title: Similarity Preserving Snippet-Based Visualization of Web Search Results

abstract: Internet users are very familiar with the results of a search query displayed as a ranked list of snippets. Each textual snippet shows a content summary of the referred document (or webpage) and a link to it. This display has many advantages, for example, it affords easy navigation and is straightforward to interpret. Nonetheless, any user of search engines could possibly report some experience of disappointment with this metaphor. Indeed, it has limitations in particular situations, as it fails to provide an overview of the document collection retrieved. Moreover, depending on the nature of the query for example, it may be too general, or ambiguous, or ill expressed the desired information may be poorly ranked, or results may contemplate varied topics. Several search tasks would be easier if users were shown an overview of the returned documents, organized so as to reflect how related they are, content wise. We propose a visualization technique to display the results of web queries aimed at overcoming such limitations. It combines the neighborhood preservation capability of multidimensional projections with the familiar snippet-based representation by employing a multidimensional projection to derive two-dimensional layouts of the query search results that preserve text similarity relations, or neighborhoods. Similarity is computed by applying the cosine similarity over a "bag-of-wordsa' vector representation of collection built from the snippets. If the snippets are displayed directly according to the derived layout, they will overlap considerably, producing a poor visualization. We overcome this problem by defining an energy functional that considers both the overlapping among snippets and the preservation of the neighborhood structure as given in the projected layout. Minimizing this energy functional provides a neighborhood preserving two-dimensional arrangement of the textual snippets with minimum overlap. The resulting visualization conveys both a global view of the query results and visual groupings that reflect related results, as illustrated in several examples shown.


title: What's causing your young patient's hip pain?

abstract: Diagnosing hip pain--which is increasingly common in children and adolescents--can be a daunting task, unless you know what to look for. These tips can help.


title: The Good Stranger Frame for Police and Military Activities

abstract: We sought to understand how some police officers and military personnel are more effective than others at increasing civilian good will following encounters. Such officers can be termed “Good Strangers” (GSs). We conducted Cognitive Task Analysis (CTA) interviews with 17 U.S. police officers and 24 warfighters (Marines and Army soldiers). The CTA interviews yielded a total of 92 incidents, which were used to identify critical skills for training warfighters to become GSs. These skills supported a professional identity as a GS – seeking opportunities to increase civilian trust in police/military. Increasing trust from civilians requires skills in gaining voluntary compliance, building rapport, deescalating conflicts, trading-off risk versus trust building, and taking the perspective of civilians.


title: Peeling the flow: a sketch-based interface to generate stream surfaces

abstract: We present a user-centric approach for stream surface generation. Given a set of densely traced streamlines over the flow field, we design a sketch-based interface that allows users to draw simple strokes directly on top of the streamline visualization result. Based on the 2D stroke, we identify a 3D seeding curve and generate a stream surface that captures the flow pattern of streamlines at the outermost layer. Then, we remove the streamlines whose patterns are covered by the stream surface. Repeating this process, users can peel the flow by replacing the streamlines with customized surfaces layer by layer. Our sketch-based interface leverages an intuitive painting metaphor which most users are familiar with. We present results using multiple data sets to show the effectiveness of our approach, and discuss the limitations and future directions.


Compact dual-band slot antenna for 2.4/5 GHz WLAN application


title: Interactive touch board using IR camera

abstract: This paper presents a novel and cost-effective approach to convert any projected display on a flat surface into interactive touch board, through the aid of computer vision techniques. An interactive touch board is similar to that of a touch screen available in mobile phones and tablets, but then uses a special stylus for its input and a LCD projector for projecting the display on the surface. Commonly used techniques fall under any of the four categories — Resistive, Capacitive, Electromagnetic and Optical. The paper proposes a method pertaining to the optical technique — involving an infrared camera, a light emitting stylus and an image processing algorithm to determine the position pointed by the user. The stylus has an infrared LED at its tip and capable of producing a tiny spot of light on the projected surface, which is invisible to human eyes. But an IR camera with IR pass filter can be used to capture the light spot and can be processed further. This is the principle technique behind the proposed method. The image from the camera is processed using an algorithm, which determines the coordinates of the IR light. This obtained position along with some calibration parameters is sufficient to calculate the mouse cursor coordinates and thus the cursor can moved to the intended position. The whole of the image processing algorithm has been implemented using the NI LabVIEW and its associated Vision and Motion toolkits. Further, the paper deals with the study of potential applications in education and scientific research.


title: Accuracy assessment of GPS navigation augmented by SAR and LiDAR-derived Digital Elevation Models

abstract: Remotely sensed Digital Elevation Models (DEM) can be used to augment a standalone Global Positioning System (GPS) by adding an extra range observation which measures the distance to the Earth centre. This method so called height aiding can reduce the number of GPS satellites required to get a 3D position fix from four to three and hence improve the performance of the GPS navigation algorithm in terms of accuracy, reliability and availability. Up until now, the accuracy of height aided GPS navigation using higher resolution Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR)-derived elevation data has not been fully evaluated in a broad spectrum of navigation scenarios. This article provides a robust and accurate analysis on how much range error is introduced by height aiding using 5 m spacing SAR and 1 m spacing LiDAR-derived DEMs under in-car and personal navigation situations. Based on the experimental results obtained from both dynamic and static tests, suggestions have been made on what level of vertical and positional accuracy can be achieved as well as the related DEM quality issues for navigation purposes.


title: Framing Reinforcement Learning from Human Reward : Reward Positivity , Temporal Discounting , Episodicity , and Performance

abstract: Several studies have demonstrated that reward from a human trainer can be a powerful feedback signal for control-learning algorithms. However, the space of algorithms for learning from such human reward has hitherto not been explored systematically. Using model-based reinforcement learning from human reward, this article investigates the problem of learning from human reward through six experiments, focusing on the relationships between reward positivity, which is how generally positive a trainer's reward values are; temporal discounting, the extent to which future reward is discounted in value; episodicity, whether task learning occurs in discrete learning episodes instead of one continuing session; and task performance, the agent's performance on the task the trainer intends to teach. This investigation is motivated by the observation that an agent can pursue different learning objectives, leading to different resulting behaviors. We search for learning objectives that lead the agent to behave as the trainer intends.We identify and empirically support a "positive circuits" problem with low discounting (i.e., high discount factors) for episodic, goal-based tasks that arises from an observed bias among humans towards giving positive reward, resulting in an endorsement of myopic learning for such domains. We then show that converting simple episodic tasks to be non-episodic (i.e., continuing) reduces and in some cases resolves issues present in episodic tasks with generally positive reward and-relatedly-enables highly successful learning with non-myopic valuation in multiple user studies. The primary learning algorithm introduced in this article, which we call "vi-tamer", is the first algorithm to successfully learn non-myopically from reward generated by a human trainer; we also empirically show that such non-myopic valuation facilitates higher-level understanding of the task. Anticipating the complexity of real-world problems, we perform further studies-one with a failure state added-that compare (1) learning when states are updated asynchronously with local bias-i.e., states quickly reachable from the agent's current state are updated more often than other states-to (2) learning with the fully synchronous sweeps across each state in the vi-tamer?algorithm. With these locally biased updates, we find that the general positivity of human reward creates problems even for continuing tasks, revealing a distinct research challenge for future work.


title: A move-and-hold pneumatic actuator enabled by self-softening variable stiffness materials

abstract: Materials exhibiting variable stiffness properties have great potential for use in the growing field of soft robotics. Soft structural materials allow a robot to fit into enclosed spaces, resist shock and vibration, or even reconfigure its geometry and adapt to various environments. Rigid structural materials on the other hand allow environmental interactions through application of force and load-bearing capabilities. Materials that can be selectively switched between these two extremes could greatly expand the functionality of a robot that requires the properties of both. In this paper, we introduce a conductive epoxy composite that is self-softening through Joule heating via direct application of electrical current. The polymer can then become load-bearing and rigid again after being formed into a new shape. We demonstrate the capabilities of this material by attaching a pneumatic actuator and showing that the resulting variable stiffness device can be softened from its initial rigid state, reshape itself using the pneumatic actuator, then become rigid again and hold this new position without additional power being supplied to the actuator.


title: Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation

abstract: This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most of the previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.


title: Experimental stability study of an Octorotor using an intelligent controller

abstract: In this paper, the attitude stabilization problem of an Octorotor with coaxial motors is studied. To this end, the new method of intelligent adaptive control is presented. The designed controller which includes fuzzy and PID controllers, is completed by resistant adaptive function of approximate external disturbance and changing in the dynamic model. In fact, the regulation factor of PID controller is done by the fuzzy logic system. At first, the Fuzzy-PID and PID controllers are simulated in MATLAB/Simulink. Then, the Fuzzy-PID controller is implemented on the Octorotor with coaxial motors as online auto-tuning. Also, LabVIEW software has been used for tests and the performance analysis of the controllers. All of this experimental operation is done in indoor environment in the presence of wind as disturbance in the hovering operation. All of these operations are real-time and telemetry wireless is done by network connection between the robot and ground station in the LABVIEW software. Finally, the controller efficiency and results are studied.


title: Cloud computing security threats and responses

abstract: Cloud computing is one of today's most exciting technologies due to its ability to reduce costs associated with computing while increasing flexibility and scalability for computer processes. During the past few years, cloud computing has grown from being a promising business idea to one of the fastest growing parts of the IT industry. IT organizations have expresses concern about critical issues (such as security) that exist with the widespread implementation of cloud computing. These types of concerns originate from the fact that data is stored remotely from the customer's location; in fact, it can be stored at any location. Security, in particular, is one of the most argued-about issues in the cloud computing field; several enterprises look at cloud computing warily due to projected security risks. The risks of compromised security and privacy may be lower overall, however, with cloud computing than they would be if the data were to be stored on individual machines instead of in a so — called "cloud" (the network of computers used for remote storage and maintenance). Comparison of the benefits and risks of cloud computing with those of the status quo are necessary for a full evaluation of the viability of cloud computing. Consequently, some issues arise that clients need to consider as they contemplate moving to cloud computing for their businesses. In this paper I summarize reliability, availability, and security issues for cloud computing (RAS issues), and propose feasible and available solutions for some of them.


title: The content and design of web sites: an empirical study

abstract: Abstract To support the emergence of a solid knowledge base for analyzing Web activity, we have developed a framework to analyze and categorize the capabilities of Web sites. This distinguishes content from design. Content refers to the information, features, or services that are offered in the Web site, design to the way the content is made available for Web visitors. Both concepts have been operationalized by means of objective and subjective measures to capture features as well as perceptions. This framework has been applied to study how different groups of companies are using the Web for commercial purposes. We have compared Web sites based on their source, industry, and size. On average, larger Web sites seem to be ‘richer’ and more advanced.


title: AN INVESTIGATION OF INFORMATION SHARING AND SEEKING BEHAVIORS IN VIRTUAL COMMUNITIES

abstract: User participations in virtual communities include both information seeking and sharing behaviors to varying degrees. The intentions to share and to seek information may have different motivations. . However, existing studies rarely analyze the intentions for both types of behaviors. This study explicates the antecedents of the two types of usage intentions – intentions to share and intentions to seek – using a model that integrates social, community, and individual factors. The model is validated using a survey of 502 online investors of the most popular online stock message board in South Korea. The results show that the sense of belonging, entertainment value, and perceived usefulness influence both the intentions to share and seek. In addition, reputation seeking enhances the intention to post while perceived knowledge reduces the intention to seek.


title: Ice-breaking: mitigating cold-start recommendation problem by rating comparison

abstract: Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the coldstart problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RAPARE strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.


title: Implicit LOD for processing, visualisation and classification in Point Cloud Servers

abstract: We propose a new paradigm to effortlessly get a portable geometric Level Of Details (LOD) for a point cloud inside a Point Cloud Server. The point cloud is divided into groups of points (patch), then each patch is reordered (MidOc ordering) so that reading points following this order provides more and more details on the patch. This LOD have then multiple applications: point cloud size reduction for visualisation (point cloud streaming) or speeding of slow algorithm, fast density peak detection and correction as well as safeguard for methods that may be sensible to density variations. The LOD method also embeds information about the sensed object geometric nature, and thus can be used as a crude multi-scale dimensionality descriptor, enabling fast classification and on-the-fly filtering for basic classes.


title: Amygdala responses to nonlinguistic emotional vocalizations

abstract: Whereas there is ample evidence for a role of the amygdala in the processing of visual emotional stimuli, particularly those with negative value, discrepant results have been reported regarding amygdala responses to emotional auditory stimuli. The present study used event-related functional magnetic resonance imaging to investigate cerebral activity underlying processing of emotional nonlinguistic vocalizations, with a particular focus on neural changes in the amygdala. Fourteen healthy volunteers were scanned while performing a gender identification task. Stimuli, previously validated on emotional valence, consisted of positive (happiness and sexual pleasure) and negative (sadness and fear) vocalizations, as well as emotionally neutral sounds (e.g., coughs). Results revealed bilateral amygdala activation in response to all emotional vocalizations when compared to neutral stimuli. These findings suggest that the generally accepted involvement of the amygdala in the perception of emotional visual stimuli, such as facial expressions, also applies to stimuli within the auditory modality. Importantly, this amygdala response was observed for both positive and negative emotional vocalizations.


title: Improving new user recommendations with rule-based induction on cold user data

abstract: With recommender systems, users receive items recommended on the basis of their profile. New users experience the cold start problem: as their profile is very poor, the system performs very poorly. In this paper, classical new user cold start techniques are improved by exploiting the cold user data, i.e. the user data that is readily available (e.g. age, occupation, location, etc.), in order to automatically associate the new user with a better first profile. Relying on the existing α-community spaces model, a rule-based induction process is used and a recommendation process based on the "level of agreement" principle is defined. The experiments show that the quality of recommendations compares to that obtained after a classical new user technique, while the new user effort is smaller as no initial ratings are asked.


title: COLIN: Planning with Continuous Linear Numeric Change

abstract: In this paper we describe COLIN, a forward-chaining heuristic search planner, capable of reasoning with COntinuous LINear numeric change, in addition to the full temporal semantics of PDDL2.1. Through this work we make two advances to the state-of-the-art in terms of expressive reasoning capabilities of planners: the handling of continuous linear change, and the handling of duration-dependent effects in combination with duration inequalities, both of which require tightly coupled temporal and numeric reasoning during planning. COLIN combines FF-style forward chaining search, with the use of a Linear Program (LP) to check the consistency of the interacting temporal and numeric constraints at each state. The LP is used to compute bounds on the values of variables in each state, reducing the range of actions that need to be considered for application. In addition, we develop an extension of the Temporal Relaxed Planning Graph heuristic of CRIKEY3, to support reasoning directly with continuous change. We extend the range of task variables considered to be suitable candidates for specifying the gradient of the continuous numeric change effected by an action. Finally, we explore the potential for employing mixed integer programming as a tool for optimising the timestamps of the actions in the plan, once a solution has been found. To support this, we further contribute a selection of extended benchmark domains that include continuous numeric effects. We present results for COLIN that demonstrate its scalability on a range of benchmarks, and compare to existing state-of-the-art planners.


title: Psilocybin can occasion mystical-type experiences having substantial and sustained personal meaning and spiritual significance

abstract: Rationale Although psilocybin has been used for centuries for religious purposes, little is known scientifically about its acute and persisting effects. Objectives This double-blind study evaluated the acute and longer-term psychological effects of a high dose of psilocybin relative to a comparison compound administered under comfortable, supportive conditions. Materials and methods The participants were hallucinogennaive adults reporting regular participation in religious or spiritual activities. Two or three sessions were conducted at 2-month intervals. Thirty volunteers received orally administered psilocybin (30 mg/70 kg) and methylphenidate hydrochloride (40 mg/70 kg) in counterbalanced order. To obscure the study design, six additional volunteers received methylphenidate in the first two sessions and unblinded psilocybin in a third session. The 8-h sessions were conducted individually. Volunteers were encouraged to close their eyes and direct their attention inward. Study monitors rated volunteers’ behavior during sessions. Volunteers completed questionnaires assessing drug effects and mystical experience immediately after and 2 months after sessions. Community observers rated changes in the volunteer’s attitudes and behavior. Results Psilocybin produced a range of acute perceptual changes, subjective experiences, and labile moods including anxiety. Psilocybin also increased measures of mystical experience. At 2 months, the volunteers rated the psilocybin experience as having substantial personal meaning and spiritual significance and attributed to the experience sustained positive changes in attitudes and behavior consistent with changes rated by community observers. Conclusions When administered under supportive conditions, psilocybin occasioned experiences similar to spontaneously occurring mystical experiences. The ability to occasion such experiences prospectively will allow rigorous scientific investigations of their causes and consequences.


title: Machine Teaching for Bayesian Learners in the Exponential Family

abstract: What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner? We propose an optimal teaching framework aimed at learners who employ Bayesian models. Our framework is expressed as an optimization problem over teaching examples that balance the future loss of the learner and the effort of the teacher. This optimization problem is in general hard. In the case where the learner employs conjugate exponential family models, we present an approximate algorithm for finding the optimal teaching set. Our algorithm optimizes the aggregate sufficient statistics, then unpacks them into actual teaching examples. We give several examples to illustrate our framework.


title: SiC MOSFET Switching Waveform Profiling Through Passive Networks

abstract: The use of Wide Band Gap (WBG) devices in high performance power converters is becoming more common, and the range of techniques for managing the high speed switching transitions is broadening. In particular, there is now a greater interest in applying techniques for shaping the switching transients to improve the converter performance. This paper explores the current state of the art in waveform profiling gate drivers and their application to WBG devices to determine the optimum gate waveform profile. This profile is then analysed with the goal of creating a passive gate drive network capable of replicating the performance. A hardware prototype of this gate driver is demonstrated and experimental results from it are compared with those from a standard drive and a fully active driver. The active gate driver shows a 31% decrease in current overshoot or a 35.4% reduction in turn-on switching losses.


title: Multi-layer Representation Fusion for Neural Machine Translation

abstract: Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.


Natural Language Interface for Databases Using a Dual-Encoder Model


Defending against malicious peripherals


title: Phase-change memory: An architectural perspective

abstract: This article surveys the current state of phase-change memory (PCM) as a nonvolatile memory technology set to replace flash and DRAM in modern computerized systems. It has been researched and developed in the last decade, with researchers providing better architectural designs which address the technology's main challenges—its limited write endurance, potential long latency, high energy writes, power dissipation, and some concerns for memory privacy. Some physical properties of the technology are also discussed, providing a basis for architectural discussions. Also briefly shown are other architectural alternatives, such as FeRAM and MRAM. The designs surveyed in this article include read before write, wear leveling, write cancellation, write pausing, some encryption schemes, and buffer organizations. These allow PCM to stand on its own as a replacement for DRAM as main memory. Designs for hybrid memory systems with both PCM and DRAM are also shown and some designs for SSDs incorporating PCM.


title: Indifference to dissonance in native Amazonians reveals cultural variation in music perception

abstract: A native Amazonian society rated consonant and dissonant chords and vocal harmonies as equally pleasant, whereas Bolivian city- and town-dwellers preferred consonance, indicating that preference for consonance over dissonance is not universal and probably develops from exposure to particular types of polyphonic music.


title: A 0.45pJ/conv-step 1.2Gs/s 6b full-Nyquist non-calibrated flash ADC in 45nm CMOS and its scaling behavior

abstract: A 6-bit 1.2 Gs/s non-calibrated flash ADC in a standard 45nm CMOS process, that achieves 0.45pJ/conv-step at full Nyquist bandwidth, is presented. Power efficient operation is achieved by a full optimization of amplifier blocks, and by innovations in the comparator and encoding stage. The performance of a non-calibrated flash ADC is directly related to device properties; a scaling analysis of our ADC in and across CMOS technologies gives insight into the excellent usability of 45nm technology for AD converter design.


title: Cybercrime forensic system in cloud computing

abstract: The cloud is a virtual computing environment that provides applications, platforms and software support as services. The applications are extended over the Internet domain to the cloud service provider (CSP) with greater flexibility and availability at lower cost. Unfortunately, the cloud computing service also provides a better environment for criminals and increases the difficulties of cybercrime forensics. In this paper, we focus on the security issues of cloud services and present a forensic framework to beat cybercrime. We set an analysis engine in the cloud as a network service and this engine is used to monitor the network behaviors and collect the evidence when network criminal behaviors happen. FTK and Encase are used to help subsequently analyze the acquired evidence and obtain ideal results.


title: Automatic CEFR Level Prediction for Estonian Learner Text

abstract: This paper reports on approaches for automatically predicting a learner’s language proficiency in Estonian according to the European CEFR scale. We used the morphological and POS tag information extracted from the texts written by learners. We compared classification and regression modeling for this task. Our models achieve a classification accuracy of 79% and a correlation of 0.85 when modeled as regression. After a comparison between them, we concluded that classification is more effective than regression in terms of exact error and the direction of error. Apart from this, we investigated the most predictive features for both multiclass and binary classification between groups and also explored the nature of the correlations between highly predictive features. Our results show considerable improvement in classification accuracy over previously reported results and take us a step closer towards the automated assessment of Estonian learner text.


title: Milli-HRG inertial navigation system

abstract: The Hemispherical Resonator Gyro (HRG) has proven itself to be an ultra-reliable technology for space application with over 18 million operation hours and 100% mission success. Northrop Grumman Navigation Systems Division is developing a terrestrial inertial navigation system (INS) based on the proven space technology that can be used for precision pointing applications. The Precision Pointing System (PPS) design yields a small size and lightweight system and will require only a few watts of power to operate. To achieve this small sized INS, the PPS utilizes a new golf-ball sized milli-HRG (mHRG) that is based on the current HRG 130P production gyro design used in extremely accurate space pointing systems. The power reduction is derived from a new electronics design based around low power elements. The new mHRG gyro design has demonstrated bias stability performance better than the navigation grade gyros and will quickly attain this accuracy due to the extremely low noise characteristics of the HRG. Instrumental in the success of the mHRG performance has been the implementation of a calibration mechanization that eliminates the requirement for thermal control or modeling. This implementation will allow the INS to align quickly and will be advantageous for applications that have a quick response time requirement. Additionally, due to the stability of the mHRG the system can operate without GPS aiding for greater than an hour, while maintaining the attitude accuracy required for precision pointing, before an internal re-alignment is needed. The simplified design of mHRG has reduced the parts count by roughly 90% when compared to the current space qualified HRG production unit. With the major parts reduction it is projected that the mHRG can be produced efficiently and at a cost making it a viable choice for terrestrial applications.


title: Alternative c-means clustering algorithms

abstract: Abstract In this paper we propose a new metric to replace the Euclidean norm in c-means clustering procedures. On the basis of the robust statistic and the influence function, we claim that the proposed new metric is more robust than the Euclidean norm. We then create two new clustering methods called the alternative hard c-means (AHCM) and alternative fuzzy c-means (AFCM) clustering algorithms. These alternative types of c-means clustering have more robustness than c-means clustering. Numerical results show that AHCM has better performance than HCM and AFCM is better than FCM. We recommend AFCM for use in cluster analysis. Recently, this AFCM algorithm has successfully been used in segmenting the magnetic resonance image of Ophthalmology to differentiate the abnormal tissues from the normal tissues.


title: Joint repairs for web wrappers

abstract: Automated web scraping is a popular means for acquiring data from the web. Scrapers (or wrappers) are derived from either manually or automatically annotated examples, often resulting in under/over segmented data, together with missing or spurious content. Automatic repair and maintenance of the extracted data is thus a necessary complement to automatic wrapper generation. Moreover, the extracted data is often the result of a long-term data acquisition effort and thus jointly repairing wrappers together with the generated data reduces future needs for data cleaning. We study the problem of computing joint repairs for XPath-based wrappers and their extracted data. We show that the problem is NP-complete in general but becomes tractable under a few natural assumptions. Even tractable solutions to the problem are still impractical on very large datasets, but we propose an optimal approximation that proves effective across a wide variety of domains and sources. Our approach relies on encoded domain knowledge, but require no per-source supervision. An evaluation spanning more than 100k web pages from 100 different sites of a wide variety of application domains, shows that joint repairs are able to increase the quality of wrappers between 15% and 60% independently of the wrapper generation system, eliminating all errors in more than 50% of the cases.


title: AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders

abstract: Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which is usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.


title: Identifying and Accounting for Task-Dependent Bias in Crowdsourcing

abstract: Models for aggregating contributions by crowd workers have been shown to be challenged by the rise of task-specific biases or errors. Task-dependent errors in assessment may shift the majority opinion of even large numbers of workers to an incorrect answer. We introduce and evaluate probabilistic models that can detect and correct task-dependent bias automatically. First, we show how to build and use probabilistic graphical models for jointly modeling task features, workers' biases, worker contributions and ground truth answers of tasks so that task-dependent bias can be corrected. Second, we show how the approach can perform a type of transfer learning among workers to address the issue of annotation sparsity. We evaluate the models with varying complexity on a large data set collected from a citizen science project and show that the models are effective at correcting the task-dependent worker bias. Finally, we investigate the use of active learning to guide the acquisition of expert assessments to enable automatic detection and correction of worker bias.


HANDGESTURERECOGNITION: A LITERATURE REVIEW


title: Evaluation of oscillatory phenomena in reverse operation for High Voltage Diodes

abstract: We have researched breaking the trade-off limitations between the overall loss and the reverse recovery softness of High Voltage (HV) freewheeling diodes. For the first time, our results show that the oscillatory phenomena in the reverse operation for HV diodes originate in the enlarging behavior of the space charge region to the cathode side during reverse operation and the extracting hole velocity at the end of the recovery period. The remaining hole of the cathode side affects the slowing and reduces the above behavior. We call this phenomenon the “relaxing electric field effect” at the cathode side. The novel HV diode structure can provide the necessary relaxing electric field effect for improving the soft recovery performance by employing the new Relaxed Field of Cathode (RFC) that utilizes LPT(II) technology. The RFC concept demonstrates a clear trade-off breakthrough between the overall loss and the reverse recovery softness of HV diodes.


title: Operation, design and control of dual H-bridge-based isolated bidirectional DC-DC converter

abstract: The operation, design and control of an isolated bidirectional DC - DC converter for hybrid electric vehicle energy management applications are discussed. Different operation modes and boundary conditions are distinguished by phase-shift angle and load conditions. The absolute and relative output voltage ripple was derived. The dead-band effect and safe operational area are further investigated. The relations between output power and leakage inductance and switching frequency are also presented. The proposed converter was simulated and a prototype was built and tested. Experiments on the converter's steady state and transient operations validated the design and simulation.


title: Safer Classification by Synthesis

abstract: The discriminative approach to classification using deep neural networks has become the de-facto standard in various fields. Complementing recent reservations about safety against adversarial examples, we show that conventional discriminative methods can easily be fooled to provide incorrect labels with very high confidence to out of distribution examples. We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models. At training time, we learn a generative model for each class, while at test time, given an example to classify, we query each generator for its most similar generation, and select the class corresponding to the most similar one. Our approach is general and can be used with expressive models such as GANs and VAEs. At test time, our method accurately"knows when it does not know,"and provides resilience to out of distribution examples while maintaining competitive performance for standard examples.


title: Requirements Engineering and Software Project Success: An Industrial Survey in Australia and the U.S.

abstract: Because requirements engineering is recognized as critical to successful software projects we surveyed a number of software practitioners regarding their software development practices during recent software projects. Relationships between requirements practices and software project outcomes enable us to better understand requirements issues and their relationship with project success. We asked three sets of questions directly related to requirements issues: 1) requirements practices, 2) the sponsor and customers/users, and 3) project management. Our respondents were from business organizations in the U.S. and Australia, and were almost exclusively involved in in-house software development. The most significant factors from each question set were: 1) the requirements were good, 2) there was a high level of Customer/User involvement, and 3) the requirements were managed effectively. Overall, the best predictor of project success was that the requirements were good together with the requirements were managed effectively (93% of projects were predicted correctly). Our survey shows that effective project management is fundamental to effective requirements engineering.


title: Characterization of interspecific hybrids and first backcross generations from crosses between two cultivated eggplants (Solanum melongena and S. aethiopicum Kumba group) and implications for eggplant breeding

abstract: Common (Solanum melongena L.) and scarlet (S. aethiopicum L.) eggplants are cultivated for their fruits and form part of the same genepool. We have studied plant and fruit characteristics, pollen viability and seed set, phenolics content, and fruit flesh browning in accessions of S. melongena and S. aethiopicum Kumba group, as well as interspecific hybrids between these species and first backcross generations to each parental species. Respective genotypes were also characterized with seven polymorphic SSR markers. The results demonstrate that many differences exist for plant and fruit morphology among S. melongena, S. aethiopicum and the interspecific hybrids. The latter are very vigorous and generally intermediate between the two parents, except for fruit size which is smaller (and parthenocarpic due to a high pollen sterility) than those of any of the parents. Backcross progenies also exhibited morphological variation with moderate heritability values for the attributes evaluated. Variation for fruit size was present in the backcross generations but fruits were small resulting in little variation for fruit shape. Backcross plants with moderate fertility produced seeded fruits. Primary hybrids had fruit phenolics content similar to that of S. aethiopicum, the parent with lowest phenolics concentration, and were heterotic for fruit flesh browning. Backcross progenies were quite variable for both traits. SSR markers did not reveal segregation distortion in the backcross generations for these interspecific hybrids. The results demonstrate that generations derived from sexual interspecific hybridization can be a powerful tool for S. melongena and S. aethiopicum Kumba group breeding.


title: COCA: Computation Offload to Clouds Using AOP

abstract: In this paper, we describe COCA -- Computation Offload to Clouds using AOP (aspect-oriented programming). COCA is a programming framework that allows smart phones application developers to offload part of the computation to servers in the cloud easily. COCA works at the source level. By harnessing the power of AOP, \name inserts appropriate offloading code into the source code of the target application based on the result of static and dynamic profiling. As a proof of concept, we integrate \name into the Android development environment and fully automate the new build process, making application programming and software maintenance easier. With COCA, mobile applications can now automatically offload part of the computation to the cloud, achieving better performance and longer battery life. Smart phones such as iPhone and Android phones can now easily leverage the immense computing power of the cloud to achieve tasks that were considered difficult before, such as having a more complicated artificial-intelligence engine.


title: Chinese Segmentation And New Word Detection Using Conditional Random Fields

abstract: Chinese word segmentation is a difficult, important and widely-studied sequence modeling problem. This paper demonstrates the ability of linear-chain conditional random fields (CRFs) to perform robust and accurate Chinese word segmentation by providing a principled framework that easily supports the integration of domain knowledge in the form of multiple lexicons of characters and words. We also present a probabilistic new word detection method, which further improves performance. Our system is evaluated on four datasets used in a recent comprehensive Chinese word segmentation competition. State-of-the-art performance is obtained.


title: Automation of Mathematical Induction as part of the History of Logic

abstract: We review the history of the automation of mathematical induction


title: Comparing and evaluating epoll, select, and poll event mechanisms

abstract: This paper uses a high-performance, eventdriven, HTTP server (the server) to compare the performance of the select, poll, and epoll event mechanisms. We subject the server to a variety of workloads that allow us to expose the relative strengths and weaknesses of each event mechanism. Interestingly, initial results show that the select and poll event mechanisms perform comparably to the epoll event mechanism in the absence of idle connections. Profiling data shows a significant amount of time spent executing a large number of epoll_ctl system calls. As a result, we examine a variety of techniques for reducing epoll_ctl overhead including edge-triggered notification, and introducing a new system call (epoll_ctlv) that aggregates several epoll_ctl calls into a single call. Our experiments indicate that although these techniques are successful at reducing epoll_ctl overhead, they only improve performance slightly.


title: Robust information encryption diffractive-imaging-based scheme with special phase retrieval algorithm for a customized data container

abstract: Abstract The diffractive-imaging-based encryption (DIBE) scheme has aroused wide interesting due to its compact architecture and low requirement of conditions. Nevertheless, the primary information can hardly be recovered exactly in the real applications when considering the speckle noise and potential occlusion imposed on the ciphertext. To deal with this issue, the customized data container (CDC) into DIBE is introduced and a new phase retrieval algorithm (PRA) for plaintext retrieval is proposed. The PRA, designed according to the peculiarity of the CDC, combines two key techniques from previous approaches, i.e., input-support-constraint and median-filtering. The proposed scheme can guarantee totally the reconstruction of the primary information despite heavy noise or occlusion and its effectiveness and feasibility have been demonstrated with simulation results.


title: SEE: Towards Semi-Supervised End-to-End Scene Text Recognition

abstract: Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.


title: Prospects of energy conservation and management in buildings – The Saudi Arabian scenario versus global trends

abstract: The volume of construction work in the GCC countries is dramatically increasing, which poses challenges on energy demand and the environment. The building construction sector in the Kingdom of Saudi Arabia is the largest and fastest in the GCC states. It is estimated that 2.32 million new homes are to be built by 2020 in order to meet the demand of growing population. This article provides a comprehensive picture of the initiatives and research progress in energy conservation and management in the Saudi Arabian building sector. Starting with a background of energy scenario, the article covers the policy-level measures and research and development activities reported so far, and compares the current situation with the global trends. This review reveals that even though there are promising research activities, policy supports and growing public awareness, the Kingdom has wide scope for further developments in order to ensure (through the utilization of its resources at their fullest potential) that all the viable state-of-the-art technologies for energy conservation and management in buildings are implemented. Accordingly, a consolidated outline of the key areas in the domain is presented, and many potential new research directions are proposed.


title: Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing

abstract: Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can parse utterances in unseen domains while only being trained on examples in other source domains. First, we map an utterance to an abstract, domain-independent, logical form that represents the structure of the logical form, but contains slots instead of KB constants. Then, we replace slots with KB constants via lexical alignment scores and global inference. Our model reaches an average accuracy of 53.4% on 7 domains in the Overnight dataset, substantially better than other zero-shot baselines, and performs as good as a parser trained on over 30% of the target domain examples.


title: Joint Spatio-Temporal Boundary Detection and Boundary Flow Prediction with a Fully Convolutional Siamese Network.

abstract: This paper addresses a new problem of joint object boundary detection and boundary motion estimation in videos, which we named boundary flow estimation. Boundary flow is an important mid-level visual cue as boundaries characterize spatial extents of objects, and the flow indicates motions and interactions of objects. Yet, most prior work on motion estimation has focused on dense object motion or feature points that may not necessarily reside on boundaries. For boundary flow estimation, we specify a new fully convolutional Siamese network (FCSN) that jointly estimates object-level boundaries in two consecutive frames. Boundary correspondences in the two frames are predicted by the same FCSN with a new, unconventional deconvolution approach. Finally, the boundary flow estimate is improved with an edgelet based filtering. Evaluation is conducted on three tasks: boundary detection in videos, boundary flow estimation, and optical flow estimation. On boundary detection, we achieve the state-of-the-art performance on the benchmark VSB100 dataset. On boundary flow estimation, we present the first results on the Sintel training dataset. For optical flow estimation, we run the recent approach CPM-Flow but on the augmented input with our boundary flow matches, and achieve significant performance improvement on the Sintel benchmark.


title: The Use of the UTAUT Model in the Adoption of E-Government Services in Kuwait

abstract: E-government initiatives are in their infancy in many developing countries. The success of these initiatives is dependent on government support as well as citizens' adoption of e-government services. This study adopted the unified of acceptance and use of technology (UTAUT) model to explore factors that determine the adoption of e-government services in a developing country, namely Kuwait. 880 students were surveyed, using an amended version of the UTAUT model. The empirical data reveal that performance expectancy, effort expectancy and peer influence determine students' behavioural intention. Moreover, facilitating conditions and behavioural intentions determine students' use of e-government services. Implications for decision makers and suggestions for further research are also considered in this study.


title: Optical simulation of time-of-flight sensor accuracy in rain

abstract: Technology for self-driving vehicles is currently under heavy development. Reliable autonomous driving requires precise information from different sensors, in order to establish a realistic and accurate model of the vehicle's environment. Time-of-Flight (ToF) sensors can contribute valuable information to such a vehicle's sensor network [1]. The influence of weather effects, in particular rain droplets, on distance and depth measurements with a ToF camera based sensor are examined based on optical simulation models in this publication.


title: YIN, a fundamental frequency estimator for speech and music

abstract: An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.


title: Robust Multiframe Super-Resolution Employing Iteratively Re-Weighted Minimization

abstract: Multiframe super-resolution algorithms reconstruct high-resolution images by exploiting complementary information in multiple low-resolution frames. However, despite their success under ideal conditions, most existing methods rely on simplistic approximations to the physics of image acquisition and show limited robustness in real-world applications. This paper proposes spatially adaptive Bayesian modeling and an iterative algorithm for robust super-resolution imaging. In particular, we introduce a weighted Gaussian observation model to consider space variant noise and weighted bilateral total variation to exploit sparsity of natural images. Based on this model, we develop a majorization–minimization algorithm implemented as iteratively re-weighted minimization. The proposed method simultaneously estimates model parameters and the super-resolved image in an iterative coarse-to-fine scheme. Compared to prior work, our approach combines the benefits of achieving robust and edge preserving image reconstruction with small amount of parameter tuning, while being flexible in terms of motion models, computationally efficient and easy to implement. Our experimental evaluation confirms that our approach outperforms state-of-the-art algorithms under various practical conditions, e.g., inaccurate geometric and photometric registration or invalid measurements.


title: Differential Cache-Collision Timing Attacks on AES with Applications to Embedded CPUs

abstract: This paper proposes a new type of cache-collision timing attacks on software implementations of AES. Our major technique is of differential nature and is based on the internal cryptographic properties of AES, namely, on the MDS property of the linear code providing the diffusion matrix used in the MixColumns transform. It is a chosen-plaintext attack where pairs of AES executions are treated differentially. The method can be easily converted into a chosen-ciphertext attack. We also thoroughly study the physical behavior of cache memory enabling this attack. ::: ::: On the practical side, we demonstrate that our theoretical findings lead to efficient real-world attacks on embedded systems implementing AES at the example of ARM9. As this is one of the most wide-spread embedded platforms today [7], our experimental results might make a revision of the practical security of many embedded applications with security functionality necessary. To our best knowledge, this is the first paper to study cache timing attacks on embedded systems.


title: Sensorless speed control of a PM synchronous motor based on sliding mode observer and extended Kalman filter

abstract: In this paper a method for rotor speed and position detection in permanent magnet synchronous motors is presented, suitable for applications where low speed and standstill operations are not required. The approach is based on the estimation of the motor back-EMF through a sliding mode observer and a cascaded Kalman filtering. Due to its characteristics, the technique can be applied to motors having distorted and whatever shaped back-EMF waveform. The equation error for the rotor position estimation is derived and discussed. Test results are presented, which show the system performance including start-up capability, which is one of the most critical conditions in back-EMF based sensorless schemes.


title: A Pragmatic Approach to Exploiting Full Force Capacity for Serial Redundant Manipulators

abstract: Considering a set of robotic tasks which involve physical interaction with the environment, the theoretical knowledge of the full force capacity of the manipulator is a key factor in the design or development of an efficient and economically attractive solution. Carrying its own weight while countering forces may be too much for a robot in certain configurations. Kinematic redundancy with regard to a task allows a robot to perform it in a continuous space of articular configurations; space in which the payload of the robot may vary dramatically. It may be impossible to withstand a physical interaction in some configurations, while it may be easily sustainable in others that bring the end-effector to the same location. This becomes obviously more prevalent for a limited payload robot. This letter describes a framework for these kind of operations, in which kinematic redundancy is used to explore the full extent of a force capacity for a given manipulator and task (in this letter, the terms “force” and “wrench” may interchangeably refer to two-, three-, or six-dimensional forces depending on the dimension of the problem and on whether they may or may not include components of translational forces and/or moments. Their dimensional definition will be explicitly given whenever specifically needed). A pragmatic force capacity index (FCI) is proposed. The FCI offers a sound basis for redundancy resolution via optimization or complete redundancy exploration, and may provide good hints for end-effector design. A practical use case involving 7-DOFs KUKA LBR iiwa was used to demonstrate the relevance of the proposed method.


title: Analysis of the Effects of Drought on Vegetation Cover in a Mediterranean Region through the Use of SPOT-VGT and TERRA-MODIS Long Time Series

abstract: The analysis of vegetation dynamics and agricultural production is essential in semi-arid regions, in particular as a consequence of the frequent occurrence of periods of drought. In this paper, a multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION (between September 1998 and August 2013) and TERRA-MODIS satellite data (between September 2000 and August 2013), was used to analyze the vegetation dynamics over the central region of Tunisia in North Africa, which is characterized by a semi-arid climate. Products derived from these two satellite sensors are generally found to be coherent. Our analysis of land use and NDVI anomalies, based on the Vegetation Anomaly Index (VAI), reveals a strong level of agreement between estimations made with the two satellites, but also some discrepancies related to the spatial resolution of these two products. The vegetation’s behavior is also analyzed during years affected by drought through the use of the Windowed Fourier Transform (WFT). Discussions of the dynamics of annual agricultural areas show that there is a combined effect between climate and farmers’ behavior, leading to an increase in the prevalence of bare soils during dry years.


title: One-To-Many Network for Visually Pleasing Compression Artifacts Reduction

abstract: We consider the compression artifacts reduction problem, where a compressed image is transformed into an artifact-free image. Recent approaches for this problem typically train a one-to-one mapping using a per-pixel $L_2$ loss between the outputs and the ground-truths. We point out that these approaches used to produce overly smooth results, and PSNR doesn't reflect their real performance. In this paper, we propose a one-to-many network, which measures output quality using a perceptual loss, a naturalness loss, and a JPEG loss. We also avoid grid-like artifacts during deconvolution using a "shift-and-average" strategy. Extensive experimental results demonstrate the dramatic visual improvement of our approach over the state of the arts.


title: SUPPLE: automatically generating user interfaces

abstract: In order to give people ubiquitous access to software applications, device controllers, and Internet services, it will be necessary to automatically adapt user interfaces to the computational devices at hand (eg, cell phones, PDAs, touch panels, etc.). While previous researchers have proposed solutions to this problem, each has limitations. This paper proposes a novel solution based on treating interface adaptation as an optimization problem. When asked to render an interface on a specific device, our supple system searches for the rendition that meets the device's constraints and minimizes the estimated effort for the user's expected interface actions. We make several contributions: 1) precisely defining the interface rendition problem, 2) demonstrating how user traces can be used to customize interface rendering to particular user's usage pattern, 3) presenting an efficient interface rendering algorithm, 4) performing experiments that demonstrate the utility of our approach.


title: The first de novo transcriptome of pepino (Solanum muricatum): assembly, comprehensive analysis and comparison with the closely related species S. caripense, potato and tomato

abstract: BackgroundSolanum sect. Basarthrum is phylogenetically very close to potatoes (Solanum sect. Petota) and tomatoes (Solanum sect. Lycopersicon), two groups with great economic importance, and for which Solanum sect. Basarthrum represents a tertiary gene pool for breeding. This section includes the important regional cultigen, the pepino (Solanum muricatum), and several wild species. Among the wild species, S. caripense is prominent due to its major involvement in the origin of pepino and its wide geographical distribution. Despite the value of the pepino as an emerging crop, and the potential for gene transfer from both the pepino and S. caripense to potatoes and tomatoes, there has been virtually no genomic study of these species.ResultsUsing Illumina HiSeq 2000, RNA-Seq was performed with a pool of three tissues (young leaf, flowers in pre-anthesis and mature fruits) from S. muricatum and S. caripense, generating almost 111,000,000 reads among the two species. A high quality de novo transcriptome was assembled from S. muricatum clean reads resulting in 75,832 unigenes with an average length of 704 bp. These unigenes were functionally annotated based on similarity of public databases. We used Blast2GO, to conduct an exhaustive study of the gene ontology, including GO terms, EC numbers and KEGG pathways. Pepino unigenes were compared to both potato and tomato genomes in order to determine their estimated relative position, and to infer gene prediction models. Candidate genes related to traits of interest in other Solanaceae were evaluated by presence or absence and compared with S. caripense transcripts. In addition, by studying five genes, the phylogeny of pepino and five other members of the family, Solanaceae, were studied. The comparison of S. caripense reads against S. muricatum assembled transcripts resulted in thousands of intra- and interspecific nucleotide-level variants. In addition, more than 1000 SSRs were identified in the pepino transcriptome.ConclusionsThis study represents the first genomic resource for the pepino. We suggest that the data will be useful not only for improvement of the pepino, but also for potato and tomato breeding and gene transfer. The high quality of the transcriptome presented here also facilitates comparative studies in the genus Solanum. The accurate transcript annotation will enable us to figure out the gene function of particular traits of interest. The high number of markers (SSR and nucleotide-level variants) obtained will be useful for breeding programs, as well as studies of synteny, diversity evolution, and phylogeny.


title: Citizen participation, perceived control, and psychological empowerment

abstract: The research integrates the citizen participation literature with research on perceived control in an effort to further our understanding of psychological empowerment. Eleven indices of empowerment representing personality, cognitive, and motivational measures were identified to represent the construct. Three studies examined the relationship between empowerment and participation. The first study examined differences among groups identified by a laboratory manipulation as willing to participate in personally relevant or community relevant situations. Study II examined differences for groups defined by actual involvement in community activities and organizations. Study III replicated Study II with a different population. In each study, individuals reporting a greater amount of participation scored higher on indices of empowerment. Psychological empowerment could be described as the connection between a sense of personal competence, a desire for, and a willingness to take action in the public domain. Discriminant function analyses resulted in one significant dimension, identified as pyschological empowerment, that was positively correlated with leadership and negatively correlated with alienation.


title: Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images

abstract: The shape-adaptive discrete cosine transform ISA-DCT) transform can be computed on a support of arbitrary shape, but retains a computational complexity comparable to that of the usual separable block-DCT (B-DCT). Despite the near-optimal decorrelation and energy compaction properties, application of the SA-DCT has been rather limited, targeted nearly exclusively to video compression. In this paper, we present a novel approach to image filtering based on the SA-DCT. We use the SA-DCT in conjunction with the Anisotropic Local Polynomial Approximation-Intersection of Confidence Intervals technique, which defines the shape of the transform's support in a pointwise adaptive manner. The thresholded or attenuated SA-DCT coefficients are used to reconstruct a local estimate of the signal within the adaptive-shape support. Since supports corresponding to different points are in general overlapping, the local estimates are averaged together using adaptive weights that depend on the region's statistics. This approach can be used for various image-processing tasks. In this paper, we consider, in particular, image denoising and image deblocking and deringing from block-DCT compression. A special structural constraint in luminance-chrominance space is also proposed to enable an accurate filtering of color images. Simulation experiments show a state-of-the-art quality of the final estimate, both in terms of objective criteria and visual appearance. Thanks to the adaptive support, reconstructed edges are clean, and no unpleasant ringing artifacts are introduced by the fitted transform.


title: Machine Learning and Its Applications to Biology

abstract: The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechanization should be acknowledged when considering machine learning in broad terms. Firstly, it is intended that the classification and prediction tasks can be accomplished by a suitably programmed computing machine. That is, the product of machine learning is a classifier that can be feasibly used on available hardware. Secondly, it is intended that the creation of the classifier should itself be highly mechanized, and should not involve too much human input. This second facet is inevitably vague, but the basic objective is that the use of automatic algorithm construction methods can minimize the possibility that human biases could affect the selection and performance of the algorithm. Both the creation of the algorithm and its operation to classify objects or predict events are to be based on concrete, observable data. ::: ::: The history of relations between biology and the field of machine learning is long and complex. An early technique [1] for machine learning called the perceptron constituted an attempt to model actual neuronal behavior, and the field of artificial neural network (ANN) design emerged from this attempt. Early work on the analysis of translation initiation sequences [2] employed the perceptron to define criteria for start sites in Escherichia coli. Further artificial neural network architectures such as the adaptive resonance theory (ART) [3] and neocognitron [4] were inspired from the organization of the visual nervous system. In the intervening years, the flexibility of machine learning techniques has grown along with mathematical frameworks for measuring their reliability, and it is natural to hope that machine learning methods will improve the efficiency of discovery and understanding in the mounting volume and complexity of biological data. ::: ::: This tutorial is structured in four main components. Firstly, a brief section reviews definitions and mathematical prerequisites. Secondly, the field of supervised learning is described. Thirdly, methods of unsupervised learning are reviewed. Finally, a section reviews methods and examples as implemented in the open source data analysis and visualization language R (http://www.r-project.org).


title: Recommender Systems from an Industrial and Ethical Perspective

abstract: Over the recent years, a plethora of recommender systems (RS) have been proposed by academics. The degree of adoptability of these algorithms by industrial e-commerce platforms remains unclear. To get an insight into real-world recommendation engines, we survey more than 30 existing shopping cart solutions and compare the performance of popular recommendation algorithms on proprietary e-commerce datasets. Our results show that deployed systems rarely go beyond trivial "best seller" lists or very basic personalized recommendation algorithms, which nevertheless exhibit superior performance to more elaborate techniques both in our experiments and other related studies. We also perform chronological dataset splits to demonstrate the importance of preserving the sequence of events during evaluation, and the recency of events during training. The second part of our research is still ongoing and focuses on various ethical challenges that complicate the design of recommender systems. We believe that this direction of research remains mostly neglected despite its increasing impact on RS' quality and safety.


title: Inventory inaccuracies in the wholesale supply chain

abstract: The inventory level shown in the Information System contrary to popular belief and assumptions in most academic papers and in spite of the considerable amounts invested in information technology, is often inaccurate. The inventory inaccuracy occurs when the inventory shown in the Information System is not in agreement with the actually available inventory. In this paper, we first describe the major factors generating inventory inaccuracy. Then, we provide situations permitting to manage an inventory system subject to errors. We provide a general framework permitting to model the inventory inaccuracy issue. In particular, we link the inaccuracy issue with the well known random yield problem. The shown link permits us to derive the optimal ordering policy of an inventory framework where demand satisfaction is done based on the inventory records (which are subject to inaccuracies). We also propose an analysis permitting to show the added value of an advanced identification system such as the RFID technology.


title: Transforming XML schemas into OWL ontologies using formal concept analysis

abstract: Ontology Web Language (OWL) is considered as a data representation format exploited by the Extensible Markup Language (XML) format. OWL extends XML by providing properties to further express the semantics of data. To this effect, transforming XML data into OWL proves important and constitutes an added value for indexing XML documents and re-engineering ontologies. In this paper, we propose a formal method to transform XSD schemas into OWL schemas using transformation patterns. To achieve this end, we extend at the beginning, a set of existing transformation patterns to allow the maximum transformation of XSD schema constructions. In addition, a formal method is presented to transform an XSD schema using the extended patterns. This method named PIXCO comprises several processes. The first process models both the transformation patterns and all the constructions of XSD schema to be transformed. The patterns are modeled using the context of Formal Concept Analysis. The XSD constructions are modeled using a proposed mathematical model. This modeling will be used in the design of the following process. The second process identifies the most appropriate patterns to transform each construction set of XSD schema. The third process generates for each XSD construction set an OWL model according to the pattern that is identified. Finally, it creates the OWL file encompassing the generated OWL models.


title: Management System for Large Li-Ion Battery Packs with a New Adaptive Multistage Charging Method

abstract: Among the wide diversity of existing technologically mature batteries, lithium-ion (Li-ion) batteries have become popular because of their longevity, high energy density, high efficiency and lack of memory effect. Differential charging of cells with age has turned balancing management systems into an important research subject. This paper proposes a new battery management system (BMS) to improve the capacity usage and lifespan of large Li-ion battery packs and a new charging algorithm based on the traditional multistage method. The main advantages of the proposed system are its versatility and ability to implement different charging and balancing methods in a very accessible way. The combination of charging methods with balancing methods represents an evolution when compared with other works in the literature.


title: Characteristics and internet marketing strategies of online auction sellers

abstract: Internet marketing is a field that is continuing to grow, and the online auction concept may be defining a totally new and unique distribution alternative. Very few studies have examined auction sellers and their internet marketing strategies. This research examines the internet auction phenomenon as it relates to the marketing mix of online auction sellers. The data in this study indicate that, whilst there is great diversity among businesses that utilise online auctions, distinct cost leadership and differentiation marketing strategies are both evident. These two approaches are further distinguished in terms of the internet usage strategies employed by each group.


title: A Gaze-Based Driver Distraction Warning System and Its Effect on Visual Behavior

abstract: Driver distraction is a contributing factor to many crashes; therefore, a real-time distraction warning system should have the potential to mitigate or circumvent many of these crashes. The objective of this paper is to investigate the usefulness of a real-time distraction detection algorithm called AttenD. The evaluation is based on data from an extended field study comprising seven drivers who drove on an average of 4351 ± 2181 km in a naturalistic setting. Visual behavior was investigated both on a global scale and on a local scale in the surroundings of each warning. An increase in the percentage of glances at the rear-view mirror and a decrease in the amount of glances at the center console were found. The results also show that visual time sharing decreased in duration from 9.94 to 9.20 s due to the warnings, that the time from fully attentive to warning decreased from 3.20 to 3.03 s, and that the time from warning to full attentiveness decreased from 6.02 to 5.46 s. The limited number of participants does not allow any generalizable conclusions, but a trend toward improved visual behavior could be observed. This is a promising start for further improvements of the algorithm and the warning strategy.


title: Fog computing through public-resource computing and storage

abstract: Fog computing is a model in which data and processing are concentrated on devices at the edge of the network, rather than almost entirely in the cloud. This new paradigm tries to solve the latency and bandwidth issues that the current cloud systems have to deal with, and also addresses the scalability problems caused by the exponential growth of the number of smart devices due to the success of the Internet of Things. In this paper we introduce the idea of using public-resource computing and storage techniques in order to process part of the workload of the current cloud systems so as to avoid saturating the cloud. This idea proposes the use of devices working as participants, which form a data center between the cloud service providers and the end-clients. A participant can be any type of device, from a traditional PC (Personal Computer), to a smartphone or tablet or even a smart TV. We have evaluated two different case studies by simulating the use of participating nodes in video-transfer applications. The results of the simulations demonstrate that public-resource computing and storage can be used to solve the latency and bandwidth issues that affect the current cloud systems. Therefore, our results represent a feasible solution for applications that process or store public data.


title: Leader–member exchange and its dimensions: Effects of self-effort and other's effort on relationship quality.

abstract: Two hundred thirty-two manager-subordinate dyads provided data on the effort expended toward the development of leader-member exchange (LMX) relationships, how such effort related to expectations about relationship quality, and intentions to exert future effort toward relationship development. For both managers and subordinates, higher quality LMX relationships were reported and expectations were met when the other member of the dyad put forth effort into relationship development. One's own higher effort coupled with lower effort by the other was associated with a lower quality LMX relationship. Examination of the 4 dimensions of LMX (R. C. Liden & J. M. Maslyn, 1998) revealed unique effects depending on the dimension considered. In addition, LMX and relationship tenure interacted, showing that future effort toward relationship development was greatest when individuals had been in higher quality relationships for longer periods of time.


title: Relative confidence sampling for efficient on-line ranker evaluation

abstract: A key challenge in information retrieval is that of on-line ranker evaluation: determining which one of a finite set of rankers performs the best in expectation on the basis of user clicks on presented document lists. When the presented lists are constructed using interleaved comparison methods, which interleave lists proposed by two different candidate rankers, then the problem of minimizing the total regret accumulated while evaluating the rankers can be formalized as a K-armed dueling bandits problem. In this paper, we propose a new method called relative confidence sampling (RCS) that aims to reduce cumulative regret by being less conservative than existing methods in eliminating rankers from contention. In addition, we present an empirical comparison between RCS and two state-of-the-art methods, relative upper confidence bound and SAVAGE. The results demonstrate that RCS can substantially outperform these alternatives on several large learning to rank datasets.


title: ACID Encountering the CAP Theorem: Two Bank Case Studies

abstract: In the era of big data, we may adopt the distributed architecture for a transaction processing system due to some reasons, including distributed branches, heavy demand and operational expenditure, etc. In terms of the CAP Theorem, a transaction processing system associated with ACID properties is infeasible to work well in the distributed architecture. It is indispensable to address how to make a trade-off between availability and partition tolerance for a bank as it favors the consistency in the distributed system. In this research, we conduct two case studies to address the question using two transaction logs collected from a bank in China. We mainly analyze the table dependency and the table concurrency, and find that (1) it is arduous to partition the data in the database system associated with ACID properties, (2) in-memory architecture for updating transactions may be an alternative for building a transaction processing system.


title: Linear N-Point Camera Pose Determination

abstract: The determination of camera position and orientation from known correspondences of 3D reference points and their images is known as pose estimation in computer vision and space resection in photogrammetry. It is well-known that from three corresponding points there are at most four algebraic solutions. Less appears to be known about the cases of four and five corresponding points. We propose a family of linear methods that yield a unique solution to 4- and 5-point pose determination for generic reference points. We first review the 3-point algebraic method. Then we present our two-step, 4-point and one-step, 5-point linear algorithms. The 5-point method can also be extended to handle more than five points. Finally, we demonstrate our methods on both simulated and real images. We show that they do not degenerate for coplanar configurations and even outperform the special linear algorithm for coplanar configurations in practice.


title: Position and Orientation Errors in Mobile Robot Absolute Self-Localization Using an Improved Version of the Generalized Geometric Triangulation Algorithm

abstract: Triangulation with active beacons is widely used in the absolute localization of mobile robots. The original generalized geometric triangulation algorithm suffers only from the restrictions that are common to all algorithms that perform self-localization through triangulation. But it is unable to compute position and orientation when the robot is over the segment of the line that goes by beacons 1 and 2 whose origin is beacon 1 and does not contain beacon 2. An improved version of the algorithm allows self-localization even when the robot is over that line segment. Simulations results suggest that a robot is able to localize itself, with small position and orientation errors, over a wide region of the plane, if measurement uncertainty is small enough.


title: ProM 6: The Process Mining Toolkit

abstract: Process mining has been around for a decade, and it has proven to be a very fertile and successful research field. Part of this success can be contributed to the ProM tool, which combines most of the existing process mining techniques as plug-ins in a single tool. ProM6 removes many limitations that existed in the previous versions, in particular with respect to the tight integration between the tool and the GUI. ProM 6 has been developed from scratch and uses a completely redesigned architecture. The changes were driven by many real-life applications and new insights into the design of process analysis software. Furthermore, the introduction of XESame in this toolkit allows for the conversion of logs to the ProM native format without programming.


title: A Cognitive Perspective on Spatial Context

abstract: This paper develops a representation-theoretic notion of spatial context for cognitive agents that interact with spatial environments. We discuss the state of the art in defining context as used in context-aware and / or location-aware systems. In contrast to existing approaches, we define context through cognitive processes. Placing cognitive processes in the focus of our context definition allows for a truly user-centered perspective: conceptuali- zations imbue spatial structures with meaning. This allows for fixing termino- logical problems and relating context definitions to work in spatial information theory and cognitive science. Although we focus on spatial context, the approach is generic and can be adapted to other domains in which cognitive aspects concerning users of information systems are central.


title: Novel technology for the provision of power to implantable physiological devices

abstract: We report the development of a novel technology that enables the wireless transmission of sufficient amounts of power to implantable physiological devices. The system involves a primary unit generating the magnetic field and a secondary pickup unit deriving power from the magnetic field and a power conditioner. The inductively coupled system was able to supply a minimum of 20 mW at all locations and pickup orientations across a rat cage, although much higher power of up to 10 W could be achieved. We hypothesized that it would be possible to use this technology to record a high-fidelity ECG signal in a conscious rat. A device was constructed in which power was utilized to recharge a battery contained within a telemetry device recording ECG signal sampled at 2,000 Hz in conscious rats (200-350 g) living in their home cage. Attributes of the ECG signal (QT, QRS, and PR interval) could be obtained with a high degree of accuracy (<1 ms). ECG and heart rate changes in response to treatment with the beta blocker propranolol and the proarrhythmic alkaloid aconitine were measured. Transmitters were implanted for up to 4 mo, and the characteristic circadian variation in heart rate was recorded. Such technology allows potentially lifetime monitoring without the need for implant refurbishment. The ability to provide suitable power levels to implanted devices without concern to the orientation of the device and without causing heating provides the basis for the development of new devices to record or influence physiological signals in animals or humans over significantly longer time periods than can currently be accommodated.


title: An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog

abstract: We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and incorporate structured KB query results into system responses to successfully complete task-oriented dialogs. The proposed model produces well-structured system responses by jointly learning belief tracking and KB result processing conditioning on the dialog history. We evaluate the model in a restaurant search domain using a dataset that is converted from the second Dialog State Tracking Challenge (DSTC2) corpus. Experiment results show that the proposed model can robustly track dialog state given the dialog history. Moreover, our model demonstrates promising results in producing appropriate system responses, outperforming prior end-to-end trainable neural network models using per-response accuracy evaluation metrics.


title: The smoke detection for early fire-alarming system base on video processing

abstract: The paper presents an smoke-detection method for early fire-alarming system based on video processing. The basic strategy of smoke-pixel judgment is composed of two decision rules: a chromaticity-based static decision rule and a diffusion-based dynamic characteristic decision rule. The chromatic decision rule is deduced by grayish color of smoke and dynamic decision rule is dependent on the spreading attributes of smoke. Experimental results show that the proposed method can provide an early alarm at a lower false alarm rate before the fire burns up, and hence is very attractive for the important military, social security, commercial applications, and so on.


title: Foreskin development in 10 421 Chinese boys aged 0–18 years

abstract: BACKGROUND ::: Few studies on foreskin development and the practice of circumcision have been done in Chinese boys. This study aimed to determine the natural development process of foreskin in children. ::: ::: ::: METHODS ::: A total of 10 421 boys aged 0 to 18 years were studied. The condition of foreskin was classified into type I (phimosis), type II (partial phimosis), type III (adhesion of prepuce), type IV (normal), and type V (circumcised). Other abnormalities of the genitalia were also determined. ::: ::: ::: RESULTS ::: The incidence of a completely retractile foreskin increased from 0% at birth to 42.26% in adolescence; however, the phimosis rate decreased with age from 99.7% to 6.81%. Other abnormalities included web penis, concealed penis, cryptorchidism, hydrocele, micropenis, inguinal hernia, and hypospadias. ::: ::: ::: CONCLUSIONS ::: Incomplete separation of foreskin is common in children. Since it is a natural phenomenon to approach the adult condition until puberty, circumcision should be performed with cautions in children.


title: A Simplified-Traditional Chinese Character Conversion Model Based on Log-Linear Models

abstract: With the growth of exchange activities between four regions of cross strait, the problem to correctly convert between Traditional Chinese (TC) and Simplified Chinese (SC) become more and more important. Numerous one-to-many mappings and term usage differences make it more difficult to convert from SC to TC. This paper proposed a novel simplified-traditional Chinese character conversion model based on log-linear models, in which features such as language models and lexical semantic consistency weighs are integrated. When estimating lexical semantic consistency weighs, cross-language word-based semantic spaces were used. Experiments were conducted and the results show that the proposed model achieve better performance.


title: Understanding the latency benefits of multi-cloud webservice deployments

abstract: To minimize user-perceived latencies, webservices are often deployed across multiple geographically distributed data centers. The premise of our work is that webservices deployed across multiple cloud infrastructure services can serve users from more data centers than that possible when using a single cloud service, and hence, offer lower latencies to users. In this paper, we conduct a comprehensive measurement study to understand the potential latency benefits of deploying webservices across three popular cloud infrastructure services - Amazon EC2, Google Compute Engine (GCE), and Microsoft Azure. We estimate that, as compared to deployments on one of these cloud services, users in up to half the IP address prefixes can have their RTTs reduced by over 20% when a webservice is deployed across the three cloud services. When we dig deeper to understand these latency benefits, we make three significant observations. First, when webservices shift from single-cloud to multi-cloud deployments, a significant fraction of prefixes will see latency benefits simply by being served from a different data center in the same location. This is because routing inefficiencies that exist between a prefix and a nearby data center in one cloud service are absent on the path from the prefix to a nearby data center in a different cloud service. Second, despite the latency improvements that a large fraction of prefixes will perceive, users in several locations (e.g., Argentina and Israel) will continue to incur RTTs greater than 100ms even when webservices span three large-scale cloud services (EC2, GCE, and Azure). Finally, we see that harnessing the latency benefits offered by multi-cloud deployments is likely to be challenging in practice; our measurements show that the data center which offers the lowest latency to a prefix often fluctuates between different cloud services, thus necessitating replication of data.


title: Rumor centrality: a universal source detector

abstract: We consider the problem of detecting the source of a rumor (information diffusion) in a network based on observations about which set of nodes possess the rumor. In a recent work [10], this question was introduced and studied. The authors proposed rumor centrality as an estimator for detecting the source. They establish it to be the maximum likelihood estimator with respect to the popular Susceptible Infected (SI) model with exponential spreading time for regular trees. They showed that as the size of infected graph increases, for a line (2-regular tree) graph, the probability of source detection goes to 0 while for d-regular trees with d ≥ 3 the probability of detection, say αd, remains bounded away from 0 and is less than 1/2. Their results, however stop short of providing insights for the heterogeneous setting such as irregular trees or the SI model with non-exponential spreading times. This paper overcomes this limitation and establishes the effectiveness of rumor centrality for source detection for generic random trees and the SI model with a generic spreading time distribution. The key result is an interesting connection between a multi-type continuous time branching process (an equivalent representation of a generalized Polya's urn, cf. [1]) and the effectiveness of rumor centrality. Through this, it is possible to quantify the detection probability precisely. As a consequence, we recover all the results of [10] as a special case and more importantly, we obtain a variety of results establishing the universality of rumor centrality in the context of tree-like graphs and the SI model with a generic spreading time distribution.


Scalable Multitask Policy Gradient Reinforcement Learning.


title: A 2.4pJ/bit ASK demodulator with 100% modulation rate for 13.56MHz NFC/RFID applications

abstract: In this paper, an Amplitude Shift Keying (ASK) demodulator circuit for Near Field Communication (NFC) target mode or Radio Frequency Identification (RFID) tag is designed by using sample and hold method. It features envelope detector, switch capacitor filter, dynamic comparator, clock extractor and phase generator. This demodulator was demonstrated and simulated in TSMC 0.18um CMOS technology with an average power of 32uW power consumption at 13.56Mbps Non-Return-to-Zero (NRZ) date rate for a carrier frequency of 13.56MHz. The minimum input voltage swing is 200mVpp.


title: A Fractal-Based Circularly Polarized UHF RFID Reader Antenna

abstract: This letter presents the design of a coplanar waveguide (CPW) circularly polarized antenna for the central frequency 900 MHz, it comes in handy for radio frequency identification (RFID) short-range reading applications within the band of 902-928 MHz where the axial ratio of proposed antenna model is less than 3 dB. The proposed design has an axial-ratio bandwidth of 36 MHz (4%) and impedance bandwidth of 256 MHz (28.5%).


title: A Survey of Wireless Path Loss Prediction and Coverage Mapping Methods

abstract: In this paper we provide a thorough and up to date survey of path loss prediction methods, spanning more than 60 years of fairly continuous research. These methods take a variety of approaches to modeling the signal attenuation between wireless transceivers: purely theoretical models, empirically fitted (often statistical) models, deterministic ray-optical models, and measurement-directed methods. Our work here extends and updates excellent, but now dated prior surveys of this important field. We provide a new taxonomy for reasoning about the similarities and differences of the many approaches and provide a brief but complete overview of the various methods as well as describing insights into future directions for research in this area.