title: A Review of Application of Data Mining in Earthquake Prediction

abstract: A natural disaster is the effect of a natural hazard (e.g., flood, tornado, hurricane, volcanic eruption, earthquake, heatwave, or landslide). Earthquakes, landslides, tsunamis and volcanos are complex physical phenomenon that leads to financial, environmental or human losses. Prediction of such geological disasters is the need of the day. Also, prediction of these disasters is a complex process that depends on many physical and environmental parameters. Many approaches exist in the literature based on scientific and statistical analysis. Data mining techniques can also be used for prediction of these natural hazards. This paper presents a review of application of data mining in the prediction of natural geological calamities. 16 journal articles on the subject published between 1989 and 2011 was analyzed. The main data mining techniques used for earthquake prediction are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the prediction of earthquakes, tsunamis, landslides and other micro seismic activities. This paper also aims to encourage additional research on topics, and concludes with several suggestions for further research.


Network on a Chip: An architecture for billion transistor era


title: Opinion Mining and Summarization of Hotel Reviews

abstract: Everyday many users purchases product, book travel tickets, buy goods and services through web. Users also share their views about product, hotel, news, and topic on web in the form of reviews, blogs, comments etc. Many users read review information given on web to take decisions such as buying products, watching movie, going to restaurant etc. Reviews contain user's opinion about product, event or topic. It is difficult for web users to read and understand contents from large number of reviews. Important and useful information can be extracted from reviews through opinion mining and summarization process. We presented machine learning and Senti Word Net based method for opinion mining from hotel reviews and sentence relevance score based method for opinion summarization of hotel reviews. We obtained about 87% of accuracy of hotel review classification as positive or negative review by machine learning method. The classified and summarized hotel review information helps web users to understand review contents easily in a short time.


title: On semidefinite relaxations for the block model

abstract: The stochastic block model (SBM) is a popular tool for community detection in networks, but fitting it by maximum likelihood (MLE) involves a computationally infeasible optimization problem. We propose a new semidefinite programming (SDP) solution to the problem of fitting the SBM, derived as a relaxation of the MLE. We put ours and previously proposed SDPs in a unified framework, as relaxations of the MLE over various sub-classes of the SBM, revealing a connection to sparse PCA. Our main relaxation, which we call SDP-1, is tighter than other recently proposed SDP relaxations, and thus previously established theoretical guarantees carry over. However, we show that SDP-1 exactly recovers true communities over a wider class of SBMs than those covered by current results. In particular, the assumption of strong assortativity of the SBM, implicit in consistency conditions for previously proposed SDPs, can be relaxed to weak assortativity for our approach, thus significantly broadening the class of SBMs covered by the consistency results. We also show that strong assortativity is indeed a necessary condition for exact recovery for previously proposed SDP approaches and not an artifact of the proofs. Our analysis of SDPs is based on primal-dual witness constructions, which provides some insight into the nature of the solutions of various SDPs. We show how to combine features from SDP-1 and already available SDPs to achieve the most flexibility in terms of both assortativity and block-size constraints, as our relaxation has the tendency to produce communities of similar sizes. This tendency makes it the ideal tool for fitting network histograms, a method gaining popularity in the graphon estimation literature, as we illustrate on an example of a social networks of dolphins. We also provide empirical evidence that SDPs outperform spectral methods for fitting SBMs with a large number of blocks.


title: CliNER 2.0: Accessible and Accurate Clinical Concept Extraction

abstract: Clinical notes often describe important aspects of a patient's stay and are therefore critical to medical research. Clinical concept extraction (CCE) of named entities - such as problems, tests, and treatments - aids in forming an understanding of notes and provides a foundation for many downstream clinical decision-making tasks. Historically, this task has been posed as a standard named entity recognition (NER) sequence tagging problem, and solved with feature-based methods using handengineered domain knowledge. Recent advances, however, have demonstrated the efficacy of LSTM-based models for NER tasks, including CCE. This work presents CliNER 2.0, a simple-to-install, open-source tool for extracting concepts from clinical text. CliNER 2.0 uses a word- and character- level LSTM model, and achieves state-of-the-art performance. For ease of use, the tool also includes pre-trained models available for public use.


title: AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter

abstract: The rise in popularity of social interacting websites such as Facebook, Twitter, and Snapchat has been challenged by the upsurge of unwelcomed and troubling bodies on these systems. This includes spam senders, malware systems, and other content contaminators. It is noted that highly automated accounts with 450 tweets per day produced almost 18% of entire Twitter circulation in the 2016 U.S. Presidential election. It is also observed that those disruptive systems called bots are inclined more towards circulating negative news than positive information. This paper introduces a novel framework named Associative Affinity Factor Analysis (AAFA) designed for stance detection and bot identification. Using AAFA, the proposed framework identifies real people from bots and detects the stance in bipolar affinities. The 2016 U.S. Presidential election campaign was used as a test use case because of its significant and unique counter-factual properties. The results show that our proposed AAFA framework achieves high accuracy when compared to several existing state-of-theart methods.


title: Nanosecond pulse Generator using fast recovery diodes for cell electromanipulation

abstract: Design and operation of a fast recovery diode based pulse generator is presented. The generator produces 3.5-ns-wide, 1200 V amplitude unipolar pulses or +/-600-V bipolar pulses into 50-/spl Omega/ load at the maximum repetition rate of 100 kHz. Pulses shorter than 10 ns are essential for the studies of biological cell response to high electric fields while avoiding ordinary electroporation effects dominant at long pulses. Bipolar pulses are used for the studies of biological cell response to high electric fields when the net transfer of charge is undesirable. The bipolar pulse is produced from a unipolar pulse with the help of a shorted transmission line. This transmission line delays and inverts the initial pulse, so the output is the sum of the initial and the inverted and delayed pulses. The use of mass-produced fast recovery surface-mount rectifier diodes in this circuit substantially simplifies the generator and results in low cost and very small footprint. Similar diode switched pulse generators have been described in the literature using mostly custom fabricated snap-recovery diodes. Here we give an example of an ordinary low-cost diode performing similarly to the custom fabricated counterpart. The diode switched circuit relaxes the requirement on the speed of the main closing switch; in our case, a low-cost power metal-oxide semiconductor field-effect transistor (MOSFET)-saturable core transformer combination.


title: A Switched-Capacitor DC–DC Converter With High Voltage Gain and Reduced Component Rating and Count

abstract: This paper proposes a bidirectional switched-capacitor dc-dc converter for applications that require high voltage gain. Some of conventional switched-capacitor dc-dc converters have diverse voltage or current stresses for the switching devices in the circuit, not suitable for modular configuration or for high efficiency demand; some suffer from relatively high power loss or large device count for high voltage gain, even if the device voltage stress could be low. By contrast, the proposed dc-dc converter features low component (switching device and capacitor) power rating, small switching device count, and low output capacitance requirement. In addition to its low current stress, the combination of two short symmetric paths of charge pumps further lowers power loss. Therefore, a small and light converter with high voltage gain and high efficiency can be achieved. Simulation and experimental results of a 450-W prototype with a voltage conversion ratio of six validate the principle and features of this topology.


title: A humanoid upper body system for two-handed manipulation

abstract: This video presents a humanoid two-arm system developed as a research platform for studying dexterous two-handed manipulation. The system is based on the modular DLR-Lightweight-Robot-III and the DLR-Hand-II. Two arms and hands are combined with a three degrees-of-freedom movable torso and a visual system to form a complete humanoid upper body. The diversity of the system is demonstrated by showing the mechanical design, several control concepts, the application of rapid prototyping and hardware-in-the-loop (HIL) development as well as two-handed manipulation experiments and the integration of path planning capabilities.


title: Which personal quality of life domains affect the happiness of older South Africans?

abstract: Objective ::: To ascertain which quality of life domains affect the happiness of older South Africans.


title: Action of acetylcholine, muscarine, nicotine and antagonists on the membrane potential of astrocytes in cultured rat brainstem and spinal cord

abstract: Abstract The actions of acetylcholine, muscarine and nicotine on the membrane potential of astrocytes of cultured rat brainstem and spinal cord were examined. All 3 cholinergic agonists produced hyperpolarizations of the majority of astrocytes tested, although a small number of cells was depolarized. The hyperpolarizations induced by acetylcholine and muscarine were blocked by the muscarinic antagonist atropine. The nicotinic antagonist mecamylamine antagonized the effects by nicotine in all cells tested. In contrast, mecamylamine completely blocked the acetylcholine induced hyperpolarizations in approximately half of the astrocytes whereas in the remaining cells the response was only reduced. Our electrophysiological studies are consistent with recent autoradiographic binding studies from our laboratory, suggesting the existence of muscarinic and nicotinic receptors on astrocytes.


title: Comic image understanding based on polygon detection

abstract: Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then ::: identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for ::: reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon ::: detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each ::: storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by ::: analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000 ::: comic images from ten printed comic series, and the experimental results demonstrate that it works well on different ::: types of comic images.


title: Flying capacitor multilevel inverters and DTC motor drive applications

abstract: In this paper, the requirements imposed by a direct torque control (DTC) strategy on multilevel inverters are analyzed. A control strategy is proposed in order to fulfill those requirements when a flying-capacitor multilevel inverter is used. Simulation and practical results will confirm the performance of the proposed strategy when using the multilevel inverter to control an induction motor by the DTC principle. Also, the advantages of using a multilevel inverter with a DTC strategy are shown by simulation results.


title: Support-free volume printing by multi-axis motion

abstract: We present a new method to fabricate 3D models on a robotic printing system so the need for supporting structures can be tremendously reduced by accumulating materials along curved tool-paths.


title: Rwandacoin: Prospects and challenges of developing a cryptocurrency for transactions in Rwanda

abstract: The use of cryptocurrencies such as Bitcoin and Ethereum in performing online transactions has been on the rise in the world. Africa as a continent is not left out in the adoption of blockchain and cryptocurrencies. This paper explores the prospects and challenges of developing a cryptocurrency in Rwanda which we denote Rwandacoin. In addition, the paper discusses the potentials of Rwandacoin easing intercountry trading in East Africa.


title: Large-scale, drift-free SLAM using highly robustified building model constraints

abstract: Constrained key-frame based local bundle adjustment is at the core of many recent systems that address the problem of large-scale, georeferenced SLAM based on a monocular camera and on data from inexpensive sensors and/or databases. The majority of these methods, however, impose constraints that result from proprioceptive sensors (e.g. IMUs, GPS, Odometry) while ignoring the possibility of explicitly constraining the structure (e.g. point cloud) resulting from the reconstruction process. Moreover, research on on-line interactions between SLAM and deep learning methods remains scarce, and as a result, few SLAM systems take advantage of deep architectures. We explore both these areas in this work: we use a fast deep neural network to infer semantic and structural information about the environment, and using a Bayesian framework, inject the results into a bundle adjustment process that constrains the 3d point cloud to texture-less 3d building models.


title: Multimodal Speech Emotion Recognition Using Audio and Text

abstract: Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, we propose a novel deep dual recurrent encoder model that utilizes text data and audio signals simultaneously to obtain a better understanding of speech data. As emotional dialogue is composed of sound and spoken content, our model encodes the information from audio and text sequences using dual recurrent neural networks (RNNs) and then combines the information from these sources to predict the emotion class. This architecture analyzes speech data from the signal level to the language level, and it thus utilizes the information within the data more comprehensively than models that focus on audio features. Extensive experiments are conducted to investigate the efficacy and properties of the proposed model. Our proposed model outperforms previous state-of-the-art methods in assigning data to one of four emotion categories (i.e., angry, happy, sad and neutral) when the model is applied to the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.


title: Character Networks for Narrative Generation: Structural Balance Theory and the Emergence of Proto-Narratives.

abstract: This paper models narrative as a complex adaptive system in which the temporal sequence of events constituting a story emerges out of cascading local interactions between nodes in a social network. The approach is not intended as a general theory of narrative, but rather as a particular generative mechanism relevant to several academic communities: (1) literary critics and narrative theorists interested in new models for narrative analysis, (2) artificial intelligence researchers and video game designers interested in new mechanisms for narrative generation, and (3) complex systems theorists interested in novel applications of agent-based modeling and network theory. The paper is divided into two parts. The first part offers examples of research by literary critics on the relationship between social networks of fictional characters and the structure of long-form narratives, particularly novels. The second part provides an example of schematic story generation based on a simulation of the structural balance network model. I will argue that if literary critics can better understand sophisticated narratives by extracting networks from them, then narrative intelligence researchers can benefit by inverting the process, that is, by generating narratives from networks.


title: Exploiting Debate Portals for Semi-Supervised Argumentation Mining in User-Generated Web Discourse

abstract: Analyzing arguments in user-generated Web discourse has recently gained attention in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learning, are usually domain dependent and suffer from the lack of large and diverse annotated corpora. However, annotating arguments in discourse is costly, error-prone, and highly context-dependent. We asked whether leveraging unlabeled data in a semi-supervised manner can boost the performance of argument component identification and to which extent is the approach independent of domain and register. We propose novel features that exploit clustering of unlabeled data from debate portals based on a word embeddings representation. Using these features, we significantly outperform several baselines in the cross-validation, cross-domain, and cross-register evaluation scenarios.


title: Wordfence: Text detection in natural images with border awareness

abstract: In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a novel pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets — 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, our end-to-end word recognition system achieves state-of-the-art 86% F-Score on ICDAR13.


Large-Scale Bird Sound Classification using Convolutional Neural Networks


title: Development of a measure of the experience of being bullied in youth

abstract: The Personal Experiences Checklist (PECK) was developed to provide a multidimensional assessment of a young person's personal experience of being bullied that covered the full range of bullying behaviors, including covert relational forms of bullying and cyber bullying. A sample of 647 school children were used to develop the scale, and a 2nd sample of 218 children completed the PECK and a battery of measures of bullying (including peer nomination), anxiety, depression, and self-esteem, to provide validity evidence. Test-retest reliability was assessed in a further sample of 78 students. Four factors emerged from a principal axis factoring consistent with the domains of relational-verbal bullying, cyber bullying, physical bullying, and bullying based on culture and were confirmed with confirmatory factor analysis. The data also supported a higher order bullying factor with direct effects on these 4 factors. All PECK scales showed good to excellent internal consistency (Cronbach's α range = .78-.91) and adequate test-retest reliability (range r = .61-.86). Most, but not all, expected relations were found with alternative methods of assessing bullying and measures of psychopathology. Taken together, the PECK provides a promising comprehensive and behaviorally focused dimensional measure of bullying. (PsycINFO Database Record (c) 2012 APA, all rights reserved). Language: en


title: Cognitive Real-Time QoE Management in Video Streaming Services

abstract: Satisfying the needs of users of online video streaming services requires not only to manage the network Quality of Service (QoS), but also to address the user's Quality of Experience (QoE) expectations. While QoS factors reflect the status of individual networks, they do not comprehensively capture the end-to-end features affecting the quality delivered to the user. In this situation, QoE management is the better option. However, traditionally used QoE management models require human interaction and have stringent requirements in terms of time and complexity. Thus, they fail to achieve successful performance in terms of real-timeliness, accuracy, scalability and adaptability. This dissertation work investigates new methods to bring QoE management to the level required by the real-time management of video services. In this paper, we highlight our main contributions. First, with the aim to perform a combined network-service assessment, we designed an experimental methodology able to map network QoS onto service QoE. Our methodology is meant to provide service and network providers with the means to pinpoint the working boundaries of their video-sets and to predict the effect of network policies on perception. Second, we developed a generic machine learning framework that allows deriving accurate predictive No Reference (NR) assessment metrics, based on simplistic NR QoE methods, that are functionally and computationally viable for real-time QoE evaluation. The tools, methods and conclusions derived from this dissertation conform a solid contribution to QoE management of video streaming services, opening new venues for further research.


title: Real-time driving behavior identification based on driver-in-the-loop vehicle dynamics and control

abstract: This paper studies to characterize driver driving behavior or driver control structure in real time. The three proposed methods use some of the signals such as the driver actuation measurements, the relative ranges between a leading and a following vehicle during a car-following maneuver, and the vehicle dynamic responses such as the vehicle's longitudinal acceleration and deceleration. All the used signals exist in various electronic control systems. Vehicle tests were conducted on a test vehicle to illustrate the effectiveness of the proposed methods in identifying aggressive and cautious driving behaviors.


title: Impact of a workplace stress reduction program on blood pressure and emotional health in hypertensive employees.

abstract: OBJECTIVES ::: This study examined the impact of a workplace-based stress management program on blood pressure (BP), emotional health, and workplace-related measures in hypertensive employees of a global information technology company. ::: ::: ::: DESIGN ::: Thirty-eight (38) employees with hypertension were randomly assigned to a treatment group that received the stress-reduction intervention or a waiting control group that received no intervention during the study period. The treatment group participated in a 16-hour program, which included instruction in positive emotion refocusing and emotional restructuring techniques intended to reduce sympathetic nervous system arousal, stress, and negative affect, increase positive affect, and improve performance. Learning and practice of the techniques was enhanced by heart rate variability feedback, which helped participants learn to self-generate physiological coherence, a beneficial physiologic mode associated with increased heart rhythm coherence, physiologic entrainment, parasympathetic activity, and vascular resonance. BP, emotional health, and workplace-related measures were assessed before and 3 months after the program. ::: ::: ::: RESULTS ::: Three months post-intervention, the treatment group exhibited a mean adjusted reduction of 10.6 mm Hg in systolic BP and of 6.3 mm Hg in diastolic BP. The reduction in systolic BP was significant in relation to the control group. The treatment group also demonstrated improvements in emotional health, including significant reductions in stress symptoms, depression, and global psychological distress and significant increases in peacefulness and positive outlook. Reduced systolic BP was correlated with reduced stress symptoms. Furthermore, the trained employees demonstrated significant increases in the work-related scales of workplace satisfaction and value of contribution. ::: ::: ::: CONCLUSIONS ::: Results suggest that a brief workplace stress management intervention can produce clinically significant reductions in BP and improve emotional health among hypertensive employees. Implications are that such interventions may produce a healthier and more productive workforce, enhancing performance and reducing losses to the organization resulting from cognitive decline, illness, and premature mortality.


abstract: Crop yields and salinity levels in the North Fork of the Red River (North Fork River) basin, located in southwestern Oklahoma and the Texas Panhandle, were analyzed based on the diverse climate in the region. Saline irrigation water is a major problem in the basin. The Elm Fork Creek flows through salt deposits, making the creek and its receiving stream, the North Fork River, too saline to use for irrigation. This greatly reduces the number of hectares that can be utilized for agricultural crops within the basin. A baseline SWAT model was setup, calibrated and validated to simulate streamflow and wheat and cotton yields. The SWAT model and a regression equation were used to analyze variable weather impacts on crop yields and salinity levels. Using the weather generator WXGEN and 58 years of observed weather data, ten 50-year weather datasets were generated. Output from the weather generator was input into the calibrated SWAT model to simulate wheat and dryland and irrigated cotton yields for the ten weather scenarios. Using an empirical relationship between ionic strength and streamflow, salinity levels were estimated. Though the crop yields varied greatly from year to year, the yields were not significantly different over the 50-year simulation period. The electrical conductivity (EC, expressed in decisiemens per meter or dS/m) at the US Geological Survey gage station just downstream of the salt deposits was significantly different with levels ranging from 40 to 65 dS/m. Though the water in the Elm Fork is much too saline to use for irrigation, the water in the North Fork River may be used as long as the flow rates in the river are greater than 0.60 m3/s. In order to optimize the available cropland, a salinity control must be installed upstream of the salt deposits on the Elm Fork Creek. ::: Keywords: salinity, SWAT, crop yield, wheat modeling, cotton modeling, Red River ::: DOI: 10.3965/j.ijabe.20150803.950 Online first on [2015-03-17] ::: ::: Citation: Mittelstet A R, Storm D E, Stoecker A L. Using SWAT to simulate crop yields and salinity levels in the North Fork River Basin, USA. Int J Agric & Biol Eng, 2015; 8(3): 110－124.


title: An Axiomatically Derived Measure for the Evaluation of Classification Algorithms

abstract: We address the general problem of finding suitable evaluation measures for classification systems. To this end, we adopt an axiomatic approach, i.e., we discuss a number of properties ("axioms") that an evaluation measure for classification should arguably satisfy. We start our analysis by addressing binary classification. We show that F1, nowadays considered a standard measure for the evaluation of binary classification systems, does not comply with a number of them, and should thus be considered unsatisfactory. We go on to discuss an alternative, simple evaluation measure for binary classification, that we call K, and show that it instead satisfies all the previously proposed axioms. We thus argue that researchers and practitioners should replace F1 with K in their everyday binary classification practice. We carry on our analysis by showing that K can be smoothly extended to deal with single-label multi-class classification, cost-sensitive classification, and ordinal classification.


title: Vehicle Detection from 3D Lidar Using Fully Convolutional Network

abstract: Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.


title: Ellipsoidal-Blob Approximation of 3D Models and Its Applications Abstract

abstract: This paper presents a technique for automatically approximating a given mesh model with an ellipsoidal blobby model. Firstly, an ellipsoid decomposition algorithm is introduced to approximate given models by ellipsoids. After that, a blobby implicit surface employing ellipsoidal blobs is modelled to fit the sampling points on the given mesh. Finally, the reconstructed ellipsoidal blobby model is applied in two applications: the geometry data reduction and the target shape controlled cloud animation.


title: Compiling C/C++ SIMD Extensions for Function and Loop Vectorizaion on Multicore-SIMD Processors

abstract: SIMD vectorization has received significant attention in the past decade as an important method to accelerate scientific applications, media and embedded applications on SIMD architectures such as Intel® SSE, AVX, and IBM* AltiVec. However, most of the focus has been directed at loops, effectively executing their iterations on multiple SIMD lanes concurrently relying upon program hints and compiler analysis. This paper presents a set of new C/C++ high-level vector extensions for SIMD programming, and the Intel® C++ product compiler that is extended to translate these vector extensions and produce optimized SIMD instruction sequences of vectorized functions and loops. For a function, our main idea is to vectorize the entire function for callers instead of just vectorizing loops (if any) inside the function. It poses the challenge of dealing with complicated control-flow in the function body, and matching caller and callee for SIMD vector calls while vectorizing caller functions (or loops) and callee functions. Our compilation methods for automatically compiling vector extensions are described. We present performance results of several non-trivial visual computing, computational, and simulation workloads, utilizing SIMD units through the vector extensions on Intel® Multicore 128-bit SIMD processors, and we show that significant SIMD speedups (3.07x to 4.69x) are achieved over the serial execution.


title: Parallels of human language in the behavior of bottlenose dolphins

abstract: A short review of similarities between dolphins and humans with the help of quantitative linguistics and information theory.


title: The impact of climate change on mental health (but will mental health be discussed at Copenhagen

abstract: Climate change will shortly be assuming centre stage when Copenhagen hosts the United Nations Climate Change Conference in early December 2009. In Copenhagen, delegates will discuss the international response to climate change (i.e. the ongoing increase in the Earth's average surface temperature) and the meeting is widely viewed as the most important of its kind ever held ( http://en.cop15.dk/ ). International agreement will be sought on a treaty to replace the 1997 Kyoto Protocol. At the time of writing it is not known whether agreement will be reached on the main issues of reducing greenhouse gas emissions and financing the impacts of climate change, and it appears that the impact of climate change on mental health is unlikely to be on the agenda. We discuss here how climate change could have consequences for global mental health and consider the implications for future research and policy.


title: Characterization and Analysis of an Innovative Gate Driver and Power Supplies Architecture for HF Power Devices With High dv/dt

abstract: This paper presents a specific architecture for a low-side/high-side gate driver implementation for power devices running at high switching frequencies and under very high switching speeds. An electromagnetic interference (EMI) optimization is done by modifying the parasitic capacitance of the propagation paths between the power and the control sides, thanks to a specific design of the circuit. Moreover, to reduce the parasitic inductances and to minimize the antenna phenomenon, the paper studies which elements of the drivers’ circuitry must be brought as close as possible to the power parts. This is important when the ambient temperature of the power device becomes critical, for instance, in automotive and aeronautic applications. Simulations and experiments validate the advantages of the proposed architecture on the conducted EMI problem.


Measuring Customers' Perceptions and Readiness to Accept E-Commerce in Iraq: An Empirical Study


title: Development of Friction Drive Transmission

abstract: A cylindrical friction drive was developed for electric oil pump applications. It was comprised of an outer ring a sun roller, a loading planet two supporting planets, and a stationary carrier. The sun roller was set eccentric to the outer ring to generate a wedge gap that facilitates a torque actuated loading mechanism for the friction drive. The loading planet was properly assembled in the wedge gap and elastically supported to the carrier. By altering the stiffness ratio of the elastic support to contact, the actual operating friction coefficient of the friction drive can be changed regardless of the wedge angle to suit for performance requirement. This provided a greater freedom for design and performance optimization. Design analysis was presented and a FE model was developed to quantify design parameters. Prototypes of the friction drive were fabricated and extensive testing was conducted to evaluate its performance. Results indicated the performance of the friction drive for exceeded the design specifications in speed, torque. and power ratings. The friction drive offered a consistent smooth and quiet performance over a wide range of operating conditions. It was capable of operating at an elevated speed of up to 12 000 rpm with adequate thermal characteristics. The friction drive demonstrated a peak efficiency above 97%. Results confirmed that the stiffness of the elastic support has an important impact on performance. The elastic support stiffness, in conjunction with the contact stiffness, determines the actual operating friction coefficient at the frictional contacts.


title: Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models

abstract: Despite the massive popularity of probabilistic (association) football forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three probability values corresponding to home win, draw, and away win) there is no agreed scoring rule to determine their forecast accuracy. Moreover, the various scoring rules used for validation in previous studies are inadequate since they fail to recognise that football outcomes represent a ranked (ordinal) scale. This raises severe concerns about the validity of conclusions from previous studies. There is a well-established generic scoring rule, the Rank Probability Score (RPS), which has been missed by previous researchers, but which properly assesses football forecasting models.


title: A 225 GHz Circular Polarization Waveguide Duplexer Based on a Septum Orthomode Transducer Polarizer

abstract: In this paper, we present the modeling and experimental validation of an efficient, compact, and high-isolation waveguide duplexer operating at 225 GHz. While duplexers based on ferrite circulators are key components of many monostatic radar systems, at W-band frequencies and above the performance of waveguide circulators rapidly deteriorates. The design presented here uses a ferrite-free duplexing concept based on transmitting and receiving in orthogonal circular polarizations. The circular polarization is efficiently generated in waveguide using a septum orthomode transducer polarizer, leading to a compact device with a single horn antenna. The complete duplexer is designed using efficient numerical algorithms, resulting in a fabricated device with a measured 10% fractional bandwidth, a return loss better than 20 dB, isolation of 30 dB, and insertion loss below 1 dB.


title: Monogenic and chromosomal causes of isolated speech and language impairment

abstract: The importance of a precise molecular diagnosis for children with intellectual disability, autism spectrum disorder and epilepsy has become widely accepted and genetic testing is an integral part of the diagnostic evaluation of these children. In contrast, children with an isolated speech or language disorder are not often genetically evaluated, despite recent evidence supporting a role for genetic factors in the aetiology of these disorders. Several chromosomal copy number variants and single gene disorders associated with abnormalities of speech and language have been identified. Individuals without a precise genetic diagnosis will not receive optimal management including interventions such as early testosterone replacement in Klinefelter syndrome, otorhinolaryngological and audiometric evaluation in 22q11.2 deletion syndrome, cardiovascular surveillance in 7q11.23 duplications and early dietary management to prevent obesity in proximal 16p11.2 deletions. This review summarises the clinical features, aetiology and management options of known chromosomal and single gene disorders that are associated with speech and language pathology in the setting of normal or only mildly impaired cognitive function.


Creating a gold standard corpus for terminological annotation from online forum data


An ontology-based approach for modeling the heavy metals’ temporal and spatial variations in groundwater resources by using fuzzy multi-criteria group decision-making models and geographical information system


title: Human Safety Mechanisms of Human-Friendly Robots: Passive Viscoelastic Trunk and Passively Movable Base

abstract: We would like to give robots the ability to secure human safety in human-robot collisions capable of arising in our living and working environments. However, unfortunately, not much attention has been paid to the technologies of human robot symbiosis to date because almost all robots have been designed and constructed on the assumption that the robots are physically separated from humans. A robot with a new concept will be required to deal with human-robot contact. In this article, we propose a passively movable human-friendly robot that consists of an elastic material-covered manipulator, passive compliant trunk, and passively movable base. The compliant trunk is equipped with springs and dampers, and the passively movable base is constrained by friction developed between the contact surface of the base and the ground. During unexpected collisions, the trunk and base passively move in response to the produced collision force. We describe the validity of the movable base and compliant trunk for collision ...


title: Community detection in weighted networks: Algorithms and applications

abstract: Community detection is an important issue due to its wide use in designing network protocols such as data forwarding in Delay Tolerant Networks (DTN) and worm containment in Online Social Networks (OSN). However, most of the existing community detection algorithms focus on binary networks. Since most networks are weighted such as social networks, DTN or OSN, in this paper, we address the problems of community detection in weighted networks and exploit community for data forwarding in DTN and worm containment in OSN. We propose a novel community detection algorithm, and then introduce two metrics called intra-centrality and inter-centrality, to characterize nodes in communities. Based on these metrics, we propose an efficient data forwarding algorithm for DTN and an efficient worm containment strategy for OSN. Extensive trace-driven simulation results show that the data forwarding algorithm and the worm containment strategy significantly outperform existing works.


title: Event-based analysis of video

abstract: Dynamic events can be regarded as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences (possibly of different lengths) based on their behavioral content. This measure is non-parametric and can thus handle a wide range of dynamic events. We use this measure for isolating and clustering events within long continuous video sequences. This is done without prior knowledge of the types of events, their models, or their temporal extent. An outcome of such a clustering process is a temporal segmentation of long video sequences into event-consistent sub-sequences, and their grouping into event-consistent clusters. Our event representation and associated distance measure can also be used for event-based indexing into long video sequences, even when only one short example-clip is available. However, when multiple example-clips of the same event are available (either as a result of the clustering process, or given manually), these can be used to refine the event representation, the associated distance measure, and accordingly the quality of the detection and clustering process.


title: Predicting Thread Discourse Structure over Technical Web Forums

abstract: Online discussion forums are a valuable means for users to resolve specific information needs, both interactively for the participants and statically for users who search/browse over historical thread data. However, the complex structure of forum threads can make it difficult for users to extract relevant information. The discourse structure of web forum threads, in the form of labelled dependency relationships between posts, has the potential to greatly improve information access over web forum archives. In this paper, we present the task of parsing user forum threads to determine the labelled dependencies between posts. Three methods, including a dependency parsing approach, are proposed to jointly classify the links (relationships) between posts and the dialogue act (type) of each link. The proposed methods significantly surpass an informed baseline. We also experiment with "in situ" classification of evolving threads, and establish that our best methods are able to perform equivalently well over partial threads as complete threads.


title: Let’s stop trying to be “sexy” – preparing managers for the (big) data-driven business era

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The approach involves analysis and review of the literature. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.


title: Ocean wave spectra from a linear polarimetric SAR

abstract: Conventional horizontal or vertical polarization synthetic aperture radar (SAR) images seldom perform well in detecting azimuthally traveling ocean waves. However, theoretical analyses suggest that linear-polarimetric backscatter measurements may be more sensitive to these waves and, therefore, that a SAR with linear polarization is expected to give a better measurement of azimuthally traveling ocean waves. We derive the polarization-orientation modulation transform function and tilt modulation transform function of the linear-polarimetric SAR. Through numerical simulations based on these formulations, we examine the effects of radar and ocean wave parameters on linear-polarimetric SAR image spectra. We suggest a method to eliminate the 180/spl deg/ directional ambiguity in determining the true wave direction. Our numerical simulations show that the correlation between ocean wave spectra and SAR image spectra is improved for larger radar incidence angles and longer ocean waves. For real aperture radar, we suggest that the polarimetric modulation allows measurement of waves traveling in the azimuth direction. Moreover, as suggested by Schuler et al., this may be the dominant modulation for many SAR aircraft measurements, particularly for moderate sea states.


title: Semantic similarity of ontology instances tailored on the application context

abstract: The paper proposes a framework to assess the semantic similarity among instances within an ontology It aims to define a sensitive measurement of semantic similarity, which takes into account different hints hidden in the ontology definition and explicitly considers the application context The similarity measurement is computed by combining and extending existing similarity measures and tailoring them according to the criteria induced by the context Experiments and evaluation of the similarity assessment are provided.


title: Phrase-Based Statistical Machine Translation

abstract: This paper is based on the work carried out in the framework of the VERBMOBIL project, which is a limited-domain speech translation task (German-English). In the final evaluation, the statistical approach was found to perform best among five competing approaches.In this paper, we will further investigate the used statistical translation models. A shortcoming of the single-word based model is that it does not take contextual information into account for the translation decisions. We will present a translation model that is based on bilingual phrases to explicitly model the local context. We will show that this model performs better than the single-word based model. We will compare monotone and non-monotone search for this model and we will investigate the benefit of using the sum criterion instead of the maximum approximation.


title: Opinion Mining for Relating Subjective Expressions and Annual Earnings in US Financial Statements

abstract: Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built tagging models based on the conditional random field (CRF) techniques, considering a variety of combinations of linguistic factors including morphology, orthography, predicate-argument structure, syntax, and simple semantics. Our results show that the CRF models are reasonably effective to find opinion holders in experiments when we adopted the popular MPQA corpus for training and testing. The contribution of our paper is to identify opinion patterns in multiword expressions (MWEs) forms rather than in single word forms. ::: We find that the managers of corporations attempt to use more optimistic words to obfuscate negative financial performance and to accentuate the positive financial performance. Our results also show that decreasing earnings were often accompanied by ambiguous and mild statements in the reporting year and that increasing earnings were stated in assertive and positive way.


title: Review: Security in wireless ad-hoc networks - A survey

abstract: Pervasive mobile and low-end wireless technologies, such as radio-frequency identification (RFID), wireless sensor networks and the impending vehicular ad-hoc networks (VANETs), make the wireless scenario exciting and in full transformation. For all the above (and similar) technologies to fully unleash their potential in the industry and society, there are two pillars that cannot be overlooked: security and privacy. Both properties are especially relevant if we focus on ad-hoc wireless networks, where devices are required to cooperate - e.g. from routing to the application layer - to attain their goals. In this paper, we survey emerging and established wireless ad-hoc technologies and we highlight their security/privacy features and deficiencies. We also identify open research issues and technology challenges for each surveyed technology.


S KIP RNN : L EARNING TO S KIP S TATE U PDATES IN R ECURRENT N EURAL N ETWORKS


title: Evaluation of transaction authentication methods for online banking

abstract: Authentication is a major research topic in the information security field. Much has been written about assessing entity (user) authentication methods, but there is a lack of literature concerning the evaluation of financial transaction authentication in online banking. Entity authentication methods have been systematized by quantifying their qualitative aspects, but there is no evaluation mechanism which also places the additional characteristics of transaction authentication in a user-centric context. Based on an existing mechanism which quantifies accessibility, memorability, security and vulnerability characteristics in entity authentication methods, we propose feasibility as an additional dimension which quantifies aspects related to the secure usability of transaction authentication methods. We also propose the use of this evaluation mechanism by multiple raters to reduce personal bias. Four implemented and eight proposed authentication methods for online banking were evaluated by seven experts. The results indicate that the mechanism can be applied on a wide range of authentication methods, since it is able to evaluate methods based on different information schemes. However, care must be taken that evaluations are performed by multiple experts, due to the amount of subjectivity inherent in the mechanism and in the different opinions of the raters. We introduced a qualitative evaluation mechanism for online banking authentication.Seven raters examine 12 online banking authentication methods with our mechanism.Bank-issued authentication devices overall have a qualitative very good fit.Most user-owned devices fit poorly compared to bank-issued devices.


title: LifeFlow: visualizing an overview of event sequences

abstract: Event sequence analysis is an important task in many domains: medical researchers may study the patterns of transfers within the hospital for quality control; transportation experts may study accident response logs to identify best practices. In many cases they deal with thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called \emph{LifeFlow}. LifeFlow is scalable, can summarize all possible sequences, and represents the temporal spacing of the events within sequences. Two case studies with healthcare and transportation domain experts are presented to illustrate the usefulness of LifeFlow. A user study with ten participants confirmed that after 15 minutes of training novice users were able to rapidly answer questions about the prevalence and temporal characteristics of sequences, find anomalies, and gain significant insight from the data.


title: Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter

abstract: Arabic is one of the languages that present special challenges to Optical character recognition (OCR). The main challenge in Arabic is that it is mostly cursive. Therefore, a segmentation process must be carried out to determine where the character begins and where it ends. This step is essential for character recognition. This paper presents Arabic character segmentation algorithm. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. This is done using profile's amplitude filter and simple edge tool to find characters separations. Our algorithm shows promising performance when applied on different machine printed documents with different Arabic fonts.


title: Spatial representation of magnitude in humans (Homo sapiens), Western lowland gorillas (Gorilla gorilla gorilla), and American black bears (Ursus americanus)

abstract: The spatial-numerical association of response codes (SNARC) effect is the tendency for humans to respond faster to relatively larger numbers on the left or right (or with the left or right hand) and faster to relatively smaller numbers on the other side. This effect seems to occur due to a spatial representation of magnitude either in occurrence with a number line (wherein participants respond to relatively larger numbers faster on the right), other representations such as clock faces (responses are reversed from number lines), or culturally specific reading directions, begging the question as to whether the effect may be limited to humans. Given that a SNARC effect has emerged via a quantity judgement task in Western lowland gorillas and orangutans (Gazes et al., Cog 168:312–319, 2017), we examined patterns of response on a quantity discrimination task in American black bears, Western lowland gorillas, and humans for evidence of a SNARC effect. We found limited evidence for SNARC effect in American black bears and Western lowland gorillas. Furthermore, humans were inconsistent in direction and strength of effects, emphasizing the importance of standardizing methodology and analyses when comparing SNARC effects between species. These data reveal the importance of collecting data with humans in analogous procedures when testing nonhumans for effects assumed to bepresent in humans.


title: DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome

abstract: Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug–human protein interactions, it is reasonable to analyze the chemical– protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user’s molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user’s molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.


title: From POS tagging to dependency parsing for biomedical event extraction

abstract: Background: Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance. Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge, there is no recent work making such comparisons in the biomedical context; specifically no detailed analysis of neural models on this data is available. Experimental results show that in general, the neural models outperform the feature-based models on two benchmark biomedical corpora GENIA and CRAFT. We also perform a task-oriented evaluation to investigate the influences of these models in a downstream application on biomedical event extraction, and show that better intrinsic parsing performance does not always imply better extrinsic event extraction performance. Conclusion: We have presented a detailed empirical study comparing traditional feature-based and neural network-based models for POS tagging and dependency parsing in the biomedical context, and also investigated the influence of parser selection for a biomedical event extraction downstream task. Availability of data and material: We make the retrained models available at https://github.com/datquocnguyen/BioPosDep


title: Fingerprint-Based Attendance Management System

abstract: In recent time, there has been high level of impersonation experienced on a daily basis in both private and public sectors, the ghost worker syndrome which has become a menace across all tiers of government, employers concerns over the levels of employee absence in their workforce and the difficulty in managing student attendance during lecture periods. Fingerprints are a form of biometric identification which is unique and does not change in one’s entire lifetime. This paper presents the attendance management system using fingerprint technology in a university environment. It consists of two processes namely; enrolment and authentication. During enrolment, the fingerprint of the user is captured and its unique features extracted and stored in a database along with the users identity as a template for the subject. The unique features called minutiae points were extracted using the Crossing Number (CN) method which extracts the ridge endings and bifurcations from the skeleton image by examining the local neighborhoods of each ridge pixel using a 3 x 3 window. During authentication, the fingerprint of the user is captured again and the extracted features compared with the template in the database to determine a match before attendance is made. The fingerprint-based attendance management system was implemented with Microsoft’s C# on the. NET framework and Microsoft’s Structured Query Language (SQL) Server 2005 as the backend. The experimental result shows that the developed system is highly efficient in the verification of users fingerprint with an accuracy level of 97.4%. The average execution time for the developed system was 4.29 seconds as against 18.48 seconds for the existing system. Moreover, the result shows a well secured and reliable system capable of preventing impersonation.


title: Warming Up to Cold Start Personalization

abstract: Smart agents face abandonment if they are unable to provide value to the users from the very first interaction. Existing smart agents take time to learn about new users before they can offer them personalized services. We present a method for learning personalization information about users quickly and without placing unnecessary hardship on them. Our method enables smart agents to pick which questions to ask the user when they first interact to maximize the agent's overall knowledge about the user. We demonstrate our method on two publically available US census datasets containing 172 user variables from 1,799,394 training and 1,618,489 testing users. The questions selected using our method improve the agent's accuracy when inferring information about future users, including information that they did not ask about. Our work enables smart agents that assist the user with personalized services soon after they start interacting.


title: Bowling online: Social networking and social capital within the organization

abstract: Within an organizational setting, social capital facilitates knowledge management processes in that it enables individuals to locate useful information, draw on resources and make contributions to the network. This paper explores the relationship between various dimensions of organizational social capital and the use of an internal social network site (SNS). We hypothesize that the use of a SNS contributes to social capital within the organization in that SNS users are able to maintain larger networks of heterogeneous contacts. Additionally, the affordances of the site support social interaction between users, thus helping individuals maintain existing relationships and deepen developing ones. We find that bonding relationships, sense of corporate citizenship, interest in connecting globally, and access to new people and expertise are all associated with greater intensity of use of the social network site.


title: A fuzzy expert system for earthquake prediction, case study: the Zagros range

abstract: A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system (FIS) and to infer the results. In this paper, we have used a Sugeno type fuzzy inference system to build the FES. At the next step, the adaptive network-based fuzzy inference system (ANFIS) is used to refine the FES parameters and improve its performance. The proposed framework is then employed to attain the performance of a human expert used to predict earthquakes in the Zagros area based on the idea of coupled earthquakes. While the prediction results are promising in parts of the testing set, the general performance indicates that prediction methodology based on coupled earthquakes needs more investigation and more complicated reasoning procedure to yield satisfactory predictions.


title: Subjective Panoramic Video Quality Assessment Database for Coding Applications

abstract: With the development of virtual reality, higher quality panoramic videos are in great demand to guarantee the immersive viewing experience. Therefore, quality assessment attaches much importance to correlated technologies. Considering the geometric transformation in projection and the limited resolution of head-mounted device (HMD), a modified display protocol of the high resolution sequences for the subjective rating test is proposed, in which an optimal display resolution is determined based on the geometry constraints between screen and human eyes. By sampling the videos to the optimal resolution before coding, the proposed method significantly alleviates the interference of HMD sampling while displaying, thus ensuring the reliability of subjective quality opinion in terms of video coding. Using the proposed display protocol, a subjective quality database for panoramic videos is established for video coding applications. The proposed database contains 50 distorted sequences obtained from ten raw panoramic video sequences. Distortions are introduced with the High Efficiency Video Coding compression. Each sequence is evaluated by 30 subjects on video quality, following the absolute category rating with hidden reference method. The rating scores and differential mean opinion scores (DMOSs) are recorded and included in the database. With the proposed database, several state-of-the-art objective quality assessment methods are further evaluated with correlation analysis. The database, including the video sequences, subjective rating scores and DMOS, can be used to facilitate future researches on coding applications.


title: CHALLENGES FOR AGILE DEVELOPMENT OF COTS COMPONENTS AND COTS-BASED SYSTEMS - A Theoretical Examination

abstract: Component-based software engineering has had great impact in the desktop and server domain and is spreading to other domains as well, such as embedded systems. Agile software development is another approach which has gained much attention in recent years, mainly for smaller-scale production of less critical systems. Both of them promise to increase system quality, development speed and flexibility, but so far little has been published on the combination of the two approaches. This paper presents a comprehensive analysis of the applicability of the agile approach in the development processes of 1) COTS components and 2) COTS-based systems. The study method is a systematic theoretical examination and comparison of the fundamental concepts and characteristics of these approaches. The contributions are: first, an enumeration of identified contradictions between the approaches, and suggestions how to bridge these incompatibilities to some extent. Second, the paper provides some more general comments, considerations, and application guidelines concerning the introduction of agile principles into the development of COTS components or COTS-based systems. This study thus forms a framework which will guide further empirical studies.


title: Transformational, transactional, and laissez-faire leadership styles: a meta-analysis comparing women and men.

abstract: A meta-analysis of 45 studies of transformational, transactional, and laissez-faire leadership styles found that female leaders were more transformational than male leaders and also engaged in more of the contingent reward behaviors that are a component of transactional leadership. Male leaders were generally more likely to manifest the other aspects of transactional leadership (active and passive management by exception) and laissez-faire leadership. Although these differences between male and female leaders were small, the implications of these findings are encouraging for female leadership because other research has established that all of the aspects of leadership style on which women exceeded men relate positively to leaders' effectiveness whereas all of the aspects on which men exceeded women have negative or null relations to effectiveness.


title: Clinical and electrophysiological study of neuropathy after organophosphorus compounds poisoning

abstract: Clinical and electrophysiological examinations were performed on 12 patients with toxic neuropathy following accidental ingestion of alcohol polluted by triorthocresyl phosphate (TOCP). Concurrent PNS and CNS lesions were found in all patients. Two to three months after ingestion, five of them showed prevalent signs of mixed, sensorimotor polyneuropathy, especially motor and distal, and the electrophysiological data pointed to the mixed process of axonal degeneration and secondary demyelination. In two of these five patients in whom examinations were repeated 13 years after TOCP ingestion, there was a marked clinical and electrophysiological improvement of signs of PNS lesions. Improvement of signs of CNS lesions was very poor even after 13 years. Signs of CNS lesions prevailed in the remaining seven patients. The clinical picture resembled that in amyotrophic lateral sclerosis and the electrophysiological data suggested a neuronal and axonal degeneration. Apart from the 12 cases of TOCP neuropathy, we also studied two cases of poisoning with organophosphorus insecticides, Dipterex and Divipan, in which a pure motor form of neuropathy was found.


title: Developing and implementing communications strategies: A descriptive model

abstract: The world-wide environment, but particularly the South African environment today has created a growing need for businesses to communicate effectively with their stakeholders. The number of stakeholders interacting with companies has burgeoned, their demands have become greater and the need for change is critical. As these pressures increase, the need for companies to communicate effectively will grow. In South Africa the possibility for misunderstanding is compounded by communication barriers such as the socio-political system, diversity of cultures and languages, as well as the many different levels of education. In an environment such as this the need for effective communication is so great that a company can clearly no longer rely on ad hoc , reactive communications to facilitate the achievement of corporate goals. In order to ensure effective communication a company needs to have an overall communications strategy which has its roots in corporate goals and strategies. Developing and implementing communications strategies involves many complex considerations such as the identification and analysis of stakeholders and their power bases, issues management, corporate image and culture, crisis communications planning, media planning and dealing with communication barriers. This article provides a suggested framework for integrating these and other considerations into the development and implementation of communications strategies.


title: Parallel multilevel k-way partitioning scheme for irregular graphs

abstract: In this paper we present a parallel formulation of a multilevel k-way graph partitioning algorithm. The multilevel k-way partitioning algorithm reduces the size of the graph by collapsing vertices and edges (coarsening phase), finds a k-way partition of the smaller graph, and then it constructs a k-way partition for the original graph by projecting and refining the partition to successively finer graphs (uncoarsening phase). A key innovative feature of our parallel formulation is that it utilizes graph coloring to effectively parallelize both the coarsening and the refinement during the uncoarsening phase. Our algorithm is able to achieve a high degree of concurrency, while maintaining the high quality partitions produced by the serial algorithm. We test our scheme on a large number of graphs from finite element methods, and transportation domains. Our parallel formulation on Cray T3D, produces high quality 128-way partitions on 128 processors in a little over two seconds, for graphs with a million vertices. Thus our parallel algorithm makes it possible to perform dynamic graph partition in adaptive computations without compromising quality.


title: "Pimp My Roomba": designing for personalization

abstract: We present a study of how householders personalize their domestic vacuuming robot, iRobot's Roomba". In particular, we build on Blom and Monk's [3] theory of personalization that argues that personalization does not only occur naturally but can also be induced by design choices. In this study, we created a personalization toolkit, which allowed people to customize their Roomba's appearance and distributed it to 15 households. Our observations of these households provide empirical support that personalization can facilitate positive experiences with a Roomba, and having materials to hand can increase the odds of customization. We conclude by discussing design implications for personalization.


title: Programming by Feedback

abstract: This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user's utility function and accounts for the user's (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.


Image Question Answering: A Visual Semantic Embedding Model and a New Dataset.


title: Pseudo-Query Reformulation

abstract: Automatic query reformulation refers to rewriting a user’s original query in order to improve the ranking of retrieval results compared to the original query. We present a general framework for automatic query reformulation based on discrete optimization. Our approach, referred to as pseudo-query reformulation, treats automatic query reformulation as a search problem over the graph of unweighted queries linked by minimal transformations (e.g. term additions, deletions). This framework allows us to test existing performance prediction methods as heuristics for the graph search process. We demonstrate the effectiveness of the approach on several publicly available datasets.


title: Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

abstract: Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and generalizes very poorly outside the domain of the training data. At the same time, unsupervised systems have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Addressing these drawbacks, in this paper, we tackle keyphrase extraction from single documents with EmbedRank: a novel unsupervised method, that leverages sentence embeddings. EmbedRank achieves higher F-scores than graph-based state of the art systems on standard datasets and is suitable for real-time processing of large amounts of Web data. With EmbedRank, we also explicitly increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrases' semantic overlap leads to no gains in F-score, our high diversity selection is preferred by humans.


title: Detecting and Explaining Causes From Text For a Time Series Event

abstract: Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.


title: Old Communication, New Literacies: Social Network Sites as Social Learning Resources

abstract: This study examined the role of a social network site (SNS) in the lives of 11 high school teenagers from low-income families in the U.S. We conducted interviews, talk-alouds and content analysis of MySpace profiles. Qualitative analysis of these data revealed three themes. First, SNSs facilitated emotional support, helped maintain relationships, and provided a platform for self-presentation. Second, students used their online social network to fulfill essential social learning functions. Third, within their SNS, students engaged in a complex array of communicative and creative endeavors. In several instances, students' use of social network sites demonstrated the new literacy practices currently being discussed within education reform efforts. Based on our findings, we suggest additional directions for related research and educational practices.


title: Raman study on surface layers and thin films by using total reflection experiments

abstract: Abstract An access to Raman spectroscopic investigations of phenomena of boundary layers is given through the excitation by means of the evanescent wave in total reflection (Total Reflection Raman Spectroscopy - TRRS). The TRRS scattering unit contains a semicylinder as TR-element and the sample placed on the planar face of it. This equipment permits the variation of the incident angle in the full range from 0° to 90°. The general theory of the TRRS is specialized for our experimental setup. The TRRS-method allows two possibilities of application: i) the investigation of surface layers by excitation above the critical angle and ii) the investigation of thin films by excitation at the critical angle. Any examples were discussed for demonstrating these applications.


title: Design of High-Order Switches for Multimode Applications on a Silicon-on-Insulator Technology

abstract: A silicon-on-insulator (SOI) CMOS technology on high-resistivity silicon substrates is presented for the design of high-power switches for cellular and wireless local area network handset applications. A design methodology is introduced to design high-order switches for optimal insertion loss and isolation performance. Sources of nonlinearities in SOI switches are discussed. To the best of our knowledge, this work is the first demonstration of high-power switches on a high-resistivity SOI CMOS technology for high-volume cellular handset applications with adequate intermodulation and harmonic distortion performance. The design details and measurement results for a variety of RF switches with general-purpose input/output and mobile industry processor interface control interfaces, flip-chip/wire-bond packaging, and for various standards are discussed.


title: Absence of synergism between exposure to asbestos and cigarette smoking in asbestosis.

abstract: To assess both independent and synergistic effects of exposure to asbestos and cigarette smoking on the development of asbestosis, survey data from 4 groups of workers exposed to asbestos were analyzed with multivariate statistical models. Survey methods were standardized and included for the 383 subjects a respiratory symptoms questionnaire, occupational history, physical examination, pulmonary function testing, and a chest radiograph. Exposure to asbestos and cigarette smoking were assessed by questionnaire. Synergism between the 2 exposures was not present for previously identified manifestations of asbestosis including bilateral fine crackles, clubbing, dyspnea, radiographic abnormality, decreased forced vital capacity, and decreased single-breath diffusing capacity of the lung for CO. However, additive, independent effects of these 2 exposures were present for each of these parameters.


title: Broadband cavity-backed slot antenna array with 45° slant polarization

abstract: The radiation characteristics of a cavity-backed four-slot antenna array are investigated in this paper. By employing cavity-backed slots antenna element, the antenna array has wide bandwidth and wide beam width. The feeding network of the antenna array mainly consists of shielded microstrip line. Both simulated and measured results of the antenna return loss and radiation patterns are presented. The measured VSWR<2 bandwidth is 55% of the center frequency 21.8GHz. It is shown that using shielded microstrip line leads to better radiation performances. Besides, good agreement can be observed between the measured radiation patterns and the simulated values. These findings may provide useful insights and guidelines for the design of 45° slant linear polarization wideband antenna array in K-band with wide beam.


title: A Fingerprint Method for Indoor Localization Using Autoencoder Based Deep Extreme Learning Machine

abstract: By growing the demand for location based services in indoor environments in recent years, fingerprint based indoor localization has attracted much research interest. The fingerprint localization method works based on received signal strength (RSS) in wireless sensor networks. This method uses RSS measurements from available transmitter sensors, which are collected by a smart phone with internal sensors. In this article, we propose a novel algorithm that takes advantage of deep learning, extreme learning machines, and high level extracted features by autoencoder to improve the localization performance in the feature extraction and the classification. Furthermore, as the fingerprint database needs to be updated (due to the dynamic nature of environment), we also increase the number of training data, in order to improve the localization performance, gradually. Simulation results indicate that the proposed method provides a significant improvement in localization performance by using high level extracted features by autoencoder and increasing the number of training data.


title: From Hashing to CNNs: Training BinaryWeight Networks via Hashing

abstract: Deep convolutional neural networks (CNNs) have shown appealing performance on various computer vision tasks in recent years. This motivates people to deploy CNNs to realworld applications. However, most of state-of-art CNNs require large memory and computational resources, which hinders the deployment on mobile devices. Recent studies show that low-bit weight representation can reduce much storage and memory demand, and also can achieve efficient network inference. To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing. In this paper, we first reveal the strong connection between inner-product preserving hashing and binary weight networks, and show that training binary weight networks can be intrinsically regarded as a hashing problem. Based on this perspective, we propose an alternating optimization method to learn the hash codes instead of directly learning binary weights. Extensive experiments on CIFAR10, CIFAR100 and ImageNet demonstrate that our proposed BWNH outperforms current state-of-art by a large margin.


title: Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing

abstract: Training deep neural networks results in strong learned representations that show good generalization capabilities. In most cases, training involves iterative modification of all weights in the network via back-propagation. In Extreme Learning Machines, it has been suggested to set the first layer of a network to fixed random values instead of learning it. In this paper, we propose to take this approach a step further and fix almost all layers of a deep convolutional neural network, allowing only a small portion of the weights to be learned. As our experiments show, fixing even the majority of the parameters of the network often results in performance which is on par with the performance of learning all of them. The implications of this intriguing property of deep neural networks are discussed and we suggest practical ways to harness it to create more robust and compact representations.


title: Critical success factors in enterprise resource planning systems: Review of the last decade

abstract: Organizations perceive ERP as a vital tool for organizational competition as it integrates dispersed organizational systems and enables flawless transactions and production. This review examines studies investigating Critical Success Factors (CSFs) in implementing Enterprise Resource Planning (ERP) systems. Keywords relating to the theme of this study were defined and used to search known Web engines and journal databases for studies on both implementing ERP systems per se and integrating ERP systems with other well- known systems (e.g., SCM, CRM) whose importance to business organizations and academia is acknowledged to work in a complementary fashion. A total of 341 articles were reviewed to address three main goals. This study structures previous research by presenting a comprehensive taxonomy of CSFs in the area of ERP. Second, it maps studies, identified through an exhaustive and comprehensive literature review, to different dimensions and facets of ERP system implementation. Third, it presents studies investigating CSFs in terms of a specific ERP lifecycle phase and across the entire ERP life cycle. This study not only reviews articles in which an ERP system is the sole or primary field of research, but also articles that refer to an integration of ERP systems and other popular systems (e.g., SCM, CRM). Finally it provides a comprehensive bibliography of the articles published during this period that can serve as a guide for future research.


title: Sentiment Analysis on Twitter to Improve Time Series Contextual Anomaly Detection for Detecting Stock Market Manipulation

abstract: In this paper, We propose a formalized method to improve the performance of Contextual Anomaly Detection (CAD) for detecting stock market manipulation using Big Data techniques. The method aims to improve the CAD algorithm by capturing the expected behaviour of stocks through sentiment analysis of tweets about stocks. The extracted insights are aggregated per day for each stock and transformed to a time series. The time series is used to eliminate false positives from anomalies that are detected by CAD. We present a case study and explore developing sentiment analysis models to improve anomaly detection in the stock market. The experimental results confirm the proposed method is effective in improving CAD through removing irrelevant anomalies by correctly identifying 28% of false positives.


title: "I spy with my little eye!": Breadth of attention, inattentional blindness, and tactical decision making in team sports.

abstract: Failures of awareness are common when attention is otherwise engaged. Such failures are prevalent in attention-demanding team sports, but surprisingly no studies have explored the inattentional blindness paradigm in complex sport game-related situations. The purpose of this paper is to explore the link between breadth of attention, inattentional blindness, and tactical decision-making in team ball sports. A series of studies revealed that inattentional blindness exists in the area of team ball sports (Experiment 1). More tactical instructions can lead to a narrower breadth of attention, which increases inattentional blindness, whereas fewer tactical instructions widen the breadth of attention in the area of team ball sports (Experiment 2). Further meaningful exogenous stimuli reduce inattentional blindness (Experiment 3). The results of all experiments are discussed in connection with consciousness and attention theories as well as creativity and training in team sports.


title: Trends in worldwide ICT electricity consumption from 2007 to 2012

abstract: Information and Communication Technology (ICT) devices and services are becoming more and more widespread in all aspects of human life. Following an increased worldwide focus on the environmental impacts of energy consumption in general, there is also a growing attention to the electricity consumption associated with ICT equipment. In this paper we assess how ICT electricity consumption in the use phase has evolved from 2007 to 2012 based on three main ICT categories: communication networks, personal computers, and data centers. We provide a detailed description of how we calculate the electricity use and evolution in these three categories. Our estimates show that the yearly growth of all three individual ICT categories (10%, 5%, and 4%, respectively) is higher than the growth of worldwide electricity consumption in the same time frame (3%). The relative share of this subset of ICT products and services in the total worldwide electricity consumption has increased from about 3.9% in 2007 to 4.6% in 2012. We find that the absolute electricity consumption of each of the three categories is still roughly equal. This highlights the need for energy-efficiency research across all these domains, rather than focusing on a single one.


title: Qualitative clinical evaluation of scapular dysfunction: a reliability study.

abstract: The purpose of this study was to determine the intrarater and interrater reliability of a clinical evaluation system for scapular dysfunction. No commonly accepted terminology presently exists for describing the abnormal dynamic scapular movement patterns that are commonly associated with shoulder injury. A method of observation was devised for clinical evaluation of scapular dysfunction. Blinded evaluators (2 physicians and 2 physical therapists) were familiarized with the evaluation method of scapular movement patterns before viewing a videotape of 26 subjects with and without scapular dysfunction. Each evaluator was asked to categorize the predominant scapular movement pattern observed during bilateral humeral scaption and abduction motions. Reliability was assessed by a kappa coefficient. Intertester reliability (kappa = 0.4) was found to be slightly lower than intratester reliability (kappa = 0.5). These results indicate that, with refinement, this qualitative evaluation method may allow clinicians to standardize the categorization of dynamic scapular dysfunction patterns.


title: Sexual Attractiveness: Sex Differences in Assessment and Criteria

abstract: Abstract Two studies of university undergraduates used novel measures to explore sex differences and intrasexual variation in mate preferences and sexual behavior. Study 1 used photographs of models to assess the effects of sex of subject and target persons' ambition/income on judgments of dating, sexual, and marital desirability, and analyzed these effects' associations with subjects' sexual attitudes and behavior (Simpson's and Gangestad's Sociosexual Orientation Inventory [SOI]). Results were consistent with the proposed model of mate selection: for men, potential partners' physical attributes establish a pool of coitally acceptable partners, some of whom may merit long-term investment. When women choose partners, nonphysical characteristics such as ambition, status, and dominance establish a pool of partners who are potentially acceptable for sexual relations and higher-investment relationships. Target persons' ambition/income strongly affected women's, but not men's, reported willingness to date and have sex with target persons, and the effects on women were not associated with their SOI scores. Study 2 used photographs of models in bathing suits to explore sex differences in the capacity to determine coital acceptability by means of a visual scan, what types of information men and women need in addition to a visual scan in order to determine coital acceptability, and whether these variables are associated with subjects' SOI scores. Results were consistent with the hypotheses. Women with high SOI scores require fewer signs of male willingness to invest in order to engage in sexual relations than do women with low scores. Nevertheless, the two groups have essentially the same perceptual filters and criteria in mate selection, and these differ dramatically from those of men. A "tradeoff-threshold" model of mate evaluation is described, and its compatibility with Singh's models is discussed.


title: 3D Reconstruction of Indoor Scenes Using RGB-D Monocular Vision

abstract: Aiming at the problem of low speed of 3D reconstruction of indoor scenes with monocular vision, the color images and depth images of indoor scenes based on ASUS Xtion monocular vision sensor were used for 3D reconstruction. The image feature extraction using the ORB feature detection algorithm, and compared the efficiency of several kinds of classic feature detection algorithm in image matching, Ransac algorithm and ICP algorithm are used to point cloud fusion. Through experiments, a fast 3D reconstruction method for indoor, simple and small-scale static environment is realized. Have good accuracy, robustness, real-time and flexibility.


title: The Shortest Path Problem Revisited: Optimal Routing for Electric Vehicles

abstract: Electric vehicles (EV) powered by batteries will play a significant role in the road traffic of the future. The unique characteristics of such EVs - limited cruising range, long recharge times, and the ability to regain energy during deceleration - require novel routing algorithms, since the task is now to determine the most economical route rather than just the shortest one. This paper proposes extensions to general shortestpath algorithms that address the problem of energy-optimal routing. Specifically, we (i) formalize energy-efficient routing in the presence of rechargeable batteries as a special case of the constrained shortest path problem (CSPP) with hard and soft constraints, and (ii) present an adaption of a general shortest path algorithm (using an energy graph, i.e., a graph with a weight function representing the energy consumption) that respects the given constraints and has a worst case complexity of O(n3). The presented algorithms have been implemented and evaluated within a prototypic navigation system for energy-efficient routing.


title: Cost-sensitive learning methods for imbalanced data

abstract: Class imbalance is one of the challenging problems for machine learning algorithms. When learning from highly imbalanced data, most classifiers are overwhelmed by the majority class examples, so the false negative rate is always high. Although researchers have introduced many methods to deal with this problem, including resampling techniques and cost-sensitive learning (CSL), most of them focus on either of these techniques. This study presents two empirical methods that deal with class imbalance using both resampling and CSL. The first method combines and compares several sampling techniques with CSL using support vector machines (SVM). The second method proposes using CSL by optimizing the cost ratio (cost matrix) locally. Our experimental results on 18 imbalanced datasets from the UCI repository show that the first method can reduce the misclassification costs, and the second method can improve the classifier performance.


title: Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction

abstract: Collaboration enables weak species to survive in an environment where different species compete for limited resources. Cooperative coevolution (CC) is a nature-inspired optimization method that divides a problem into subcomponents and evolves them while genetically isolating them. Problem decomposition is an important aspect in using CC for neuroevolution. CC employs different problem decomposition methods to decompose the neural network training problem into subcomponents. Different problem decomposition methods have features that are helpful at different stages in the evolutionary process. Adaptation, collaboration, and competition are needed for CC, as multiple subpopulations are used to represent the problem. It is important to add collaboration and competition in CC. This paper presents a competitive CC method for training recurrent neural networks for chaotic time-series prediction. Two different instances of the competitive method are proposed that employs different problem decomposition methods to enforce island-based competition. The results show improvement in the performance of the proposed methods in most cases when compared with standalone CC and other methods from the literature.