title: E-Participation, Transparency, and Trust in Local Government

abstract: This article examines the relationship between electronic participation (e-participation) and trust in local government by focusing on five dimensions of the e-participationprocess: (1) satisfaction with e-participation applications, (2) satisfaction with government responsiveness to e-participants, (3) e-participants’ development through the participation, (4) perceived influence on decision making, and (5) assessment of government transparency. Using data from the 2009 E-Participation Survey in Seoul Metropolitan Government, this article finds that e-participants’ satisfaction with e-participation applications is directly associated with their development and their assessment of government transparency. The findings reveal that e-participants’ satisfaction with government responsiveness is positively associated with their perceptions of influencing government decision making. Furthermore, there is a positive association between e-participants’ perception of influencing government decision making and their assessment of government transparency. Finally, the article finds that there is a positive association between e-participants’ assessment of government transparency and their trust in the local government providing the e-participation program.


title: Optimization of ReLU Neural Networks using Quotient Stochastic Gradient Descent

abstract: It has been well known that neural networks with rectified linear hidden units (ReLU) as activation functions are positively scale invariant, which results in severe redundancy in their weight space (i.e., many ReLU networks with different weights are actually equivalent). In this paper, we formally characterize this redundancy/equivalence using the language of \emph{quotient space} and discuss its negative impact on the optimization of ReLU neural networks. Specifically, we show that all equivalent ReLU networks correspond to the same vector in the quotient space, and each such vector can be characterized by the so-called skeleton paths in the ReLU networks. With this, we prove that the dimensionality of the quotient space is $\#$weight$-\#$(hidden nodes), indicating that the redundancy of the weight space is huge. In this paper, we propose to optimize ReLU neural networks directly in the quotient space, instead of the original weight space. We represent the loss function in the quotient space and design a new stochastic gradient descent algorithm to iteratively learn the model, which we call \emph{Quotient stochastic gradient descent } (abbreviated as Quotient SGD). We also develop efficient tricks to ensure that the implementation of Quotient SGD almost requires no extra computations as compared to standard SGD. According to the experiments on benchmark datasets, our proposed Quotient SGD can significantly improve the accuracy of the learned model.


title: Mining Education Data to Predict Student's Retention: A comparative Study

abstract: The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student retention data.


title: A Survey on Sensor-Cloud: Architecture, Applications, and Approaches

abstract: Nowadays, wireless sensor network (WSN) applications have been used in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. However, due to the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the WSN data as well as analysis (online and offline) of the processed information under context using inherently complex models to extract events of interest. In this scenario, cloud computing is becoming a promising technology to provide a flexible stack of massive computing, storage, and software services in a scalable and virtualized manner at low cost. Therefore, in recent years, Sensor-Cloud infrastructure is becoming popular that can provide an open, flexible, and reconfigurable platform for several monitoring and controlling applications. In this paper, we present a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor-Cloud platform including its definition, architecture, and applications. The research challenges, existing solutions, and approaches as well as future research directions are also discussed in this paper.


title: A complementary filter for attitude estimation of a fixed-wing UAV

abstract: This paper considers the question of using a nonlinear complementary filter for attitude estimation of fixed-wing unmanned aerial vehicle (UAV) given only measurements from a low-cost inertial measurement unit. A nonlinear complementary filter is proposed that combines accelerometer output for low frequency attitude estimation with integrated gyrometer output for high frequency estimation. The raw accelerometer output includes a component corresponding to airframe acceleration, occurring primarily when the aircraft turns, as well as the gravitational acceleration that is required for the filter. The airframe acceleration is estimated using a simple centripetal force model (based on additional airspeed measurements), augmented by a first order dynamic model for angle-of-attack, and used to obtain estimates of the gravitational direction independent of the airplane manoeuvres. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/INS that was available for the data set.


title: Community detection in content-sharing social networks

abstract: Network structure and content in microblogging sites like Twitter influence each other ---user A on Twitter follows user B for the tweets that B posts on the network, and A may then re-tweet the content shared by B to his/her own followers. In this paper, we propose a probabilistic model to jointly model link communities and content topics by leveraging both the social graph and the content shared by users. We model a community as a distribution over users, use it as a source for topics of interest, and jointly infer both communities and topics using Gibbs sampling. While modeling communities using the social graph, or modeling topics using content have received a great deal of attention, a few recent approaches try to model topics in content-sharing platforms using both content and social graph. Our work differs from the existing generative models in that we explicitly model the social graph of users along with the user-generated content, mimicking how the two entities co-evolve in content-sharing platforms. Recent studies have found Twitter to be more of a content-sharing network and less a social network, and it seems hard to detect tightly knit communities from the follower-followee links. Still, the question of whether we can extract Twitter communities using both links and content is open. In this paper, we answer this question in the affirmative. Our model discovers coherent communities and topics, as evinced by qualitative results on sub-graphs of Twitter users. Furthermore, we evaluate our model on the task of predicting follower-followee links. We show that joint modeling of links and content significantly improves link prediction performance on a sub-graph of Twitter (consisting of about 0.7 million users and over 27 million tweets), compared to generative models based on only structure or only content and paths-based methods such as Katz.


title: Reciprocal frame structures made easy

abstract: A reciprocal frame (RF) is a self-supported three-dimensional structure made up of three or more sloping rods, which form a closed circuit, namely an RF-unit. Large RF-structures built as complex grillages of one or a few similar RF-units have an intrinsic beauty derived from their inherent self-similar and highly symmetric patterns. Designing RF-structures that span over large domains is an intricate and complex task. In this paper, we present an interactive computational tool for designing RF-structures over a 3D guiding surface, focusing on the aesthetic aspect of the design. There are three key contributions in this work. First, we draw an analogy between RF-structures and plane tiling with regular polygons, and develop a computational scheme to generate coherent RF-tessellations from simple grammar rules. Second, we employ a conformal mapping to lift the 2D tessellation over a 3D guiding surface, allowing a real-time preview and efficient exploration of wide ranges of RF design parameters. Third, we devise an optimization method to guarantee the collinearity of contact joints along each rod, while preserving the geometric properties of the RF-structure. Our tool not only supports the design of wide variety of RF pattern classes and their variations, but also allows preview and refinement through interactive controls.


title: Estimating 3D Human Shapes from Measurements

abstract: Recent advances in 3D imaging technologies give rise to databases of human shapes, from which statistical shape models can be built. These statistical models represent prior knowledge of the human shape and enable us to solve shape reconstruction problems from partial information. Generating human shape from traditional anthropometric measurements is such a problem, since these 1D measurements encode 3D shape information. Combined with a statistical shape model, these easy-to-obtain measurements can be leveraged to create 3D human shapes. However, existing methods limit the creation of the shapes to the space spanned by the database and thus require a large amount of training data. In this paper, we introduce a technique that extrapolates the statistically inferred shape to fit the measurement data using non-linear optimization. This method ensures that the generated shape is both human-like and satisfies the measurement conditions. We demonstrate the effectiveness of the method and compare it to existing approaches through extensive experiments, using both synthetic data and real human measurements.


Short Text Clustering via Convolutional Neural Networks


Credit Scoring using Machine Learning Techniques


title: Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods

abstract: Partial least squares-based structural equation modelling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are used. One of the most fundamental issues in PLS-SEM is that of minimum sample size estimation. The ‘10-times rule’ has been a favourite because of its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum sample size estimation in PLS-SEM: the inverse square root method, and the gamma-exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application. © 2016 John Wiley & Sons Ltd


title: A cross-language acoustic study of initial and final allophones of /l/

abstract: Formant frequency data for /l/ in 23 languages/dialects where the consonant may be typically clear or dark show that the two varieties of /l/ are set in contrast mostly in the context of /i/ but also next to /a/, and that a few languages/dialects may exhibit intermediate degrees of darkness in the consonant. F2 for /l/ is higher utterance initially than utterance finally, more so if the lateral is clear than if it is dark; moreover, the initial and final allophones may be characterized as intrinsic (in most languages/dialects) or extrinsic (in several English dialects, Czech and Dutch) depending on whether the position-dependent frequency difference in question is below or above 200/300Hz. The paper also reports a larger degree of vowel coarticulation for clear /l/ than for dark /l/ and in initial than in final position. These results are interpreted in terms of the production mechanisms involved in the realization of the two /l/ varieties in the different positional and vowel context conditions subjected to investigation.


title: EMAN 2 : An extensible image processing suite for electron microscopy

abstract: EMAN is a scientiWc image processing package with a particular focus on single particle reconstruction from transmission electron microscopy (TEM) images. It was Wrst released in 1999, and new versions have been released typically 2–3 times each year since that time. EMAN2 has been under development for the last two years, with a completely refactored image processing library, and a wide range of features to make it much more Xexible and extensible than EMAN1. The user-level programs are better documented, more straightforward to use, and written in the Python scripting language, so advanced users can modify the programs’ behavior without any recompilation. A completely rewritten 3D transformation class simpliWes translation between Euler angle standards and symmetry conventions. The core C++ library has over 500 functions for image processing and associated tasks, and it is modular with introspection capabilities, so programmers can add new algorithms with minimal eVort and programs can incorporate new capabilities automatically. Finally, a Xexible new parallelism system has been designed to address the shortcomings in the rigid system in EMAN1.


title: Artificial Intelligence in Video Games: Towards a Unified Framework

abstract: With modern video games frequently featuring sophisticated and realistic environments, the need for smart and comprehensive agents that understand the various aspects of complex environments is pressing. Since video game AI is often specifically designed for each game, video game AI tools currently focus on allowing video game developers to quickly and efficiently create specific AI. One issue with this approach is that it does not efficiently exploit the numerous similarities that exist between video games not only of the same genre, but of different genres too, resulting in a difficulty to handle the many aspects of a complex environment independently for each video game. Inspired by the human ability to detect analogies between games and apply similar behavior on a conceptual level, this paper suggests an approach based on the use of a unified conceptual framework to enable the development of conceptual AI which relies on conceptual views and actions to define basic yet reasonable and robust behavior. The approach is illustrated using two video games, Raven and StarCraft: Brood War.


title: Self-assembly of fibronectin into fibrillar networks underneath dipalmitoyl phosphatidylcholine monolayers: Role of lipid matrix and tensile forces

abstract: The cell-mediated assembly of fibronectin (Fn) into fibrillar matrices is a complex multistep process that is incompletely understood because of the chemical complexity of the extracellular matrix and a lack of experimental control over molecular interactions and dynamic events. We have identified conditions under which Fn assembles into extended fibrillar networks after adsorption to a dipalmitoyl phosphatidylcholine (DPPC) monolayer in contact with physiological buffer. We propose a sequential model for the Fn assembly pathway, which involves the orientation of Fn underneath the lipid monolayer by insertion into the liquid expanded (LE) phase of DPPC. Attractive interactions between these surface-anchored proteins and the liquid condensed (LC) domains leads to Fn enrichment at domain edges. Spontaneous self-assembly into fibrillar networks, however, occurs only after expansion of the DPPC monolayer from the LC phase though the LC/LE phase coexistence. Upon monolayer expansion, the domain boundaries move apart while attractive interactions among Fn molecules and between Fn and domain edges produce a tensile force on the proteins that initiates fibril assembly. The resulting fibrils have been characterized in situ by using fluorescence and light-scattering microscopy. We have found striking similarities between fibrils produced under DPPC monolayers and those found on cellular surfaces, including their assembly pathways.


ANDROINSPECTOR: A System for Comprehensive Analysis of Android Applications


title: Optimal Power Dispatch of Multi-Microgrids at Future Smart Distribution Grids

abstract: In this paper, future distribution network operation is discussed under assumption of multimicrogrids (MMGs) concept. The economic operation of MMGs is formulated as an optimization problem. A stochastically and probabilistic modeling of both small-scale energy resources (SSERs) and load demand at each microgrids (MGs) is done to determine the optimal economic operation of each MGs with minimum cost based on the power transaction between the MGs and main grids. The balance between the total power generation in each MGs and the load demand is determined regarding the sold or purchase power either by MG or by main grid. Based on the results, the mean, standard deviation, and probability density function of each generated power with SSERs is determined considering optimization constraints. A statistical analysis for generated power and costs is given. The power interchange between MGs is considered. The particle swarm optimization is applied to minimize the cost function as an optimization algorithm. Results show that it is possible to regulate the power demand and power transaction between each MGs and the main grid. Moreover, it is indicated that the power sharing between MGs with main grid can reduce the total operation cost of the future distribution network.


title: Structural Equation Modeling of Mediation and Moderation With Contextual Factors

abstract: Researchers often grapple with the idea that an observed relationship may be part of a more complex chain of effects. These complex relationships are described in terms such as indirect influences, distal vs. proximal causes, intermediate outcomes, and ultimate causes; all of which share the concept of mediation. Similarly, researchers must often consider that an observed relationship may be part of a more complex, qualified system. These relationships are described using concepts such as interactions, subgroup differences, and shocks; all of which share the concept of moderation. Generally speaking, a mediator can be thought of as the carrier or transporter of information along the causal chain of effects. A moderator, on the other hand, is the changer of a relationship in as ystem. In this chapter, we explore both empirical and theoretical considerations in modeling mediation and moderation using structural equation modeling. Our


What Is Instructional-Design Theory and How Is It Changing?


title: A single-system interpretation of dissociations between recognition and categorization in a task involving object-like stimuli

abstract: In previous research (Reed, Squire, Patalano, Smith, & Jonides, 1999), amnesic patients performed at near normal levels in a categorization task involving stimuli with discrete features, but showed impaired recall of the features. These results were taken as evidence of the existence of separate memory systems for categorization and recall/recognition. The present research addresses a single-system account of this dissociation. In Experiment 1, results closely matching the dissociation pattern were obtained when normal participants' classification and recognition performance was tested either immediately or after a week delay. In addition, formal modeling of the data suggested that participants use only a few of the dimensions in the categorization task, whereas they must use many dimensions in the cued-recall task. In Experiment 2, we found that many participants could perform the categorization task without any exposure to the training sequence. These results suggest that different memory demands across the two tasks may be responsible for the observed dissociation—separate memory systems are not necessarily involved.


title: Noise Reduction Topology for Concentrate Winding High-Speed Brushless DC Motors

abstract: In this paper, we report an innovative Brushless DC stator topology for high-speed applications. This stator is designed as a trade-off between acoustic, electro-mechanic performances, and mass production feasibility. This topology is an improvement of an existing architecture fulfilling only the last two criteria.


Sensors and IEDs Required by Smart Distribution Applications


title: Behavioral Patterns of Older Adults in Assisted Living

abstract: In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average n.umber of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.


title: Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

abstract: We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum, but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources form a distributed system where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Theoretical analysis shows that the proposed approach not only yields vanishing regret in comparison to the global optimal solution but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.


title: Scientific Discovery Learning with Computer Simulations of Conceptual Domains

abstract: Scientific discovery learning is a highly self-directed and constructivistic form of learning. A computer simulation is a type of computer-based environment that is well suited for discovery learning, the main task of the learner being to infer, through experimentation, characteristics of the model underlying the simulation. In this article we give a review of the observed effectiveness and efficiency of discovev learning in simulation environments together with problems that learners may encounter in discovery learning, and we discuss how simulations may be combined with instructional support in order to overcome these problems.


The Causes and Consequences of Local Voting in Solomon Islands


title: From Mobile Phone Data to Transport Network -- Gaining Insight about Human Mobility

abstract: In this paper we present methods for mapping trajectories of cell tower latitude-longitudes in mobile phone data to transport network. Compared to other forms of mobile phone data such as GPS traces, this form is the most popular and inexpensive but the least accurate and highly irregular. Unlike some earlier works on this type of data where only long trips are studied, our work is based on everyday transportation inside a city. The problems due to inaccuracy in determining position of a user and irregularity of phone events are felt even more intensely for such small distances. Our approach in processing this data takes into account its repetitive nature, i.e., A typical user makes the same trips every week. Moreover, many users follow the same transport route to go from one place to another. First of all, we defined stay region taking into consideration irregularity of phone events. Then we extracted trajectories between each pair of stay regions as trips. Then, we map these trips to existing transportation network consisting of bus services, train lines, expressways, and their combinations such as bus-train-bus. For a given origin and destination, we create a heat map that shows the most commonly used route. A round-the-clock heat map, both for weekdays and weekends, shows people's mobility at different times of a week. We validate our findings by both direct (sample survey) and indirect (comparison with available statistics) methods.


title: Vision-based Human Gender Recognition: A Survey

abstract: Gender is an important demographic attribute of people. This paper provides a survey of human gender recognition in computer vision. A review of approaches exploiting information from face and whole body (either from a still image or gait sequence) is presented. We highlight the challenges faced and survey the representative methods of these approaches. Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.


title: An Asynchronous Binary-Search ADC Architecture With a Reduced Comparator Count

abstract: This paper reports an asynchronous binary-search analog-to-digital converter (ADC) with reference range prediction. An original N-bit binary-search ADC requires 2N - 1 comparators while the proposed one only needs 2N - 1 ones. Compared to the (high speed, high power) flash ADC and (low speed, low power) successive approximation register ADC, the proposed architecture achieves the balance between power consumption and operation speed. The proof-of-concept 5-bit prototype only consists of a passive track-and-hold circuit, a reference ladder, 9 comparators, 56 switches and 26 static logic gates. This compact ADC occupies an active area of 120 × 50 μm2 and consumes 1.97 mW from a 1-V supply. At 800 MS/s, the effective number of bits is 4.40 bit and the effective resolution bandwidth is 700 MHz. The resultant figure of merit is 116 fJ/conversion-step.


title: The Performance Evaluation of Speech Recognition by Comparative Approach

abstract: For more than five decades, Automatic speech recognition has been the area of active research. This problem was thoroughly studied with the advent of digital computing and signal processing. Increased awareness of the advantages of conversational systems led to the development of this problem. Speech recognition has wide applications and includes voice-controlled appliances fully featured speech-to-text software, automation of operatorassisted services and voice recognition aids for the handicapped.


title: Multi-view 3D reconstruction in underwater environments: Evaluation and benchmark

abstract: Mapping of underwater environments is a critical task for a range of activities from monitoring coral reef habitats to surveying submerged archaeological sites. In recent years, optical reconstruction methods developed for terrestrial (in air) applications have increasingly been applied to the underwater environment by the marine science community. However, assumptions such as the brightness constancy constraint (BCC) and traditional camera pinhole models, which hold in air, do not apply in the underwater environment. There is a lack of literature about how the violation of these assumptions affects the accuracy of the reconstruction. The main contributions of this paper is to develop a quantitative evaluation of computer vision-based 3D reconstruction methods applied to the underwater domain. Comparisons are presented between low- and high-cost camera systems to quantify the tradeoff between equipment cost and performance.


title: Deep learning fusion conceptual frameworks for complex human activity recognition using mobile and wearable sensors

abstract: Human Activity recognition (HAR) is an important area of research in ubiquitous computing and Human Computer Interaction. To recognize activities using mobile or wearable sensor, data are collected using appropriate sensors, segmented, needed features extracted and activities categories using discriminative models (SVM, HMM, MLP etc.). Feature extraction is an important stage as it helps to reduce computation time and ensure enhanced recognition accuracy. Earlier researches have used statistical features which require domain expert and handcrafted features. However, the advent of deep learning that extracts salient features from raw sensor data and has provided high performance in computer vision, speech and image recognition. Based on the recent advances recorded in deep learning for human activity recognition, we briefly reviewed the different deep learning methods for human activities implemented recently and then propose a conceptual deep learning frameworks that can be used to extract global features that model the temporal dependencies using Gated Recurrent Units. The framework when implemented would comprise of seven convolutional layer, two Gated recurrent unit and Support Vector Machine (SVM) layer for classification of activity details. The proposed technique is still under development and will be evaluated with benchmarked datasets and compared with other baseline deep learning algorithms.


title: Factors associated with willingness to participate in a vaccine clinical trial among elderly Hispanic patients

abstract: A population specific understanding of barriers and facilitators to participation in clinical trials could improve recruitment of elderly and minority populations. We investigated how prior exposure to clinical trials and incentives were associated with likelihood of participation in a vaccine clinical trial through a questionnaire administered to 200 elderly patients in an academic general internal medicine clinic. Wilcoxon signed rank sum test compared likelihood of participation with and without monetary incentives. Logistic regression evaluated characteristics associated with intent to participate in an influenza vaccine trial, adjusted for age, gender, language, and education history. When asked about likelihood of participation if there was monetary compensation, there was a 12.2% absolute increase in those reporting that they would not participate, with a significant difference in the distribution of likelihood before and after mentioning a monetary incentive (Wilcoxon signed rank test, p = 0.001). Those with previous knowledge of clinical trials (54.4%) were more likely to report they would participate vs. those without prior knowledge (OR 2.5, 95% CI [1.2, 5.2]). The study highlights the importance of pre-testing recruitment materials and incentives in key group populations prior to implementing clinical trials.


title: Ensemble Convolutional Neural Networks for Pose Estimation

abstract: Abstract Human pose estimation is a challenging task due to significant appearance variations. An ensemble of models, each of which is optimized for a limited variety of poses, is capable of modeling a large variety of human body configurations.However, ensembling models is not a straightforward task due to the complex interdependence among noisy and ambiguous pose estimation predictions acquired by each model.We propose to capture this complex interdependence using a convolutional neural network. Our network achieves this interdependence representation using a combination of deep convolution and deconvolution layers for robust and accurate pose estimation. We evaluate the proposed ensemble model on publicly available datasets and show that our model compares favorably against baseline models and state-of-the-art methods.


title: 3.38 Mhz operation of 1.2kV SiC MOSFET with integrated ultra-fast gate drive

abstract: With the commercialization of wide bandgap power devices such as SiC MOSFETs and JBS diodes, power electronics converters used in the harsh environments such as hybrid electric vehicles and aerospace attract more and more attentions. The low loss, high temperature and fast switching capabilities are utilized in the converters to improve the power density and efficiency. However, the EMI problem caused by the fast switching is a major constrain for improving switching frequency. For this reason, an integrated SiC module with 1.2kV MOSFET and ultra-fast gate drive circuits is proposed and developed. Two 1.2kV SiC MOSFETs bare dies and two high current gate driver chips are integrated in a compact integrated module package to reduce the parasitic inductance. 0Ω gate resistor therefore can be used in this module to improve the device at maximum speed. Noise free operation of the tested module is verified even under extremely high dV/dt and dI/dt conditions. The ultra-low turn-off loss of the module is being demonstrated. Finally, the integrated module is demonstrated in two megahertz converters: an 800W 1.5MHz synchronous boost converter and a 3.38MHz half bridge inverter. The era for high voltage-megahertz switching has arrived.


title: Recognizing Text with Perspective Distortion in Natural Scenes

abstract: This paper presents an approach to text recognition in natural scene images. Unlike most existing works which assume that texts are horizontal and frontal parallel to the image plane, our method is able to recognize perspective texts of arbitrary orientations. For individual character recognition, we adopt a bag-of-key points approach, in which Scale Invariant Feature Transform (SIFT) descriptors are extracted densely and quantized using a pre-trained vocabulary. Following [1, 2], the context information is utilized through lexicons. We formulate word recognition as finding the optimal alignment between the set of characters and the list of lexicon words. Furthermore, we introduce a new dataset called StreetViewText-Perspective, which contains texts in street images with a great variety of viewpoints. Experimental results on public datasets and the proposed dataset show that our method significantly outperforms the state-of-the-art on perspective texts of arbitrary orientations.


title: School-Based Interventions for Aggressive and Disruptive Behavior: Update of a Meta-Analysis

abstract: Background Research about the effectiveness of school-based psychosocial prevention programs for reducing aggressive and disruptive behavior was synthesized using meta-analysis. This work updated previous work by the authors and further investigated which program and student characteristics were associated with the most positive outcomes. Methods Two hundred forty-nine experimental and quasi-experimental studies of school-based programs with outcomes representing aggressive and/or disruptive behavior were obtained. Effect sizes and study characteristics were coded from these studies and analyzed. Results Positive overall intervention effects were found on aggressive and disruptive behavior and other relevant outcomes. The most common and most effective approaches were universal programs and targeted programs for selected/indicated children. The mean effect sizes for these types of programs represent a decrease in aggressive/disruptive behavior that is likely to be of practical significance to schools. Multicomponent comprehensive programs did not show significant effects and those for special schools or classrooms were marginal. Different treatment modalities (e.g., behavioral, cognitive, social skills) produced largely similar effects. Effects were larger for better-implemented programs and those involving students at higher risk for aggressive behavior. Conclusions Schools seeking prevention programs may choose from a range of effective programs with some confidence that whatever they pick will be effective. Without the researcher involvement that characterizes the great majority of programs in this meta-analysis, schools might be well-advised to give priority to those that will be easiest to implement well in their settings.


title: Evolutionary Computation: An Overview

abstract: Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computerbased problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and differences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research.


title: A Diffraction Measurement Model and Particle Filter Tracking Method for RSS-Based DFL

abstract: Device-free localization (DFL) based on received signal strength (RSS) measurements functions by measuring RSS variation due to the presence of the target. The accuracy of a certain localization method closely depends on the accuracy of the measurement model itself. Existing models have been found not accurate enough under certain circumstances as they cannot explain some phenomena observed in DFL practices. In light of this, we propose a new model to characterize the RSS variation, which invokes diffraction theory and regards the target as a cylinder instead of a point mass. It is observed that the proposed model agrees well with experimental measurements, particularly when the target crosses the link or is in the vicinity of the link. Since the proposed measurement model is highly nonlinear, a particle filter-based tracking method is used to generate the approximate Bayesian estimate of the target position. As a performance benchmark, we have also derived the posterior Cramer–Rao lower bound of DFL for a diffraction model. A field test has shown that the proposed diffraction model may improve the tracking accuracy at least by 45% in a single-target case and by 27% in a double-target case.


title: Pathogenesis of enteric Campylobacter infection

abstract: Campylobacter jejuni and related species are important human pathogens, causing acute human enterocolitis, and they are the most common cause of food-borne diarrhoea in many industrialized countries. Previous infection with certain strains of C. jejuni is also linked with the development of the neurological disorder Guillain-Barre syndrome (GBS). Despite its importance as a human pathogen, relatively little is understood of the mechanisms of C. jejuni-associated disease. The recent release of the complete genome sequence of C. jejuni strain NCTC 11168, together with new strategies for random and directed mutagenesis, have allowed a better insight into some of the genetic determinants of C. jejuni virulence. In this review current knowledge on the pathogenesis of C. jejuni infection is summarized, and possible directions of future research indicated.


title: CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web

abstract: The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.


title: The mode of delivery affects the diversity and colonization pattern of the gut microbiota during the first year of infants' life: a systematic review

abstract: The human gut is the habitat for diverse and dynamic microbial ecosystem. The human microbiota plays a critical role in functions that sustain health and is a positive asset in host defenses. Establishment of the human intestinal microbiota during infancy may be influenced by multiple factors including delivery mode. Present review compiles existing evidences on the effect of delivery mode on the diversity and colonization pattern of infants gut microbiota. Two investigators searched for relevant scientific publications from four databases (Pubmed, Medline, Embase, and Web of Science). The last search was performed on September 21, 2015, using key terms ((delivery mode OR caesarean delivery OR cesarean section OR vaginal delivery) AND (gut microbiota OR gut microbiome OR gut microflora OR intestinal microflora OR microbial diversity) AND (infants OR children)). All included studies described at least two types of gut microbiota in relation to delivery mode (caesarean section vs vaginal delivery) and used fecal samples to detect gut microbiota. Seven out of 652 retrieved studies met inclusion criteria, were included in systematic analysis. Caesarean Section (CS) was associated with both lower abundance and diversity of the phyala Actinobacteria and Bacteroidetes, and higher abundance and diversity of the phylum Firmicute from birth to 3 months of life. At the colonization level, Bifidobacterium, and Bacteroides genera seems to be significantly more frequent in vaginally delivered infants compared with CS delivered. These infants were more colonized by the Clostridium, and Lactobacillus genera. From the reports, it is tempting to say that delivery mode has less effect on colonization and diversity of Bifidobacteria, Bacteroides, Clostridium, and Lactobacillus genera from the age of 6 to 12 months of life. The diversity and colonization pattern of the gut microbiota were significantly associated to the mode of delivery during the first three months of life, however the observed significant differences disappears after 6 months of infants life. The healthy gut microbiota is considered to promote development and maturation of the immune system while abnormal gut is considered as the major cause of severe gastrointestinal infections during the infancy. Further studies should investigate the diversity and colonization levels of infant gut microbiota in relation to the mode of delivery and its broad impact on infants’ health at each stage of life.


title: Vision-based automated parking system

abstract: This paper describes an approach to overcome a situation of monitoring and managing a parking area using a vision based automated parking system. With the rapid increase of cars the need to find available parking space in the most efficient manner, to avoid traffic congestion in a parking area, is becoming a necessity in car park management. Current car park management is dependent on either human personnel keeping track of the available car park spaces or a sensor based system that monitors the availability of each car park space or the overall number of available car park spaces. In both situations, the information available was only the total number of car park spaces available and not the actual location available. In addition, the installation and maintenance cost of a sensor based system is dependent on the number of sensors used in a car park. This paper shows a vision based system that is able to detect and indicate the available parking spaces in a car park. The methods utilized to detect available car park spaces were based on coordinates to indicate the regions of interest and a car classifier. This paper shows that the initial work done here has an accuracy that ranges from 90% to 100% for a 4 space car park. The work done indicated that the application of a vision based car park management system would be able to detect and indicate the available car park spaces


title: A New Method for Line Segmentation of Handwritten Hindi Text

abstract: In this paper, we have discussed the new method for Line Segmentation of Handwritten Hindi text. The method is based on header line detection, base line detection and contour following technique. No preprocessing like skew correction, thinning or noise removal has been done on the data. The purpose of this paper is three fold. Firstly, we explained by experiments that this method is suitable for fluctuating lines or variable skew lines of text. Also, we confirm that this method is invariant of non uniform skew between words in a line (non uniform text line skew). Secondly, the contour following after header line detection correctly separates some of the overlapped lines of text. Thirdly, this paper provides a brief review of text line segmentation techniques for handwritten text which can be very useful for the beginners who want to work on text line segmentation.


title: CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

abstract: Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image — a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.


title: Deep Complex Networks

abstract: At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks.


title: Reputation, Trust, and Rebates: How Online Auction Markets can Improve their Feedback Mechanisms

abstract: Reputation systems constitute an important institution, helping sustain trust in online auction markets. However, only half of buyers leave feedback after transactions, and nearly all feedback is positive. In this paper, I propose a mechanism whereby sellers can provide rebates (not necessarily in monetary form) to buyers contingent upon buyers' provision of reports. Using a game theoretical model, I show how the mechanism can increase unbiased reporting. There exists a pooling equilibrium where both good and bad sellers choose the rebate option, even though their true types are revealed through feedback. The mechanism also induces bad sellers to improve the quality of the contract.


title: How Music and Instruments Began: A Brief Overview of the Origin and Entire Development of Music, from Its Earliest Stages

abstract: Music must first be defined and distinguished from speech, and from animal and bird cries. We discuss the stages of hominid anatomy that permit music to be perceived and created, with the likelihood of both H. neanderthalensis and H. sapiens both being capable. The earlier hominid ability to emit sounds of variable pitch with some meaning, shows that music at its simplest level must have predated speech. The possibilities of anthropoid motor impulse suggest that rhythm may have preceded melody, though full control of rhythm may well not have come any earlier than the perception of music above. There are four evident purposes for music: dance, ritual, entertainment personal and communal, and above all social cohesion, again on both personal and communal levels. We then proceed to how instruments began, with a brief survey of the surviving examples from the Mousterian period onwards, including the possible Neanderthal evidence and the extent to which they showed ‘artistic’ potential in other fields. We warn that our performance on replicas of surviving instruments may bear little or no resemblance to that of the original players. We continue with how later instruments, strings and skin-drums began and developed into instruments we know in worldwide cultures today. The sound of music is then discussed, scales and intervals, and the lack of any consistency of consonant tonality around the world. This is followed by iconographic evidence of the instruments of later antiquity into the European Middle Ages, and finally the history of public performance, again from the possibilities of early humanity into more modern times. This paper draws the ethnomusicological perspective on the entire development of music, instruments, and performance, from the times of H. neanderthalensis and H. sapiens into those of modern musical history, and it is written with the deliberate intention of informing readers who are without special education in music, and providing necessary information for inquiries into the origin of music by cognitive scientists.


title: A handwritten Chinese characters recognition method based on sample set expansion and CNN

abstract: Convolutional neural networks (CNN) is a powerful technology for classification of visual inputs. However, both the scale and quality of the training set are an important factor to the performance of a learned system. In real applications, it is generally difficult to obtain a high-quality and large-scale handwritten Chinese characters sample set. Insufficient samples of handwritten Chinese characters would cause poor recognition performance. In this paper, we propose a handwritten Chinese character recognition method based on dataset expansion and CNNs. Firstly, the topology of proposed Convolutional neural networks model is addressed. Then, several dataset expansion techniques are utilized to expand the scale of available samples, which include random elastic deformation, shear transformation and rotation within a small range, etc. A series of experiments on the HCL2000 Chinese character handwriting database have shown that our method can effectively improve the recognition performance, with a reduction in error rate of 35.01%, verified the effectiveness of our proposed approach.


Clustering in the Cloud: Clustring Algorithms to Hadoop Map/Reduce Framework


Consecutive lynestrenol and cross-sex hormone treatment in biological female adolescents with gender dysphoria: a retrospective analysis


title: Behaviour, realism and immersion in games

abstract: Immersion is recognised as an important element of good games. However, it is not always clear what is meant by immersion. Earlier work has identified possible barriers to immersion including a lack of coherence between different aspects of the game. Building on this work, we designed an experiment to examine people's expectations of how a game should behave and what would happen if that behaviour was deliberately made to be incoherent. The idea then is to understand immersion through seeing how immersion can be broken. The main manipulation was to alter the behaviour and realism of the graphics in the course of a simple game situation. Surprisingly, results indicated that participants could be so immersed within a simple environment such that even significant changes in behaviour had little effect on the level of immersion. In some cases, the attempted disruptions went completely unnoticed. These results suggest that immersion within an application can overcome effects which are completely against user expectations.


title: A GRASP algorithm for constrained two-dimensional non-guillotine cutting problems

abstract: This paper presents a greedy randomized adaptive search procedure (GRASP) for the constrained two-dimensional non-guillotine cutting problem, the problem of cutting the rectangular pieces from a large rectangle so as to maximize the value of the pieces cut. We investigate several strategies for the constructive and improvement phases and several choices for critical search parameters. We perform extensive computational experiments with well-known instances previously reported, first to select the best alternatives and then to compare the efficiency of our algorithm with other procedures.


title: Performance optimization of Hadoop cluster using linux services

abstract: Hadoop is an open source tool. It enables the processing and distributed storage of big data sets using commodity cluster computing. With Hadoop occupying a core status in the current processing era, its performance optimization is also being heavily studied. This paper introduces one such method to improve Hadoop cluster performance by using a Remote Procedure Call (RPC), rpcbind service of the Linux system. The comparison is done by executing multiple Hadoop benchmarks on a configured multi-node Hadoop cluster. The final outcome turns in rpcbind favor depicting how the service improves the cluster performance by reducing the elapsed time of the benchmark executed.


title: Speedy transactions in multicore in-memory databases

abstract: Silo is a new in-memory database that achieves excellent performance and scalability on modern multicore machines. Silo was designed from the ground up to use system memory and caches efficiently. For instance, it avoids all centralized contention points, including that of centralized transaction ID assignment. Silo's key contribution is a commit protocol based on optimistic concurrency control that provides serializability while avoiding all shared-memory writes for records that were only read. Though this might seem to complicate the enforcement of a serial order, correct logging and recovery is provided by linking periodically-updated epochs with the commit protocol. Silo provides the same guarantees as any serializable database without unnecessary scalability bottlenecks or much additional latency. Silo achieves almost 700,000 transactions per second on a standard TPC-C workload mix on a 32-core machine, as well as near-linear scalability. Considered per core, this is several times higher than previously reported results.


It Takes Three to Tango: Triangulation Approach to Answer Ranking in Community Question Answering


title: Computational Methods for Residential Energy Cost Optimization in Smart Grids: A Survey

abstract: A smart power grid transforms the traditional electric grid into a user-centric, intelligent power network. The cost-saving potential of smart homes is an excellent motivating factor to involve users in smart grid operations. To that end, this survey explores the contemporary cost-saving strategies for smart grids from the users’ perspective. The study shows that optimization methods are the most popular cost-saving techniques reported in the literature. These methods are used to plan scheduling and power utilization schemes of household appliances, energy storages, renewables, and other energy generation devices. The survey shows that trading energy among neighborhoods is one of the effective methods for cost optimization. It also identifies the prediction methods that are used to forecast energy price, generation, and consumption profiles, which are required to optimize energy cost in advance. The contributions of this article are threefold. First, it discusses the computational methods reported in the literature with their significance and limitations. Second, it identifies the components and their characteristics that may reduce energy cost. Finally, it proposes a unified cost optimization framework and addresses the challenges that may influence the overall residential energy cost optimization problem in smart grids.


title: From the body's viscera to the body's image: Is there a link between interoception and body image concerns?

abstract: Abstract Interoception, the sense of the physiological condition of the body originating from within its internal organs, and body image, namely the perception, feelings and attitudes one has about one’s body, are two fundamental components of our sense of personal identity and overall well-being. However, the relation between interoception and body image remains poorly understood. We here review recent behavioural and neuroimaging evidence from non-clinical and clinical populations (e.g. eating disorders) to propose that basic interoceptive processes and interoceptive awareness may crucially contribute to the complex formation of body image, as well as to its disturbances. In particular, lower interoceptive accuracy and awareness are associated with body-image concerns. We provide a potential mechanistic explanation of the link between interoception and body image, which aims to integrate interoceptive and exteroceptive representations of the body. The suggested link between interoception and body image can inform new empirically testable hypotheses on the underlying neurocognitive processes that are central to body image concerns and disturbances, and motivate relevant clinical implications.


title: The G-spot: an observational MRI pilot study.

abstract: OBJECTIVES ::: To identify a G-spot complex (GSC) in vivo in MRI examinations at 1.5 Tesla field strength. ::: ::: ::: DESIGN ::: Observational study. ::: ::: ::: SETTING ::: Single centre. ::: ::: ::: POPULATION ::: Twenty-one consecutive patients (January-March 2014). ::: ::: ::: METHODS ::: Imaging analysis of routine imaging protocols for usual medical indications with and without concomitant opacification of the vaginal cavity with inert ultrasound gel. The gel distends the otherwise collapsed vaginal walls, allowing for an improved discrimination of anatomic features. The macroscopic and histological results recently derived from the dissections of fresh cadavers by Ostrzenski et al. were translated into imaging characteristics to be expected in the respective MRI sequences (e.g. T1- and T2-weighted) in search of an in vivo correlate of the GSC. Age, menopause status, medical indication and diagnosis were co-variables. ::: ::: ::: MAIN OUTCOME MEASURES ::: To analyse primarily whether MRI imaging is able to depict a distinct morphological entity in vivo matching the GSC, based on anatomical descriptions published recently. The elaboration of an appropriate MRI-imaging protocol was a secondary aim. ::: ::: ::: RESULTS ::: A total of 21 studies were obtained. A GSC was identified within the anterior vaginal wall in 13/21 patients (62%). In all, 10/21 (48%) had vaginal gel opacification. We identified a GSC in 10/10 patients (100%) with opacification in all three planes of the T2 images. This was only true for 3/11 cases (27%) without opacification. ::: ::: ::: CONCLUSIONS ::: There is evidence for an in vivo morphological correlate to the postmortem anatomical findings of a GSC described by Ostrzenski et al.; its visibility in MRI imaging can be significantly improved with vaginal opacification by ultrasound gel. ::: ::: ::: TWEETABLE ABSTRACT ::: Identification of G-spot by MRI with vaginal gel-opacification in 13/21 patients.


Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks for Grapheme-to-Phoneme Conversion Utilizing Complex Many-to-Many Alignments


title: Discontinuous-Current Mode Operation of a Two-Phase Interleaved Boost DC–DC Converter With Coupled Inductor

abstract: A two-phase interleaved boost dc–dc converter with an inversely coupled inductor in a discontinuous-current mode (DCM) is analyzed by the equivalent inductance method. Coupling effects on the circuit statuses are described, and a forced conduction of the power diode or reverse-paralleled diode of MOSFET is caused by coupling. As a result, three major circuit statuses are figured out according to the physical relationship between the input–output voltage ratio and the coupling coefficient, and their condition boundaries are used to classify the DCM operation modes. Then, considering the load (duty cycle) variation, ten DCM operation modes are comprehensively analyzed. The analysis can be used to make an easy prediction of operation modes, and extended to the analysis of an interleaved buck converter or buck/boost bidirectional converter with a coupled inductor in DCM. At last, a 300-W prototype is built and tested in the lab to verify the analysis.


title: Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events

abstract: Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process. In many cases, such events can be described as a collection of categorical values that are considered as entities of different types, which we call heterogeneous categorical events. Due to the lack of intrinsic distance measures among entities, and the exponentially large event space, most existing work relies heavily on heuristics to calculate abnormal scores for events. Different from previous work, we propose a principled and unified probabilistic model APE (Anomaly detection via Probabilistic pairwise interaction and Entity embedding) that directly models the likelihood of events. In this model, we embed entities into a common latent space using their observed co-occurrence in different events. More specifically, we first model the compatibility of each pair of entities according to their embeddings. Then we utilize the weighted pairwise interactions of different entity types to define the event probability. Using Noise-Contrastive Estimation with"context-dependent"noise distribution, our model can be learned efficiently regardless of the large event space. Experimental results on real enterprise surveillance data show that our methods can accurately detect abnormal events compared to other state-of-the-art abnormal detection techniques.


title: Who overuses Smartphones? Roles of virtues and parenting style in Smartphone addiction among Chinese college students

abstract: Several empirical studies have linked negative parenting style (i.e., parental rejection and overprotection) with a higher degree of Smartphone addiction. However, few studies have analyzed the potential protective factors (e.g., virtues) that may prevent addiction and promote effective Smartphone addiction interventions. Therefore, we examined if virtues (i.e., relationship, vitality, and conscientiousness) mediated the association between parenting style and Smartphone addiction among college students. Moreover, we examined whether these relationships were moderated by gender. Chinese college students (N=742) ages 1625 years completed the: 1) short-Egna Minnen av Barndoms Uppfostran-Chinese measure of parenting style, 2) 96-item Chinese Virtues Questionnaire, and the 3) Mobile Phone Addiction Index of Smartphone addiction. The results showed that negative parenting style significantly influenced college students' Smartphone addiction. Furthermore, virtues acted as a cognitive mechanism that mediated the association between negative parenting style and Smartphone addiction. Moreover, male virtues may be more sensitive to negative parenting style than female virtues. Relevant suggestions for college administrators, educators, parents, and future researchers are offered. Negative parenting style could directly predict the severity of Smartphone addiction.Parenting style has influence on college students' virtues.Virtues could negatively predict Smartphone addiction.Virtues mediated the association between negative parenting style and Smartphone addiction.Male virtues were more sensitive to negative parenting style than female virtues.


CoLink: An Unsupervised Framework for User Identity Linkage


title: Fully integrated 54nm STT-RAM with the smallest bit cell dimension for high density memory application

abstract: A compact STT(Spin-Transfer Torque)-RAM with a 14F2 cell was integrated using modified DRAM processes at the 54nm technology node. The basic switching performance (R-H and R-V) of the MTJs and current drivability of the access transistors were characterized at the single bit cell level. Through the direct access capability and normal chip operation in our STT-RAM test blocks, the switching behavior of bit cell arrays was also analyzed statistically. From this data and from the scaling trend of STT-RAM, we estimate that the unit cell dimension below 30nm can be smaller than 8F2.


abstract: Precisely recommending relevant items from massive candidates to a large number of users is an indispensable yet computationally expensive task in many online platforms (e.g., Amazon.com and Netflix.com). A promising way is to project users and items into a Hamming space and then recommend items via Hamming distance. However, previous studies didn't address the cold-start challenges and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue. We further develop an efficient discrete optimization algorithm for parameter learning. Based on extensive experiments on three real-world datasets, we show that DCFM outperforms the state-of-the-arts on both regression and classification tasks.


title: Cloud-Based Design and Manufacturing Systems: A Social Network Analysis

abstract: A Cloud-Based Design and Manufacturing (CBDM) System refers to an information and communication technology (ICT) system that facilitates design and manufacturing knowledge sharing between actors (e.g., CBDM service providers and consumers) in the distributed and collaborative socio-technical network. The aim of this study is to address the challenge of information sharing and technical communication during the CBDM product development process. Specifically, we model a CBDM system as a socio-technical network. The research questions are: (1) What measures can be used to analyze the socio-technical network generated by CBDM? (2) How to detect communities/clusters and key actors in the socio-technical network? To answer these questions, a social network analysis (SNA) approach is formulated to analyze the socio-technical network generated by CBDM systems. The results indicate that SNA allows for visualizing collaborative relationship patterns of actors as well as detecting the community structure of CBDM systems.


title: Sensing techniques for tablet+stylus interaction

abstract: We explore grip and motion sensing to afford new techniques that leverage how users naturally manipulate tablet and stylus devices during pen + touch interaction. We can detect whether the user holds the pen in a writing grip or tucked between his fingers. We can distinguish bare-handed inputs, such as drag and pinch gestures produced by the nonpreferred hand, from touch gestures produced by the hand holding the pen, which necessarily impart a detectable motion signal to the stylus. We can sense which hand grips the tablet, and determine the screen's relative orientation to the pen. By selectively combining these signals and using them to complement one another, we can tailor interaction to the context, such as by ignoring unintentional touch inputs while writing, or supporting contextually-appropriate tools such as a magnifier for detailed stroke work that appears when the user pinches with the pen tucked between his fingers. These and other techniques can be used to impart new, previously unanticipated subtleties to pen + touch interaction on tablets.


title: Graph-Based Label Propagation in Digital Media: A Review

abstract: The expansion of the Internet over the last decade and the proliferation of online social communities, such as Facebook, Googlep, and Twitter, as well as multimedia sharing sites, such as YouTube, Flickr, and Picasa, has led to a vast increase of available information to the user. In the case of multimedia data, such as images and videos, fast querying and processing of the available information requires the annotation of the multimedia data with semantic descriptors, that is, labels. However, only a small proportion of the available data are labeled. The rest should undergo an annotation-labeling process. The necessity for the creation of automatic annotation algorithms gave birth to label propagation and semi-supervised learning. In this study, basic concepts in graph-based label propagation methods are discussed. Methods for proper graph construction based on the structure of the available data and label inference methods for spreading label information from a few labeled data to a larger set of unlabeled data are reviewed. Applications of label propagation algorithms in digital media, as well as evaluation metrics for measuring their performance, are presented.


title: Mining Indonesian cyber bullying patterns in social networks

abstract: Bullying in social media such as Twitter and Facebook has been recognised as a serious issue in Indonesia. Bullying in social media is a type of human rights violation that involves other people following an initial perpetrator in sending bullying messages repeatedly and intentionally in order to cause distress and risk to the victims. Moreover, some people use Twitter for different, more innocuous, but still unpleasant, purposes such as embarrassing someone. ::: ::: Our research analyses Indonesian bullying words on Twitter so as to discover Indonesian bullying patterns. It also discusses how to mine Indonesian bullying words on Twitter by using text mining techniques. Analysing Indonesian bullying words is one of the challenges in this work. ::: ::: Our research has successfully identified that "bangsat" and "anjing" terms are the trend of Indonesian bullying patterns on Twitter. ::: ::: This work also compares Indonesian bullying patterns in Jakarta and Surabaya. The results are quite similar. The "bangsat" and "anjing" terms usually occur on Twitter located in both cities. Finally, our research discusses how text mining could provide a solution towards analysing Indonesian bullying words patterns in Twitter messages.


title: Varied Definitions of Nasolabial Angle: Searching for Consensus Among Rhinoplasty Surgeons and an Algorithm for Selecting the Ideal Method

abstract: Background:The nasolabial angle (NLA) is an important aesthetic metric for nasal assessment and correction. Although the literature offers many definitions, none has garnered universal acceptance.Methods:To gauge the consensus level among practitioners, surveys were administered to a convenience sam


title: Fall in thyroid stimulating hormone (TSH) may be an early marker of ipilimumab-induced hypophysitis

abstract: Hypophysitis develops in up to 19% of melanoma patients treated with ipilimumab, a cytotoxic T-lymphocyte antigen-4 antibody. Early detection may avert life-threatening hypopituitarism. We aimed to assess the incidence of ipilimumab-induced hypophysitis (IH) at a quaternary melanoma referral centre, and to determine whether cortisol or thyroid stimulating hormone (TSH) monitoring could predict IH onset. We performed a retrospective cohort study of ipilimumab-treated patients at a quaternary melanoma referral centre in Australia. The inclusion criteria were patients with metastatic or unresectable melanoma treated with ipilimumab monotherapy, and cortisol and TSH measurements prior to ≥ 2 infusions. The main outcomes were IH incidence and TSH and cortisol patterns in patients who did and did not develop IH. Of 78 ipilimumab-treated patients, 46 met the study criteria and 9/46 (20%) developed IH at a median duration of 13.0 weeks (range 7.7–18.1) following ipilimumab initiation. All patients whose TSH fell ≥ 80% compared to baseline developed IH, and, in 5/9 patients with IH, TSH fell prior to cortisol fall and IH diagnosis. Pre-cycle-4 TSH was significantly lower in those who developed IH (0.31 vs. 1.73 mIU/L, P = 0.006). TSH fall was detected at a median time of 9.2 (range 7.7–16.4) weeks after commencing ipilimumab, and a median of 3.6 (range of − 1.4 to 9.7) weeks before IH diagnosis. There was no difference in TSH between the groups before cycles 1–3 or in cortisol before cycles 1–4. TSH fall ≥ 80% may be an early marker of IH. Serial TSH measurement during ipilimumab therapy may be an inexpensive tool to expedite IH diagnosis.


title: The psychopathic individual: A lack of responsiveness to distress cues?

abstract: In this study, we investigated the psychophysiological responsiveness of psychopathic individuals to distress cues and to threatening and neutral stimuli. Eighteen psychopathic individuals and 18 incarcerated control individuals, identified using the Revised Psychopathy Checklist (Hare, 1991, The Hare Pscyhoparhy Checklist-Revised, Toronto: Multi-Health Systems), were shown slides of these three types of stimuli, and their electrodermal responses were recorded. The psychopathic individuals showed (relative to the controls) reduced electrodermal responses to the distress cues. In contrast, the two groups did not differ in their electrodermal responses to the threatening stimuli and to the neutral stimuli. The results are interpreted within the Violence Inhibition Mechanism model (Blair, 1995, Cognirion, 57, 1-29) of the psychopathic individual.


title: Illuminant Aware Gamut-Based Color Transfer

abstract: This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white-balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same 'white axis' and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant-aware strategy gives a better result than directly working with the original source and target image's luminance channel as done by many previous methods. Afterwards, our method performs a full gamut-based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant-aware and gamut-based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.


title: Awareness, Perception of Risk and Behaviors Related to Retail Marijuana Among a Sample of Colorado Youth

abstract: Youth marijuana use is a growing concern with increasingly permissive views towards marijuana use. Little is known about attitudes and beliefs toward marijuana use among youth in the context of legalization. This study describes youth attitudes and beliefs about health risks associated with marijuana use, social norms of peer use, conversations with parents about marijuana use, and knowledge of recreational marijuana laws, using a venue-day-time sampling approach with diverse Colorado youth (n = 241) post-legalization. We considered demographic (gender, racial/ethnic and geographic) differences in knowledge of laws and perceptions of risk using bivariate and multivariate analyses. While many youth are knowledgeable about retail marijuana laws in Colorado, males were 2.12 times more likely to be familiar with laws compared to females. While 40 % of the sample perceived a moderate to high risk from weekly marijuana consumption and 57 % from daily consumption, fewer males perceived these risks. Over ¾ of the sample indicate they discuss marijuana with parents, but many fewer indicate discussing consequences and health effects of use with parents. Results suggest opportunities for parents and clinicians to influence youth attitudes and behaviors towards marijuana use. It may be worthwhile to target educational campaigns to different demographic groups, and to offer training and capacity building for parents to discuss marijuana with their teenaged children.


title: Discovering highly reliable subgraphs in uncertain graphs

abstract: In this paper, we investigate the highly reliable subgraph problem, which arises in the context of uncertain graphs. This problem attempts to identify all induced subgraphs for which the probability of connectivity being maintained under uncertainty is higher than a given threshold. This problem arises in a wide range of network applications, such as protein-complex discovery, network routing, and social network analysis. Since exact discovery may be computationally intractable, we introduce a novel sampling scheme which enables approximate discovery of highly reliable subgraphs with high probability. Furthermore, we transform the core mining task into a new frequent cohesive set problem in deterministic graphs. Such transformation enables the development of an efficient two-stage approach which combines novel peeling techniques for maximal set discovery with depth-first search for further enumeration. We demonstrate the effectiveness and efficiency of the proposed algorithms on real and synthetic data sets.


title: Parallel Reservoir Computing Using Optical Amplifiers

abstract: Reservoir computing (RC), a computational paradigm inspired on neural systems, has become increasingly popular in recent years for solving a variety of complex recognition and classification problems. Thus far, most implementations have been software-based, limiting their speed and power efficiency. Integrated photonics offers the potential for a fast, power efficient and massively parallel hardware implementation. We have previously proposed a network of coupled semiconductor optical amplifiers as an interesting test case for such a hardware implementation. In this paper, we investigate the important design parameters and the consequences of process variations through simulations. We use an isolated word recognition task with babble noise to evaluate the performance of the photonic reservoirs with respect to traditional software reservoir implementations, which are based on leaky hyperbolic tangent functions. Our results show that the use of coherent light in a well-tuned reservoir architecture offers significant performance benefits. The most important design parameters are the delay and the phase shift in the system's physical connections. With optimized values for these parameters, coherent semiconductor optical amplifier (SOA) reservoirs can achieve better results than traditional simulated reservoirs. We also show that process variations hardly degrade the performance, but amplifier noise can be detrimental. This effect must therefore be taken into account when designing SOA-based RC implementations.


title: Moral decision-making, ToM, empathy and the default mode network

abstract: Abstract Automatic intuitions and deliberate reasoning, sourcing internal representations of our personal norms and values, contribute to our beliefs of what is right and wrong. We used fMRI to directly compare moral (M) and non-moral (NM) decision-making processes using scenarios requiring conscious deliberation, whereby the main character declared an intention to take a course of action. Furthermore, we examined the relationship between BOLD signal, associated with M > NM decision-making, and moral judgment competence, psychopathy, and empathy. We observed greater activity in various parts of Theory of Mind, empathy and default mode networks during M > NM decision-making. There was a trend for high scores on primary psychopathy to correlate with decreased M > NM BOLD activation in an area extending from dorsolateral prefrontal cortex to medial prefrontal cortex. We suggest that moral decision-making entails a greater degree of internally directed processing, such as self-referential mental processing and the representation of intentions and feelings, than non-moral decision-making.


title: Is computer science science?

abstract: Computer science meets every criterion for being a science, but it has a self-inflicted credibility problem.


title: Causality: Models, Reasoning, and Inference

abstract: 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.


title: Design of a RFID tag using dipole antenna with Electromagnetic Band Gap

abstract: Passive RFID is a promising technology for products tracking in logistics. However, the UHF RFID tag is greatly affected by backside goods especially metallic object. This paper describes the design of a dipole antenna with Electromagnetic Band Gap (EBG) for RFID tag, so that the tag could still work on metallic surface. The design of EBG material for UHF RFID is operated at 922 MHz and its reflection phase has an effect on the antenna characteristic.


title: CHIP IMPEDANCE MATCHING FOR UHF RFID TAG ANTENNA DESIGN

abstract: Passive UHF RFID tag consists of a microchip attached directly to an antenna. Proper impedance match between the antenna and the chip is crucial in RFID tag design. It directly influences RFID system performance characteristics such as the range of a tag. It is known that an RFID microchip is a nonlinear load whose complex impedance in each state varies with the frequency and the input power. This paper illustrates a proper calculation of the tag power reflection coefficient for maximum power transfer by taking into account of the changing chip impedance versus frequency.


title: Wireless Secrecy Regions With Friendly Jamming

abstract: Inspired by recent results on information-theoretic security, we consider the transmission of confidential messages over wireless networks, in which the legitimate communication partners are aided by friendly jammers. We characterize the security level of a confined region in a quasi-static fading environment by computing the probability of secrecy outage in connection with two new measures of physical-layer security: the jamming coverage and the jamming efficiency. Our analysis for various jamming strategies based on different levels of channel state information provides insight into the design of optimal jamming configurations and shows that a single jammer is not sufficient to maximize both figures of merit simultaneously. Moreover, a single jammer requires full channel state information to provide security gains in the vicinity of the legitimate receiver.


title: SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach

abstract: We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing high-dimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.


title: Is Q-learning Provably Efficient?

abstract: Model-free reinforcement learning (RL) algorithms, such as Q-learning, directly parameterize and update value functions or policies without explicitly modeling the environment. They are typically simpler, more flexible to use, and thus more prevalent in modern deep RL than model-based approaches. However, empirical work has suggested that model-free algorithms may require more samples to learn [Deisenroth and Rasmussen 2011, Schulman et al. 2015]. The theoretical question of "whether model-free algorithms can be made sample efficient" is one of the most fundamental questions in RL, and remains unsolved even in the basic scenario with finitely many states and actions. ::: We prove that, in an episodic MDP setting, Q-learning with UCB exploration achieves regret $\tilde{O}(\sqrt{H^3 SAT})$, where $S$ and $A$ are the numbers of states and actions, $H$ is the number of steps per episode, and $T$ is the total number of steps. This sample efficiency matches the optimal regret that can be achieved by any model-based approach, up to a single $\sqrt{H}$ factor. To the best of our knowledge, this is the first analysis in the model-free setting that establishes $\sqrt{T}$ regret without requiring access to a "simulator."


title: An efficient page-level FTL to optimize address translation in flash memory

abstract: Flash-based solid state disks (SSDs) have been very popular in consumer and enterprise storage markets due to their high performance, low energy, shock resistance, and compact sizes. However, the increasing SSD capacity imposes great pressure on performing efficient logical to physical address translation in a page-level flash translation layer (FTL). Existing schemes usually employ a built-in RAM cache for storing mapping information, called the mapping cache, to speed up the address translation. Since only a fraction of the mapping table can be cached due to limited cache space, a large number of extra operations to flash memory are required for cache management and garbage collection, degrading the performance and lifetime of an SSD. In this paper, we first apply analytical models to investigate the key factors that incur extra operations. Then, we propose an efficient page-level FTL, named TPFTL, which employs two-level LRU lists to organize cached mapping entries to minimize the extra operations. Inspired by the models, we further design a workload-adaptive loading policy combined with an efficient replacement policy to increase the cache hit ratio and reduce the writebacks of replaced dirty entries. Finally, we evaluate TPFTL using extensive trace-driven simulations. Our evaluation results show that compared to the state-of-the-art FTLs, TPFTL reduces random writes caused by address translation by an average of 62% and improves the response time by up to 24%.


Improving PILCO with Bayesian Neural Network Dynamics Models


title: Wireless Optogenetic Nanonetworks for Brain Stimulation: Device Model and Charging Protocols

abstract: In recent years, numerous research efforts have been dedicated toward developing efficient implantable devices for brain stimulation. However, there are limitations and challenges with the current technologies. They include neuron population stimulation instead of single neuron level, the size, the biocompatibility, and the device lifetime reliability in the patient's brain. We have recently proposed the concept of wireless optogenetic nanonetworking devices (WiOptND) that could address the problem of long term deployment, and at the same time target single neuron stimulation utilizing ultrasonic as a mode for energy harvesting. In addition, a number of charging protocols are also proposed, in order to minimize the quantity of energy required for charging, while ensuring minimum number of neural spike misfirings. These protocols include the simple charge and fire, which requires the full knowledge of the raster plots of neuron firing patterns, and the predictive sliding detection window, and its variant Markov-chain based time-delay patterns, which minimizes the need for full knowledge of neural spiking patterns as well as number of ultrasound charging frequencies. Simulation results exhibit a drop for the stimulation ratio of ~ 25% and more stable trend in its efficiency ratio (standard deviation of ~0.5%) for the Markov-chain based time-delay patterns protocol compared with the baseline change and fire. The results show the feasibility of utilizing WiOptND for long-term implants in the brain, and a new direction toward precise stimulation of neurons in the cortical microcolumn of the brain cortex.


title: Position control of mobile two wheeled inverted pendulum robot by sliding mode control

abstract: Mobile two wheeled inverted pendulum (TWIP) robot is one type of mobile robot that have a number of advantages. Because dynamics of a TWIP is a complex nonlinear form, several studies have linearized the dynamic equation at the equilibrium point and designed a controller with linear dynamic equation. The limitation of the approach is, because of ignoring a high order term, the performance of that controller cannot be guaranteed at a point far from equilibrium. This paper discusses the modeling of TWIP systems and shows a new control method with nonlinear dynamics.


Dynamic Differential Location Privacy with Personalized Error Bounds.


title: Adoption and Diffusion of in-store Mobile Payment : Lessons from China

abstract: With the evolution of technology and the Internet, the world is heading to a "cashless society". China has moved towards one solution - mobile payment in the recent decade. In every corner of urba ...


title: Scientific Collaborator Recommendation in Heterogeneous Bibliographic Networks

abstract: Most of the previous studies on scientific collaborator recommendation are based on social proximity analysis to suggest collaborators. However, the extracted homogeneous features cannot well represent the multiple factors which may implicitly affect the future scientific collaboration. In this paper we propose an approach based on the multiple heterogeneous network features, which has produced good results in our experiments based on a dataset of more than 30,000 ISI papers. This method can help solving the similar problems of people to people recommendation. It generates high quality expert's profiles via integrating research expertise, co-author network characteristics and researchers' institutional connectivity (local and global) through a SVM-Rank based information merging mechanism to perform intelligent matching. The generated comprehensive profiles alleviate information asymmetry and the multiple similarity measures overcome problems related to information overloading. The proposed method has been implemented in Scholar Mate research network (www.scholarmate.com) which is a research 2.0 innovation, promoting research collaboration in virtual scientific community.


title: Quarter-Mode Cavity Filters in Substrate Integrated Waveguide Technology

abstract: This paper presents a systematic investigation of quarter-mode filters in substrate integrated waveguide (SIW) technology. This class of filters is particularly convenient because it combines the features of SIW structures with the improvement of size reduction. After a thorough analysis of the quarter-mode SIW cavity, this paper presents different coupling mechanisms and feeding techniques for the design of quarter-mode SIW filters: side coupling and corner coupling are considered, highlighting the advantages and disadvantages of the two techniques. Novel filter topologies are introduced, with the design and experimental verification of simple filters and their extension to higher order filter structures. Techniques to introduce transmission zeros are described and demonstrated. Moreover, the combination of quarter-mode SIW cavities and coplanar waveguide resonators leads to increasing the filter order to higher order and allows the implementation of quasi-elliptic filters.


title: Distributed Robust Power Minimization for the Downlink of Multi-Cloud Radio Access Networks

abstract: Conventional cloud radio access networks (CRANs) assume single cloud processing and treat inter-cloud interference as background noise. This paper considers the downlink of a multi-CRAN where each cloud is connected to several base-stations (BSs) through limited-capacity wireline backhaul links. The set of BSs connected to each cloud, called cluster, serves a set of pre-known mobile users. The performance of the system becomes therefore a function of both inter-cloud and intra-cloud interference, as well as the compression schemes of the limited capacity backhaul links. This paper assumes independent compression scheme and imperfect channel state information (CSI) where the CSI errors belong to an ellipsoidal bounded region. The problem of interest becomes one of minimizing the network total transmit power subject to BS power and quality of service constraints, as well as backhaul capacity and CSI error constraints. This paper suggests solving the problem using the alternating direction method of multipliers (ADMMs). One highlight of this paper is that the proposed ADMM-based algorithm can be implemented in a distributed fashion across the multi-cloud network by allowing a limited amount of information exchange between the coupled clouds. Simulation results show that the proposed distributed algorithm provides a similar performance to the centralized algorithm in a reasonable number of iterations.


title: A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units

abstract: This paper proposes a novel framework for recurrent neural networks (RNNs) inspired by the human memory models in the field of cognitive neuroscience to enhance information processing and transmission between adjacent RNNs' units. The proposed framework for RNNs consists of three stages that is working memory, forget, and long-term store. The first stage includes taking input data into sensory memory and transferring it to working memory for preliminary treatment. And the second stage mainly focuses on proactively forgetting the secondary information rather than the primary in the working memory. And finally, we get the long-term store normally using some kind of RNN's unit. Our framework, which is generalized and simple, is evaluated on 6 datasets which fall into 3 different tasks, corresponding to text classification, image classification and language modelling. Experiments reveal that our framework can obviously improve the performance of traditional recurrent neural networks. And exploratory task shows the ability of our framework of correctly forgetting the secondary information.