title: Modeling the Helpful Opinion Mining of Online Consumer Reviews as a Classification Problem

abstract: The paper addresses an opinion mining problem: how to find the helpful reviews from online consumer reviews via the quality of the content. Since there are too many reviews, efficiently identifying the helpful ones earlier can benefit both consumers and companies. Consumers can read only the helpful opinions from helpful reviews before they purchase a product, while companies can acquire the true reasons a product is liked or hated. A system is built to assess the difficulty of the problem. The experimental results show that helpful reviews can be distinguished from unhelpful ones with high precision.


title: Zero-voltage-switching PWM three-level converter with current-doubler-rectifier

abstract: This paper proposes a zero-voltage-switching (ZVS) pulse-width modulation three-level converter with current-doubler-rectifier, which achieves ZVS for all the switches under wide load range and in a wide line range. The rectifier diodes commutate naturally, therefore no oscillation occurs. The determination of the output filter inductance and the blocking capacitor is discussed in details. The experimental results are presented to verify the operation principle of the proposed converter.


title: Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

abstract: We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.


Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment


title: PV generation enhancement with a virtual inertia emulator to provide inertial response to the grid

abstract: With high-penetration levels of renewable generating sources being integrated into the existing electric power grid, conventional generators are being replaced and grid inertial response is deteriorating. This technical challenge is more severe with photovoltaic (PV) generation than with wind generation because PV generation systems cannot provide inertial response unless special countermeasures are adopted. To enhance the inertial response, this paper proposes to synthesize a virtual inertia emulator (VIE) by using a battery energy storage system (BESS) and a three-phase grid-tied inverter to simulate a traditional generator such that the inertial response can be appropriately enhanced without sacrificing energy efficiency. Control systems for the VIE are presented in this paper, along with simulation results from PSCAD and MATLAB to validate the effectiveness of the proposed scheme.


title: Assessment of jerk performance s-curve and trapezoidal velocity profiles

abstract: nowadays, there are several techniques for the acceleration and deceleration of computer numerical control (CNC) machine tools and industrial robots (Robot manipulators) in order to plan smoother trajectories avoiding the jerk and another kind of stress. The aim of this article is to show a comparison between the trapezoidal and s-curve velocity profile used in robotics and CNC machinery. After doing that, the reader will be able to select a velocity profile in order to design their trajectories.


title: Air quality monitoring system based on IoT using Raspberry Pi

abstract: Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on Human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. Particulate matter is one of the most important parameter having the significant contribution to the increase in air pollution. This creates a need for measurement and analysis of real-time air quality monitoring so that appropriate decisions can be taken in a timely period. This paper presents a real-time standalone air quality monitoring system which includes various parameters: PM 2.5, carbon monoxide, carbon dioxide, temperature, humidity and air pressure. Internet of Things is nowadays finding profound use in each and every sector, plays a key role in our air quality monitoring system too. Internet of Things converging with cloud computing offers a novel technique for better management of data coming from different sensors, collected and transmitted by low power, low cost ARM based minicomputer Raspberry pi. The system is tested in Delhi and the measurements are compared with the data provided by the local environment control authority and are presented in a tabular form. The values of the parameters measured are shown in IBM Bluemix Cloud.


title: Structural patterns of information cascades and their implications for dynamics and semantics

abstract: Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although the dynamics and semantics of information cascades have been studied, the structural patterns and their correlations with dynamics and semantics are largely unknown. Here we explore a large-scale dataset including $432$ million information cascades with explicit records of spreading traces, spreading behaviors, information content as well as user profiles. We find that the structural complexity of information cascades is far beyond the previous conjectures. We first propose a ten-dimensional metric to quantify the structural characteristics of information cascades, reflecting cascade size, silhouette, direction and activity aspects. We find that bimodal law governs majority of the metrics, information flows in cascades have four directions, and the self-loop number and average activity of cascades follows power law. We then analyze the high-order structural patterns of information cascades. Finally, we evaluate to what extent the structural features of information cascades can explain its dynamic patterns and semantics, and finally uncover some notable implications of structural patterns in information cascades. Our discoveries also provide a foundation for the microscopic mechanisms for information spreading, potentially leading to implications for cascade prediction and outlier detection.


title: Family factors in adolescent problematic Internet gaming: A systematic review

abstract: Background and aimsFamilial influences are known to affect the likelihood of an adolescent becoming a problem gamer. This systematic review examined some of the key findings in empirical research o...


title: Graph based anomaly detection and description: a survey

abstract: Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.


title: Regression Shrinkage and Selection Via the Lasso

abstract: SUMMARY We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.


title: Evaluation of Mindful Sport Performance Enhancement (MSPE): A New Approach to Promote Flow in Athletes

abstract: The construct of mindfulness appears to be compatible with theories of flow and peak performance in sport. The present study assessed how Mindful Sport Performance Enhancement (MSPE), a new 4-week program, affected flow states, performance, and psychological characteristics of 11 archers and 21 golfers from the community. Participants completed trait measures of anxiety, perfectionism, thought disruption, confidence, mindfulness, and flow. They additionally provided data on their performances and state levels of mindfulness and flow. Analyses revealed that some significant changes in dimensions of the trait variables occurred during the training. Levels of state flow attained by the athletes also increased between the first and final sessions. The findings suggest that MSPE is a promising intervention to enhance flow, mindfulness, and aspects of sport confidence. An expanded workshop to allot more time for mindfulness practice is recommended for future studies.


title: An LDA-Based Approach to Scientific Paper Recommendation

abstract: Recommendation of scientific papers is a task aimed to support researchers in accessing relevant articles from a large pool of unseen articles. When writing a paper, a researcher focuses on the topics related to her/his scientific domain, by using a technical language.


title: Design and optimization of multi-band Wilkinson power divider

abstract: In this paper, a general and easy procedure for designing the symmetrical Wilkinson power divider that achieves equal-power split at N arbitrary frequencies is introduced. Each quarter-wave branch in the conventional Wilkinson divider is replaced by N sections of transmission lines, and the isolation between the output ports is achieved by using N resistors. The design parameters are the characteristic impedances and lengths of the N transmission line sections, and the N isolation resistors. The even–odd modes of analysis are used to derive the design equations. Closed-form expressions, which are suitable for CAD purposes, are derived for the dual-band divider. For N ≥ 3, closed-form expressions are not available, and therefore, the powerful particle swarm optimization method is used to obtain the design parameters. Examples of the dual-, triple-, and quad-band dividers are presented to validate the proposed design procedure, and the results are compared, wherever possible, with published results using other methods. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.


title: [Evaluation of fiber reinforcement composites in restoring lower dentition defect and fixing loose teeth for chronic periodontitis].

abstract: PURPOSE ::: To evaluate the effect of fiber reinforcement composites (FRC) in treating chronic periodontitis with dentition defect. ::: ::: ::: METHODS ::: Thirty patients who had chronic periodontitis and missed less than 2 lower anterior teeth were chosen. After appropriate periodontal treatment, EverStick fibers were used to fabricate splint with the missing teeth repaired. One month, 6 months, 1 year and 2 years after treatment, periodontal indexes such as plaque index (PLI), gingival index (GI) and probe depth (PD) were recorded and the patients' satisfaction was evaluated. SPSS 17.0 software package was used for statistical analysis. ::: ::: ::: RESULTS ::: The results showed that no significant difference was found in PLI and GI during observation period (P>0.05). PD decreased significantly after 6 months of observation (P<0.05). Patients' satisfaction degree for aesthetics, pronunciation, comfort and function was beyond 80%. ::: ::: ::: CONCLUSIONS ::: Using FRC technique in chronic periodontitis with dentition defect can effectively fix the loosen teeth and maintain physical function. Appropriate periodontal treatment every 6 months is the key factor for satisfactory results.


title: Complex beam epitaxy of metal oxide films

abstract: YBa2Cu3O7 films were prepared by the new deposition technique ”complex beam epitaxy”. In this technique, metal-EDTA complexes are evaporated by excimer laser ablation. The metal-EDTA complexes are decomposed by heating on the single crystalline (100)SrTiO3 substrate and form the crystal of metal oxides. The epitaxial relationship between the film and the substrate was determined as [100]YBCO//[100]SrTiO3 and [001]YBCO//[001]SrTiO3. There were no peaks for carbon and nitrogen on the AES and EPMA spectra obtained from surface and inside of the resulting samples. On the surface of the samples, sub-micron crystallites of YBa2Cu3O7 were mainly seen. There was an optimum distance between the target and the substrate for good transcription of the target composition to the substrate.


title: Dilemmas in a general theory of planning

abstract: The search for scientific bases for confronting problems of social policy is bound to fail, becuase of the nature of these problems. They are “wicked” problems, whereas science has developed to deal with “tame” problems. Policy problems cannot be definitively described. Moreover, in a pluralistic society there is nothing like the undisputable public good; there is no objective definition of equity; policies that respond to social problems cannot be meaningfully correct or false; and it makes no sense to talk about “optimal solutions” to social problems unless severe qualifications are imposed first. Even worse, there are no “solutions” in the sense of definitive and objective answers.


title: Solid texturing of complex surfaces

abstract: Texturing is an effective method of simulating surface detail at relatively low cost. Traditionally, texture functions have been defined on the two-dimensional surface coordinate systems of individual surface patches. This paper introduces the notion of "solid texturing". Solid texturing uses texture functions defined throughout a region of three-dimensional space. Many nonhomogeneous materials, including wood and stone, may be more realistically rendered using solid texture functions. In addition, solid texturing can easily be applied to complex surface which are difficult to texture using two-dimensional texture functions. The paper gives examples of solid texture functions based on Fourier synthesis, stochastic texture models, projections of two-dimensional textures, and combinations of other solid textures.


title: Physics-Inspired Neural Networks for Efficient Device Compact Modeling

abstract: We present a novel physics-inspired neural network (Pi-NN) approach for compact modeling. Development of high-quality compact models for devices is a key to connect device science with applications. One recent approach is to treat compact modeling as a regression problem in machine learning. The most common learning algorithm to develop compact models is the multilayer perceptron (MLP) neural network. However, device compact models derived using the MLP neural networks often exhibit unphysical behavior, which is eliminated in the Pi-NN approach proposed in this paper, since the Pi-NN incorporates fundamental device physics. As a result, smooth, accurate, and computationally efficient device models can be learned from discrete data points by using Pi-NN. This paper sheds new light on the future of the neural network compact modeling.


title: Social representation of cyberbullying and adolescent suicide: a mixed-method analysis of news stories

abstract: ABSTRACTCyberbullying has provoked public concern after well-publicized suicides of adolescents. This mixed-methods study investigates the social representation of these suicides. A content analysis of 184 U.S. newspaper articles on death by suicide associated with cyberbullying or aggression found that few articles adhered to guidelines suggested by the World Health Organization and the American Foundation for Suicide Prevention to protect against suicidal behavioral contagion. Few articles made reference to suicide or bullying prevention resources, and most suggested that the suicide had a single cause. Thematic analysis of a subset of articles found that individual deaths by suicide were used as cautionary tales to prompt attention to cyberbullying. This research suggests that newspaper coverage of these events veers from evidence-based guidelines and that more work is needed to determine how best to engage with journalists about the potential consequences of cyberbullying and suicide coverage.


title: Image Data Sharing for Biomedical Research—Meeting HIPAA Requirements for De-identification

abstract: Data sharing is increasingly recognized as critical to cross-disciplinary research and to assuring scientific validity. Despite National Institutes of Health and National Science Foundation policies encouraging data sharing by grantees, little data sharing of clinical data has in fact occurred. A principal reason often given is the potential of inadvertent violation of the Health Insurance Portability and Accountability Act privacy regulations. While regulations specify the components of private health information that should be protected, there are no commonly accepted methods to de-identify clinical data objects such as images. This leads institutions to take conservative risk-averse positions on data sharing. In imaging trials, where images are coded according to the Digital Imaging and Communications in Medicine (DICOM) standard, the complexity of the data objects and the flexibility of the DICOM standard have made it especially difficult to meet privacy protection objectives. The recent release of DICOM Supplement 142 on image de-identification has removed much of this impediment. This article describes the development of an open-source software suite that implements DICOM Supplement 142 as part of the National Biomedical Imaging Archive (NBIA). It also describes the lessons learned by the authors as NBIA has acquired more than 20 image collections encompassing over 30 million images.


title: Pre-harvest sprays of hexanal formulation for extending retention and shelf-life of mango (Mangifera indica L.) fruits

abstract: Abstract Field experiments were conducted to study the response of mango (var. Alphonso and Banganapalli) to pre-harvest sprays of a nano-emulsion of hexanal (Enhanced Freshness Formulation) in extending retention and shelf-life of fruits. Hexanal, a naturally occurring plant derived compound is known to inhibit phospholipase-D and facilitates extension of shelf-life of fruits during storage. Pre-harvest sprays of EFF (1.6 mM) on 30 and 15 days before harvest were done and the observations on fruit retention, incidence of post-harvest diseases and net fruit yield were recorded. After harvest, the fruits were kept under ambient (25 ± 2 °C; 70–75% RH) and cold (14 ± 2 °C; 85–90% RH) storage conditions and the physiological and biochemical parameters were measured regularly till the end of the shelf-life. The data clearly indicated that pre-harvest sprays of EFF significantly reduced the post-harvest diseases such as anthracnose, stem end rot and other infestations which minimized the post-harvest losses while increasing the net fruit yield by 4–5 kg per tree. The sprayed trees retained the fruits in the tree by 3–4 weeks longer than control. Further, the fruits harvested from the sprayed trees remained fresh for a longer period of time under both ambient (24–25 days) and cold (36–40 days) storage conditions in comparison to their respective controls (16–18 days; 20–22 days). The physiological loss in weight was registered lower while other quality attributes such as firmness, total sugars, acidity of treated fruits were higher than their respective controls regardless of storage conditions. Overall, the results suggested that pre-harvest sprays of EFF assisted in retention fruits for 3–4 weeks longer in the orchard itself besides extending shelf-life under storage conditions without the loss of quality of fruits.


title: Pow! Boom! Kablam! Effects of Viewing Superhero Programs on Aggressive, Prosocial, and Defending Behaviors in Preschool Children

abstract: Many schools and parents try to motivate children to become defenders of victimized peers. Defending behavior is common in the media (particularly in superhero programs); however, no study has examined the effect of media on defending behavior. The aim of the study was to examine longitudinal associations between superhero engagement and a variety of aggressive, prosocial, and defending behaviors in preschool children. Participants consisted of 240 preschoolers (49% male) and their parents who reported on child media use and outcomes at 2 different time points. Preschooler’s engagement with superheroes was related to increased physical and relational aggression 1 year later. Engagement with superheroes was not related to prosocial or defending behaviors. Implications of the results are discussed.


title: An argument-annotated corpus of scientific publications

abstract: Argumentation is an essential feature of scientific language. We present an annotation study resulting in a corpus of scientific publications annotated with argumentative components and relations. The argumentative annotations have been added to the existing Dr. Inventor Corpus, already annotated for four other rhetorical aspects. We analyze the annotated argumentative structures and investigate the relations between argumentation and other rhetorical aspects of scientific writing, such as discourse roles and citation contexts.


title: Minimum Error Rate Training In Statistical Machine Translation

abstract: Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality. These training criteria make use of recently proposed automatic evaluation metrics. We describe a new algorithm for efficient training an unsmoothed error count. We show that significantly better results can often be obtained if the final evaluation criterion is taken directly into account as part of the training procedure.


title: E-textiles in Clinical Rehabilitation: A Scoping Review

abstract: Electronic textiles have potential for many practical uses in clinical rehabilitation. This scoping review appraises recent and emerging developments of textile-based sensors with applications to rehabilitation. Contributions published from 2009 to 2013 are appraised with a specific focus on the measured physiological or biomechanical phenomenon, current measurement practices, textile innovations, and their merits and limitations. While fabric-based signal quality and sensor integration have advanced considerably, overall system integration (including circuitry and power) has not been fully realized. Validation against clinical gold standards is inconsistent at best, and feasibility with clinical populations remains to be demonstrated. The overwhelming focus of research and development has been on remote sensing but the opportunity for textile-mediated feedback to the wearer remains unexplored. Recommendations for future research are provided.


title: An Immune Genetic Algorithm for Software Test Data Generation

abstract: This paper aims at incorporating immune operators in genetic algorithms as an advanced method for solving the problem of test data generation. The new proposed hybrid algorithm is called immune genetic algorithm (IGA). A full description of this algorithm is presented before investigating its application in the context of software test data generation using some benchmark programs. Moreover, the algorithm is compared with other evolutionary algorithms.


title: Combining Per-Frame and Per-Track Cues for Multi-Person Action Recognition

abstract: We propose a model to combine per-frame and per-track cues for action recognition. With multiple targets in a scene, our model simultaneously captures the natural harmony of an individual's action in a scene and the flow of actions of an individual in a video sequence, inferring valid tracks in the process. Our motivation is based on the unlikely discordance of an action in a structured scene, both at the track level and the frame level (e.g., a person dancing in a crowd of joggers). While we can utilize sampling approaches for inference in our model, we instead devise a global inference algorithm by decomposing the problem and solving the subproblems exactly and efficiently, recovering a globally optimal joint solution in several cases. Finally, we improve on the state-of-the-art action recognition results for two publicly available datasets.


title: Empty container management for intermodal transportation networks

abstract: Abstract We present a computational analysis of the effect of planning horizon length on empty container management for intermodal transportation networks. The analysis is based on an integer program that seeks to minimize total costs related to moving empty containers, subject to meeting requirements for moving loaded containers. A case study of potential container-on-barge operations within the Mississippi River basin illustrates the effects of planning horizon length on mode selection. A longer planning horizon can encourage the use of inexpensive, slow transportation modes, such as barge. The impact depends on the number and location of container storage pools.


title: Standalone Photovoltaic Water Pumping System Using Induction Motor Drive With Reduced Sensors

abstract: A simple and efficient solar photovoltaic (PV) water pumping system utilizing an induction motor drive (IMD) is presented in this paper. This solar PV water pumping system comprises two stages of power conversion. The first stage extracts the maximum power from a solar PV array by controlling the duty ratio of a dc–dc boost converter. The dc bus voltage is maintained by the controlling the motor speed. This regulation helps in the reduction of motor losses by reducing motor currents at higher voltage for the same power injection. To control the duty ratio, an incremental conductance based maximum power point tracking (MPPT) control technique is utilized. A scalar-controlled voltage source inverter serves the purpose of operating an IMD. The stator frequency reference of IMD is generated by the proposed control scheme. The proposed system is modeled, and its performance is simulated in detail. The scalar control eliminates the requirement of a speed sensor/encoder. Consequently, the need of motor current sensor is also eliminated. Moreover, the dynamics are improved by an additional speed feedforward term in the control scheme. The proposed control scheme makes the system inherently immune to the variation in the pump constant. The prototype of PV-powered IMD emulating the pump characteristics is developed in the laboratory to examine the performance under different operating conditions.


title: Predictors of New Graduate Nurses’ Organizational Commitment During a Nurse Residency Program

abstract: Retaining newly graduated nurses is critical for organizations because of the significant cost of turnover. Since commitment to an organization is associated with decreased turnover intent, understanding factors that influence new graduates’ organizational commitment is important. In a sample of nurse residency program participants, predictors of organizational commitment over time were explored. Perceptions of the work environment, particularly job satisfaction and job stress, were found to be most influential.


title: Catastrophic forgetting in connectionist networks

abstract: Abstract All natural cognitive systems, and, in particular, our own, gradually forget previously learned information. Plausible models of human cognition should therefore exhibit similar patterns of gradual forgetting of old information as new information is acquired. Only rarely does new learning in natural cognitive systems completely disrupt or erase previously learned information; that is, natural cognitive systems do not, in general, forget ‘catastrophically'. Unfortunately, though, catastrophic forgetting does occur under certain circumstances in distributed connectionist networks. The very features that give these networks their remarkable abilities to generalize, to function in the presence of degraded input, and so on, are found to be the root cause of catastrophic forgetting. The challenge in this field is to discover how to keep the advantages of distributed connectionist networks while avoiding the problem of catastrophic forgetting. In this article the causes, consequences and numerous solutions to the problem of catastrophic forgetting in neural networks are examined. The review will consider how the brain might have overcome this problem and will also explore the consequences of this solution for distributed connectionist networks.


title: Efficient model selection for regularized linear discriminant analysis

abstract: Classical Linear Discriminant Analysis (LDA) is not applicable for small sample size problems due to the singularity of the scatter matrices involved. Regularized LDA (RLDA) provides a simple strategy to overcome the singularity problem by applying a regularization term, which is commonly estimated via cross-validation from a set of candidates. However, cross-validation may be computationally prohibitive when the candidate set is large. An efficient algorithm for RLDA is presented that computes the optimal transformation of RLDA for a large set of parameter candidates, with approximately the same cost as running RLDA a small number of times. Thus it facilitates efficient model selection for RLDA.An intrinsic relationship between RLDA and Uncorrelated LDA (ULDA), which was recently proposed for dimension reduction and classification is presented. More specifically, RLDA is shown to approach ULDA when the regularization value tends to zero. That is, RLDA without any regularization is equivalent to ULDA. It can be further shown that ULDA maps all data points from the same class to a common point, under a mild condition which has been shown to hold for many high-dimensional datasets. This leads to the overfitting problem in ULDA, which has been observed in several applications. Thetheoretical analysis presented provides further justification for the use of regularization in RLDA. Extensive experiments confirm the claimed theoretical estimate of efficiency. Experiments also show that, for a properly chosen regularization parameter, RLDA performs favorably in classification, in comparison with ULDA, as well as other existing LDA-based algorithms and Support Vector Machines (SVM).


title: A Meta-analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT)

abstract: The originating article of the Unified Theory of Acceptance and Use of Technology (UTAUT) has been cited by a large number of studies. However, a detailed examination of such citations revealed that only small proportion (43 articles) of these citations actually utilized the theory or its constructs in their empirical research for examining IS/IT related issues. In order to examine whether the theory is performing consistently well across various studies, this research aims to undertake a statistical meta-analysis of findings reported in 43 published studies that have actually utilized UTAUT or its constructs in their empirical research. Findings reveal the underperformance of theory in subsequent studies in comparison to the performance of UTAUT reported in the originating article. The limitations experienced while conducting the meta-analysis, recommendations, and the future scope for the further research in this area have also been briefly explained in concluding section.


title: A robust mass spectrometry method for rapid profiling of erythrocyte ghost membrane proteomes

abstract: Background ::: Red blood cell (RBC) physiology is directly linked to many human disorders associated with low tissue oxygen levels or anemia including chronic obstructive pulmonary disease, congenital heart disease, sleep apnea and sickle cell anemia. Parasites such as Plasmodium spp. and phylum Apicomplexa directly target RBCs, and surface molecules within the RBC membrane are critical for pathogen interactions. Proteomics of RBC membrane 'ghost' fractions has therefore been of considerable interest, but protocols described to date are either suboptimal or too extensive to be applicable to a larger set of clinical cohorts. ::: ::: ::: Methods ::: Here, we describe an optimised erythrocyte isolation protocol from blood, tested for various storage conditions and explored using different fractionation conditions for isolating ghost RBC membranes. Liquid chromatography mass spectrometry (LC-MS) analysis on a Q-Exactive Orbitrap instrument was used to profile proteins isolated from the comparative conditions. Data analysis was run on the MASCOT and MaxQuant platforms to assess their scope and diversity. ::: ::: ::: Results ::: The results obtained demonstrate a robust method for membrane enrichment enabling consistent MS based characterisation of > 900 RBC membrane proteins in single LC-MS/MS analyses. Non-detergent based membrane solubilisation methods using the tissue and supernatant fractions of isolated ghost membranes are shown to offer effective haemoglobin removal as well as diverse recovery including erythrocyte membrane proteins of high and low abundance. ::: ::: ::: Conclusions ::: The methods described in this manuscript propose a medium to high throughput framework for membrane proteome profiling by LC-MS of potential applicability to larger clinical cohorts in a variety of disease contexts.


title: Towards end-to-end polyphonic music transcription: Transforming music audio directly to a score

abstract: We present a neural network model that learns to produce music scores directly from audio signals. Instead of employing commonplace processing steps, such as frequency transform front-ends, harmonicity and scale priors, or temporal pitch smoothing, we show that a neural network can learn such steps on its own when presented with the appropriate training data. We show how such a network can perform monophonic transcription with very high accuracy, and how it also generalizes well to transcribing polyphonic music.


title: Synthesis, antifungal activity of caffeic acid derivative esters, and their synergism with fluconazole and nystatin against Candida spp.

abstract: We tested the antifungal potential of caffeic acid and 8 of its derivative esters against Candidaalbicans ATCC 90028 and 9 clinical isolatesand carried out a synergism assay with fluconazole and nystatin. Propyl caffeate (C3) showed the best antifungal activity against the tested strains. When in combination, C3 markedly reduced the MIC of fluconazole and nystatin with synergistic effect up to 64-fold. Finally, C3 showed a high IC50 value and selective indexagainst oral keratinocytes, demonstrating low toxicity against this cell type and selectivity for yeast cells. Further research should confirm its antifungal potential for development of combined therapy to treat C. albicans infections.


title: A real-time ellipse detection based on edge grouping

abstract: In this paper, we present a efficient algorithm for real-time ellipse detection. Unlike Hough transform algorithm that is computationally intense and requires a higher dimensional parameter space, our proposed method reduces the computational complexity significantly, and accurately detects ellipses in realtime. We present a new method of detecting arc-segments from the image, based on the properties of ellipse. We then group the arc-segments into elliptical arcs in order to estimate the parameters of the ellipse, which are calculated using the least-square method. Our method has been tested and implemented on synthetic and real-world images containing both complete and incomplete ellipses. The performance is compared to existing ellipse detection algorithms, demonstrating the robustness, accuracy and effectiveness of our algorithm.


title: A predictive current control for solar PV fed VSI in distribution system

abstract: A control technique based on predictive current control for a voltage source inverter fed by a solar PV system integrating to the distribution grid with Perturb and Observe (P&O) maximum power point tracking (MPPT) technique is presented in this paper. The proposed control is implemented for a three phase system to support the utility grid by providing the power to grid as well as to the connected loads with improved quality of power. This control approach for solar PV fed voltage source inverter (VSI) provides fast transient response and improved accuracy in control. Proposed control algorithm efficiently and continuously tracks reference current to make grid current sinusoidal in steady state and transient conditions. When the solar irradiance is available, the developed system acts as power quality conditioner and supply the PV generated power to the loads and the grid. During unavailability of sun power the system transfer the power from the grid to the load and capable to provide multidirectional power flow. Simulation for the proposed system has been carried out and results are presented.


title: A Bayesian approach to covariance estimation and data fusion

abstract: In this paper, we address the fusion problem of two estimates, where the cross-correlation between the estimates is unknown. To solve the problem within the Bayesian framework, we assume that the covariance matrix has a prior distribution. We also assume that we know the covariance of each estimate, i.e., the diagonal block of the entire co-variance matrix (of the random vector consisting of the two estimates). We then derive the conditional distribution of the off-diagonal blocks, which is the cross-correlation of our interest. The conditional distribution happens to be the inverted matrix variate t-distribution. We can readily sample from this distribution and use a Monte Carlo method to compute the minimum mean square error estimate for the fusion problem. Simulations show that the proposed method works better than the popular covariance intersection method.


title: An ECG monitoring system for prediction of cardiac anomalies using WBAN

abstract: Cardiovascular diseases (CVD) are known to be the most widespread causes to death. Therefore, detecting earlier signs of cardiac anomalies is of prominent importance to ease the treatment of any cardiac complication or take appropriate actions. Electrocardiogram (ECG) is used by doctors as an important diagnosis tool and in most cases, it's recorded and analyzed at hospital after the appearance of first symptoms or recorded by patients using a device named holter ECG and analyzed afterward by doctors. In fact, there is a lack of systems able to capture ECG and analyze it remotely before the onset of severe symptoms. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real time systems able to accurately analyze ECG and detect cardiac abnormalities. In this paper, we propose a new CVD detection system using Wireless Body Area Networks (WBAN) technology. This system processes the captured ECG using filtering and Undecimated Wavelet Transform (UWT) techniques to remove noises and extract nine main ECG diagnosis parameters, then the system uses a Bayesian Network Classifier model to classify ECG based on its parameters into four different classes: Normal, Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC) and Myocardial Infarction (MI). The experimental results on ECGs from real patients databases show that the average detection rate (TPR) is 96.1% for an average false alarm rate (FPR) of 1.3%.


title: Effects of Antenna Characteristics and Placements on a Vehicle-to-Vehicle Channel Scenario

abstract: The time-varying propagation channel in vehicle-to-vehicle (V2V) communications strongly influences the performance of a wireless system in terms of e.g. channel capacity or outage probability. Assuming knowledge of the channel characteristics, in simulations these parameters are generally calculated by using isotropic radiators instead of real antennas, thus neglecting the influence of the antennas' parameters. Therefore, the goal of this paper is to investigate a possible antenna configuration consisting of three antennas mounted at different positions on the car and to describe the difference between the real and a corresponding isotropic configuration for a defined channel scenario.


title: Psidium guajava: a review of its traditional uses, phytochemistry and pharmacology.

abstract: Abstract Psidium guajava , is an important food crop and medicinal plant in tropical and subtropical countries is widely used like food and in folk medicine around of the world. This aims a comprehensive of the chemical constituents, pharmacological, and clinical uses. Different pharmacological experiments in a number of in vitro and in vivo models have been carried out. Also have been identified the medicinally important phyto-constituents. A number of metabolites in good yield and some have been shown to possess useful biological activities belonging mainly to phenolic, flavonoid, carotenoid, terpenoid and triterpene. Extracts and metabolites of this plant, particularly those from leaves and fruits possess useful pharmacological activities. A survey of the literature shows P. guajava is mainly known for its antispasmodic and antimicrobial properties in the treatment of diarrhoea and dysentery. Has also been used extensively as a hypoglycaemic agent. Many pharmacological studies have demonstrated the ability of this plant to exhibit antioxidant, hepatoprotection, anti-allergy, antimicrobial, antigenotoxic, antiplasmodial, cytotoxic, antispasmodic, cardioactive, anticough, antidiabetic, antiinflamatory and antinociceptive activities, supporting its traditional uses. Suggest a wide range of clinical applications for the treatment of infantile rotaviral enteritis, diarrhoea and diabetes.


title: EQ-SD: a measure of health status from the EuroQol Group

abstract: Established in 1987, the EuroQol Group initially comprised a network of international, multilingual and multidisciplinary researchers from seven centres in Finland, the Netherlands, Norway, Sweden and the UK. Nowadays, the Group comprises researchers from Canada, Denmark, Germany, Greece, Japan, New Zealand, Slovenia, Spain, the USA and Zimbabwe. The process of shared development and local experimentation resulted in EQ-5D, a generic measure of health status that provides a simple descriptive profile and a single index value that can be used in the clinical and economic evaluation of health care and in population health surveys. Currently, EQ-5D is being widely used in different countries by clinical researchers in a variety of clinical areas. EQ-5D is also being used by eight out of the first 10 of the top 50 pharmaceutical companies listed in the annual report of Pharma Business (November/December 1999). Furthermore, EQ-5D is one of the handful of measures recommended for use in cost-effectiveness analyses by the Washington Panel on Cost Effectiveness in Health and Medicine. EQ-5D has now been translated into most major languages with the EuroQol Group closely monitoring the process.


title: An empirical study of best practices in virtual teams

abstract: Abstract There is nothing quite so useless as doing with great efficiency that which should not be done at all. –Peter Drucker This study explores the issue of effectiveness within virtual teams — groups of people who work together although they are often dispersed across space, time, and/or organizational boundaries. Due to the recent trend towards corporate restructuring, which can, in part, be attributed to an increase in corporate layoffs, mergers and acquisitions, competition, and globalization, virtual teams have become critical for companies to survive. Globalization of the marketplace alone, for that matter, makes such distributed work groups the primary operating units needed to achieve a competitive advantage in this ever-changing business environment. In an effort to determine the factors that contribute to/inhibit the success of a virtual team, a survey was distributed to a total of eight companies in the high technology, agriculture, and professional services industries. Data was then collected from 67 individuals who comprised a total of 12 virtual teams from these companies. Results indicated that several factors were positively correlated to the effectiveness of the participating teams. The teams’ processes and team members’ relations presented the strongest relationships to team performance and team member satisfaction, while the selection procedures and executive leadership styles also exhibited moderate associations to these measures of effectiveness. Analysis of predictor variables such as the design process, other internal group dynamics, and additional external support mechanisms, however, depicted weaker relations. Although the connections between the teams’ tools and technologies and communication patterns and the teams’ effectiveness measures did not prove significant, content analysis of the participants’ narrative responses to questions regarding the greatest challenges to virtual teams suggested otherwise. Beyond the traditional strategies used to enhance a team’s effectiveness, further efforts directed towards the specific technology and communication-related issues that concern dispersed team members are needed to supplement the set of best practices identified in the current study.


title: EMI Filter design using high frequency models of the passive components

abstract: The aim of this paper is to propose a new design method of EMI filters for the power converters using high frequency models of the passive components. It is well known that the static converters are the main source of conducted disturbances of the common mode and differential mode. Often, the solution used to reduce conducted emissions consists to use the EMI filters. The design of these filters is very difficult because it requires complete mastery of the design process. In this paper, a high frequency modeling method of the coupled inductors of filter is proposed. Coupled inductors and capacitor models are used to make a complete EMI filters. The high frequency models of these components are used in the simulations and the obtained results are compared to a prototype measurement data. A new EMI filters design method based on the optimization of the stray elements is detailed. It is based on the analysis of conducted EMI induce by the DC-DC converter.


title: Dimensions of oral care management in Texas hospitals.

abstract: PURPOSE ::: There is a growing body of evidence that suggests improved oral health can help patient outcomes in hospitals. Yet there are indicators that oral care in hospitals is less than ideal. This study was conducted to quantify and qualify the dimensions of oral care in Texas hospitals with a focus on the dental knowledge, attitudes and practices of nurses and barriers to providing such care. ::: ::: ::: METHODS ::: A random sample of 582 registered nurses, licensed and employed in Texas, was surveyed about oral care attitudes, practices and knowledge. Frequencies and Spearman correlations were used to describe and analyze the data with SPSS. ::: ::: ::: RESULTS ::: Ninety-eight respondents returned surveys for a 16.8% response rate. Of these, 52% reported their nursing education did not ("minimally/not at all") prepare them for oral care management. However, they felt oral health was important (95%), felt responsible for oral care (79%) and assessed the oral cavity of their patients (78.6%). Although they reported being "knowledgeable" or "very knowledgeable" about oral health management (67%), their score on the knowledge questions was low (mean=51%, sd=0.132). There was not a significant correlation between the knowledge scores and education levels (ρ=0.136, p>0.05) or knowledge scores and work areas (ρ=-0.080, p>0.05). They also reported such barriers as low priority for oral care, lack of time, no mandate and the need for more resources. ::: ::: ::: CONCLUSION ::: This study revealed that nurses experienced a disconnect between feeling responsible yet somewhat incapable and/or ill-prepared to provide adequate oral care for their patients. This suggests a possible need for revising nurse education, hospital requirements and protocols for performing oral care and employing dental professionals in hospitals.


title: English-Japanese Neural Machine Translation with Encoder-Decoder-Reconstructor

abstract: Neural machine translation (NMT) has recently become popular in the field of machine translation. However, NMT suffers from the problem of repeating or missing words in the translation. To address this problem, Tu et al. (2017) proposed an encoder-decoder-reconstructor framework for NMT using back-translation. In this method, they selected the best forward translation model in the same manner as Bahdanau et al. (2015), and then trained a bi-directional translation model as fine-tuning. Their experiments show that it offers significant improvement in BLEU scores in Chinese-English translation task. We confirm that our re-implementation also shows the same tendency and alleviates the problem of repeating and missing words in the translation on a English-Japanese task too. In addition, we evaluate the effectiveness of pre-training by comparing it with a jointly-trained model of forward translation and back-translation.


title: An annotated review of past papers on attack graphs

abstract: Abstract : This report reviews past research papers that describe how to construct attack graphs, how to use them to improve security of computer networks, and how to use them to analyze alerts from intrusion detection systems. Two commercial systems are described 1, 2, and a summary table compares important characteristics of past research studies. For each study, information is provided on the number of attacker goals, how graphs are constructed, sizes of networks analyzed, how well the approach scales to larger networks, and the general approach. Although research has made significant progress in the past few years, no system has analyzed networks with more than 20 hosts, and computation for most approaches scales poorly and would be impractical for networks with more than even a few hundred hosts. Current approaches also are limited because many require extensive and difficult-to-obtain details on attacks, many assume that host-to-host reachability information between all hosts is already available, and many produce an attack graph but do not automatically generate recommendations from that graph. Researchers have suggested promising approaches to alleviate some of these limitations, including grouping hosts to improve scaling, using worst-case default values for unknown attack details, and symbolically analyzing attack graphs to generate recommendations that improve security for critical hosts. Future research should explore these and other approaches to develop attack graph construction and analysis algorithms that can be applied to large enterprise networks.


title: An Analysis of Dropout for Matrix Factorization

abstract: Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem classes, such as linear or logistic regression, several theoretical studies have demonstrated the equivalence between dropout and a fully deterministic optimization problem with data-dependent Tikhonov regularization. This work presents a theoretical analysis of dropout for matrix factorization, where Bernoulli random variables are used to drop a factor, thereby attempting to control the size of the factorization. While recent work has demonstrated the empirical effectiveness of dropout for matrix factorization, a theoretical understanding of the regularization properties of dropout in this context remains elusive. This work demonstrates the equivalence between dropout and a fully deterministic model for matrix factorization in which the factors are regularized by the sum of the product of the norms of the columns. While the resulting regularizer is closely related to a variational form of the nuclear norm, suggesting that dropout may limit the size of the factorization, we show that it is possible to trivially lower the objective value by doubling the size of the factorization. We show that this problem is caused by the use of a fixed dropout rate, which motivates the use of a rate that increases with the size of the factorization. Synthetic experiments validate our theoretical findings.


title: A Compact 38 GHz Multibeam Antenna Array With Multifolded Butler Matrix for 5G Applications

abstract: A compact 38 GHz multibeam antenna array is presented for fifth-generation applications. A beamforming network featuring a substrate integrated waveguide (SIW) multifolded 4 × 8 Butler matrix is constructed. Benefiting from multilayer structure, the Butler matrix is highly integrated and assembled underneath the radiators to achieve compactness. The Butler matrix is, then, combined with an 8 × 10 SIW slot-coupled patch array to establish four steering beams along the H-plane and to accomplish size reduction of 53.5% in its longitudinal direction. For demonstration, one prototype of the proposed antenna array was fabricated. The measured four beams point toward ±36° and ±12° with gains equal to 19.8 and 21 dBi, respectively.


title: Stabilizing cell phone video using inertial measurement sensors

abstract: We present a system that rectifies and stabilizes video sequences on mobile devices with rolling-shutter cameras. The system corrects for rolling-shutter distortions using measurements from accelerometer and gyroscope sensors, and a 3D rotational distortion model. In order to obtain a stabilized video, and at the same time keep most content in view, we propose an adaptive low-pass filter algorithm to obtain the output camera trajectory. The accuracy of the orientation estimates has been evaluated experimentally using ground truth data from a motion capture system. We have conducted a user study, where the output from our system, implemented in iOS, has been compared to that of three other applications, as well as to the uncorrected video. The study shows that users prefer our sensor-based system.


title: Improving performance by matching imbalanced workloads with heterogeneous platforms

abstract: Although GPUs are considered ideal to accelerate massively data-parallel applications, there are still exceptions to this rule. For example, imbalanced applications cannot be efficiently processed by GPUs: despite the massive data parallelism, a varied computational workload per data point remains GPU-unfriendly. To efficiently process imbalanced applications, we exploit the use of heterogeneous platforms (GPUs and CPUs) by partitioning the workload to fit the usage patterns of the processors. In this work, we present our flexible and adaptive method that predicts the optimal partitioning. Our method aims to match a quantitative model of the application with the hardware capabilities of the platform, and calculates the optimal match according to a user-given criterion. We evaluate our method in terms of overall performance gain, prediction accuracy, flexibility and adaptivity. Our results, gathered from both synthetic and real-world workloads, show performance gains of up to 60%, accurate predictions for more than 90% of all the 1395 imbalanced workloads we have tested, and confirm that the method adapts correctly to application, dataset, and platform changes (both hardware and software). We conclude that model-based prediction of workload partitioning for heterogeneous platforms is feasible and useful for performance improvement.


title: CSI-MIMO: Indoor Wi-Fi fingerprinting system

abstract: Wi-Fi based fingerprinting systems, mostly utilize the Received Signal Strength Indicator (RSSI), which is known to be unreliable due to environmental and hardware effects. In this paper, we present a novel Wi-Fi fingerprinting system, exploiting the fine-grained information known as Channel State Information (CSI). The frequency diversity of CSI can be effectively utilized to represent a location in both frequency and spatial domain resulting in more accurate indoor localization. We propose a novel location signature CSI-MIMO that incorporates Multiple Input Multiple Output (MIMO) information and use both the magnitude and the phase of CSI of each sub-carrier. We experimentally evaluate the performance of CSI-MIMO fingerprinting using the k-nearest neighbor and the Bayes algorithm. The accuracy of the proposed CSI-MIMO is compared with Finegrained Indoor Fingerprinting System (FIFS) and a simple CSI-based system. The experimental result shows an accuracy improvement of 57% over FIFS with an accuracy of 0.95 meters.


title: The economic value of predictability in portfolio management

abstract: This paper evaluates the evidence on return predictability from an economic perspective: it asks whether investors would have been able and willing to exploit dividend price signals in order to allocate capital efficiently. We use a simple model that incorporates a time varying investment opportunity set into a mean-variance portfolio maximization framework, and derive the optimal capital allocation weights for: (i) a naive strategy based on average realized returns; and (ii) a class of strategies that condition on dividend-price signals. While our data supports in-sample evidence of return predictability, the out-of-sample returns of the naive strategy are higher than those of all conditional portfolio specifications based on a certainty equivalent metric and portfolio turnover. The degree of underperformance is most dramatic in the last three decades: an investor who had used dividend-price ratios as signals for capital allocation in the period 1990-2012 would have consistently generated lower returns than by following a naive strategy. These results suggest that dividend-price predictability offers no economic value to investors.


title: Development of a wireless physiological computing platform using a national instruments' myRIO embedded device

abstract: We designed a physiological computing platform, which is easy to configure and setup using National Instruments' myRIO embedded device. Physiological signal acquisition, processing, and visualization are required for the most basic sort of physiological computing platform. Therefore, the proposed platform consisted of these three basic modules. (1) Signal acquisition module: the physiological signals captured via the biosensors were sent wirelessly using the myRIO's analog input channels to a PC or mobile handheld devices such as, tablet or smartphone. (2) Signal processing module: the signal processing procedures were conducted using a graphical design software i.e., LabVIEW. (3) Visualization module: the signals and computing results were on the screen for the user. In this paper, we demonstrated the effectiveness of proposed platform on non-contact measurement of the heart and respiration rates using microwave radar.


title: Achieving High Scalability Through Hybrid Switching in Software-Defined Networking

abstract: Traditional networks rely on aggregate routing and decentralized control to achieve scalability. On the contrary, software-defined networks achieve near optimal network performance and policy-based management through per-flow routing and centralized control, which, however, face scalability challenge due to: 1) limited ternary content addressable memory and on-die memory for storing the forwarding table and 2) per-flow communication/computation overhead at the controller. This paper presents a novel hybrid switching (HS) design, which integrates traditional switching and software-defined networking (SDN) switching for the purpose of achieving both scalability and optimal performance. We show that the integration also leads to unexpected benefits of making both types of switching more efficient under the hybrid design. We also design the general optimization framework via HS and propose an approximation algorithm for load-balancing optimization as a case study. Testing and numerical evaluation demonstrate the superior performance of HS when comparing with the state-of-the-art SDN design.


Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning


title: Preceding vehicle detection using Histograms of Oriented Gradients

abstract: This paper presents a monocular vision-based preceding vehicle detection system using Histogram of Oriented Gradient (HOG) based method and linear SVM classification. Our detection algorithm consists of three main components: HOG feature extraction, linear SVM classifier training and vehicles detection. Integral Image method is adopted to improve the HOG computational efficiency, and hard examples are generated to reduce false positives in the training phase. In detection step, the multiple overlapping detections due to multi-scale window searching are very well fused by non-maximum suppression based on mean-shift. The monocular system is tested under different traffic scenarios (e.g., simply structured highway, complex urban environments, local occlusion conditions), illustrating good performance.


title: Anomaly detection model based on data stream clustering

abstract: Intrusion detection provides important protection for network security and anomaly detection as a type of intrusion detection, which can recognize the pattern of normal behaviors and label the behaviors which departure from normal pattern as anomaly behaviors. The updating of network equipment and broadband speed makes the data mining object change from static data sets to dynamic data streams. We think that the traditional methods based on data set do not satisfy the needs of dynamic network environment. The network data stream is temporal and cannot be treated as static data set. The concept and distribution of data objects is variety in different time stamps and the changing is unpredictable. Therefore, we propose an improved data stream clustering algorithm and design the anomaly detection model according to the improved algorithm. The established model can be modified with the changing of data stream and detect anomaly behaviors in time.


title: Pegasus, a workflow management system for science automation

abstract: Modern science often requires the execution of large-scale, multi-stage simulation and data analysis pipelines to enable the study of complex systems. The amount of computation and data involved in these pipelines requires scalable workflow management systems that are able to reliably and efficiently coordinate and automate data movement and task execution on distributed computational resources: campus clusters, national cyberinfrastructures, and commercial and academic clouds. This paper describes the design, development and evolution of the Pegasus Workflow Management System, which maps abstract workflow descriptions onto distributed computing infrastructures. Pegasus has been used for more than twelve years by scientists in a wide variety of domains, including astronomy, seismology, bioinformatics, physics and others. This paper provides an integrated view of the Pegasus system, showing its capabilities that have been developed over time in response to application needs and to the evolution of the scientific computing platforms. The paper describes how Pegasus achieves reliable, scalable workflow execution across a wide variety of computing infrastructures. Comprehensive description of the Pegasus Workflow Management System.Detailed explanation of Pegasus workflow transformations.Data management in Pegasus.Earthquake science application example.


title: Indoor navigation using QR codes and WiFi signals with an implementation on mobile platform

abstract: In this paper we demonstrate how to extend an indoor personal navigation system based upon fusing pedestrian dead reckoning data and WiFi fingerprints by using simple, unobtrusive visual landmarks perceived by the user's smartphone camera. The proposed navigation system employs a factor graph to represent the localization constraints stemming from measurements obtained using the sensors available in a mobile device. The novelty of this work lies in integration of the constraints imposed by opportunistic observations of simple landmarks based on QR codes in the graph-based formulation of the localization problem. The experiments concern feasibility of detecting and reading QR codes under real-life conditions, and the accuracy of user position estimation. The experimental results confirm that QR codes can provide valuable localization information in real-time, especially when the site lacks a rich landscape of WiFi signals.


title: Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD

abstract: In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4- D (color) video data completion and de-noising. We exploit the recently proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11] to characterize informational and structural complexity of multilinear data. We first show that videos with linear camera motion can be represented more efficiently using t-SVD compared to the approaches based on vectorizing or flattening of the tensors. Since efficiency in representation implies efficiency in recovery, we outline a tensor nuclear norm penalized algorithm for video completion from missing entries. Application of the proposed algorithm for video recovery from missing entries is shown to yield a superior performance over existing methods. We also consider the problem of tensor robust Principal Component Analysis (PCA) for de-noising 3-D video data from sparse random corruptions. We show superior performance of our method compared to the matrix robust PCA adapted to this setting as proposed in [4].


title: Access point assignment in hybrid LiFi and WiFi networks in consideration of LiFi channel blockage

abstract: As a promising complement to the increasingly congested wireless fidelity (WiFi) system, light fidelity (LiFi) has recently drawn massive attention due to the wide and unregulated visible light spectrum. Also, the light waves cannot penetrate walls which enables interference avoidance between compartments. Meanwhile, LiFi access points (APs) have a much shorter range, relative to WiFi APs. A hybrid network can combine the high-speed transmission of LiFi and the ubiquitous coverage of WiFi. For such a network, access point assignment (APA) becomes challenging because of the completely overlaying coverage of different networks. In addition, LiFi is susceptible to channel blockage due to opaque obstacles, adding difficulties to the APA issue. In this paper, a novel APA method is proposed for hybrid LiFi and WiFi networks in consideration of LiFi channel blockage for the first time. By exploiting users' statistical information of channel blockage, the proposed method formulates the issue as an optimisation problem that maximises the system throughput over a period of time. Results show that the proposed method can improve throughput by up to 90% over the conventional load balancing method.


title: Development of a Safety- and Energy-Aware Impedance Controller for Collaborative Robots

abstract: In contexts where robots share their workspace with humans, safety is of utmost importance. Consequently, in recent years, a big impulse has been given to the design of human-friendly robots by involving both mechanical and control design aspects. Regarding controller design, this often involves introducing compliance and ensuring asymptotic stability using an interaction control scheme and passivity theory. Moreover, when human operators physically interact with the robot during work, strict safety measures become necessary with some of these including power and force limitations [1]. In this letter, a novel impedance control technique for collaborative robots is presented. The featured controller allows a safe human–robot interaction through energy and power limitations, assuring passivity through energy tanks. The proposed controller is evaluated with a KUKA LWR 4+ arm in a comanipulation environment.


title: Simulating Cellular Communications in Vehicular Networks: Making SimuLTE Interoperable with Veins

abstract: The evolution of cellular technologies toward 5G progressively enables efficient and ubiquitous communications in an increasing number of fields. Among these, vehicular networks are being considered as one of the most promising and challenging applications, requiring support for communications in high-speed mobility and delay-constrained information exchange in proximity. In this context, simulation frameworks under the OMNeT++ umbrella are already available: SimuLTE and Veins for cellular and vehicular systems, respectively. In this paper, we describe the modifications that make SimuLTE interoperable with Veins and INET, which leverage the OMNeT++ paradigm, and allow us to achieve our goal without any modification to either of the latter two. We discuss the limitations of the previous solution, namely VeinsLTE, which integrates all three in a single framework, thus preventing independent evolution and upgrades of each building block.


title: Determinants of E-Commerce in Indian MSME Sector: A Conceptual Research Model Based on TOE Framework

abstract: This paper proposes research framework to study impact of organizational factors on E-Commerce Implementation in Indian MSME sector. It also shows the profound impact of MSME sector on Indian Economy, status of E-commerce in India & imperative of E-Commerce for Indian MSME sector. A literature review in this area.


Forensic Data Extraction and Analysis of Left Artifacts on emulated Android Phones: A Case Study of Instant Messaging Applications


title: Ad Recommendation for Sponsored Search Engine via Composite Long-Short Term Memory

abstract: Search engine logs contain a large amount of users' click-through data that can be leveraged as implicit indicators of relevance. In this paper we address ad recommendation problem that finding and ranking the most relevant ads with respect to users' search queries. Due to the click sparsity, the conventional methods can hardly model the both inter- and intra-relations among users, queries and ads. We utilize the long-short term memory(LSTM) network to effectively encode two kinds of sequences: the (user, query) sequence and the query word sequence to represent users' query intention in a continuous vector space and decode them as distributions over ads respectively. Further more, we combine these two LSTM networks in an appropriate way to build up a more robust model referred as composite LSTM model(cLSTM) for ad recommendation. We evaluate the proposed cLSTM on real world click-through data set comparing with two baseline methods, the results demonstrate that our proposed model outperforms two baselines and mitigate the click sparsity problem to a certain degree.


title: Semantic Classes And Syntactic Ambiguity

abstract: In this paper we propose to define selectional preference and semantic similarity as information-theoretic relationships involving conceptual classes, and we demonstrate the applicability of these definitions to the resolution of syntactic ambiguity. The space of classes is defined using WordNet [8], and conceptual relationships are determined by means of statistical analysis using parsed text in the Penn Treebank.


Defendable Architectures Achieving Cyber Security by Designing for Intelligence Driven Defense


title: Fabrication of solid lipid microcapsules containing ascorbic acid using a microfluidic technique.

abstract: The importance of ascorbic acid (AA) in the human diet has motivated food researchers to develop AA-fortified food products. However, this compound is very unstable. The aim of this work was to produce solid lipid microcapsules (SLMs) loaded with AA using microfluidic technology. The morphology of the SLMs was analysed by optical, scanning electron and confocal microscopy. We determined the encapsulation efficiency, particle size and stability of the encapsulated material. Two different means of enhancing the encapsulation efficiency and stability of AA were demonstrated: a pore blocking method and a micromolecule-chelating agent within the core. The results indicated the enormous potential of the designed vehicle to prevent AA degradation in a food product; additionally, this vehicle could mask the acidic taste of AA.


title: Integration of Atheros ath5k device driver in wireless ad-hoc router

abstract: Research in Mobile ad-hoc networks (MANETs) has been in-progress towards real-time implementation, such as routing protocols, and device drivers for wireless cards. This paper presents and discusses a real architecture of mobile wireless ad-hoc routers on open-source Linux platform. An exploration of Atheros ath5k driver for wireless ad-hoc routers is shown in three-fold. Firstly, operations of WLAN MAC layer (IEEE 802.11), both in the infrastructure mode and the infrastructure-less (ad-hoc) mode, are described and analyzed. Secondly, the source code of ath5k device driver is analyzed and discussed on different characteristics. These ones include: i) data structures to be used in the ath5k, ii) reception and transmission packet flows, iii) configuration flow. Finally, realistic scenarios are presented to illustrate for the operations of ath5k device driver, and to collect statistical wireless information.


Mobile Robot Navigation Using Active Vision


167-29: Segmenting Children's Narratives with PROC CLUSTER: An Application of SAS Tools to Child Language Studies


title: Making snapshot isolation serializable

abstract: Snapshot Isolation (SI) is a multiversion concurrency control algorithm, first described in Berenson et al. [1995]. SI is attractive because it provides an isolation level that avoids many of the common concurrency anomalies, and has been implemented by Oracle and Microsoft SQL Server (with certain minor variations). SI does not guarantee serializability in all cases, but the TPC-C benchmark application [TPC-C], for example, executes under SI without serialization anomalies. All major database system products are delivered with default nonserializable isolation levels, often ones that encounter serialization anomalies more commonly than SI, and we suspect that numerous isolation errors occur each day at many large sites because of this, leading to corrupt data sometimes noted in data warehouse applications. The classical justification for lower isolation levels is that applications can be run under such levels to improve efficiency when they can be shown not to result in serious errors, but little or no guidance has been offered to application programmers and DBAs by vendors as to how to avoid such errors. This article develops a theory that characterizes when nonserializable executions of applications can occur under SI. Near the end of the article, we apply this theory to demonstrate that the TPC-C benchmark application has no serialization anomalies under SI, and then discuss how this demonstration can be generalized to other applications. We also present a discussion on how to modify the program logic of applications that are nonserializable under SI so that serializability will be guaranteed.


title: Broadband Circularly Polarized Crossed Dipole With Parasitic Loop Resonators and Its Arrays

abstract: A novel printed crossed dipole with broad axial ratio (AR) bandwidth is proposed. The proposed dipole consists of two dipoles crossed through a 90°phase delay line, which produces one minimum AR point due to the sequentially rotated configuration and four parasitic loops, which generate one additional minimum AR point. By combining these two minimum AR points, the proposed dipole achieves a broadband circularly polarized (CP) performance. The proposed antenna has not only a broad 3 dB AR bandwidth of 28.6% (0.75 GHz, 2.25-3.0 GHz) with respect to the CP center frequency 2.625 GHz, but also a broad impedance bandwidth for a voltage standing wave ratio (VSWR) ≤2 of 38.2% (0.93 GHz, 1.97-2.9 GHz) centered at 2.435 GHz and a peak CP gain of 8.34 dBic. Its arrays of 1 t 2 and 2 t 2 arrangement yield 3 dB AR bandwidths of 50.7% (1.36 GHz, 2-3.36 GHz) with respect to the CP center frequency, 2.68 GHz, and 56.4% (1.53 GHz, 1.95-3.48 GHz) at the CP center frequency, 2.715 GHz, respectively. This paper deals with the designs and experimental results of the proposed crossed dipole with parasitic loop resonators and its arrays.


title: Spoken Natural Language Dialogue Systems: A Practical Approach

abstract: 1. Achieving Spoken Communication with Computers 2. Foundational Work in Integrated Dialog Processing 3. Dialog Processing Theory 4. Computational Model 5. Parsing 6. System Implementation 7. Experimental Results 8. Performance of the Speech Recognizer and Parser 9. Enhanced Dialog Processing: Verifying Doubtful Inputs 10. Extending the State of the Art A. The Goal and Action Description Language B. User's Guide for the Interruptible Prolog Simulator (IPSIM) C. Obtaining the System Software Via the Anonymous FTP


title: A New Comprehensive Approach of Surround Sound Recording

abstract: Many techniques have been developed concerning surround sound recording and the issue has turned out to be a challenge without a comprehensive theory. This paper presents an approach based on a full 3D acoustic field theory and the use of a 3D microphone array. Our research work has lead to a new spatial digital processing technique which allows the use of freely positioned capsules of any type such as omnidirectionals, bidirectionals, or cardioids. This technique can be seen as an extension of full sphere, generalized Ambisonics which provides a spatial resolution never achieved before, but requires neither high order directivity capsules nor assumes that all capsules are coincident. The theory has been validated with a full sphere 3rd-order prototype using 24 omnidirectional capsules. The theory also allows for a 5th order spherical harmonic, multichannel 5.0 microphone. 5717 LABORIE ET AL. A NEW COMPREHENSIVE APPROACH OF SURROUND SOUND RECORDING 0 INTRODUCTION Spatial sound recording has been an issue for a long time. Beginning with stereo, important steps have been made. However, high spatial resolution sound recording is still a challenge [9, 14, 18] mainly because of the lack of good high order elementary microphones and of a comprehensive theory [20]. Fundamental acoustics has been understood for a long time but has long been unused. It does not currently offer ready-to-use tools except for Wave Field Synthesis. Surround sound reveals the need for further application of this theory. In the early 70’s, Michael Gerzon [8] developed Ambisonics, a recording technique based on the encoding of the incoming direction of the sound within the sound itself [1, 2, 10, 13]. This approach considers the three dimensional sound field as a source distribution around a central point and was an innovative extension of stereo in the same spirit as its inventor, Blumlein. Ambisonics allows separation of the recording stage from the reconstruction stage via the so-called encoding and decoding processes. The Ambisonic microphone contains four cardioid capsules which deliver the A-format. An encoder transforms these signals to the B-format [7]. The B-format signals have the advantage of being independent of the characteristics of either the recording or the reconstruction systems, contrary to stereophonic, quadraphonic or today’s other multichannel schemes. During the reconstruction stage, a decoder generates the loudspeaker’s feed signals from the B-format. The decoder depends on the loudspeaker’s configuration and several decoders have been designed for various configurations, such as quadraphony or 5.1 multichannel format. The spatial resolution is limited, however, to first order spherical harmonics because the capsules used to record the sound are first order. More precisely, the tetrahedral microphone array known as the SoundField microphone, patented by Gerzon, has cardioid capsules [4]. Many extensions and developments concerning Ambisonics have been studied to reach higher orders [5, 6, 11, 12, 15, 16] but these extensions have been limited by the fact that there do not exist any higher order microphones with adequate characteristics. Moreover, the theory assumes that all microphones are physically at the same point, which leads either to imperfections or to requiring compensation of the signals. In a recent article [17], M. A. Poletti developed a new theory for a circular microphone array using only omnidirectional or cardioid capsules which leads to a technique that does not require that the capsules be all at the same point and also provides high order directivity in the horizontal plane. He achieves this by no longer considering a source distribution around the origin, but rather by considering the physical sound field in a horizontal plane near the origin. This has the advantage of not requiring the knowledge of high order derivatives of the sound field at the origin to obtain high order directivity in the horizontal plane. This theory still has some drawbacks. The capsules are necessarily on a circle, and omnidirectional capsules can not be used because they lead to infinite gains. Moreover, the capsule noise is not taken into account and could be amplified during the encoding process. Finally, this theory uses a two dimensional model, which constitutes a significant departure from reality. In this paper, a new spatial recording technique based on a three dimensional theoretical approach is presented. The goal of this technique is to extract and deliver as much spatial information as possible from the set of signals delivered by a microphone array. In order to do this, we use fundamental acoustics [3, 19] and develop new tools to apply it to spatial sound recording. This leads to a sound representation in a form compatible with full sphere, high order, generalized Ambisonics. However, this work is not derived from Ambisonics, as we do not consider the source repartition around the origin. This work can be seen as an extension to Ambisonics that overcomes its limitations. Our technique makes it possible to obtain high order Ambisonic signals from an array of capsules which can be of any type (omnidirectional, bidirectional, cardioid, etc.) and can have any position and orientation in space. Nothing is presumed a priori about the position of the capsules, so they are not necessarily on a plane or on a sphere. The resulting microphone consists of an array of capsules and a processing system based on a DSP or a PC. This paper will first present the theoretical principle including the spatial sampling and encoding aspects. The practical results we have arrived at will then AES 114 CONVENTION, AMSTERDAM, THE NETHERLANDS, 2003 MARCH 22–25 2 LABORIE ET AL. A NEW COMPREHENSIVE APPROACH OF SURROUND SOUND RECORDING


title: SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems

abstract: SentiNet is a novel detection framework for localized universal attacks on neural networks. These attacks restrict adversarial noise to contiguous portions of an image and are reusable with different images---constraints that prove useful for generating physically-realizable attacks. Unlike most other works on adversarial detection, SentiNet does not require training a model or preknowledge of an attack prior to detection. Our approach is appealing due to the large number of possible mechanisms and attack-vectors that an attack-specific defense would have to consider. By leveraging the neural network's susceptibility to attacks and by using techniques from model interpretability and object detection as detection mechanisms, SentiNet turns a weakness of a model into a strength. We demonstrate the effectiveness of SentiNet on three different attacks---i.e., data poisoning attacks, trojaned networks, and adversarial patches (including physically realizable attacks)--- and show that our defense is able to achieve very competitive performance metrics for all three threats. Finally, we show that SentiNet is robust against strong adaptive adversaries, who build adversarial patches that specifically target the components of SentiNet's architecture.


title: Scrum-based Methodology for Distributed Software Development

abstract: There are many companies doing software development in a distributed way nowadays, due to the great benefits that it provides, however, this type of development also leads to multiple complications such as deteriorated communications. Among the most widespread methodologies for software development is the Rational Unified Process (RUP). However, in recent decades a series of methodologies have emerged, called "agile methodologies", which aim to develop software quickly, focusing on people and frequent delivery of software. Of the existing agile methodologies, Scrum is one of the most widely-applied, due to its ability to complement other methods and processes. For this reason, the strategies proposed by Scrum may be suitable for the distributed management and deployment of the phases and disciplines of RUP. In this paper we present a methodology based on the integration of RUP and Scrum, called scrum UP, which has been so designed as to be appropriate in a distributed environment.


title: Question Answering System for Non-factoid Type Questions and Automatic Evaluation based on BE Method

abstract: In this paper, we describe answer extraction method for non-factoid questions. We classified non-factoid type questions into three types: why type, definition type and how type. We analyzed each type of questions and developed answer extraction patterns for these types of questions. For automatic evaluation, we have developed BE based evaluation tool for answers of questions. BE method is originally proposed by Hovy et. and we applied BE method for question answering evaluation. Evaluation is done by comparison between BEs of system answer and BEs of correct answers.


title: Visualization Criticism - The Missing Link Between Information Visualization and Art

abstract: Classifications of visualization are often based on technical criteria, and leave out artistic ways of visualizing information. Understanding the differences between information visualization and other forms of visual communication provides important insights into the way the field works, though, and also shows the path to new approaches. We propose a classification of several types of information visualization based on aesthetic criteria. The notions of artistic and pragmatic visualization are introduced, and their properties discussed. Finally, the idea of visualization criticism is proposed, and its rules are laid out. Visualization criticism bridges the gap between design, art, and technical/pragmatic information visualization. It guides the view away from implementation details and single mouse clicks to the meaning of a visualization.


title: “Hunger Hurts but Starving Works”: Characterizing the Presentation of Eating Disorders Online

abstract: Within the CSCW community, little has been done to systematically analyze online eating disorder (ED) user generated content. In this paper, we present the results of a cross-platform content analysis of ED-related posts. We analyze the way that hashtags are used in ad-hoc ED- focused networks and present a comprehensive corpus of ED-terminology that frequently accompanies ED activities online. We provide exemplars of the types of ED-related content found online. Through this characterization of activities, we draw attention to the increasingly important role that these platforms play and how they are used and misappropriated for negative health purposes. We also outline specific challenges associated with researching these types of networks online. CAUTION: This paper includes media that could potentially be a trigger to those dealing with an eating disorder or with other self-injury illnesses. Please use caution when reading, printing, or disseminating this paper.


title: Natural Language Object Retrieval

abstract: In this paper, we address the task of natural language object retrieval, to localize a target object within a given image based on a natural language query of the object. Natural language object retrieval differs from text-based image retrieval task as it involves spatial information about objects within the scene and global scene context. To address this issue, we propose a novel Spatial Context Recurrent ConvNet (SCRC) model as scoring function on candidate boxes for object retrieval, integrating spatial configurations and global scene-level contextual information into the network. Our model processes query text, local image descriptors, spatial configurations and global context features through a recurrent network, outputs the probability of the query text conditioned on each candidate box as a score for the box, and can transfer visual-linguistic knowledge from image captioning domain to our task. Experimental results demonstrate that our method effectively utilizes both local and global information, outperforming previous baseline methods significantly on different datasets and scenarios, and can exploit large scale vision and language datasets for knowledge transfer.


title: A Tradeoff Analysis of a Cloud-Based Robot Navigation Assistant Using Stereo Image Processing

abstract: The use of Cloud Computing for computation offloading in the robotics area has become a field of interest today. The aim of this work is to demonstrate the viability of cloud offloading in a low level and intensive computing task: a vision-based navigation assistance of a service mobile robot. In order to do so, a prototype, running over a ROS-based mobile robot (Erratic by Videre Design LLC) is presented. The information extracted from on-board stereo cameras will be used by a private cloud platform consisting of five bare-metal nodes with AMD Phenom 965 $\,\times\,$ 4 CPU, with the cloud middleware Openstack Havana. The actual task is the shared control of the robot teleoperation, that is, the smooth filtering of the teleoperated commands with the detected obstacles to prevent collisions. All the possible offloading models for this case are presented and analyzed. Several performance results using different communication technologies and offloading models are explained as well. In addition to this, a real navigation case in a domestic circuit was done. The tests demonstrate that offloading computation to the Cloud improves the performance and navigation results with respect to the case where all processing is done by the robot.


Machine Translation using Neural Networks and Finite-State Models


title: Modeling and forecasting cumulative average temperature and heating degree day indices for weather derivative pricing

abstract: In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein–Uhlenbeck temperature process, with seasonality in the level and volatility and time-varying speed of mean reversion. We forecast up to 2 months ahead out of sample daily temperatures, and we simulate the corresponding Cumulative Average Temperature and Heating Degree Day indices. The proposed model is validated in 8 European and 5 USA cities all traded in the Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods, proposed in prior studies, in most cases. We find that wavelet networks can model the temperature process very well and consequently they constitute an accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Cooling and Heating Degree Day indices.


SciData: a data model and ontology for semantic representation of scientific data


title: Minimum Translation Modeling with Recurrent Neural Networks

abstract: We introduce recurrent neural networkbased Minimum Translation Unit (MTU) models which make predictions based on an unbounded history of previous bilingual contexts. Traditional back-off n-gram models suffer under the sparse nature of MTUs which makes estimation of highorder sequence models challenging. We tackle the sparsity problem by modeling MTUs both as bags-of-words and as a sequence of individual source and target words. Our best results improve the output of a phrase-based statistical machine translation system trained on WMT 2012 French-English data by up to 1.5 BLEU, and we outperform the traditional n-gram based MTU approach by up to 0.8 BLEU.


title: Automatic detection and prevention of cyberbullying

abstract: The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on average, 20% to 40% of all teenagers have been victimized online. In this paper, we focus on cyberbullying as a particular form of cybervictimization. Successful prevention depends on the adequate detection of potentially harmful messages. However, given the massive information overload on the Web, there is a need for intelligent systems to identify potential risks automatically. We present the construction and annotation of a corpus of Dutch social media posts annotated with fine-grained cyberbullying-related text categories, such as insults and threats. Also, the specific participants (harasser, victim or bystander) in a cyberbullying conversation are identified to enhance the analysis of human interactions involving cyberbullying. Apart from describing our dataset construction and annotation, we present proof-of-concept experiments on the automatic identification of cyberbullying events and fine-grained cyberbullying categories.


title: Confirmation Bias: A Ubiquitous Phenomenon in Many Guises

abstract: Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a h...


The association between pelvic girdle pain and sick leave during pregnancy; a retrospective study of a Norwegian population


Modeling Approximate Computing Techniques


title: Emotional Competence and Leadership Excellence at Johnson & Johnson

abstract: A study was conducted on three hundred and fifty-eight Managers across the Johnson & Johnson Consumer & Personal Care Group (JJC&PC Group) globally to assess if there are specific leadership competencies that distinguish high performers from average performers. Participants were randomly selected, then coded for performance rating, potential code, gender, functional group and regional area. More than fourteen hundred employees took part in a one hundred and eighty three question multi-rater survey that measured a variety of competencies associated with leadership performance including those commonly referred to as Emotional Intelligence. Results showed that the highest performing managers have significantly more “emotional competence” than other managers. There was strong inter-rater agreement among Supervisors, Peers, and Subordinates that the competencies of Self-Confidence, Achievement Orientation, Initiative, Leadership, Influence and Change Catalyst differentiate superior performers. The high potential managers received higher scores in the emotional competencies by Peers and Supervisors, but not by Subordinates. Some gender difference was found, with Supervisors rating Females higher in Adaptability and Service Orientation, while Peers rated Females higher on Emotional Self-Awareness, Conscientiousness, Developing Others, Service Orientation, and Communication. Direct reports scored Males higher in Change Catalyst.