the Fisher information to local spike-dependent rules accessible to biological neural networks. We conjecture that, if combined with other learning procedures, it could provide a building-block for content-aware learning strategies that use only quantities computable in biological neural networks to attenuate pattern interference and catastrophic forgetting. Additionally, this work clarifies how global information-geometric structure in a learning problem can be exposed in local model statistics, building a deeper theoretical connection between the statistics of single units in a network, and the global structure of the collective learning space.


Intelligent code reviews using deep learning


title: High-performance NiO/Ag/NiO transparent electrodes for flexible organic photovoltaic cells.

abstract: Transparent electrodes with a dielectric-metal-dielectric (DMD) structure can be implemented in a simple manufacturing process and have good optical and electrical properties. In this study, nickel oxide (NiO) is introduced into the DMD structure as a more appropriate dielectric material that has a high conduction band for electron blocking and a low valence band for efficient hole transport. The indium-free NiO/Ag/NiO (NAN) transparent electrode exhibits an adjustable high transmittance of ∼82% combined with a low sheet resistance of ∼7.6 Ω·s·q(-1) and a work function of 5.3 eV after UVO treatment. The NAN electrode shows excellent surface morphology and good thermal, humidity, and environmental stabilities. Only a small change in sheet resistance can be found after NAN electrode is preserved in air for 1 year. The power conversion efficiencies of organic photovoltaic cells with NAN electrodes deposited on glass and polyethylene terephthalate (PET) substrates are 6.07 and 5.55%, respectively, which are competitive with those of indium tin oxide (ITO)-based devices. Good photoelectric properties, the low-cost material, and the room-temperature deposition process imply that NAN electrode is a striking candidate for low-cost and flexible transparent electrode for efficient flexible optoelectronic devices.


title: Complex networks: The fragility of interdependency

abstract: A study of failures in interconnected networks highlights the vulnerability of tightly coupled infrastructures and shows the need to consider mutually dependent network properties in designing resilient systems.


title: S1 guidelines: Lipedema.

abstract: The present, revised guidelines on lipedema were developed under the auspices of and funded by the German Society of Phlebology (DGP). The recommendations are based on a systematic literature search and the consensus of eight medical societies and working groups. The guidelines contain recommendations with respect to diagnosis and management of lipedema. The diagnosis is established on the basis of medical history and clinical findings. Characteristically, there is a localized, symmetrical increase in subcutaneous adipose tissue in arms and legs that is in marked disproportion to the trunk. Other findings include edema, easy bruising, and increased tenderness. Further diagnostic tests are usually reserved for special cases that require additional workup. Lipedema is a chronic, progressive disorder marked by the individual variability and unpredictability of its clinical course. Treatment consists of four therapeutic mainstays that should be combined as necessary and address current clinical symptoms: complex physical therapy (manual lymphatic drainage, compression therapy, exercise therapy, and skin care), liposuction and plastic surgery, diet, and physical activity, as well as psychotherapy if necessary. Surgical procedures are indicated if - despite thorough conservative treatment - symptoms persist, or if there is progression of clinical findings and/or symptoms. If present, morbid obesity should be therapeutically addressed prior to liposuction.


Types of religious philosophy


title: Strength-power parameters as predictors of sprinting performance.

abstract: Aim. The purpose of this study was to determine the relationship between strength - power parameters and sprint performance and to predict sprint times from strength - power parameters. Methods. Twenty-five male young sprinters participated in this study. Squat jump(SJ), counter-movement jump (CMJ), drop jump height (DJH), repeated jump(RJ) and 100m sprint time from block start, including reaction time (RT) and times at 10m, 30m and 60m were measured. Reactive strength index (RSI), the difference between counter-movement and squat jump (CMJ-SJ) and the mean velocities of the intermediate sections 0-lOm, 10-30m, 30-60m, 60-100 m (V0-10, V10-30, V30-60 and V60-100) were also calculated. Results. The canonical correlation analysis with strength - power parameters as predictors and reaction time and mean velocities as dependent variables revealed two canonical variables that explained 89.6% of the total variance. The first canonical variable (R=0.840) explained the association between SJ, RJ, DJH, RSI and all mean velocities. The second canonical variable (R=0.707) had only one predictor, CMJ-SJ, and loaded only on RT. Stepwise multiple regression analysis confirmed that RT depends only on CMJ-SJ. V0-10 depends on both DJ and SJ, while V10-30 depends only on SJ. Finally, V30-60 and V60-100 are primarily dependent on RSI. Multiple regression analysis of the 100m sprint time revealed that 46.5% of the variability could be explained by the variability of the strength- power predictors. Conclusion. Performance at 100m sprint is strongly associated with strength- power parameters. The best predictor of the overall performance is probably SJ (or CMJ).


title: Sustaining Superior Performance in Business Ecosystems: Evidence from Application Software Developers in the iOS and Android Smartphone Ecosystems

abstract: We study the phenomenon of business ecosystems in which platform firms orchestrate the functioning of ecosystems by providing platforms and setting the rules for participation by complementor firms. We develop a theoretical framework to explain how the structural and evolutionary features of the ecosystem may shape the extent to which participating complementor firms can sustain their superior performance. The structural feature, which we refer to as ecosystem complexity, is a function of the number of unique components or subsystems that interact with the complementor’s product. We incorporate the evolutionary features by considering the role of generational transitions initiated by platform firms over time as well as the role of complementors’ ecosystem-specific experience. Evidence from Apple’s iOS and Google’s Android smartphone ecosystems supports our arguments that higher ecosystem complexity helps app developers sustain their superior performance, and that this effect is stronger for more experienc...


title: The attentional requirements of consciousness

abstract: It has been widely claimed that attention and awareness are doubly dissociable and that there is no causal relation between them. In support of this view are numerous claims of attention without awareness, and awareness without attention. Although there is evidence that attention can operate on or be drawn to unconscious stimuli, various recent findings demonstrate that there is no empirical support for awareness without attention. To properly test for awareness without attention, we propose that a stimulus be studied using a battery of tests based on diverse, mainstream paradigms from the current attention literature. When this type of analysis is performed, the evidence is fully consistent with a model in which attention is necessary, but not sufficient, for awareness.


title: A highly stretchable tactile distribution sensor for smooth surfaced humanoids

abstract: Stretchable tactile distribution sensors are significantly important in realizing natural and rich tactile interactions for next generation humanoids with soft and smooth surfaces. We have developed such a sensor which has no wiring in most of the sensing area and is based on inverse problem technique - EIT. At first, we show the ability of the sensor to detect novel and rich tactile interactions involving skin deformation such as pinching and rubbing. Furthermore, by using our newly developed conductive knitted fabric, we demonstrate how the sensor can be extremely useful for sensing pressure distributions as well as for covering complex 3D surfaces such as the face. Also, we show a successful implementation of the highly stretchable sensor over movable joints, the repeatability of the sensor value against joint movements and the detectability of external tactile stimuli for various joint angles are also demonstrated.


title: Basic vibration signal processing for bearing fault detection

abstract: Faculty in the College of Engineering at the University of Alabama developed a multidisciplinary course in applied spectral analysis that was first offered in 1996. The course is aimed at juniors majoring in electrical, mechanical, industrial, or aerospace engineering. No background in signal processing or Fourier analysis is assumed; the requisite fundamentals are covered early in the course and followed by a series of laboratories in which the fundamental concepts are applied. In this paper, a laboratory module on fault detection in rolling element bearings is presented. This module is one of two laboratory modules focusing on machine condition monitoring applications that were developed for this course. Background on the basic operational characteristics of rolling element bearings is presented, and formulas given for the calculation of the characteristic fault frequencies. The shortcomings of conventional vibration spectral analysis for the detection of bearing faults is examined in the context of a synthetic vibration signal that students generate in MATLAB. This signal shares several key features of vibration signatures measured on bearing housings. Envelope analysis and the connection between bearing fault signatures and amplitude modulation/demodulation is explained. Finally, a graphically driven software utility (a set of MATLAB m-files) is introduced. This software allows students to explore envelope analysis using measured data or the synthetic signal that they generated. The software utility and the material presented in this paper constitute an instructional module on bearing fault detection that can be used as a stand-alone tutorial or incorporated into a course.


title: Corporate Social Responsibility and Financial Performance: An Empirical Analysis on Greek Companies

abstract: This paper is an attempt to explore the relationship of CSR and firms’ financial performance in Greek firms. Based on stakeholder theory and mainly on the theory of “good management”, we try to find out if an improvement in CSR actions results in higher stock returns. Our empirical analysis will test whether there is an impact of CSR performance on stock returns, using voluntary disclosures, based on a sample of Greek listed companies. The findings show that there is a positive correlation among stock returns and CSR performance in Greek companies. In operational level, these results aim at persuading managers to implement CSR actions in a greater extent in order to enhance firm market efficiency.


Fitting Cox Model Using PROC PHREG and Beyond in SAS


title: A dataset for point of gaze detection using head poses and eye images

abstract: This paper presents a new, publicly available dataset, aimed to be used as a benchmark for Point of Gaze (PoG) detection algorithms. The dataset consists of two modalities that can be combined for PoG definition: (a) a set of videos recording the eye motion of human participants as they were looking at, or following, a set of predefined points of interest on a computer visual display unit (b) a sequence of 3D head poses synchronized with the video. The eye motion was recorded using a Mobile Eye-XG, head mounted, infrared monocular camera and the head position by using a set of Vicon motion capture cameras. The ground truth of the point of gaze and head location and direction in the three-dimensional space are provided together with the data. The ground truth regarding the point of gaze is known in advance since the participants are always looking at predefined targets on a monitor.


Design of One Stage Operational Transconductance Amplifiers at 65 nm and 90 nm for Low Power Applications


title: Symmetric Variational Autoencoder and Connections to Adversarial Learning

abstract: A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previously distinct techniques of VAE and adversarially learning, and provides insights that allow us to ameliorate shortcomings with some previously developed adversarial methods. In addition to an analysis that motivates and explains the sVAE, an extensive set of experiments validate the utility of the approach.


title: An adaptive algorithm for learning changes in user interests

abstract: In this paper, we describe a new scheme to learn dynamic user's interests in an automated information filtering and gathering system running on the Internet. Our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. We developed a 3-descriptor approach to represent the user's interest categories. Using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.


title: Metacognitive Control and the Spacing Effect

abstract: This study investigates whether the use of a spacing strategy absolutely improves final performance, even when the learner had chosen, metacognitively, to mass. After making judgments of learning, adult and child participants chose to mass or space their study of word pairs. However, 1/3 of their choices were dishonored. That is, they were forced to mass after having chosen to space and forced to space after having chosen to mass. Results showed that the spacing effect obtained for both adults and children when choices were honored. However, using a spacing strategy when it was in disagreement with the participant's own choice, or forced, did not enhance performance for the adults (Experiment 1). And although performance was enhanced for the children (beyond massing strategies), it was not as good as when the spacing decisions were self-chosen (Experiment 2). The data suggest that although spacing is an effective strategy for learning, it is not universal, particularly when the strategy is not chosen by the learner. In short, metacognitive control is often crucial and should be honored.


Sentiment Analysis and E-Learning : a Proposal


Word-Context Character Embeddings for Chinese Word Segmentation


title: A 25–30 GHz 8-antenna 2-stream hybrid beamforming receiver for MIMO communication

abstract: This paper presents a 65 nm CMOS 25–30 GHz hybrid beamforming receiver with eight antenna inputs and two baseband output streams. The receiver uses the Cartesian-Combining architecture, which is introduced for two baseband streams. Each antenna signal is complex-weighted independently and combined with weighted signals from other antennas prior to downconversion. Each RF-domain complex weight is realized using a pair of 5-bit digitally controlled VGA's. The receiver achieves 34 dB conversion gain, 7.3 dB minimum noise figure, and 5 GHz of RF bandwidth while consuming only 27.5 mW power per antenna element (340 mW for the entire receiver). Two-element Cartesian-combining achieves a peak-to-null ratio of 20 dB. Use of mostly active phase shifting and combining approach made the design ultra-compact with 3.86 mm2 core area for entire 8-element 2-stream receiver.


title: Convolutional Neural Networks for Movement Prediction in Videos

abstract: In this work we present a convolutional neural network-based (CNN) model that predicts future movements of a ball given a series of images depicting the ball and its environment. For training and evaluation, we use artificially generated images sequences. Two scenarios are analyzed: Prediction in a simple table tennis environment and a more challenging squash environment. Classical 2D convolution layers are compared with 3D convolution layers that extract the motion information of the ball from contiguous frames. Moreover, we investigate whether networks with stereo visual input perform better than those with monocular vision only. Our experiments suggest that CNNs can indeed predict physical behaviour with small error rates on unseen data but the performance drops for very complex underlying movements.


title: "Like Having a Really Bad PA": The Gulf between User Expectation and Experience of Conversational Agents

abstract: The past four years have seen the rise of conversational agents (CAs) in everyday life. Apple, Microsoft, Amazon, Google and Facebook have all embedded proprietary CAs within their software and, increasingly, conversation is becoming a key mode of human-computer interaction. Whilst we have long been familiar with the notion of computers that speak, the investigative concern within HCI has been upon multimodality rather than dialogue alone, and there is no sense of how such interfaces are used in everyday life. This paper reports the findings of interviews with 14 users of CAs in an effort to understand the current interactional factors affecting everyday use. We find user expectations dramatically out of step with the operation of the systems, particularly in terms of known machine intelligence, system capability and goals. Using Norman's 'gulfs of execution and evaluation' [30] we consider the implications of these findings for the design of future systems.


title: Two Markov Models of Open System Occupational Mobility: Underlying Conceptualizations and Empirical Tests

abstract: The principal purposes of this paper are to introduce a more general area of application for a refreshing approach to occupational mobility (open systems), to illustrate the general open system approach with two currently existing alternative models including an explication of the two models' distinct representations of the process and to test each model's predictive accuracy for a given job system. The approach focuses on intragenerational occupational mobility in a continuously operative job system. One model views mobility in terms of manflows, the other as interrelated job vacancy moves. Application is to an internal labor market-a state police system. The data are continuous for 43 years (I 927-1970). Both models are found to be quite accurate in predictions once stationarity was approximated: 1949-1970. This study along with other open system studies suggests that Markov chain models are more generally adequate than has been concluded from closed cohort analyses. The models have general applicability to job systems, whether or not formally organized.


title: NMODE --- Neuro-MODule Evolution

abstract: Modularisation, repetition, and symmetry are structural features shared by almost all biological neural networks. These features are very unlikely to be found by the means of structural evolution of artificial neural networks. This paper introduces NMODE, which is specifically designed to operate on neuro-modules. NMODE addresses a second problem in the context of evolutionary robotics, which is incremental evolution of complex behaviours for complex machines, by offering a way to interface neuro-modules. The scenario in mind is a complex walking machine, for which a locomotion module is evolved first, that is then extended by other modules in later stages. We show that NMODE is able to evolve a locomotion behaviour for a standard six-legged walking machine in approximately 10 generations and show how it can be used for incremental evolution of a complex walking machine. The entire source code used in this paper is publicly available through GitHub.


title: FPGA Implementation of SRAM-based Ternary Content Addressable Memory

abstract: Content Addressable Memory (CAM) is a special memory that accomplishes search operation in a single clock cycle but CAM has disadvantages like low bit density and high cost per bit. In this paper, we present an implementation of a 512 x 36 SRAM-based TCAM (SR-TCAM) on a Virtex-5 FPGA, which is the strength of SR-TCAM because currently classical TCAMs cannot be implemented on FPGA. We have used two synthesis optimizations (BRAM-AUTO and BRAM = BLOCK_POWER2) using BRAMs on FPGA. Thus, user can choose design parameters that are suitable for his application. The power data has been measured using Xilinx X-Power by using activities for search operations of SR-TCAM. We have computed power consumption by taking the average of 1000 search operations with 100 MHz clock speed to have better power estimation, which results, for one of the design, in an average power consumption of 2.11 mW. SR-TCAM exploits dense SRAM and achieves comparable search performance in two clock cycles. Thus, SR-TCAM is a feasible and practical alternative to traditional CAMs.


title: Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions

abstract: BackgroundThe main objective of this research is to identify, categorize, and analyze barriers perceived by physicians to the adoption of Electronic Medical Records (EMRs) in order to provide implementers with beneficial intervention options.MethodsA systematic literature review, based on research papers from 1998 to 2009, concerning barriers to the acceptance of EMRs by physicians was conducted. Four databases, "Science", "EBSCO", "PubMed" and "The Cochrane Library", were used in the literature search. Studies were included in the analysis if they reported on physicians' perceived barriers to implementing and using electronic medical records. Electronic medical records are defined as computerized medical information systems that collect, store and display patient information.ResultsThe study includes twenty-two articles that have considered barriers to EMR as perceived by physicians. Eight main categories of barriers, including a total of 31 sub-categories, were identified. These eight categories are: A) Financial, B) Technical, C) Time, D) Psychological, E) Social, F) Legal, G) Organizational, and H) Change Process. All these categories are interrelated with each other. In particular, Categories G (Organizational) and H (Change Process) seem to be mediating factors on other barriers. By adopting a change management perspective, we develop some barrier-related interventions that could overcome the identified barriers.ConclusionsDespite the positive effects of EMR usage in medical practices, the adoption rate of such systems is still low and meets resistance from physicians. This systematic review reveals that physicians may face a range of barriers when they approach EMR implementation. We conclude that the process of EMR implementation should be treated as a change project, and led by implementers or change managers, in medical practices. The quality of change management plays an important role in the success of EMR implementation. The barriers and suggested interventions highlighted in this study are intended to act as a reference for implementers of Electronic Medical Records. A careful diagnosis of the specific situation is required before relevant interventions can be determined.


title: Climate change threats to plant diversity in Europe

abstract: Climate change has already triggered species distribution shifts in many parts of the world. Increasing impacts are expected for the future, yet few studies have aimed for a general understanding of the regional basis for species vulnerability. We projected late 21st century distributions for 1,350 European plants species under seven climate change scenarios. Application of the International Union for Conservation of Nature and Natural Resources Red List criteria to our projections shows that many European plant species could become severely threatened. More than half of the species we studied could be vulnerable or threatened by 2080. Expected species loss and turnover per pixel proved to be highly variable across scenarios (27-42% and 45-63% respectively, averaged over Europe) and across regions (2.5-86% and 17-86%, averaged over scenarios). Modeled species loss and turnover were found to depend strongly on the degree of change in just two climate variables describing temperature and moisture conditions. Despite the coarse scale of the analysis, species from mountains could be seen to be disproportionably sensitive to climate change (≈60% species loss). The boreal region was projected to lose few species, although gaining many others from immigration. The greatest changes are expected in the transition between the Mediterranean and Euro-Siberian regions. We found that risks of extinction for European plants may be large, even in moderate scenarios of climate change and despite inter-model variability.


title: Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia

abstract: Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety.


title: Exploring female infertility using predictive analytic

abstract: With the availability of medical data for large number of patients in hospitals, early detection of diseases has been made easier in the recent past. Conditions like Infertility which are hard to detect or diagnose can be now diagnosed with greater precision with the help of predictive modeling. One of the key challenges for early detection and timely treatment is in identifying and recording key variables that contribute to specific variance of infertility. In this paper, we consider 26 variables and identify relevant variables for early detection of 8 variant classes of female infertility. We compared various techniques and determined that the Random forest is the best method offerings 88% of accuracy for a reasonably large hospital dataset of size 965.


title: Personal Verification Using Palmprint and Hand Geometry Biometric

abstract: A new approach for the personal identification using hand images is presented. This paper attempts to improve the performance of palmprint-based verification system by integrating hand geometry features. Unlike other bimodal biometric systems, the users does not have to undergo the inconvenience of passing through two sensors since the palmprint and hand geometry features can be are acquired from the same image, using a digital camera, at the same time. Each of these gray level images are aligned and then used to extract palmprint and hand geometry features. These features are then examined for their individual and combined performance. The image acquisition setup used in this work was inherently simple and it does not employ any special illumination nor does it use any pegs to cause any inconvenience to the users. Our experimental results on the image dataset from 100 users confirm the utility of hand geometry features with those from palmprints and achieve promising results with a simple image acquisition setup.


title: Genetic and familial aspects of narcolepsy

abstract: Narcolepsy-cataplexy is a disabling sleep disorder characterized by excessive daytime sleepiness and abnormal REM sleep. The development of human narcolepsy involves environmental factors acting on a specific genetic background. The importance of environmental factors is evidenced by the reported 25 to 31% of monozygotic twins who are concordant for narcolepsy. One of the predisposing genetic factors is located in the MHC DQ region. More than 85% of all narcoleptic patients with definite cataplexy share a specific HLA allele, HLA DQB1*0602 (most often in combination with HLA DR2), compared with 12 to 38% of the general population, as evaluated in various ethnic groups. Genetic factors other than HLA are also likely to be involved. Even if genuine multiplex families are rare, 1 to 2% of the first-degree relatives of narcolepsy patients manifest the disorder, compared with 0.02 to 0.18% in the general population. Studies using a canine model of narcolepsy illustrate the importance of non-MHC genes in disease predisposition. In this model, narcolepsy is transmitted as a single autosomal recessive trait, canarc-1 . In spite of an association with immune-related polymorphisms, narcolepsy does not appear to be a classic autoimmune disease. Other pathophysiologic models involving the microglia and the release of specific cytokines in the CNS may be involved and are being explored. This approach, together with positional cloning studies in humans and canines, should reveal the cause of narcolepsy and open new therapeutic avenues.


title: Depth-from-defocus: blur equalization technique

abstract: A new spatial-domain Blur Equalization Technique (BET) is presented. BET is based on Depth-from-Defocus (DFD) technique. It relies on equalizing the blur or defocus of two different images recorded with different camera parameters. Also, BET facilitates modeling of images locally by higher order polynomials with lower series truncation errors. The accuracy of BET is further enhanced by discarding pixels with low Signal-to-Noise ratio by thresholding image Laplacians, and relying more on sharper of the two blurred images in estimating the blur parameters. BET is found to be superior to some of the best comparable DFD techniques in a large number of both simulation and actual experiments. Actual experiments used a large variety of objects including very low contrast digital camera test charts located at many different distances. In autofocusing experiments, BET gave an RMS error of 1.2% in lens position.


title: Stator heat extraction system for axial flux yokeless and segmented armature machines

abstract: The torque density of electric motors strongly depends on the performance of the cooling system. In the axial flux yokeless and segmented armature machine, cooling inherently occurs through self-ventilation by both rotor discs. Nevertheless, an independent cooling system remains necessary for most applications. In the axial flux yokeless and segmented armature machine, the individual stator cores with concentrated windings are potted in the stator housing using an epoxy resin. These epoxy resins are relatively bad thermally conductive, and therefore, limit the heat transfer to the surface of the machine. Therefore, radially inward heat extraction fins are introduced in this paper. These fins provide an excellent thermal conduction path from the windings to the stator surface, where they can be abducted by conventional cooling techniques such as forced air or water jacket cooling. The focus of this paper is on the study of the radially inward heat extraction fins using thermal finite element modelling as well as experimental validation on a 4kW axial flux yokeless and segmented armature machine stator. At steady state and for the rated losses, the temperature difference between the winding and stator housing is less than 10 degrees Celcius.


title: Do Differences in Password Policies Prevent Password Reuse?

abstract: Password policies were originally designed to make users pick stronger passwords. However, research has shown that they often fail to achieve this goal. In a systematic audit of the top 100 web sites in Germany, we explore if diversity in current real-world password policies prevents password reuse. We found that this is not the case: we are the first to show that a single password could hypothetically fulfill 99% of the policies under consideration. This is especially problematic because password reuse exposes users to similar risks as weak passwords. We thus propose a new approach for policies that focuses on password reuse and respects other websites to determine if a password should be accepted. This re-design takes current user behavior into account and potentially boosts the usability and security of password-based authentication.


title: High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications

abstract: This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together, DeLMA and the ADA algorithm provide a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Ornithology, Cornell University. This paper provides a high level technical overview of the system, and discusses various aspects of the design. Basic runtime performance and project summary are presented. The DeLMA-ADA baseline performance comparing desktop serial configuration to a 64 core distributed HPC system shows as much as a 44 times faster increase in runtime execution. Performance tests using 48 cores on the HPC shows a 9x to 12x efficiency over a 4 core desktop solution. Project summary results for 19 east coast deployments show that the DeLMA-ADA solution has processed over three million channel hours of sound to date.


title: Teaching-to-Learn and Learning-to-Teach for Multi-Label propagation

abstract: Multi-label propagation aims to transmit the multi-label information from labeled examples to unlabeled examples based on a weighted graph. Existing methods ignore the specific propagation difficulty of different unlabeled examples and conduct the propagation in an imperfect sequence, leading to the error-prone classification of some difficult examples with uncertain labels. To address this problem, this paper associates each possible label with a "teacher", and proposes a "Multi-Label Teaching-to-Learn and Learning-to-Teach" (ML-TLLT) algorithm, so that the entire propagation process is guided by the teachers and manipulated from simple examples to more difficult ones. In the teaching-to-learn step, the teachers select the simplest examples for the current propagation by investigating both the definitiveness of each possible label of the unlabeled examples, and the dependencies between labels revealed by the labeled examples. In the learning-to-teach step, the teachers reversely learn from the learner's feedback to properly select the simplest examples for the next propagation. Thorough empirical studies show that due to the optimized propagation sequence designed by the teachers, ML-TLLT yields generally better performance than seven state-of-the-art methods on the typical multi-label benchmark datasets.


title: Comparative study of advanced signal processing techniques for islanding detection in a hybrid distributed generation system

abstract: In this paper, islanding detection in a hybrid distributed generation (DG) system is analyzed by the use of hyperbolic S-transform, time-time transform and mathematical morphology methods. The merits of these methods are thoroughly compared against commonly adopted wavelet transform and S-transform techniques, as a new contribution to earlier studies. The hybrid DG system consists of photovoltaic and wind energy systems connected to the grid within the IEEE 30-bus system. Negative sequence component of the voltage signal is extracted at the point of common coupling and passed through the above mentioned techniques. The efficacy of the proposed methods is also compared by energy based technique with proper threshold selection to accurately detect the islanding phenomena. Further, to augment the accuracy of the result, the classification is done using support vector machine to distinguish islanding from other power quality disturbances. The results demonstrate effective performance and feasibility of the proposed techniques for islanding detection under both noise-free and noisy environments, and also in the presence of harmonics.


title: Structured Learning from Heterogeneous Behavior for Social Identity Linkage

abstract: Social identity linkage across different social media platforms is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. In this paper, we propose a solution framework, $\sf{ HYDRA}$ , which consists of three key steps: (I) we model heterogeneous behavior by long-term topical distribution analysis and multi-resolution temporal behavior matching against high noise and information missing, and the behavior similarity are described by multi-dimensional similarity vector for each user pair; (II) we build structure consistency models to maximize the structure and behavior consistency on users’ core social structure across different platforms, thus the task of identity linkage can be performed on groups of users, which is beyond the individual level linkage in previous study; and (III) we propose a normalized-margin-based linkage function formulation, and learn the linkage function by multi-objective optimization where both supervised pair-wise linkage function learning and structure consistency maximization are conducted towards a unified Pareto optimal solution. The model is able to deal with drastic information missing, and avoid the curse-of-dimensionality in handling high dimensional sparse representation. Extensive experiments on 10 million users across seven popular social networks platforms demonstrate that $\sf{ HYDRA}$ correctly identifies real user linkage across different platforms from massive noisy user behavior data records, and outperforms existing state-of-the-art approaches by at least 20 percent under different settings, and four times better in most settings.


title: Comparison of Geometric and Probabilistic Shaping with Application to ATSC 3.0

abstract: In this work, geometric shaping (GS) and probabilistic shaping (PS) for the AWGN channel is reviewed. Both approaches are investigated in terms of symbol-metric decoding (SMD) and bit-metric decoding (BMD). For GS, an optimization algorithm based on differential evolution is formulated. Achievable rate analysis reveals that GS suffers from a 0.4 dB performance degradation compared to PS when BMD is used. Forward-error correction simulations of the ATSC 3.0 modulation and coding formats (modcods) confirm the theoretical findings. In particular, PS enables seamless rate adaptation with one single modcod and it outperforms ATSC 3.0 GS modcods by more than 0.5 dB for spectral efficiencies larger than 3.2 bits per channel use.


title: Finding Tiny Faces

abstract: Though tremendous strides have been made in object recognition, one of the remaining open challenges is detecting small objects. We explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, and contextual reasoning. While most recognition approaches aim to be scale-invariant, the cues for recognizing a 3px tall face are fundamentally different than those for recognizing a 300px tall face. We take a different approach and train separate detectors for different scales. To maintain efficiency, detectors are trained in a multi-task fashion: they make use of features extracted from multiple layers of single (deep) feature hierarchy. While training detectors for large objects is straightforward, the crucial challenge remains training detectors for small objects. We show that context is crucial, and define templates that make use of massively-large receptive fields (where 99% of the template extends beyond the object of interest). Finally, we explore the role of scale in pre-trained deep networks, providing ways to extrapolate networks tuned for limited scales to rather extreme ranges. We demonstrate state-of-the-art results on massively-benchmarked face datasets (FDDB and WIDER FACE). In particular, when compared to prior art on WIDER FACE, our results reduce error by a factor of 2 (our models produce an AP of 82% while prior art ranges from 29-64%).


title: Optimal autoscaling in a IaaS cloud

abstract: An application provider leases resources (i.e., virtual machine instances) of variable configurations from a IaaS provider over some lease duration (typically one hour). The application provider (i.e., consumer) would like to minimize their cost while meeting all service level obligations (SLOs). The mechanism of adding and removing resources at runtime is referred to as autoscaling. The process of autoscaling is automated through the use of a management component referred to as an autoscaler. This paper introduces a novel autoscaling approach in which both cloud and application dynamics are modeled in the context of a stochastic, model predictive control problem. The approach exploits trade-off between satisfying performance related objectives for the consumer's application while minimizing their cost. Simulation results are presented demonstrating the efficacy of this new approach.


title: Exploring User-Specific Information in Music Retrieval

abstract: With the advancement of mobile computing technology and cloud-based streaming music service, user-centered music retrieval has become increasingly important. User-specific information has a fundamental impact on personal music preferences and interests. However, existing research pays little attention to the modeling and integration of user-specific information in music retrieval algorithms/models to facilitate music search. In this paper, we propose a novel model, named User-Information-Aware Music Interest Topic (UIA-MIT) model. The model is able to effectively capture the influence of user-specific information on music preferences, and further associate users' music preferences and search terms under the same latent space. Based on this model, a user information aware retrieval system is developed, which can search and re-rank the results based on age- and/or gender-specific music preferences. A comprehensive experimental study demonstrates that our methods can significantly improve the search accuracy over existing text-based music retrieval methods.


title: An SEM-artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline

abstract: The study examines the effects of SERVPERF on customer satisfaction and loyalty.A comparison between full service and low cost carriers is conducted.A predictive analytic approach via SEM-artificial-neural-networks is engaged.Multiple group analysis is used to assess the group invariance.63.1% and 55.6% of variance in customer satisfaction and loyalty are explained. There is a dearth of studies pertaining to the influence of SERVPERF on customer satisfaction and customer loyalty among low cost and full service airlines. Prior studies have measured service quality using the GAP-5 model with SERVQUAL; however this study offers a new perspective by using the SERVPERF with an SEM-artificial-neural-networks predictive analytic approach. This is different from the previous studies as it contributes to application of expert systems and intelligent algorithms in the context of low cost and full service airline. The findings revealed significant influences of SERVPERF dimensions on customer satisfaction towards customer loyalty with 63.1% and 55.6% variance explained. The implications from this research may help CEOs and managers of the air travel and tourism industry players to make better decisions in their resource planning stage, at the same time improving customer satisfaction and loyalty.


title: Soft-switching two-switch flyback converter with wide range

abstract: A novel soft-switching two-switch flyback converter with wide duty cycle range is proposed in this paper. Compared with conventional two-switch flyback converter, it employed an auxiliary switch and a clamp capacitor to instead of two reset diodes. Therefore, its duty cycle can exceed 0.5, soft switching operation can be achieved easy for all switches and voltage stress across main switches are clamped at 1/2Vin, voltage stress across auxiliary switch is clamped at Vin. Analysis of steady-state and simulation result verified above performance. So that it is very suitable for high input, high efficiency application.


title: Machine Translation of Legal Information and Its Evaluation

abstract: This paper presents the machine translation system known as TransLI (Translation of Legal Information) developed by the authors for automatic translation of Canadian Court judgments from English to French and from French to English. Normally, a certified translation of a legal judgment takes several months to complete. The authors attempted to shorten this time significantly using a unique statistical machine translation system which has attracted the attention of the federal courts in Canada for its accuracy and speed. This paper also describes the results of a human evaluation of the output of the system in the context of a pilot project in collaboration with the federal courts of Canada.


A guide to synthesising qualitative research for researchers undertaking health technology assessments and systematic reviews


title: The Effects of Interleaved Practice

abstract: Previous research shows that interleaving rather than blocking practice of different skills (e.g. abcbcacab instead of aaabbbccc) usually improves subsequent test performance. Yet interleaving, but not blocking, ensures that practice of any particular skill is distributed, or spaced, because any two opportunities to practice the same task are not consecutive. Hence, because spaced practice typically improves test performance, the previously observed test benefits of interleaving may be due to spacing rather than interleaving per se. In the experiment reported herein, children practiced four kinds of mathematics problems in an order that was interleaved or blocked, and the degree of spacing was fixed. The interleaving of practice impaired practice session performance yet doubled scores on a test given one day later. An analysis of the errors suggested that interleaving boosted test scores by improving participants' ability to pair each problem with the appropriate procedure. Copyright © 2009 John Wiley & Sons, Ltd.


title: Evaluation of speech under stress and emotional conditions

abstract: This paper presents results from an investigation of how stress and emotion affect speech characteristics with specific application to improving automatic speech recognition. Past studies have been limited in scope, often using only one or two subjects and analyzing only one or two parameters (typically involving pitch). A comprehensive speech under stress database has been established at Georgia Tech for the purposes of stress research. The database is partitioned into five domains, encompassing a wide variety of stresses that include: various talking styles (slow, fast, soft, loud, angry, clear, question, in noise), single and dual tracking workload stress inducing tasks, emotional speech from psychiatric analysis session, and subject motion‐fear tasks. A total of 32 speakers was employed to generate in excess of 16 000 utterances. The database evaluation was partitioned into three areas. Analysis was first performed on (i) speech with simulated stress and (ii) speech from stress inducing workload tasks...


title: The Violent True Believer as a “Lone Wolf” - Psychoanalytic Perspectives on Terrorism

abstract: The existing research on lone wolf terrorists and case experience are reviewed and interpreted through the lens of psychoanalytic theory. A number of characteristics of the lone wolf are enumerated: a personal grievance and moral outrage; the framing of an ideology; failure to affiliate with an extremist group; dependence on a virtual community found on the Internet; the thwarting of occupational goals; radicalization fueled by changes in thinking and emotion – including cognitive rigidity, clandestine excitement, contempt, and disgust – regardless of the particular ideology; the failure of sexual pair bonding and the sexualization of violence; the nexus of psychopathology and ideology; greater creativity and innovation than terrorist groups; and predatory violence sanctioned by moral (superego) authority. A concluding psychoanalytic formulation is offered.


The Visual Display of Quantitative Information


title: Second Language Learning in the Context of MOOCs

abstract: Massive Open Online Courses are becoming popular educational vehicles through which universities reach out to non-traditional audiences. Many enrolees hail from other countries and cultures, and struggle to cope with the English language in which these courses are invariably offered. Moreover, most such learners have a strong desire and motivation to extend their knowledge of academic English, particularly in the specific area addressed by the course. ::: ::: Online courses provide a compelling opportunity for domain-specific language learning. They supply a large corpus of interesting linguistic material relevant to a particular area, including supplementary images (slides), audio and video. We contend that this corpus can be automatically analysed, enriched, and transformed into a resource that learners can browse and query in order to extend their ability to understand the language used, and help them express themselves more fluently and eloquently in that domain. ::: ::: To illustrate this idea, an existing online corpus-based language learning tool (FLAX) is applied to a Coursera MOOC entitled Virology 1: How Viruses Work, offered by Columbia University.


Light-Field Database Creation and Depth Estimation


title: Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

abstract: Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier's performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35% by using additional, noisy data and handling the noise.


title: Optimization and Implementation of SIW Slot Array for Both Medium- and Long-Range 77 GHz Automotive Radar Application

abstract: In this communication, a novel substrate integrated waveguide slot array with flat-shoulder-shaped radiation pattern is introduced for both 77 GHz automotive medium-range radar and long-range radar applications. The operational principle of the antenna is introduced, while the synthesis of the desired radiation pattern is facilitated by a mixed optimization method, including nonlinear fitting and aggressive space mapping. Experiments are carried out and the measured peak gain of the proposed array is 21.7 dBi. The demonstrated antenna presents to be an excellent candidate for 77 GHz automotive radar application.


title: Formation Control of Nonholonomic Mobile Robots Without Position and Velocity Measurements

abstract: Most existing formation control approaches are based on the assumption that the global/relative position and/or velocity measurements of mobile robots are directly available. To extend the application domain and to improve the formation control performance, it is extremely necessary to avoid the use of position and velocity measurements in the design of formation controllers. In this paper, we propose new leader-following formation tracking control schemes for nonholonomic mobile robots with onboard perspective cameras, without using both position and velocity measurements. To address the unavailability issue of position measurements, the leader–follower kinematics model in the image space is developed, which can facilitate the complete elimination of measurement/estimation of the position information. Furthermore, feedback information from the perspective camera of the follower robot is used to design adaptive observers to estimate the leader linear velocity for feedforward compensation, which can handle the absence of velocity measurements such that the proposed schemes can be applied to control formations of mobile robots without mutual communication abilities. By using the Lyapunov stability theory, a rigorous stability analysis based on the nonlinear formation dynamics is provided to show that the global stability of the combined observer–controller closed-loop system can be guaranteed. Both simulation and experimental results are also given to demonstrate the performance of the proposed formation tracking control schemes.


title: Human Activity Recognition using Recurrent Neural Networks

abstract: Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.


title: An effective similarity metric for application traffic classification

abstract: Application level traffic classification is one of the major issues in network monitoring and traffic engineering. In our previous study, we proposed a new traffic classification method that utilizes a flow similarity function based on Cosine Similarity. This paper compares the classification accuracy of three similarity metrics, Jaccard Similarity, Cosine Similarity, and Gaussian Radius Based Function, to select appropriate similarity metrics for application traffic classification. This paper also defines a new two-stage traffic classification algorithm that can guarantee high classification accuracy even under an asymmetric routing environment, with reasonable complexity.


title: Analysis of KDD CUP 99 Dataset using Clustering based Data Mining

abstract: The KDD Cup 99 dataset has been the point of attraction for many researchers in the field of intrusion detection from the last decade. Many researchers have contributed their efforts to analyze the dataset by different techniques. Analysis can be used in any type of industry that produces and consumes data, of course that includes security. This paper is an analysis of 10% of KDD cup’99 training dataset based on intrusion detection. We have focused on establishing a relationship between the attack types and the protocol used by the hackers, using clustered data. Analysis of data is performed using k-means clustering; we have used the Oracle 10g data miner as a tool for the analysis of dataset and build 1000 clusters to segment the 494,020 records. The investigation revealed many interesting results about the protocols and attack types preferred by the hackers for intruding the networks. Keyword: KDD 99 dataset, clustering, k-means, intrusion detection


title: Peroneus quartus: prevalance and clinical importance

abstract: INTRODUCTION ::: The most common variant muscle of the ankle, peroneus quartus muscle, is located in the lateral leg compartment. In literature there is ambiguous nomenclature of this muscle because of its different origin and insertion sides. It is related to many pathologic conditions in the lateral ankle compartment but also it can be used as a tendon graft for reconstructive procedures. ::: ::: ::: MATERIAL AND METHOD ::: We dissected 115 cadaver legs and investigated prevalence of peroneus quartus. We also present 2 year result of a patient who had torn superior peroneal retinaculum reconstructed with peroneus quartus tendon. ::: ::: ::: RESULTS ::: The peroneus quartus muscle, with a number of different attachments, was present in 5.2 % (6/115) of the legs. It most commonly arose from the peroneus brevis muscle and inserted into the retrotrochlear eminence of the calcaneus. Associated pathologies are longitudinal degeneration and tear in the tendon of peroneus brevis. There is no any association between the prevalence of peroneus quartus and the height of retrotrochlear eminence or presence of peroneal tubercule (p > 0.05). But there is strong relationship between peroneus brevis degeneration and peroneus quartus existence (p: 0.03). We also defined a new type of peroneus quartus with a bifurcated insertion around the peroneus brevis. In literature our case report is unique because we present a patient who has torn superior peroneal retinaculum which is reconstructed with peroneus quartus tendon with 2 year follow up. ::: ::: ::: CONCLUSION ::: Peroneus quartus may lead to some pathologic conditions (pain, snapping, tear, synovitis, etc.) in the lateral ankle compartment but it may be used to reconstruct some pathologic conditions. Orthopaedics, anatomists and radiologists should be aware of this accessory tendon structure because of its clinical importance.


title: A framework and methodology for knowledge management system implementation

abstract: Knowledge assets are of far greater value than any tangible asset and provide organisations with the basis for creating a sustainable competitive advantage. The nature of knowledge and knowledge management have given rise to a range of different definitions of knowledge management systems that enable organisations to convert their knowledge into actionable information that provides them with a competitive edge and innovative capability. Such an implementation process is a comprehensive course of action that requires focus and commitment through-out the organisation in order to achieve results, yet no universally accepted framework or methodology for such a process exists. This paper analyses and describes enhancements to a 12-step process derived by Calabrese and Orlando [6] to implement a knowledge management system aimed at providing a more comprehensive framework and methodology for knowledge management system implementation. These enhancements are based on findings relating to a knowledge management proof of concept implementation conducted at one of the major mobile telecommunication operators in South Africa.


title: On the Discrepancy Between Kleinberg's Clustering Axioms and $k$-Means Clustering Algorithm Behavior

abstract: This paper investigates the validity of Kleinberg's axioms for clustering functions with respect to the quite popular clustering algorithm called $k$-means. While Kleinberg's axioms have been discussed heavily in the past, we concentrate here on the case predominantly relevant for $k$-means algorithm, that is behavior embedded in Euclidean space. We point at some contradictions and counter intuitiveness aspects of this axiomatic set within $\mathbb{R}^m$ that were evidently not discussed so far. Our results suggest that apparently without defining clearly what kind of clusters we expect we will not be able to construct a valid axiomatic system. In particular we look at the shape and the gaps between the clusters. Finally we demonstrate that there exist several ways to reconcile the formulation of the axioms with their intended meaning and that under this reformulation the axioms stop to be contradictory and the real-world $k$-means algorithm conforms to this axiomatic system.


title: Search-Based Cost-Effective Software Remodularization

abstract: Software modularization is a technique used to divide a software system into independent modules (packages) that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet new requirements, their modularizations become complex and gradually loose their quality. Thus, it is challenging to automatically optimize the classes’ distribution in packages, also known as remodularization. To alleviate this issue, we introduce a new approach to optimize software modularization by moving classes to more suitable packages. In addition to improving design quality and preserving semantic coherence, our approach takes into consideration the refactoring effort as an objective in itself while optimizing software modularization. We adapt the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) of Deb et al. to find the best sequence of refactorings that 1) maximize structural quality, 2) maximize semantic cohesiveness of packages (evaluated by a semantic measure based on WordNet), and 3) minimize the refactoring effort. We report the results of an evaluation of our approach using open-source projects, and we show that our proposal is able to produce a coherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer’s points of view.


title: A Systematic Literature Review on Intertemporal Choice in Software Engineering - Protocol and Results

abstract: When making choices in software projects, engineers and other stakeholders engage in decision making that involves uncertain future outcomes. Research in psychology, behavioral economics and neuroscience has questioned many of the classical assumptions of how such decisions are made. This literature review aims to characterize the assumptions that underpin the study of these decisions in Software Engineering. We identify empirical research on this subject and analyze how the role of time has been characterized in the study of decision making in SE. The literature review aims to support the development of descriptive frameworks for empirical studies of intertemporal decision making in practice.


title: IEEE 802.11s wireless mesh networks: Framework and challenges

abstract: Wireless mesh networking based on 802.11 wireless local area network (WLAN) has been actively explored for a few years. To improve the performance of WLAN mesh networks, a few new communication protocols have been developed in recent years. However, these solutions are usually proprietary and prevent WLAN mesh networks from interworking with each other. Thus, a standard becomes indispensable for WLAN mesh networks. To meet this need, an IEEE 802.11 task group, i.e., 802.11s, is specifying a standard for WLAN mesh networks. Although several standard drafts have been released by 802.11s, many issues still remain to be resolved. In order to understand what performance can be expected from the existing framework of 802.11s standard and what functionalities shall be added to 802.11s standard to improve performance, a detailed study on the existing 802.11s standard is given in this paper. The existing framework of 802.11s standard is first presented, followed by pointing out the challenging research issues that still exist in the current 802.11 standard. The purpose of this paper is to motivate other researchers to develop new scalable protocols for 802.11 wireless mesh networks.


title: Design of an underactuated parallelogram crawler module for an in-pipe robot

abstract: This paper presents a design method to determine the output ratio of a differential mechanism, which is installed in an in-pipe robot with three underactuated parallelogram crawler modules. The crawler module can automatically shift its shape to a parallelogram when encountering with obstacles. To clarify the requirements for the mechanism, two outputs of each crawler mechanism (torque of the arms and front pulley) are quasi-statically analyzed, and how the environmental and design parameters affect the system are verified.


title: Architectural Complexity Measures of Recurrent Neural Networks

abstract: In this paper, we systematically analyse the connecting architectures of recurrent neural networks (RNNs). Our main contribution is twofold: first, we present a rigorous graph-theoretic framework describing the connecting architectures of RNNs in general. Second, we propose three architecture complexity measures of RNNs: (a) the recurrent depth, which captures the RNN's over-time nonlinear complexity, (b) the feedforward depth, which captures the local input-output nonlinearity (similar to the"depth"in feedforward neural networks (FNNs)), and (c) the recurrent skip coefficient which captures how rapidly the information propagates over time. Our experimental results show that RNNs might benefit from larger recurrent depth and feedforward depth. We further demonstrate that increasing recurrent skip coefficient offers performance boosts on long term dependency problems, as we improve the state-of-the-art for sequential MNIST dataset.


title: Identifying the singularity conditions of Canadarm2 based on elementary Jacobian transformation

abstract: The Canadarm2, also named Space Station Remote Manipulator System (SSRMS), is a 7-joint redundant manipulator. Without spherical wrists, the singularity analysis and avoidance of these manipulators are very difficult. In this paper, a method is presented to analytically identify its singular configurations based on the elementary transformation of Jacobian matrix. Firstly, we constructed a general kinematics model to describe them in a united manner. Correspondingly, the differential kinematics equation and the modified form are derived. Secondly, the singularity conditions are isolated and collected in a 3×4 sub-matrix by several times row transformation of the modified Jacobian matrix, which is partitioned into a block-triangle matrix. Finally, all the singularity configurations are determined by analyzing the rank degeneracy conditions of the 3×4 sub-matrix. The proposed method isolates the singularity conditions, and collects them in a 3×4 sub-matrix, largely reducing the computation workload.


title: Classifying The Semantic Relations In Noun Compounds Via A Domain-Specific Lexical Hierarchy

abstract: We are developing corpus-based techniques for identifying semantic relations at an intermediate level of description (more specific than those used in case frames, but more general than those used in traditional knowledge representation systems). In this paper we describe a classification algorithm for identifying relationships between two-word noun compounds. We find that a very simple approach using a machine learning algorithm and a domain-specific lexical hierarchy successfully generalizes from training instances, performing better on previously unseen words than a baseline consisting of training on the words themselves.


title: Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models

abstract: Wind power is an increasingly used form of renewable energy. The uncertainty in wind generation is very large due to the inherent variability in wind speed, and this needs to be understood by operators of power systems and wind farms. To assist with the management of this risk, this paper investigates methods for predicting the probability density function of generated wind power from one to ten days ahead at five U.K. wind farm locations. These density forecasts provide a description of the expected future value and the associated uncertainty. We construct density forecasts from weather ensemble predictions, which are a relatively new type of weather forecast generated from atmospheric models. We also consider density forecasting from statistical time series models. The best results for wind power density prediction and point forecasting were produced by an approach that involves calibration and smoothing of the ensemble-based wind power density.


title: Positional asphyxia: reflection on 2 cases.

abstract: Positional asphyxia, a fatal condition arising because of the adoption of particular body positions, causing mechanical interference with pulmonary ventilation, can occur in various circumstances that are likely to come under the observation of the specialist in legal medicine (work, car accidents, torture, kidnapping, etc.). It is difficult to diagnose the cause of death in such cases because they generally present with an aspecific anatomopathologic picture. In some situations, positional asphyxia can be hard to distinguish from asphyxia because of chest compression. The main difference is in the way the event occurred: whether the particular position causing the asphyxia had been adopted by choice or by compulsion or necessity when an extrinsic mechanical action would result in traumatic asphyxia. The diagnosis of positional asphyxia is essentially based on 3 criteria: the body position must obstruct normal gas exchange, it must be impossible to move to another position, and other causes of natural or violent death must be excluded. To illustrate the main physiopathologic and diagnostic causes of positional asphyxia, the authors report 2 cases taken from the records of events that came under the observation of the Medico-Legal Sector of Bari University Hospital throughout the last 10 years.


title: Lay dispositionism and implicit theories of personality

abstract: Lay dispositionism refers to lay people's tendency to use traits as the basic unit of analysis in social perception (L. Ross & R. E. Nisbett, 1991). Five studies explored the relation between the practices indicative of lay dispositionism and people's implicit theories about the nature of personal attributes. As predicted, compared with those who believed that personal attributes are malleable (incremental theorists), those who believed in fixed traits (entity theorists) used traits or trait-relevant information to make stronger future behavioral predictions (Studies 1 and 2) and made stronger trait inferences from behavior (Study 3). Moreover, the relation between implicit theories and lay dispositionism was found in both the United States (a more individualistic culture) and Hong Kong (a more collectivistic culture), suggesting this relation to be generalizable across cultures (Study 4). Finally, an experiment in which implicit theories were manipulated provided preliminary evidence for the possible causal role of implicit theories in lay dispositionism (Study 5).


title: Wearable Health Monitors and Physician-Patient Communication: The Physician's Perspective

abstract: Studies have shown that effective physician-patient communication can result in improved health outcomes. Wearable health monitors, which are steadily growing in adoption, may help to improve this communication. In this paper we report the findings of a survey conducted among physicians, in which we assessed their views on the potential integration of wearables into patient care. Our analysis suggests that physicians think of wearables in terms of getting accurate information on patient compliance and the efficacy of treatment to inform their decision-making. Respondents expressed interest in whether wearables would be convenient, feasible, and applicable. They also showed concern for regulations, intrusiveness, and interruptions. If integrated into physician-patient communication and interaction, wearables may be more effective to improve health and to have lasting effects. This study is a first step in understanding how this might be realized and how issues surrounding integration might be investigated.


title: Receiver structures for signals with unknown frequency and time-of-arrival

abstract: Receiver structures for an optimal noncoherent receiver when a transmitted signal experiences a random frequency shift and/or a random time-of-arrival are derived. Error rate performance, obtained by simulation, is then developed and compared with the standard noncoherent correlator for the random frequency shift.


title: Trajectory Generation and Tracking Using the AR.Drone 2.0 Quadcopter UAV

abstract: Trajectory generation and tracking algorithms were developed using ROS (Robot Operating System) for the AR. Drone 2.0 quad copter. Flight paths were created with cubic polynomials and Bezier curves. A PID controller was implemented to drive the AR. Drone from its current position to the desired coordinate point for the trajectory tracking task. Additionally, a navigation method was implemented using attractive and repulsive artificial potential fields to carry out obstacle-avoidance and goal-reaching tasks. The software was tested using simulations in Gazebo and the real AR. Drone. The drone's global position was estimated using an Extended Kalman Filter and visual landmarks.


title: Harnessing Social Media for Health Promotion and Behavior Change

abstract: Rapid and innovative advances in participative Internet communications, referred to as "social media," offer opportunities for modifying health behavior. Social media let users choose to be either anonymous or identified. People of all demographics are adopting these technologies whether on their computers or through mobile devices, and they are increasingly using these social media for health-related issues. Although social media have considerable potential as tools for health promotion and education, these media, like traditional health promotion media, require careful application and may not always achieve their desired outcomes. This article summarizes current evidence and understanding of using social media for health promotion. More important, it discusses the need for evaluating the effectiveness of various forms of social media and incorporating outcomes research and theory in the design of health promotion programs for social media.


title: Enabling Usage Control through Reputation Objects: A Discussion on e-Commerce and the Internet of Services Environments

abstract: This paper discusses the meaning and the role of Trust and Reputation in Internet-of-Service and e-Commerce environments following a comparative case study. Both environments represent paradigms through which the Internet is seen as a huge infrastructure where electronic services or real products are traded on. In comparison to electronic commerce, participating in an Internet-of-Services can be full of risks for all participants. Even well known security mechanisms are not able to close all gaps of access and usage control. This paper discusses the concepts of trust and reputation and brings to light the relation between these concepts to security mechanisms, Service-Level-Agreements, and quality measurements in order to enable Usage Control. The proposed solution is based on our previous model of reputation objects. The discussion also introduces a new concept of what we call reputation auditing where quality processes are considered part of reputation management not the other way around.


title: Distributional Footprints of Deceptive Product Reviews

abstract: This paper postulates that there are natural distributions of opinions in product reviews. In particular, we hypothesize that for a given domain, there is a set of representative distributions of review rating scores. A deceptive business entity that hires people to write fake reviews will necessarily distort its distribution of review scores, leaving distributional footprints behind. In order to validate this hypothesis, we introduce strategies to create dataset with pseudo-gold standard that is labeled automatically based on different types of distributional footprints. A range of experiments confirm the hypothesized connection between the distributional anomaly and deceptive reviews. This study also provides novel quantitative insights into the characteristics of natural distributions of opinions in the TripAdvisor hotel review and the Amazon product review domains.


title: A new brain region for coordinating speech articulation

abstract: HUMAN speech requires complex planning and coordination of mouth and tongue movements. Certain types of brain injury can lead to a condition known as apraxia of speech, in which patients are impaired in their ability to coordinate speech movements but their ability to perceive speech sounds, including their own errors, is unaffected1,3. The brain regions involved in coordinating speech, however, remain largely unknown. In this study, brain lesions of 25 stroke patients with a disorder in the motor planning of articulatory movements were compared with lesions of 19 patients without such deficits. A robust double dissociation was found between these two groups. All patients with articulatory planning deficits had lesions that included a discrete region of the left precentral gyms of the insula, a cortical area beneath the frontal and temporal lobes. This area was completely spared in all patients without these articulation deficits. Thus this area seems to be specialized for the motor planning of speech.


title: Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering

abstract: This paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values.


title: The role of notch 1 activation in cardiosphere derived cell differentiation.

abstract: Cardiosphere derived cells (CDC) are present in the human heart and include heterogeneous cell populations of cardiac progenitor cells, multipotent progenitors that play critical roles in the physiological and pathological turnover of heart tissue. Little is known about the molecular pathways that control the differentiation of CDC. In this study, we examined the role of Notch 1/J kappa-recombining binding protein (RBPJ) signaling, a critical cell-fate decision pathway, in CDC differentiation. We isolated CDC from mouse cardiospheres and analyzed the differentiation of transduced cells expressing the Notch1 intracellular domain (N1-ICD), the active form of Notch1, using a terminal differentiation marker polymerase chain reaction (PCR) array. We found that Notch1 primarily supported the differentiation of CDC into smooth muscle cells (SMC), as demonstrated by the upreguation of key SMC proteins, including smooth muscle myosin heavy chain (Myh11) and SM22α (Tagln), in N1-ICD expressing CDC. Conversely, genetic ablation of RBPJ in CDC diminished the expression of SMC differentiation markers, confirming that SMC differentiation CDC is dependent on RBPJ. Finally, in vivo experiments demonstrate enhanced numbers of smooth muscle actin-expressing implanted cells after an injection of N1-ICD-expressing CDC into ischemic myocardium (44±8/high power field (hpf) vs. 11±4/high power field (hpf), n=7 sections, P<0.05). Taken together, these results provide strong evidence that Notch1 promotes SMC differentiation of CDC through an RBPJ-dependent signaling pathway in vitro, which may have important implications for progenitor cell-mediated angiogenesis.


Elucidating User Behavior in Music Services Through Persona and Gender