title: Novel antipodal linearly tapered slot antenna using GCPW-to-SIW transition for passive millimeter-wave focal plane array imaging

abstract: In this work, a novel antipodal linearly tapered slot antenna (ALTSA) using grounded coplanar waveguide (GCPW)-to-substrate integrated waveguide (SIW) transition is proposed and demonstrated. The antenna is well designed for using as the feed antenna in the focal plane array (FPA) imaging system. The S11 of the proposed antenna is below −13dB on Ka-band, meanwhile, the E-plane side lobe is almost 15dB down and 40dB down after reflecting by a parabolic antenna when placing along a line on its focal plane, which show us its high availability for passive millimeter-wave (PMMW) focal plane array imaging system.


HYBED: HYPERBOLIC NEURAL GRAPH EMBEDDING


title: Single depth image super-resolution with multiple residual dictionary learning and refinement

abstract: Learning-based image super-resolution methods often use large datasets to learn texture features. When these methods are applied to depth images, emphasis should be given on learning the geometrical structures at object boundaries, since depth images do not have much texture information. In this paper, we develop a scheme to learn multiple residual dictionaries from only one external image. After depth image super-resolution, some artifacts may appear. An adaptive depth map refinement method is then proposed to remove these artifacts along the depth edges, based on the shape-adaptive weighted median filtering method. Experimental results demonstrate the advantage of the proposed method over many other methods.


title: Genes, environment and sport performance: Why the Nature-Nurture dualism is no longer relevant

abstract: One of the most enduring and passionate debates in sports science concerns the relative importance of genes (i.e., nature) and environment (i.e., nurture) on human behaviour. This debate has considerable relevance for understanding athletic performance and exercise behaviours. The purpose of this position paper is to provide a synopsis of current evidence regarding the nature-nurture debate and to consider the implications of this research for researchers in sport science and medicine. To this end, we begin by providing a historical overview of the nature-nurture debate before reviewing current evidence for the influence of nature and nurture on variability in sports performance. Based on our analysis, we conclude that the influence of genes on phenotypes of complex systems is context-based which provides a challenge for understanding the role of variants of single genes in shaping behaviour. For this reason, the dualist approach of nature versus nurture is inadequate to account for the complexity of the interactions between these factors and advocate an approach that considers a more dynamic, interactive relationship.


title: Research and application of cluster analysis algorithm

abstract: With the popularity of database technology matures and data applications, the amount of data accumulated by the human increases rapidly. Facing the extremely large amount of data, we gradually step into a “rich data, poor knowledge” embarrassing situation. The data mining (Data Mining) rise to solve this problem. In this paper, we study the means and methods of clustering analysis that processing data partition or grouping, which is an important field in data mining. Based on the understanding of theoretical basis of clustering analysis, firstly, analyze in detail main algorithms of partitioning methods, hierarchical methods, density-based methods, grid-based methods and model-based methods. Secondly, compare performance of different clustering algorithms from scalability, the shape of cluster, sensitivity to the “noise”, and sensitivity to the data input sequence, high dimension and algorithm efficiency. Finally, use MATLAB for simulating and verifying applications of the algorithms based on K-means clustering analysis and hierarchical clustering.


title: Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems

abstract: Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other.


title: Health and economic burden of the projected obesity trends in the USA and the UK

abstract: Rising prevalence of obesity is a worldwide health concern because excess weight gain within populations forecasts an increased burden from several diseases, most notably cardiovascular diseases, diabetes, and cancers. In this report, we used a simulation model to project the probable health and economic consequences in the next two decades from a continued rise in obesity in two ageing populations--the USA and the UK. These trends project 65 million more obese adults in the USA and 11 million more obese adults in the UK by 2030, consequently accruing an additional 6-8·5 million cases of diabetes, 5·7-7·3 million cases of heart disease and stroke, 492,000-669,000 additional cases of cancer, and 26-55 million quality-adjusted life years forgone for USA and UK combined. The combined medical costs associated with treatment of these preventable diseases are estimated to increase by $48-66 billion/year in the USA and by £1·9-2 billion/year in the UK by 2030. Hence, effective policies to promote healthier weight also have economic benefits.


title: Plight of the bumble bee: pathogen spillover from commercial to wild populations.

abstract: Abstract Pathogen spread or ‘spillover’ can occur when heavily infected, domestic hosts interact with closely-related wildlife populations. Commercially-produced bumble bees used in greenhouse pollination often have higher levels of various pathogens than wild bumble bees. These pathogens may spread to wild bees when commercial bees escape from greenhouses and interact with their wild counterparts at nearby flowers. We examined the prevalence of four pathogens in wild bumble bee populations at locations near and distant to commercial greenhouses in southern Ontario, Canada. Bumble bees collected near commercial greenhouses were more frequently infected by those pathogens capable of being transmitted at flowers ( Crithidia bombi and Nosema bombi ) than bees collected at sites away from greenhouses. We argue that the spillover of pathogens from commercial to wild bees is the most likely cause of this pattern and we discuss the implications of such spillover for bumble bee conservation.


title: Portfolio Construction: Using Bootstrapping and Portfolio Weight Resampling for Construction of Diversified Portfolios

abstract: In this paper we introduce a framework for constructing portfolios, addressing two of the major problems of classical mean-variance optimization in practice: Low diversification and sensitivity to information ambiguity. In order to address these issues, we incorporate a prior regarding investors preferences as well as using a bootstrapping method to incorporate the effects of input parameter variation.


title: Human-aware robot navigation: A survey

abstract: Navigation is a basic skill for autonomous robots. In the last years human-robot interaction has become an important research field that spans all of the robot capabilities including perception, reasoning, learning, manipulation and navigation. For navigation, the presence of humans requires novel approaches that take into account the constraints of human comfort as well as social rules. Besides these constraints, putting robots among humans opens new interaction possibilities for robots, also for navigation tasks, such as robot guides. This paper provides a survey of existing approaches to human-aware navigation and offers a general classification scheme for the presented methods.


title: Detecting SQL injection attacks using SNORT IDS

abstract: SQL injection attack poses a serious security threats among the Internet community nowadays and it's continue to increase exploiting flaws found in the Web applications. In SQL injection attack, the attackers can take advantage of poorly coded web application software to introduce malicious code into the system and/or could retrieve important information. Web applications are under siege from cyber criminals seeking to steal confidential information and disable or damage the services offered by these application. Therefore, additional steps must be taken to ensure data security and integrity of the applications. In this paper we propose an innovative solution to filter the SQL injection attack using SNORT IDS. The proposed detection technique uses SNORT tool by augmenting a number of additional SNORT rules. We evaluate the proposed solution by comparing our method with several existing techniques. Experimental results demonstrate that the proposed method outperforms other similar techniques using the same data set.


title: Interactive topography simulation sandbox for geography learning course

abstract: Recently, there are huge number of researches focusing on development of teaching tools to enhance learning result. However, for some courses, they require a novel system to enhance learning environment. This research focuses on geography course in the use of color and contour lines in height differentiation according to altitude terrain. We implement an augmented reality sandbox to be an interaction terrain simulation learning aid by using sandbox, which show the elevation tint and contour line precisely and correctly based on the geography principle. When a user manipulate the height of sand in the box, the system will calculate the color and contour line that respect to the sand automatically. This system allows teacher to have interaction with student to decrease quantitative, but increase qualitative teaching. We conducted a pilot study with several high school students which gave us a good feedback and experimental results. Such results are also shown in this paper to demonstrate our achievement.


title: Customer Purchase Behavior Prediction from Payment Datasets

abstract: With the advances in the development of mobile payments, a huge amount of payment data are collected by banks. User payment data offer a good dataset to depict customer behavior patterns. A comprehensive understanding of customers' purchase behavior is crucial to developing good marketing strategies, which may trigger much greater purchase amounts. For example, by exploring customer behavior patterns, given a target store, a set of potential customers is able to be identified. In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Here we propose a probability graphical model that exploits the payment data to discover customer purchase behavior in the spatial, temporal, payment amount and product category aspects, named STPC-PGM. As a result, the mobility behavior of an individual user could be predicted with a probabilistic graphical model that accounts for all aspects of each customer's relationship with the payment platform. To achieve real time advertising, we then develop an online framework that efficiently computes the prediction results. Our experiment results show that STPC-PGM is effective in discovering customers' profiling features, and outperforms the state-of-the-art methods in purchase behavior prediction. In addition, the prediction results are being deployed in the marketing of real-world credit card users, and have presented a significant growth in the advertising conversion rate.


title: Collecting the internet AS-level topology

abstract: At the inter-domain level, the Internet topology can be represented by a graph with Autonomous Systems (ASes) as nodes and AS peerings as links. This AS-level topology graph has been widely used in a variety of research efforts. Conventionally this topology graph is derived from routing tables collected by Route Views or RIPE RIS. In this work, we assemble the most complete AS-level topology by extending the conventional method along two dimensions. First, in addition to using data from RouteViews and RIPE RIS, we also collect data from many other sources, including route servers, looking glasses, and routing registries. Second, in addition to using routing tables, we also accumulate topological information from routing updates over time. The resulting topology graph on a recent day contains 44% more links and 3% more nodes than that from using RouteViews routing tables alone. Our data collection and topology generation process have been automated, and we publish the latest topology on the web on a daily basis.


title: CQArank: jointly model topics and expertise in community question answering

abstract: Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. Experiments carried out on Stack Overflow data, the largest CQA focused on computer programming, show that our method achieves significant improvement over existing methods on multiple metrics.


title: Differences between self-reported and observed pleasure in depression and schizophrenia

abstract: Observer ratings of positive affect and self-ratings of pleasurable experience were collected daily on six schizophrenic and five depressed inpatients during baseline medication-free periods ranging from 11 to 27 days. Schizophrenics were observed to display significantly higher degrees of positive affect than depressed patients, but they reported significantly lower degrees of experienced pleasure. Depressed patients, conversely, were observed to demonstrate significantly lower degrees of positive affect than schizophrenics, but they reported significantly higher degrees of experienced pleasure. These results have important implications for the further specification of mechanisms underlying abnormalities in pleasure associated with psychiatric disorders.


title: Latent Fingerprint Value Prediction: Crowd-Based Learning

abstract: Latent fingerprints are one of the most crucial sources of evidence in forensic investigations. As such, development of automatic latent fingerprint recognition systems to quickly and accurately identify the suspects is one of the most pressing problems facing fingerprint researchers. One of the first steps in manual latent processing is for a fingerprint examiner to perform a triage by assigning one of the following three values to a query latent: Value for Individualization (VID), Value for Exclusion Only (VEO), or No Value (NV). However, latent value determination by examiners is known to be subjective, resulting in large intra-examiner and inter-examiner variations. Furthermore, in spite of the guidelines available, the underlying bases that examiners implicitly use for value determination are unknown. In this paper, we propose a crowdsourcing based framework for understanding the underlying bases of value assignment by fingerprint examiners, and use it to learn a predictor for quantitative latent value assignment. Experimental results are reported using four latent fingerprint databases, two from forensic casework (NIST SD27 and MSP) and two collected in laboratory settings (WVU and IIITD), and a state-of-the-art latent automated fingerprint identification system (AFIS). The main conclusions of this paper are as follows: 1) crowdsourced latent value is more robust than prevailing value determination (VID, VEO, and NV) and latent fingerprint image quality for predicting AFIS performance; 2) two bases can explain expert value assignments, which can be interpreted in terms of latent features; and 3) our value predictor can rank a collection of latents from most informative to least informative.


title: Virtual reality in scientific visualization

abstract: Immersing the user in the solution, virtual reality reveals the spatially complex structures in computational science in a way that makes them easy to understand and study. But beyond adding a 3D interface, virtual reality also means greater computational complexity.


title: Cellular reprogramming for pancreatic β-cell regeneration: clinical potential of small molecule control

abstract: Recent scientific breakthroughs in stem cell biology suggest that a sustainable treatment approach to cure diabetes mellitus (DM) can be achieved in the near future. However, the transplantation complexities and the difficulty in obtaining the stem cells from adult cells of pancreas, liver, bone morrow and other cells is a major concern. The epoch-making strategy of transcription-factor based cellular reprogramming suggest that these barriers could be overcome, and it is possible to reprogram any cells into functional β cells. Contemporary biological and analytical techniques help us to predict the key transcription factors needed for β-cell regeneration. These β cell-specific transcription factors could be modulated with diverse reprogramming protocols. Among cellular reprogramming strategies, small molecule approach gets proclaimed to have better clinical prospects because it does not involve genetic manipulation. Several small molecules targeting certain epigenetic enzymes and/or signaling pathways have been successful in helping to induce pancreatic β-cell specification. Recently, a synthetic DNA-based small molecule triggered targeted transcriptional activation of pancreas-related genes to suggest the possibility of achieving desired cellular phenotype in a precise mode. Here, we give a brief overview of treating DM by regenerating pancreatic β-cells from various cell sources. Through a comprehensive overview of the available transcription factors, small molecules and reprogramming strategies available for pancreatic β-cell regeneration, this review compiles the current progress made towards the generation of clinically relevant insulin-producing β-cells.


title: Beam-Blocked Channel Estimation for FDD Massive MIMO With Compressed Feedback

abstract: To fully exploit both multiplexing gain and array gain of massive multiple-input–multiple-output (MIMO), the channel state information must be obtained accurately at transmitter side (CSIT). However, conventional channel estimation solutions are not suitable for frequency-division duplexing (FDD) multiuser massive MIMO because of overwhelming pilot and feedback overhead. To reduce the pilot and feedback overhead of channel estimation in FDD systems, we propose a compressive channel estimation scheme for FDD massive MIMO systems in this paper, where the beam-blocked sparsity of massive MIMO channels in beamspace is leveraged. Particularly, we first propose a beam-blocked compressive channel estimation scheme, which can reduce the overhead for downlink training. Then, an optimal block orthogonal matching pursuit algorithm at the BS is proposed to acquire reliable CSIT from the limited number of pilots. Furthermore, an efficient algorithm for channel matrix recovery from separately quantized amplitude and phase of received signals is developed to efficiently decrease feedback load. Simulation results demonstrate that our proposed scheme outperforms conventional solutions.


title: Remote Sensing of Wetlands: Case Studies Comparing Practical Techniques

abstract: Abstract To plan for wetland protection and sensible coastal development, scientists and managers need to monitor the changes in coastal wetlands as the sea level continues to rise and the coastal population keeps expanding. Advances in sensor design and data analysis techniques are making remote sensing systems practical and attractive for monitoring natural and man-induced wetland changes. The objective of this paper is to review and compare wetland remote sensing techniques that are cost-effective and practical and to illustrate their use through two case studies. The results of the case studies show that analysis of satellite and aircraft imagery, combined with on-the-ground observations, allows researchers to effectively determine long-term trends and short-term changes of wetland vegetation and hydrology.


title: A silicon-based, all-passive, 60 GHz, 4-element, phased-array beamformer featuring a differential, reflection-type phase shifter

abstract: This paper presents an all-passive, 4-element, phased-array beamformer based on a differential, reflection-type phase shifter (RTPS) operating in the 60GHz band. The RTPS consists of a differential, vertically-coupled, coupled-line hybrid and variable, parallel-LC, resonant, reflective loads, both of which enable low-loss millimeter-wave operation. The design considerations for a silicon-based implementation of all the beamformer elements are discussed in detail. In particular, the influence of the different RTPS components on its insertion loss is analyzed. The beamformer IC and a breakout of the RTPS are implemented using CMOS-only features of IBM's 8HP 0.13ijm SiGe BiCMOS process, and employ areas of 2.1mm2 and 0.33mm2, respectively, without probe pads. Differential s-parameter measurements at 60GHz show a phase-shift range greater than 150°, insertion losses of 4–6.2dB in the RTPS and 14–16dB in the beamformer, and an isolation better than 35dB between adjacent beamformer channels. Measurements across temperature and process variations are also presented.


title: LINKED OPEN GOVERNMENT DATA AS BACKGROUNDKNOWLEDGE IN PREDICTING FOREST FIRE

abstract: Nowadays with linked open data, we can access numerous data over the world that more easily and semantically. ::: This research focus on technique for accessing linked open government data LOGD from SPARQL ::: Endpoint for resulting time series historical of Forest Fire data. Moreover, the data will automatically uses ::: as background knowledge for predicting the number of forest fire and size of burn area with machine learning. ::: By using this technique, LOGD could be used as an online background knowledge that provide time ::: series data for predicting trend of fire disaster. In evaluation, mean square error MSE and root mean square ::: error RMSE are used to evaluate the performance of prediction in this research. We also compare several ::: algorithm such as Linear Regression, Neural Network and SVM in different window size. ::: Keywords: Linked Open Government Data, Forest Fire Prediction, Time Series Data, Data Mining


title: A Content-Based Phishing Email Detection Method

abstract: Phishing emails have affected users seriously due to the enormous increasing in numbers and exquisite camouflage. Users spend much more effort on distinguishing the email properties, therefore current phishing email detection system demands more creativity and consideration in filtering for users. The proposed research tries to adopt creative computing in detecting phishing emails for users through a combination of computing techniques and social engineering concepts. In order to achieve the proposed target, the fraud type is summarised in social engineering criteria through literature review; a semantic web database is established to extract and store information; a fuzzy logic control algorithm is constructed to allocate email categories. The proposed approach will help users to distinguish the categories of emails, furthermore, to give advice based on different categories allocation. For the purpose of illustrating the approach, a case study will be presented to simulate a phishing email receiving scenario.


title: Stabilization and Tracking Control of Inverted Pendulum Using Fractional Order PID Controllers

abstract: This work focuses on the use of fractional calculus to design robust fractional-order PID (PIλDμ) controller for stabilization and tracking control of inverted pendulum (IP) system. A particle swarm optimisation (PSO) based direct tuning technique is used to design two PIλDμ controllers for IP system without linearizing the actual nonlinear model. The fitness function is minimized by running the SIMULINK model of IP system according to the PSO program in MATLAB. The performance of proposed PIλDμ controllers is compared with two PID controllers. Simulation results are also obtained by adding disturbances to the model to show the robustness of the proposed controllers.


title: Linking Loneliness, Shyness, Smartphone Addiction Symptoms, and Patterns of Smartphone Use to Social Capital

abstract: The purpose of this study is to explore the roles of psychological attributes (such as shyness and loneliness) and smartphone usage patterns in predicting smartphone addiction symptoms and social capital. Data were gathered from a sample of 414 university students using online survey in Mainland China. Results from exploratory factor analysis identified five smartphone addiction symptoms: disregard of harmful consequences, preoccupation, inability to control craving, productivity loss, and feeling anxious and lost, which formed the Smartphone Addiction Scale. Results show that the higher one scored in loneliness and shyness, the higher the likelihood one would be addicted to smartphone. Furthermore, this study shows the most powerful predictor inversely affecting both bonding and bridging social capital was loneliness. Moreover, this study presents clear evidence that the use of smartphones for different purposes (especially for information seeking, sociability, and utility) and the exhibition of different addiction symptoms (such as preoccupation and feeling anxious and lost) significantly impacted social capital building. The significant links between smartphone addiction and smartphone usage, loneliness, and shyness have clear implications for treatment and intervention for parents, educators, and policy makers. Suggestions for future research are discussed.


title: An OpenNaaS Based SDN Framework for Dynamic QoS Control

abstract: Network Service Providers should offer provisioning services with guaranteed Quality of Service (QoS), specifically adapted to the characteristics of the applications running on their network. In this paper we propose the Network Control Layer (NCL), a software framework solution based on Software Defined Networks (SDN), OpenFlow and Network as a Service (NaaS) paradigms. It addresses a major innovation area in the field of network control and management providing on-demand end-to-end network provisioning services with guaranteed QoS, based on the specific requirements of on-top running interactive applications. The NCL implementation is based on the OpenNaaS framework, and it includes mechanisms for network status monitoring and SDN switches configuration based on the interactive applications' QoS network requirements. We demonstrate NCL's utility in the context of control plane models making use of a practical use case.


title: So far away from one's partner, yet so close to romantic alternatives: avoidant attachment, interest in alternatives, and infidelity.

abstract: Temptation pervades modern social life, including the temptation to engage in infidelity. The present investigation examines one factor that may put individuals at a greater risk of being unfaithful to their partner: dispositional avoidant attachment style. The authors hypothesize that avoidantly attached people may be less resistant to temptations for infidelity due to lower levels of commitment in romantic relationships. This hypothesis was confirmed in 8 studies. People with high, vs. low, levels of dispositional avoidant attachment had more permissive attitudes toward infidelity (Study 1), showed attentional bias toward attractive alternative partners (Study 2), expressed greater daily interest in meeting alternatives to their current relationship partner (Study 5), perceived alternatives to their current relationship partner more positively (Study 6), and engaged in more infidelity over time (Studies 3, 4, 7, and 8). This effect was mediated by lower levels of commitment (Studies 5–8). Thus, avoidant attachment predicted a broad spectrum of responses indicative of interest in alternatives and propensity to engage in infidelity, which were mediated by low levels of commitment.


title: A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain

abstract: Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images. These detection and segmentation approaches are reviewed with an importance placed on enlightening the advantages and drawbacks of these methods for brain tumor detection and segmentation. The use of MRI image detection and segmentation in different procedures are also described. Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.


title: A Case Study on Quality Attribute Measurement using MARF and GIPSY

abstract: This literature focuses on doing a comparative analysis between Modular Audio Recognition Framework (MARF) and the General Intentional Programming System (GIPSY) with the help of different software metrics. At first, we understand the general principles, architecture and working of MARF and GIPSY by looking at their frameworks and running them in the Eclipse environment. Then, we study some of the important metrics including a few state of the art metrics and rank them in terms of their usefulness and their influence on the different quality attributes of a software. The quality attributes are viewed and computed with the help of the Logiscope and McCabe IQ tools. These tools perform a comprehensive analysis on the case studies and generate a quality report at the factor level, criteria level and metrics level. In next step, we identify the worst code at each of these levels, extract the worst code and provide recommendations to improve the quality. We implement and test some of the metrics which are ranked as the most useful metrics with a set of test cases in JDeodorant. Finally, we perform an analysis on both MARF and GIPSY by doing a fuzzy code scan using MARFCAT to find the list of weak and vulnerable classes.


title: CENTRAL VALUES OF HECKE L-FUNCTIONS OF CM NUMBER FIELDS

abstract: 0. Introduction. It is well known that the zeta function of CM (complex multiplication) abelian varieties can be given in terms of L-functions of associated Hecke characters. In this paper, we prove a formula expressing the central special value of the L-function of certain Hecke characters in terms of theta functions. The formula easily implies that the central value is nonnegative and yields a criterion for its positivity. Combining this criterion with the work of Arthaud and Rubin, we show that certain CM elliptic curves have Mordell-Weil rank zero over their field of definition. Let F be a totally real number field of degree t and let µ be a quadratic Hecke character of F of conductor f such that (2F , f) = 1. Given a CM extension E of F , we consider the twist χ = χcan ˜ µ of µ by a “canonical” Hecke character χcan of


title: SoC Implementation of VGA Driver using Spartan 3AN Series FPGA

abstract: As the microelectronic industry grows there has been an exponential increase in the importance of FPGAs in modern digital circuit design. FPGAs not only provided an advantage of being reconfigurable but also come in extremely small packaging. In this paper we use the Xilinx Spartan 3AN series FPGA and propose its use as part of a System on Chip controller for a Video Graphics Array (VGA), that serves as one of the most popular display units today.


title: Power Differential based Wide Area Protection

abstract: Abstract Current differential based wide area protection (WAP) has recently been proposed as a technique to increase the reliability of protection systems. It increases system stability and can prevent large contingencies such as cascading outages and blackouts. This paper describes how power differential protection (PDP) can be used within a WAP and shows that the algorithm operates correctly for all types of system faults whilst preventing unwanted tripping, even if the data has been distorted by CT saturation or by data mismatches caused by delays in the WAP data collection system. The PDP algorithm has been simulated and tested on an Iranian 400 kV transmission line under different fault and system operating conditions. The proposed operating logic and the PDP algorithm have also been evaluated using simulation studies based on the Northern Ireland Electricity (NIE) 275 kV network. The results presented illustrate the validity of the proposed protection.


title: Identifying the presence of quokkas (Setonix brachyurus) and other macropods using cytochrome b analyses from faeces

abstract: Non-invasive methods have the potential to circumvent problems associated with using more traditional techniques when surveying for rare and elusive species. In this study, non-invasive molecular-based methods have been used to analyse the scats of several species of marsupials. DNA was successfully extracted from scats of the quokka, Setonix brachyurus, and three other macropods (Macropus fuliginosus, M. irma and M. eugenii) sympatric with the quokka and with similar-appearing scats. Partial sequence from the mitochondrial cytochrome b gene from these four species and seven other macropods was used to measure genetic differentiation among them to determine whether the quokka could be unambiguously identified from the scats alone. The results confirm that molecular approaches can be used for macropod species identification using scats as the source material. The approach will have potential survey and management applications, and, more specifically, may lead to more accurate assessment of the quokka's geographic range, leading to implementation of more appropriate management strategies for its conservation.


title: Demography-based facial retouching detection using subclass supervised sparse autoencoder

abstract: Digital retouching of face images is becoming more widespread due to the introduction of software packages that automate the task. Several researchers have introduced algorithms to detect whether a face image is original or retouched. However, previous work on this topic has not considered whether or how accuracy of retouching detection varies with the demography of face images. In this paper, we introduce a new Multi-Demographic Retouched Faces (MDRF) dataset, which contains images belonging to two genders, male and female, and three ethnicities, Indian, Chinese, and Caucasian. Further, retouched images are created using two different retouching software packages. The second major contribution of this research is a novel semi-supervised autoencoder incorporating "subclass" information to improve classification. The proposed approach outperforms existing state-of-the-art detection algorithms for the task of generalized retouching detection. Experiments conducted with multiple combinations of ethnicities show that accuracy of retouching detection can vary greatly based on the demographics of the training and testing images.


title: Towards A Holistic Analysis of Mobile Payments: A Multiple Perspectives Approach. Electronic Commerce Research and Application

abstract: Abstract As mobile technologies and services are in constant evolution, many speculate on whether or not mobile payments will be a killer application for mobile commerce. To have a better understanding of the market, there is a need to analyze not only the technology but also the different actors that are involved. For this purpose, we propose to conduct two disruption analyses to draw the disruptiveness profile of mobile payment solutions compared to other payment instruments. Then, we try to discover what factors have hindered the technical and commercial development by using a DSS based on a multi-criteria decision making method called Electre I.


title: Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

abstract: In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, and in many online social media platforms. In fact, past scientific work focused on studying these forms in popular media, such as Facebook and Twitter. ::: Building on such work, we present an 8-month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior, at the same time. We propose an incremental and iterative methodology, that utilizes the power of crowdsourcing to annotate a large scale collection of tweets with a set of abuse-related labels. In fact, by applying our methodology including statistical analysis for label merging or elimination, we identify a reduced but robust set of labels. Finally, we offer a first overview and findings of our collected and annotated dataset of 100 thousand tweets, which we make publicly available for further scientific exploration.


title: Technology adoption model and a road map to successful implementation of ITIL

abstract: Purpose – The purpose of this paper is to propose the use of technology adoption model UTAUT as an adoption model of IT governance frameworks, specifically Information Technology and Infrastructure Library (ITIL). The aim is to help the decision maker to better map ITIL processes with business performance, to make a better decision on what processes to implement, and how to implement them. Design/methodology/approach – The paper uses a literature review detailing critical success factors of ITIL implementation. The proposed model was then implemented by applying it to a case study of a company in the United Arab Emirates where the ITIL implementation project failed. Findings – The proposed model gives ITIL the flexibility to work for a wide variety of industries and businesses, but it also introduces many challenges. One of the most apparent challenges is to map ITIL processes to the real world. As a result to analyzing the case study in light of the adoption model, a road map to successful implementation...


title: Deep Neural Networks in Machine Translation: An Overview

abstract: Deep neural networks (DNNs) are widely used in machine translation (MT). This article gives an overview of DNN applications in various aspects of MT.


title: The security of machine learning

abstract: Machine learning's ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine learning systems. We show how these classes influence the costs for the attacker and defender, and we give a formal structure defining their interaction. We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing how it can guide attacks against SpamBayes, a popular statistical spam filter. Finally, we discuss how our taxonomy suggests new lines of defenses.


title: Data selection for speech recognition

abstract: This paper presents a strategy for efficiently selecting informative data from large corpora of transcribed speech. We propose to choose data uniformly according to the distribution of some target speech unit (phoneme, word, character, etc). In our experiment, in contrast to the common belief that "there is no data like more data", we found it possible to select a highly informative subset of data that produces recognition performance comparable to a system that makes use of a much larger amount of data. At the same time, our selection process is efficient and fast.


title: A New Approach to Design Circularly Polarized Beam-Steering Antenna Arrays Without Phase Shift Circuits

abstract: A new approach to generate beam steering for the circularly polarized (CP) antenna array without phase shift circuits is proposed. The proposed approach is a low cost one compared with those with phase shifters. It is found that in spherical coordinate system ( $r$ , $\theta $ , and $\varphi $ ), for any constant $r$ and $\theta $ , as $\varphi $ varies from 0 to $2\pi $ , the phase of the CP antenna’s copolarized far field will progress linearly by $2\pi $ , and the magnitude is constant. If a CP antenna is rotated around its own phase center by certain degrees, there is a phase difference between the copolarized electric far fields generated by the antenna before and after rotation. An $N \times N$ array with such CP antennas elements is analyzed. The array can generate beam steering with different rotation angles of adjacent elements. The beam-steering mechanism is discussed from the orthogonal electric current elements and array perspective. Then, a $4 \times 4$ planar spiral array is designed to verify the mechanism. The array has a good beam-steering characteristic within $X$ -band. At the planes of $\varphi = 0^{\circ }$ , 45°, and 90°, the mainlobe can be scanned from −25° to +24°, −36° to +35°, and −24° to +25°, respectively.


title: Highly Efficient Single-Phase Transformerless Inverters for Grid-Connected Photovoltaic Systems

abstract: Driven by worldwide demand for renewable sources, the photovoltaic market saw in the last years a considerable amount of innovations regarding the construction and operation of inverters connected to the grid. One significant advance, among some that will be here discussed is, for example, the abolition of the galvanic isolation in inverters installed in Germany. There, transformerless topologies, like the H5 and Heric, can reach very high levels of efficiency and allow the best cost-benefit ratio for low-power grid-tied systems. This paper will follow this direction and propose a single-phase transformerless inverter circuit being composed of the association of two step-down converters. Each one modulates a half-wave of the output current, as the correct polarity of the connection to the grid is provided by low-frequency switches. Due to its straightforward design, reduced amount of semiconductors, and simple operation, it is possible to achieve a high level of efficiency and reliability. These and some other characteristics will be benchmarked against other existing circuits, being followed by a theoretical analysis on the properties of the proposed solution. The project of a laboratory prototype will be presented, along with a discussion about the obtained experimental results.


Modelling football match results and the efficiency of fixed-odds betting


title: Real-Time Online Attendance System Based on Fingerprint and GPS in the Smartphone

abstract: Real-time online attendance method is helpful for workers who do a lot of activities outside the office or workers with multi-schedule. The attendance system using online biometric fingerprint system will reduce the problems caused by manual system usage such as lags in data management. The purpose of the research is to constructs an online presence system that combines fingerprint modules and GPS. The ZFM-20 fingerprint module is used as the system's main input as well as a security tool as an entrance to get access to the entire system. GPS module is applied to determine the user's location and sends it to the smartphone. Arduino module in the system will send a text message to the parties concerned about the user's location data automatically. Each module works well and testing the entire system showed the system work reliable according to the initial scenario. The User can access the report using SMS, website, and application on the Android smartphone. The fingerprint sensor can determine the fingerprint stored in the database with an average response time of 1.39 seconds, and GPS can determine latitude and longitude with an average error of 0.007352% and 0.0003% respectively.


title: An Initial Attempt of Combining Visual Selective Attention with Deep Reinforcement Learning

abstract: Visual attention serves as a means of feature selection mechanism in the perceptual system. Motivated by Broadbent's leaky filter model of selective attention, we evaluate how such mechanism could be implemented and affect the learning process of deep reinforcement learning. We visualize and analyze the feature maps of DQN on a toy problem Catch, and propose an approach to combine visual selective attention with deep reinforcement learning. We experiment with optical flow-based attention and A2C on Atari games. Experiment results show that visual selective attention could lead to improvements in terms of sample efficiency on tested games. An intriguing relation between attention and batch normalization is also discovered.


title: Dermatoscopy of genital warts

abstract: Background Genital warts may mimic a variety of conditions, thus complicating their diagnosis and treatment. The recognition of early flat lesions presents a diagnostic challenge. Objective We sought to describe the dermatoscopic features of genital warts, unveiling the possibility of their diagnosis by dermatoscopy. Methods Dermatoscopic patterns of 61 genital warts from 48 consecutively enrolled male patients were identified with their frequencies being used as main outcome measures. Results The lesions were examined dermatoscopically and further classified according to their dermatoscopic pattern. The most frequent finding was an unspecific pattern, which was found in 15/61 (24.6%) lesions; a fingerlike pattern was observed in 7 (11.5%), a mosaic pattern in 6 (9.8%), and a knoblike pattern in 3 (4.9%) cases. In almost half of the lesions, pattern combinations were seen, of which a fingerlike/knoblike pattern was the most common, observed in 11/61 (18.0%) cases. Among the vascular features, glomerular, hairpin/dotted, and glomerular/dotted vessels were the most frequent finding seen in 22 (36.0%), 15 (24.6%), and 10 (16.4%) of the 61 cases, respectively. In 10 (16.4%) lesions no vessels were detected. Hairpin vessels were more often seen in fingerlike (χ 2 = 39.31, P = .000) and glomerular/dotted vessels in knoblike/mosaic (χ 2 = 9.97, P = .008) pattern zones; vessels were frequently missing in unspecified (χ 2 = 8.54, P = .014) areas. Limitations Only male patients were examined. Conclusions There is a correlation between dermatoscopic patterns and vascular features reflecting the life stages of genital warts; dermatoscopy may be useful in the diagnosis of early-stage lesions.


title: The safety critical electric machines and drives in the more electric aircraft: A survey

abstract: In order to improve aircraft efficiency, reliability and maintainability, the aerospace world has found in the progressive electrification of on-board services the way to reduce or to remove the presence of the hydraulic, mechanical and the bleed air/pneumatic systems. This concept is called More Electric Aircraft (MEA), which involves the introduction of the Electromechanical actuators (EMAs) and the electro-hydraulics actuators (EHAs) for the actuation of the flight surfaces of wide-body aircraft, moving from the “fly-by wire” to the “power-by wire” concept. The resulting step change in aircraft electrical loading will have far reaching implications for the electrical generation system. Considerable effort is being directed towards realizing the so-called More Electric Engine (MEE), which foresee an integration of the electrical generator directly inside the main gas turbine engine. Also the entire electrical distribution system is subject to radical revisiting, with a trend which is leaving the constant frequency AC energy distribution in favor of variable frequency or DC solutions. Hence, it is evident the MEA trend increments the technical challenges and research topics for the electrical engineering in the aeronautic applications. The aim of the paper is the presentation, from the electrical engineering point of view, of some of the most challenging application of electric machines and drives in the incoming new aircraft generation. There are too many on board electric components and systems to be analyzed: the authors centre the attention on the most safety critical drives: flight surface actuators, fuel pumps and generators.


title: Reasoning about Actions and State Changes by Injecting Commonsense Knowledge

abstract: Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. Although several recent systems have shown impressive progress in this task, their predictions can be globally inconsistent or highly improbable. In this paper, we show how the predicted effects of actions in the context of a paragraph can be improved in two ways: (1) by incorporating global, commonsense constraints (e.g., a non-existent entity cannot be destroyed), and (2) by biasing reading with preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier methods, we treat the problem as a neural structured prediction task, allowing hard and soft constraints to steer the model away from unlikely predictions. We show that the new model significantly outperforms earlier systems on a benchmark dataset for procedural text comprehension (+8% relative gain), and that it also avoids some of the nonsensical predictions that earlier systems make.


title: A data-supported history of bioinformatics tools

abstract: Since the advent of next-generation sequencing in the early 2000s, the volume of bioinformatics software tools and databases has exploded and continues to grow rapidly. Documenting this evolution on a global and time-dependent scale is a challenging task, limited by the scarcity of comprehensive tool repositories. We collected data from over ~23,000 references classified in the OMICtools database, spanning the last 26 years of bioinformatics to present a data-supported snapshot of bioinformatics software tool evolution and the current status, to shed light on future directions and opportunities in this field. The present review explores new aspects of computational biology, including country partnerships, trends in technologies and area of development, research and development (R&D) investments and coding languages. This is the most comprehensive systematic overview of the field to date and provides the community with insights and knowledge on the direction of the development and evolution of bioinformatics software tools, highlighting the increasing complexity of analysis.


title: Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel

abstract: We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different datasets and compare its performance with the current state-of-the-art techniques for emotion detection, including a recursive auto-encoder. The results of the experimental study suggest that RBEM-Emo is a promising approach advancing the current state-of-the-art in emotion detection.


Error-Correcting Output Coding for Text Classification


title: NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations

abstract: As interest in adopting Cloud computing for various applications is rapidly growing, it is important to understand how these applications and systems will perform when deployed on Clouds. Due to the scale and complexity of shared resources, it is often hard to analyze the performance of new scheduling and provisioning algorithms on actual Cloud test beds. Therefore, simulation tools are becoming more and more important in the evaluation of the Cloud computing model. Simulation tools allow researchers to rapidly evaluate the efficiency, performance and reliability of their new algorithms on a large heterogeneous Cloud infrastructure. However, current solutions lack either advanced application models such as message passing applications and workflows or scalable network model of data center. To fill this gap, we have extended a popular Cloud simulator (CloudSim) with a scalable network and generalized application model, which allows more accurate evaluation of scheduling and resource provisioning policies to optimize the performance of a Cloud infrastructure.


title: D.C. Drive System With the Insulated Gate Bipolar Transistors

abstract: A reversible D.C. drive system, which employs a four-quadrant chopper with the insulated gate bipolar transistors (IGBT), is presented. The chopper circuit employs a "half-bridge" configuration. The operation of the converter was tested at a command frequency of [1÷10] kHz. Except at very high power levels, the four-quadrant chopper has certain advantages such as high operational frequency, smooth and linear control, high efficiency and fast response.


title: Ultra-Wideband Printed Slot Antenna With Graded Index Superstrate

abstract: An ultra-wideband (UWB) compact slot antenna with a directional radiation pattern is presented in this communication. The concept is based on dielectric loaded multi-element slot antennas. Wide bandwidth operation is achieved using a driven wide slot antenna fed via an off-centered microstrip line capable of creating a fictitious short along the slot and a number of parasitic antenna elements. The proposed slot antenna uses a graded index superstrate with tapered dielectric constants, from high index to low index , in order to further improve the bandwidth and achieve directional radiation pattern. The superstrate dimensions are carefully chosen so that a dielectric resonator mode is excited to provide radiation at the lowest frequency. A sensitivity study is carried out to optimize the geometric parameters of the slot antennas and the graded index superstrate in order to achieve the maximum bandwidth as well as an unidirectional and frequency invariant radiation pattern. Through this optimization, a compact antenna is designed, fabricated, and tested to show a VSWR value of lower than 2.5 across a 2.9:1 frequency range whose dimensions are 0.27 λ×0.2 λ×0.068 λ at the lowest frequency of operation.


title: Speed closed-loop drive system of permanent magnet synchronous motor using low-count QEP

abstract: A speed closed-loop drive system of permanent magnet synchronous motor (PMSM) typically requires the rotor position feedback from the high-count quadrature encoder pulse (QEP) sensor equipped with the motor. Unlike the brushless dc motor, the high resolution of rotor position is necessary in PMSM drive because the instantaneous rotor position is required for its field orientation on the synchronously rotating dq-axis coordinate. In addition, the bandwidth of the calculated speed depends on the resolution of measured rotor position as well. Unfortunately, the resolution of the QEP (e.g., 1000–2000 pulse/rev) heavily affects the overall cost. In low cost applications, the PMSM drive using the high-count QEP is therefore impossible. This paper proposes the rotor position extrapolation using the polynomial estimator from the low-count QEP and implement in the complete speed closed-loop drive system of PMSM. Simulation results using MATLAB/SIMULINK are presented to initially validate the proposed algorithm in the speed closed-loop drive system. According to these results, the improved speed control in a high speed range can be observed when using low-count QEP of 3 and 6 pulse/rev. However, in the low speed range the speed control still requires the higher count QEP. Finally, the experimental results are included and discussed to fully validate the overall system.


title: Freudbot: An Investigation of Chatbot Technology in Distance Education

abstract: A chatbot named Freudbot was constructed using the open source architecture of AIML to determine if a famous person application of chatbot technology could improve student-content interaction in distance education. Fifty-three students in psychology completed a study in which they chatted with Freudbot over the web for 10 minutes under one of two instructional sets. They then completed a questionnaire to provide information about their experience and demographic variables. The results from the questionnaire indicated a neutral evaluation of the chat experience although participants positively endorsed the expansion of chatbot technology and provided clear direction for future development and improvement. A basic analysis of the chatlogs indicated a high proportion of on-task behaviour. There was no effect of instructional set. Altogether, the findings indicate that famous person applications of chatbot technology may be promising as a teaching and learning tool in distance and online education. Chatbots are agents programmed to mimic human conversationalists. The first and still quite successful chatbot was ELIZA (Weizenbaum, 1966), a computer program designed to emulate a Rogerian therapist, a type of self-directed therapy where the patient’s discourse is redirected back to the patient by the therapist usually in the form of a question. “Its name was chosen to emphasize that it may be incrementally improved by its users, since its language abilities may be continually improved by a "teacher". Like the ELIZA of Pygmalion fame, it can be made to appear even more civilized, the relation of appearance to reality, however, remaining in the domain of the playwright.” (Weizenbaum, 1966, p.2) The playwright in this case is the programmer but instead of classic Artificial Intelligence, ELIZA was programmed with rules to give the illusion of understanding. Essentially, ELIZA was programmed to recognize keywords and choose an appropriate transformation based on the immediate linguist context. Weizenbaum used the term ‘script’ to refer to the collection of keywords and associated transformation rules. Even though ELIZA is easily exposed as a fraud in the Turing sense, the popularity of the Rogerian therapist script remains high and there are a number of sites that allow you access to ELIZA. It is interesting to note that of all the scripts planned and developed by Weisenbaum, the Rogerian therapist script was the most enduring. Arguably the most successful chatbot today is ALICE (Artificial Linguistic Internet Chat Entity), 3 time winner of the Loebner Prize, the holy grail for chatbots. ALICE was written by Richard Wallace and although no chatbot has passed the Turing test in the Loebner competition, ALICE has been judged the most human-like chatbot in 2000, 2001, and 2004. Like ELIZA, ALICE has no true understanding and is programmed to recognize templates and respond with patterns according to the context. Moreover, like ELIZA, ALICE is incrementally improved with the addition of new responses. Unlike ELIZA, ALICE is programmed to talk to people on the web for as long as possible on any topic. Compared to the ELIZA’s knowledge of 200 keywords and rules, ALICE is embodied by approximately 41,000 templates and associated patterns. Perhaps the most important difference between ALICE and ELIZA is that ALICE is written in AIML (Artificial Intelligence Markup Language), an XML-based open source language with a reasonably active development community. There are also a variety of AIML parsers available written in Java, Perl, PHP, and C++ that permit interaction through a variety of interfaces, from simple web pages to Flash-based (or other) animation, instant messaging, and even voice input/output. In addition, Pandorabots, a web service that promotes and supports the use of ALICE and AIML is reporting support for over 20,000 chatbots on their site (http://www.pandorabots.com). At Pandorabots, would-be botmasters can easily create their own chatbot by modifying the personality of ALICE or by starting from scratch. An AIML chatbot is suitable for many educational applications but our interest was in the famous personality application. Specifically, we were interested in whether students would enjoy and benefit from chatting with famous historical figures in psychology. As a distance education provider, we are always looking for ways to improve the interaction between student and course content over the web. Chatting with an historical figure via the internet may be intrinsically more interesting than the same information presented in a standard third party format over the web. In terms of a theoretical rationale, there are several bases for investigating a famous personality application of chatbot technology as learning tool in distance education. Social constructionist theories of learning emphasize collaboration and conversation as a natural and effective means of knowledge construction and elaboration. The work of Graesser and colleagues on AutoTutor is based largely on these theories (see Graesser,Wiemer-Hastings, Wiemer-Hastings, Kreuz, & Tutoring Research Group 1999). A second rationale is found in the work of Cassell and colleagues on Embodied Conversational Agents (ECA). Cassell indicates that motivation for their research is based on the primacy of conversation as a natural skill learned early and effortlessly in life (Cassell, Bickmore, Campbell, Vilhjalmsson, & Yan, 2000). A conversational interface to a famous psychologist should be engaging and intuitive. A third rationale is provided through cognitive resource theory that argues linguistic rules governing conversational exchanges are automatic in nature due to frequency of use and consequently, free up additional resources to devote to encoding, understanding, and learning. Finally, according to the media equation (Reeves & Nass, 1996), people are predisposed to treat computers, television and other instances of media as people. They describe a number of experimental studies that generally show no differences in how media is ‘treated’ in comparison to people. The social rules that govern human-human interactions appear to govern human-computer interactions as well. If this is the case, then people may be predisposed to interact with a famous person application on the computer given the close fit of the application to human and conversational characteristics.


title: The interdisciplinary study of coordination

abstract: This survey characterizes an emerging research area, sometimes called coordination theory , that focuses on the interdisciplinary study of coordination. Research in this area uses and extends ideas about coordination from disciplines such as computer science, organization theory, operations research, economics, linguistics, and psychology. A key insight of the framework presented here is that coordination can be seen as the process of managing dependencies among activities. Further progress, therefore, should be possible by characterizing different kinds of dependencies and identifying the coordination processes that can be used to manage them. A variety of processes are analyzed from this perspective, and commonalities across disciplines are identified. Processes analyzed include those for managing shared resources, producer/consumer relationships, simultaneity constraints , and task/subtask dependencies . Section 3 summarizes ways of applying a coordination perspective in three different domains:(1) understanding the effects of information technology on human organizations and markets, (2) designing cooperative work tools, and (3) designing distributed and parallel computer systems. In the final section, elements of a research agenda in this new area are briefly outlined.


title: Predicting outcome of cricket matches using classification learners

abstract: Cricket is an outdoor sport played between two teams of eleven players each. It is one of the most followed game, with a world of riches on offer. The goal is to predict the outcome of a cricket match and improve upon the currently reported accuracy of the learners. The paper found feature engineering and attribute selection plays a significant part in improving the accuracy of the classifiers. Following learners: k-NN, Naive Bayes and Random forest classifiers were used. Each of the models recorded major performance improvement, with Random forest performing best with an accuracy of 84 %, closely followed by Naive Bayes at 83% and k-NN at 82%.


title: Class redundancy for face recognition by SVM

abstract: We propose to describe a new SVM-based method of face recognition, with RBF kernels, relying on class redundancy. The images extracted from the video flow are formatted then reduced by a PCA technique. The built vectors are used in the training phase to set up the SVMs. Some image pre-processing steps are applied in order to improve the results. We present results, in the form of a set of curves, that allow the quality of our approach to be assessed.


title: The pursuit of citizens' privacy: a privacy-aware smart city is possible

abstract: Cities are growing steadily, and the process of urbanization is a common trend in the world. Although cities are getting bigger, they are not necessarily getting better. With the aim to provide citizens with a better place to live, a new concept of a city was born: the smart city. The real meaning of smart city is not strictly defined, but it has gained much attention, and many cities are taking action in order to be considered 'smart'. These smart cities, founded on the use of information and communication technologies, aim at tackling many local problems, from local economy and transportation to quality of life and e-governance. Although technology helps to solve many of these local problems, their ability to gather unprecedented amounts of information could endanger the privacy of citizens. In this article we identify a number of privacy breaches that can appear within the context of smart cities and their services. We leverage some concepts of previously defined privacy models and define the concept of citizens' privacy as a model with five dimensions: identity privacy, query privacy, location privacy, footprint privacy and owner privacy. By means of several examples of smart city services, we define each privacy dimension and show how existing privacy enhancing technologies could be used to preserve citizens' privacy.


title: An Empirical Comparison of Any-Angle Path-Planning Algorithms.

abstract: We compare five any-angle path-planning algorithms, Theta*, Block A*, Field D*, ANYA, and Any-Angle Subgoal Graphs in terms of solution quality and runtime. Any-angle path-planning is a fairly new research area, and no direct comparison exists between these algorithms. We implement each algorithm from scratch and use similar implementations to provide a fair comparison.


title: Antecedent- and Response-Focused Emotion Regulation: Divergent Consequences for Experience, Expression, and Physiology

abstract: Using a process model of emotion, a distinction between antecedent-focused and response-focused emotion regulation is proposed. To test this distinction, 120 participants were shown a disgusting film while their experiential, behavioral, and physiological responses were recorded. Participants were told to either (a) think about the film in such a way that they would feel nothing (reappraisal, a form of antecedent-focused emotion regulation), (b) behave in such a way that someone watching them would not know they were feeling anything (suppression, a form of response-focused emotion regulation), or (c) watch the film (a control condition). Compared with the control condition, both reappraisal and suppression were effective in reducing emotion-expressive behavior. However, reappraisal decreased disgust experience, whereas suppression increased sympathetic activation. These results suggest that these 2 emotion regulatory processes may have different adaptive consequences. What happens when we get so angry with an erratic driver that we feel like yelling at him, yet we do not? Or when we feel down but want to be in good spirits for a party? Or when we find something outrageously funny but need to stifle our laughter during a formal ceremony? At times such as these, we regulate our emotions. That is, we attempt to influence which


title: Equivalence class transformation based mining of frequent itemsets from uncertain data

abstract: Numerous frequent itemset mining algorithms have been proposed over the past two decades. Most of them mine traditional databases of precise data. However, there are many real-life applications for which data are uncertain. This leads to the mining of uncertain data. In this paper, we propose an equivalence class transformation based algorithm---called UV-Eclat---which transforms probabilistic databases of uncertain data from their usual horizontal format into a vertical format, from which frequent itemsets are mined.


title: Approximate logic synthesis for error tolerant applications

abstract: Error tolerance formally captures the notion that -- for a wide variety of applications including audio, video, graphics, and wireless communications -- a defective chip that produces erroneous values at its outputs may be acceptable, provided the errors are of certain types and their severities are within application-specified thresholds. All previous research on error tolerance has focused on identifying such defective but acceptable chips during post-fabrication testing to improve yield. In this paper, we explore a completely new approach to exploit error tolerance based on the following observation: If certain deviations from the nominal output values are acceptable, then we can exploit this flexibility during circuit design to reduce circuit area and delay as well as to increase yield. The specific metric of error tolerance we focus on is error rate, i.e., how often the circuit produces erroneous outputs. We propose a new logic synthesis approach for the new problem of identifying how to exploit a given error rate threshold to maximally reduce the area of the synthesized circuit. Experiment results show that for an error rate threshold within 1%, our approach provides 9.43% literal reductions on average for all the benchmarks that we target.


title: Characterizing Piezoelectric Cantilevers for Vibration Energy Harvesting under Ambient Conditions

abstract: We report on measurement and modeling of dynamic energy harvesters based on oscillating piezoelectric cantilevers, along with careful calibration of energy conversion properties of such devices in their dynamic responses. We employ thin-film lead zirconate titanate (PZT)-based cantilevers fabricated by laser micromachining, with efficient proof masses enabled by a heavy alloy with a low melting temperature (65°C) for tuning frequency and damping. By measuring devices with different circuit parameters, and analyzing the energy conversion in time-domain oscillations, we show a model that quantitatively reveals the effects of the loading circuit for energy harvesting. We also show the effects of device dimensions on their vibrations and converted voltage output waveforms. In harvesting vibrational energy through cycles of oscillations (in 80Hz-1kHz devices), energy conversion efficiency as high as 25% has been attained.


title: A 2-phase labeling and choice architecture intervention to improve healthy food and beverage choices.

abstract: OBJECTIVES ::: We assessed whether a 2-phase labeling and choice architecture intervention would increase sales of healthy food and beverages in a large hospital cafeteria. ::: ::: ::: METHODS ::: Phase 1 was a 3-month color-coded labeling intervention (red = unhealthy, yellow = less healthy, green = healthy). Phase 2 added a 3-month choice architecture intervention that increased the visibility and convenience of some green items. We compared relative changes in 3-month sales from baseline to phase 1 and from phase 1 to phase 2. ::: ::: ::: RESULTS ::: At baseline (977,793 items, including 199,513 beverages), 24.9% of sales were red and 42.2% were green. Sales of red items decreased in both phases (P < .001), and green items increased in phase 1 (P < .001). The largest changes occurred among beverages. Red beverages decreased 16.5% during phase 1 (P < .001) and further decreased 11.4% in phase 2 (P < .001). Green beverages increased 9.6% in phase 1 (P < .001) and further increased 4.0% in phase 2 (P < .001). Bottled water increased 25.8% during phase 2 (P < .001) but did not increase at 2 on-site comparison cafeterias (P < .001). ::: ::: ::: CONCLUSIONS ::: A color-coded labeling intervention improved sales of healthy items and was enhanced by a choice architecture intervention.


title: Estimation of Current and Sag in Overhead Power Transmission Lines With Optimized Magnetic Field Sensor Array Placement

abstract: Power distribution mechanism in smart grid necessitates the development of an easy-to-install and contactless sensing system to monitor the operational state of overhead high-voltage transmission lines. Here, we propose a robust phase current and sag estimation method at support structures. Novelty in our work is the use of dual-axis magnetic field (MF) sensors equal to the number of phase conductors. This is realized by installing an array of sensors optimally placed in the same vertical plane as of conductors on the tower. The optimal position of sensor array was found while minimizing the condition number of governing linear system close to unity. For any circuit configuration, our method processes the sensed MF vector projections through a linear system, which is based on the Biot–Savart law. It considers the practical factors, such as sag, span length, and sensor-to-conductor distance. An algorithm is then designed to estimate the electric current and sag by iterative comparison between the measured and calculated MF. The method is first tested by numerical simulations for a typical one-circuit configuration, which involves three scenarios of symmetrical and unsymmetrical sag in conductors. The algorithm converges to a maximum error of $\le 1$ % within 300 iterations. We then experimentally verify our scheme on a scaled laboratory setup. Retrieved current and sag values were verified with the readings from ammeter and vernier caliper, respectively. The results prove the viability of our approach within $\le 2.6$ % deviation for current and $\le 1$ % for sag in all conductors.


title: Detecting correlated columns in relational databases with mixed data types

abstract: In a database, besides known dependencies among columns (e.g., foreign key and primary key constraints), there are many other correlations unknown to the database users. Extraction of such hidden correlations is known to be useful for various tasks in database optimization and data analytics. However, the task is challenging due to the lack of measures to quantify column correlations. Correlations may exist among columns of different data types and value domains, which makes techniques based on value matching inapplicable. Besides, a column may have multiple semantics, which does not allow disjoint partitioning of columns. Finally, from a computational perspective, one has to consider a huge search space that grows exponentially with the number of columns. In this paper, we present a novel method for detecting column correlations (DeCoRel). It aims at discovering overlapping groups of correlated columns with mixed data types in relational databases. To handle the heterogeneity of data types, we propose a new correlation measure that combines the good features of Shannon entropy and cumulative entropy. To address the huge search space, we introduce an efficient algorithm for the column grouping. Compared to state of the art techniques, we show our method to be more general than one of the most recent approaches in the database literature. Experiments reveal that our method achieves both higher quality and better scalability than existing techniques.


title: Sensitivity of the Earthquake Response of Tall Steel Moment Frame Buildings to Ground Motion Features

abstract: The seismic response of two tall steel moment frame buildings and their variants is explored through parametric nonlinear analysis using idealized sawtooth-like ground velocity waveforms, with a characteristic period (T), amplitude (peak ground velocity, PGV), and duration (number of cycles, N). Collapse-level response is induced only by long-period, moderate to large PGV ground excitation. This agrees well with a simple energy balance analysis. The collapse initiation regime expands to lower ground motion periods and amplitudes with increasing number of ground motion cycles.


title: Deep Cross Residual Learning for Multitask Visual Recognition

abstract: Residual learning has recently surfaced as an effective means of constructing very deep neural networks for object recognition. However, current incarnations of residual networks do not allow for the modeling and integration of complex relations between closely coupled recognition tasks or across domains. Such problems are often encountered in multimedia applications involving large-scale content recognition. We propose a novel extension of residual learning for deep networks that enables intuitive learning across multiple related tasks using cross-connections called cross-residuals. These cross-residuals connections can be viewed as a form of in-network regularization and enables greater network generalization. We show how cross-residual learning (CRL) can be integrated in multitask networks to jointly train and detect visual concepts across several tasks. We present a single multitask cross-residual network with >40% less parameters that is able to achieve competitive, or even better, detection performance on a visual sentiment concept detection problem normally requiring multiple specialized single-task networks. The resulting multitask cross-residual network also achieves better detection performance by about 10.4% over a standard multitask residual network without cross-residuals with even a small amount of cross-task weighting.


title: Efficient temporal mining of micro-blog texts and its application to event discovery

abstract: In this paper we present a novel method for clustering words in micro-blogs, based on the similarity of the related temporal series. Our technique, named SAX*, uses the Symbolic Aggregate ApproXimation algorithm to discretize the temporal series of terms into a small set of levels, leading to a string for each. We then define a subset of "interesting" strings, i.e. those representing patterns of collective attention. Sliding temporal windows are used to detect co-occurring clusters of tokens with the same or similar string. To assess the performance of the method we first tune the model parameters on a 2-month 1 % Twitter stream, during which a number of world-wide events of differing type and duration (sports, politics, disasters, health, and celebrities) occurred. Then, we evaluate the quality of all discovered events in a 1-year stream, "googling" with the most frequent cluster n-grams and manually assessing how many clusters correspond to published news in the same temporal slot. Finally, we perform a complexity evaluation and we compare SAX* with three alternative methods for event discovery. Our evaluation shows that SAX* is at least one order of magnitude less complex than other temporal and non-temporal approaches to micro-blog clustering.


title: Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks

abstract: Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big data analytics and machine learning, this work contributes in three ways. First, we utilize the call detail records (CDR) data to detect anomalies in the network. For authentication and verification of anomalies, we use k-means clustering, an unsupervised machine learning algorithm. Through effective detection of anomalies, we can proceed to suitable design for resource distribution as well as fault detection and avoidance. Second, we prepare anomaly-free data by removing anomalous activities and train a neural network model. By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data. Lastly, we use an autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Through simple visualization, we show that anomaly free data better generalizes the learning models and performs better on prediction task.


title: Dual band printed MIMO antennas for 5G handsets

abstract: In this article, a compact MIMO dual-band (2.6/3.6 GHz) antenna array for future 5G handsets is presented. The antenna element has a compact with size of 10.5×14.5 mm2. The proposed antenna array elements are symmetrically placed along the long edges of the smartphone, and they are composed of four same antenna types. The reference element antenna consists of U Shape structure and a connected meandered monopole. The simulated result shows that the presented antenna has dual-band at 2.5–2.7 GHz and 3.4–3.82 GHz, which has a bandwidth of 200 MHz and 400 MHz respectively for reflection coefficient less than −6dB. The port isolation is more than 11.5 dB across the whole band of interest without using any decoupling network for two antenna structure.


title: Wildest Faces: Face Detection and Recognition in Violent Settings

abstract: With the introduction of large-scale datasets and deep learning models capable of learning complex representations, impressive advances have emerged in face detection and recognition tasks. Despite such advances, existing datasets do not capture the difficulty of face recognition in the wildest scenarios, such as hostile disputes or fights. Furthermore, existing datasets do not represent completely unconstrained cases of low resolution, high blur and large pose/occlusion variances. To this end, we introduce the Wildest Faces dataset, which focuses on such adverse effects through violent scenes. The dataset consists of an extensive set of violent scenes of celebrities from movies. Our experimental results demonstrate that state-of-the-art techniques are not well-suited for violent scenes, and therefore, Wildest Faces is likely to stir further interest in face detection and recognition research.


title: Superintelligence cannot be contained: Lessons from Computability Theory

abstract: Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potential catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that such containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) infeasible.


title: Australia: an under performing knowledge nation?

abstract: Understanding the concept of a knowledge‐based economy; having a vision to make a new knowledge economy competitive; and creating and implementing a strategy to achieve that vision, would all seem to be fundamental steps for any economy striving to compete in the new global knowledge marketplace. While such issues may appear obvious, how many economies have actually responded effectively to these challenges? This paper first examines the concept of a knowledge‐based economy and then evaluates the relative performance of Australia across a profile of key knowledge‐based performance indicators. Finally, the paper addresses some key challenges facing Australia, and most other OECD economies, as they try to compete in this knowledge race in the new millennium.


title: CenWits: a sensor-based loosely coupled search and rescue system using witnesses

abstract: This paper describes the design, implementation and evaluation of a search and rescue system called CenWits. CenWits uses several small, commonly-available RF-based sensors, and a small number of storage and processing devices. It is designed for search and rescue of people in emergency situations in wilderness areas. A key feature of CenWits is that it does not require a continuously connected sensor network for its operation. It is designed for an intermittently connected network that provides only occasional connectivity. It makes a judicious use of the combined storage capability of sensors to filter, organize and store important information, combined battery power of sensors to ensure that the system remains operational for longer time periods, and intermittent network connectivity to propagate information to a processing center. A prototype of CenWits has been implemented using Berkeley Mica2 motes. The paper describes this implementation and reports on the performance measured from it.


title: Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

abstract: Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. The generation of long, informative, coherent, and diverse responses remains a hard task. In this work, we focus on the single turn setting. We add self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.


title: An Introduction to Social Semantic Web Mining & Big Data Analytics for Political Attitudes and Mentalities Research

abstract: The social web has become a major repository of social and behavioral data that is of exceptional interest to the social science and humanities research community. Computer science has only recently developed various technologies and techniques that allow for harvesting, organizing and analyzing such data and provide knowledge and insights into the structure and behavior or people on-line. Some of these techniques include social web mining, conceptual and social network analysis and modeling, tag clouds, topic maps, folksonomies, complex network visualizations, modeling of processes on networks, agent based models of social network emergence, speech recognition, computer vision, natural language processing, opinion mining and sentiment analysis, recommender systems, user profiling and semantic wikis. All of these techniques are briefly introduced, example studies are given and ideas as well as possible directions in the field of political attitudes and mentalities are given. In the end challenges for future studies are discussed.


A Bi-directional Visualization Pipeline that Enables Visual to Parametric Interation ( V 2 PI )


title: The Effect of Early Childhood Developmental Program Attendance on Future School Enrollment and Grade Progression in Rural North India

abstract: This paper examines the effect of prior participation in early childhood developmental programs, considered endogenous, upon 7-19 years olds' school enrollment and grade progression in rural North India. It hopes both to extend to less developed countries recent influential research on the long-term benefits of early childhood interventions in the United States, and to make a case for the inclusion of such interventions amongst developing nations’ policy initiatives toward expanding schooling. Analysis of data from the World Bank's 1997-98 Survey of Living Conditions in Uttar Pradesh and Bihar yields the findings that early childhood developmental program attendance at ages 0-6 raises the probability of school enrollment among average 7-19 year olds by 31 percentage points, and that this beneficial early experience also significantly hastens students’ grade progression.


Tracking of Police Patrol


Detection of copy-move forgery in digital images


title: The Human Microbiome Project

abstract: A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.


title: An Unequal Wilkinson Power Divider for a Frequency and Its First Harmonic

abstract: This letter presents a Wilkinson power divider operating at a frequency and its first harmonic with unequal power divider ratio. To obtain the unequal property, four groups of 1/6 wavelength transmission lines with different characteristic impedances are needed to match all ports. Theoretically, closed-form equations for the design are derived based on transmission line theory. Experimental results have indicated that all the features of this novel power divider can be fulfilled at f 0 and 2f 0 simultaneously.


title: New Multilevel Converters With Coupled Inductors: Properties and Control

abstract: In the paper, a new five-level (5L) converter is presented. It is called 5L-active-neutral-point-clamped with coupled-inductor (5L-ANPC-CI) and consists of a three-level bidirectional (3L-B) commutation cell connected to two parallel basic commutation cells. The proposed topology can be seen as two cascaded current stages controlled at different frequencies. The high-current stage is specific to the 3L-B cell controlled at low-frequency, while the low-current stage corresponds to the parallel cells with coupled-inductor controlled at high-frequency (HF). Due to this advantage, the new converter appears as a useful solution to increase the output current, while the switched current through the HF power devices is reduced. The operation principle of the new structure is verified by simulations both for single- and for three-phase topologies. Experimental results for single-phase 5L-ANPC-CI converter on a low-voltage test bench are also provided to validate the proper operation of this concept.


title: Clustering Uncertain Data Based on Probability Distribution Similarity

abstract: Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. The previous methods extend traditional partitioning clustering methods like $(k)$-means and density-based clustering methods like DBSCAN to uncertain data, thus rely on geometric distances between objects. Such methods cannot handle uncertain objects that are geometrically indistinguishable, such as products with the same mean but very different variances in customer ratings. Surprisingly, probability distributions, which are essential characteristics of uncertain objects, have not been considered in measuring similarity between uncertain objects. In this paper, we systematically model uncertain objects in both continuous and discrete domains, where an uncertain object is modeled as a continuous and discrete random variable, respectively. We use the well-known Kullback-Leibler divergence to measure similarity between uncertain objects in both the continuous and discrete cases, and integrate it into partitioning and density-based clustering methods to cluster uncertain objects. Nevertheless, a naive implementation is very costly. Particularly, computing exact KL divergence in the continuous case is very costly or even infeasible. To tackle the problem, we estimate KL divergence in the continuous case by kernel density estimation and employ the fast Gauss transform technique to further speed up the computation. Our extensive experiment results verify the effectiveness, efficiency, and scalability of our approaches.


title: Underwater optical communications systems. Part 2: basic design considerations

abstract: Acoustic systems may provide suitable underwater communications because sound propagates well in water. However, the maximum data transmission rates of these systems in shallow littoral waters are ~10 kilobits per second (kbps) which may be achieved only at ranges of less than 100 m. Although underwater (u/w) wireless optical communications systems can have even shorter ranges due to greater attenuation of light propagating through water, they may provide higher bandwidth (up to several hundred kbps) communications as well as covertness. To exploit these potential advantages, we consider the basic design issues for u/w optical communications systems in this paper. In addition to the basic physics of u/w optical communications with environmental noise, we consider system performance with some state-of-the-art commercial off-the-shelf (COTS) components, which have promise for placing u/w optical communications systems in a small package with low power consumption and weight. We discuss light sources which show promise for u/w optical transmitters such as laser diodes (LDs) and light emitting diodes (LEDs). Laser diodes with their output frequency shifted into the 500- to 650-nm range can emit more energy per pulse than LEDs but are more expensive. Currently, LEDs emit substantial amounts of light and are typically very inexpensive. Also, COTS photodiodes can be used as detectors which can respond to pulses several nanoseconds wide. Transmitter broadcast angles and detector fields of view (FOVs) with pointing considerations are discussed. If the transmitter broadcast angle and the detector FOV are both narrow, the signal-to-noise ratio (SNR) of the received pulse is higher but the pointing accuracy of transmitter and receiver is critical. If, however, the transmitter broadcast angle and/or the detector FOV is wide, pointing is less critical but SNR is lower and some covertness may be lost. The propagation of the transmitted light in various clear oceanic and turbid coastal water types is considered with range estimates for some COTS light sources and detectors. We also consider the effects of environmental noise such as background solar radiation, which typically limits performance of these systems


title: 19.5 Three-dimensional 128Gb MLC vertical NAND Flash-memory with 24-WL stacked layers and 50MB/s high-speed programming

abstract: In the past few years, various 3D NAND Flash memories have been demonstrated, from device feasibility to chip implementation, to overcome scaling challenges in conventional planar NAND Flash [1-3]. The difficulties include shrinking the NAND cell and increasing manufacturing costs due to quadruple patterning and extreme ultraviolet lithography, motivating the development of the next-generation node beyond 16nm-class NAND Flash [4]. In this paper, as a new 3D memory device with lower manufacturing cost and superior device scalability, we present a true 3D 128Gb 2b/cell vertical-NAND (V-NAND) Flash. The chip accomplishes 50MB/s write throughput with 3K endurance for typical embedded applications such as mobile and personal computer. Also, extended endurance of 35K is achieved with 33MB/s of write throughput for data center and enterprise SSD applications.


title: Use of physiological signals to predict cybersickness

abstract: Cybersickness is a common and unpleasant side effect of virtual reality immersion. We measured physiological changes that were experienced by seated subjects who interacted with a virtual environment (VE) first while viewing a display monitor and second while using a head-mounted display (HMD). Comparing results for these two conditions let us identify physiological consequences of HMD use. In both viewing conditions, subjects rated the severity of their symptoms verbally and completed a post-immersion cybersickness assessment questionnaire. In the HMD viewing condition but not in the display monitor condition, verbal reports of cybersickness severity increased significantly relative to baseline. Half of the subjects chose to exit the VE after six minutes of HMD use and reported feeling some nausea at that time. We found that changes in stomach activity, blinking, and breathing can be used to estimate post-immersion symptom scores, with R2 values reaching as high as 0.75. These results suggest that HMD use by seated subjects is strongly correlated with the development of cybersickness. Finally, a linear discriminant analysis shows that physiological measures alone can be used to classify subject data as belonging to the HMD or monitor viewing condition with an accuracy of 78%.