title: On-board Vehicle-to-Grid (V2G) integrator for power transaction in smart grid environment

abstract: The growing oil demands and the economic concerns have inspired the governments, car manufactures and the environmentalist to promote the manufacturing of Electrical Vehicles (EV). With developments in the power grid and advancements in the vehicle technology the EVs can contribute to the grid by selling or buying power from the grid. For the effective and efficient use of the V2G there is a major scope to develop the V2G integrators like On-Board and Off-Board. In this paper the On-Board V2G integrator is proposed and is modeled using MATLAB/SIMULINK. The conversion efficiency of 90% is obtained and the benefits of V2G are listed.


title: Let's talk about it: evaluating contributions through discussion in GitHub

abstract: Open source software projects often rely on code contributions from a wide variety of developers to extend the capabilities of their software. Project members evaluate these contributions and often engage in extended discussions to decide whether to integrate changes. These discussions have important implications for project management regarding new contributors and evolution of project requirements and direction. We present a study of how developers in open work environments evaluate and discuss pull requests, a primary method of contribution in GitHub, analyzing a sample of extended discussions around pull requests and interviews with GitHub developers. We found that developers raised issues around contributions over both the appropriateness of the problem that the submitter attempted to solve and the correctness of the implemented solution. Both core project members and third-party stakeholders discussed and sometimes implemented alternative solutions to address these issues. Different stakeholders also influenced the outcome of the evaluation by eliciting support from different communities such as dependent projects or even companies. We also found that evaluation outcomes may be more complex than simply acceptance or rejection. In some cases, although a submitter's contribution was rejected, the core team fulfilled the submitter's technical goals by implementing an alternative solution. We found that the level of a submitter's prior interaction on a project changed how politely developers discussed the contribution and the nature of proposed alternative solutions.


title: Breast cancer relapse prognosis by classic and modern structures of machine learning algorithms

abstract: According to medical reports, cancers are big problems in the world society. In this paper we are supposed to predict breast cancer recurrence by multi-layer perceptron with two different outputs, a deep neural network as a feature extraction and multi-layer perceptron as a classifier, rough neural network with two different outputs, and finally, support vector machine. Then, we compare the results achieved by each method. It can be understood that rough neural network with two outputs leads to the highest accuracy and the lowest variance among other structures.


title: Active Capacitor Voltage Balancing in Single-Phase Flying-Capacitor Multilevel Power Converters

abstract: Two active capacitor voltage balancing schemes are proposed for single-phase (H-bridge) flying-capacitor multilevel converters. They are based on the circuit equations of flying-capacitor converters. Consequently, they can be implemented using straightforward control rules. In particular, the first technique is based on an algorithm which follows the standard multilevel modulation. Then, it utilizes a redundant state selection table for capacitor voltage balancing. In the second method, multiple duty cycles are defined and modulated in direct response to the capacitor voltages. The most important advantage of these two proposed methods is that they can be utilized to converters with any desired number of levels in their output voltage. Moreover, the analysis and implementation of both methods are straightforward. Through simulation and experimental implementation, these methods are shown to be effective on capacitor voltage regulation in flying-capacitor multilevel converters.


title: The higher the better? : The limits of analytical resolution in conflict event datasets

abstract: The majority of conflict event datasets rely on media reports as their sole source of information. Because of the various difficulties associated with media reports, it is useful to compare conflict events based on them with those obtained from other observers. A paper published in 2010 by O’Loughlin and colleagues makes a first attempt to do this by using (1) a media-based event dataset and (2) military records on Afghanistan. While the authors conclude that the level of agreement between the two datasets is high, my results show that this goes away once we aggregate to finer analytical resolutions – those that are typically used in micro-level conflict analyses. Thus, rather than giving us the ‘all-clear’ for the accuracy and quality of media-based conflict data, my results once again point to the importance of robustness tests in quantitative conflict research, but also to the need to study the discrepancies in different reporting mechanisms to find out what they can and what they cannot tell us.


title: Relations between personality and coping: A meta-analysis.

abstract: Personality may directly facilitate or constrain coping, but relations of personality to coping have been inconsistent across studies, suggesting a need for greater attention to methods and samples. This meta-analysis tested moderators of relations between Big Five personality traits and coping using 2,653 effect sizes drawn from 165 samples and 33,094 participants. Personality was weakly related to broad coping (e.g., Engagement or Disengagement), but all 5 traits predicted specific strategies. Extraversion and Conscientiousness predicted more problem-solving and cognitive restructuring, Neuroticism less. Neuroticism predicted problematic strategies like wishful thinking, withdrawal, and emotion-focused coping but, like Extraversion, also predicted support seeking. Personality more strongly predicted coping in young samples, stressed samples, and samples reporting dispositional rather than situation-specific coping. Daily versus retrospective coping reports and self-selected versus researcher-selected stressors also moderated relations between personality and coping. Cross-cultural differences were present, and ethnically diverse samples showed more protective effects of personality. Richer understanding of the role of personality in the coping process requires assessment of personality facets and specific coping strategies, use of laboratory and daily report studies, and multivariate analyses.


title: The Semantics of English Prepositions: Spatial Scenes, Embodied Meaning, and Cognition

abstract: Preface Acknowledgements 1. The nature of meaning 2. Embodied meaning and spatial experience 3. Towards a model of principled polysemy: spatial scenes and conceptualization 4. The semantic network for over 5. The vertical axis 6. Spatial particles of orientation 7. Bounded landmarks 8. Conclusion References Index.


title: Spelling it out: Real-time ASL fingerspelling recognition

abstract: This article presents an interactive hand shape recognition user interface for American Sign Language (ASL) finger-spelling. The system makes use of a Microsoft Kinect device to collect appearance and depth images, and of the OpenNI+NITE framework for hand detection and tracking. Hand-shapes corresponding to letters of the alphabet are characterized using appearance and depth images and classified using random forests. We compare classification using appearance and depth images, and show a combination of both lead to best results, and validate on a dataset of four different users. This hand shape detection works in real-time and is integrated in an interactive user interface allowing the signer to select between ambiguous detections and integrated with an English dictionary for efficient writing.


Do we need more training data or better models for object detection


title: Communicating robotic navigational intentions

abstract: This paper presents a study on intention communication in a navigational context using a robotic wheelchair. The robotic wheelchair uses light projection to communicate its motion intentions. The novelty of the work is threefold: the communication of robot intentions to the passenger, the consideration of passenger and robot as a group (“in-group”) [1] who share motion intentions and the communication of the in-group intentions to other pedestrians (the “out-group”). A comparison in an autonomous navigation task where the robotic wheelchair autonomously navigates the environment with and without intention communication was performed showing that passengers and walking people found intention communication intuitive and helpful for passing by actions. Evaluation results significantly show human participant preference for having navigational intention communication for the wheelchair passenger and the person passing by it. Quantitative results show the motion of the person passing by the wheelchair with intention communication was significantly smoother compared to without intention communication.


title: The genomic landscapes of human breast and colorectal cancers

abstract: Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.


title: Radical Embedding: Delving Deeper to Chinese Radicals

abstract: Languages using Chinese characters are mostly processed at word level. Inspired by recent success of deep learning, we delve deeper to character and radical levels for Chinese language processing. We propose a new deep learning technique, called “radical embedding”, with justifications based on Chinese linguistics, and validate its feasibility and utility through a set of three experiments: two in-house standard experiments on short-text categorization (STC) and Chinese word segmentation (CWS), and one in-field experiment on search ranking. We show that radical embedding achieves comparable, and sometimes even better, results than competing methods.


title: Single Shot Multibox Detector With Kalman Filter for Online Pedestrian Detection in Video

abstract: Pedestrian detection is a valuable and challenging problem in computer vision. To fully exploit the interframe information to improve the detector's performance, many frameworks with high complexity for offline detection have been proposed. These methods cannot provide spontaneous responses or alerts. In this paper, we present a Kalman filter-based convolutional neural network (CNN) for online pedestrian detection in videos. First, the single shot multibox detector is implemented as the CNN detector, which incorporates the pedestrian's aspect ratios. Fusion modules are implemented to improve the detector's robustness for medium and far scale pedestrians. Then, bounding boxes are propagated according to the prediction from the Kalman filter. Finally, the location and confidence of the bounding boxes are refined by the Kalman filter. Our method is evaluated on two datasets with respect to both the miss rate and speed, and the results show that our method has a lower miss rate, more stable confidence, and a much higher speed.


title: ATL: a QVT-like transformation language

abstract: In the context of Model Driven Engineering (MDE), models are the main development artifacts and model transformations are among the most important operations applied to models. A number of specialized languages have been proposed in order to specify model transformations. The OMG has, for instance, adopted the QVT specification. Apart from the software engineering properties of transformation languages, the availability of high quality tool support is also of major importance for the industrial adoption and ultimate success of MDE. In this paper, we present ATL: a QVT-like model transformation language and its execution environment based on the Eclipse framework.


title: Design, Modeling, and Evaluation of a Scalable Multi-level Checkpointing System

abstract: High-performance computing (HPC) systems are growing more powerful by utilizing more hardware components. As the system mean-time-before-failure correspondingly drops, applications must checkpoint more frequently to make progress. However, as the system memory sizes grow faster than the bandwidth to the parallel file system, the cost of checkpointing begins to dominate application run times. Multi-level checkpointing potentially solves this problem through multiple types of checkpoints with different costs and different levels of resiliency in a single run. This solution employs lightweight checkpoints to handle the most common failure modes and relies on more expensive checkpoints for less common, but more severe failures. This theoretically promising approach has not been fully evaluated in a large- scale, production system context. We have designed the Scalable Checkpoint/Restart (SCR) library, a multi-level checkpoint system that writes checkpoints to RAM, Flash, or disk on the compute nodes in addition to the parallel file system. We present the performance and reliability properties of SCR as well as a probabilistic Markov model that predicts its performance on current and future systems. We show that multi-level checkpointing improves efficiency on existing large-scale systems and that this benefit increases as the system size grows. In particular, we developed low-cost checkpoint schemes that are 100x-1000x faster than the parallel file system and effective against 85% of our system failures. This leads to a gain in machine efficiency of up to 35%, and it reduces the the load on the parallel file system by a factor of two on current and future systems.


title: A Compact Wide-Band Rat-Race Hybrid Using Microstrip Lines

abstract: A compact microstrip rat-race hybrid with a 50% bandwidth employing space-filling curves is reported in this letter. The footprint of the proposed design occupies 31% of the area of the conventional similar design. Across the frequency bandwidth, the maximum amplitude unbalance is 0.5 dB, the phase variation is plusmn5deg , the isolation is better than 25 dB and the return loss is greater than 10 dB. Moreover, the circuit is planar, easy to design, and consists of only one layer without requiring plated thru holes, slots or bonding wires.


title: Image Warping by Radial Basis Functions: Application to Facial Expressions

abstract: Abstract The human face is an elastic object. A natural paradigm for representing facial expressions is to form a complete 3D model of facial muscles and tissues. However, determining the actual parameter values for synthesizing and animating facial expressions is tedious; evaluating these parameters for facial expression analysis out of gray-level images is ahead of the state of the art in computer vision. Using only 2D face images and a small number of anchor points, we show that the method of radial basis functions provides a powerful mechanism for processing facial expressions. Although constructed specifically for facial expressions, our method is applicable to other elastic objects as well.


title: An educational robot system of visual question answering for preschoolers

abstract: The educational robotics is a novel technology for preschooler's companion and can be used for lower level education. This paper presents an AI-based robot system for achieving educational aims, such as metacognition tutoring and geometrical thinking training, with characteristics of contextual teaching by mining knowledge from the real world directly. For metacognition tutoring in our system, objects in real world are detected and a set of learning materials associated with the objects is presented for learners. For example, when a cat is detected, the robot will teach learners to pronounce the “Cat” in different languages, and more knowledge about cat will be pushed to the learners. For geometrical thinking training, an automatic questioning-and-answering section is employed to engage the learner to think, which is carried by a voice interaction between learners and robots. In our experiment, a set of specific object images are captured to validate the feasibility and efficiency of the proposed system. Our study indicated that the proposed system succeeded in captivating the children and parents in maximizing the children's desire to explore.


title: Design and Characterization of a Low-Cost FPGA-Based TDC

abstract: We present a field-programmable gate array (FPGA) implementation of a time-to-digital converter (TDC) based on a low-cost, low-area Spartan 6 device. The converter is based on a tapped delay line model. Several implementation details are discussed with a particular focus on critical blocks such as the input stage and thermometer-to-binary decoding techniques. We implemented a tap filtering technique to improve the differential nonlinearity (DNL) of the single delay line while keeping a good LSB value of 25.57 ps with a single-shot precision (SSP) between $0.69\div 1.46$ LSB. Measured DNL and integral nonlinearity (INL) lie in the range between $-0.90\div 1.23$ and $-0.43\div 2.96$ LSB, respectively. Measured DNL and INL lie in the range between $-0.90\div 1.23$ and $-0.43\div 2.96$ LSB, respectively. We then implemented an interpolating TDC to overcome the limitations of a single delay line in terms of linearity and measurement range. The interpolating TDC uses the sliding scale technique, where the time interval to be measured is asynchronous with respect to the FPGA clock, achieving DNL and INL in the range $-0.072\div 0.070$ and $-0.755\div 0.872$ LSB. SSP is in the $1.096\div 2.815$ range. Moreover, we present a novel comparison between the DNLs obtained with two different methods: statistical code density test and using a finely controlled delay source. Finally, we present the results of a Monte Carlo simulation used to investigate the effects of nonlinear propagation of the signal through the delay line.


title: Stigmergy as a universal coordination mechanism II: Varieties and evolution

abstract: Abstract The concept of stigmergy, a mechanism for the coordination of actions via the trace they leave in a medium, can explain self-organizing activities in a broad range of domains, including social insects, collaborative websites, and human institutions. The present paper attempts to bring some order to these diverse applications by classifying varieties of stigmergy according to general aspects: the number of agents involved, the relative persistence or transience of the trace, the use of sematectonic or marker-based traces, and the quantitative or qualitative characteristics of traces. The resulting cases are essentially continuous, as more complex cases can be understood as having evolved out of simpler ones. One application is cognition, which can be viewed as an interiorization of the individual stigmergy that helps an agent to perform a complex project by registering the state of the work in the trace, thus providing an external memory. Another application is the evolution of cooperation, in which agents learn to profit from the synergy produced by the spontaneous stigmergic coordination between their initially independent actions, thus bypassing the problem of “free riders” that exploit the cooperators’ efforts.


title: Detecting Credit Card Fraud by Decision Trees and Support

abstract: Duman, Ekrem (Dogus Author) -- Conference full title: International MultiConference of Engineers and Computer Scientists: IMECS 2011: 16-18 March, 2011, the Royal Garden Hotel, Kowloon, Hong Kong -- Series: Lecture notes in engineering and computer science, v. 2188-2189.


title: Wearable Sensors for Remote Health Monitoring

abstract: Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.


Extending media richness theory to explain social media adoption by microbusinesses


Large Scale Multiview Stereopsis Evaluation


title: A Map for Big Data Research in Digital Humanities

abstract: This article is an attempt to represent Big Data research in digital humanities as a structured research field. A division in three concentric areas of study is presented. Challenges in the first circle – focusing on the processing and interpretations of large cultural datasets – can be organized linearly following the data processing pipeline. Challenges in the second circle – concerning digital culture at large – can be structured around the different relations linking massive datasets, large communities, collective discourses, global actors, and the software medium. Challenges in the third circle – dealing with the experience of big data – can be described within a continuous space of possible interfaces organized around three poles: immersion, abstraction, and language. By identifying research challenges in all these domains, the article illustrates how this initial cartography could be helpful to organize the exploration of the various dimensions of Big Data Digital Humanities research.


title: Experimental Investigation of Light-Gauge Steel Plate Shear Walls for the Seismic Retrofit of Buildings

abstract: This paper describes the prototype design, specimen design, experimental setup, and experimental results of three light-gauge steel plate shear wall concepts. Prototype light-gauge steel plate shear walls are designed as seismic retrofits for a hospital structure in an area of high seismicity, and emphasis is placed on minimizing their impact on the existing framing. Three single-story test specimens are designed using these prototypes as a basis, two specimens with flat infill plates (thicknesses of 0.9 mm) and a third using a corrugated infill plate (thickness of 0.7 mm). Connection of the infill plates to the boundary frames is achieved through the use of bolts in combination with industrial strength epoxy or welds, allowing for mobility of the infills if desired. Testing of the systems is done under quasi-static conditions. It is shown that one of the flat infill plate specimens, as well as the specimen utilizing a corrugated infill plate, achieve significant ductility and energy dissipation while minimizing the demands placed on the surrounding framing. Experimental results are compared to monotonic pushover predictions from computer analysis using a simple model and good agreement is observed.


title: Probabilistic Latent Semantic Indexing

abstract: Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.


title: A fast visual line segment tracker

abstract: We present a fast line segment tracker which does not require any knowledge about the motion of the camera nor the structure of the observed scene. It runs on 320 times 240 pixel images at 30 Hz. We adapted the RAPiD tracker with a new way of handling multiple line hypotheses to deal with the simple model of a single line segment. We discuss the difficulty of using a chi2-test as merging criterion and also present a new approach to overcome it. Furthermore, instead of making assumptions about the camera motion, a constant velocity motion model to predict the line segment position in the following frame is used. We explain how to deal with the instability of the endpoint extraction in this motion model to avoid unintentional motion along the line. Finally, we present results on real world indoor and urban outdoor image sequences.


title: Impact of common source inductance on switching loss of SiC MOSFET

abstract: Common source inductance is a critical issue in power electronics, especially when devices switch at high frequency. Development of SiC MOSFETs allows fast switching commutation. In order to fully utilize their ability in high frequency application, the impact of common source inductance on the switching loss should be explored. This paper proposes a method to experimentally study the influence of common source inductance on switching loss of SiC MOSFET. The switching loss is calculated by measuring the device junction temperature and calibrating thermal resistance from the SiC chip to the heatsink. ZVS soft switch method is used to separate the turn on and turn off losses by comparing the switching loss and substituting the turn off loss. A 1kW/800V output all-SiC boost DC-DC converter is built to accomplish the study.


title: Planar High-Gain Dielectric-Loaded Antipodal Linearly Tapered Slot Antenna for $E$ - and $W$ -Band Gigabyte Point-to-Point Wireless Services

abstract: The concept of substrated integrated waveguide (SIW)-fed horn and antipodal linearly tapered slot antenna (ALTSA) are jointly used to design a low-cost, high-gain, and efficient planar dielectric-loaded antenna for ultra-wideband gigabyte wireless services, which are demonstrated over $E$ - and $W$ -bands. SIW is used to feed the proposed antenna. To increase the gain of the antenna, a dielectric-loaded slab is used in front of the antenna and works as a dielectric guiding structure. The SIW feed, the proposed antenna, and the loaded dielectric are all integrated in a planar single layer substrate, resulting in low cost and easy fabrication. Low-cost printed circuit board (PCB) process is utilized to fabricate the antenna structure with rectangular and elliptical shaped loaded dielectrics. Measured bandwidth of the antenna covers both $E$ - and $W$ -bands (70–110 GHz). Measured gain of the single-element antenna is ${\hbox{14}}\pm {\hbox{0.5}}$ dBi, while the measured radiation efficiency of 84.23% is obtained at 80 GHz. Wideband SIW power dividers are used to form a 1 $\,\times\,$ 4 array structure. Measured gain of the 1 $\,\times\,$ 4 array antenna is ${\hbox{19}}\pm {\hbox{1}}$ dBi, while the measured radiation pattern and gain are almost constant within the wide bandwidth of the antenna.


title: Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning

abstract: Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted for recommendation by learning a generative model for the time series of user-preferred items. By recasting the energy function as the feature function, the proposed EB-SeqGANs is interpreted as an instance of maximum-entropy imitation learning.


Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure


title: An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets

abstract: When intense redaction is needed to protect the confidentiality of data subjects' identities and sensitive attributes, statistical agencies can use synthetic data approaches. To create synthetic data, the agency replaces identifying or sensitive values with draws from statistical models estimated from the confidential data. Many agencies are reluctant to implement this idea because (i) the quality of the generated data depends strongly on the quality of the underlying models, and (ii) developing effective synthesis models can be a labor-intensive and difficult task. Recently, there have been suggestions that agencies use nonparametric methods from the machine learning literature to generate synthetic data. These methods can estimate non-linear relationships that might otherwise be missed and can be run with minimal tuning, thus considerably reducing burdens on the agency. Four synthesizers based on machine learning algorithms-classification and regression trees, bagging, random forests, and support vector machines-are evaluated in terms of their potential to preserve analytical validity while reducing disclosure risks. The evaluation is based on a repeated sampling simulation with a subset of the 2002 Uganda census public use sample data. The simulation suggests that synthesizers based on regression trees can result in synthetic datasets that provide reliable estimates and low disclosure risks, and that these synthesizers can be implemented easily by statistical agencies.


title: LiDAR Data Classification Using Morphological Profiles and Convolutional Neural Networks

abstract: In recent years, deep learning-based methods, especially convolutional neural networks (CNNs), have shown their capabilities in remote sensing data processing. The efficacy of light detection and ranging (LiDAR) has been already proven in a wide variety of research areas. Most of the existing methods do not extract the informative features from LiDAR-derived rasterized digital surface models (LiDAR-DSM) data in a deep manner. In order to utilize the advantages of deep models for the classification of LiDAR-derived features, deep CNN is proposed here to hierarchically extract the robust and discriminant features of the input data. Moreover, morphological profiles and multiattribute profiles (MAPs) are investigated to enrich the inputs of the CNN and further to improve the ultimate classification performance. Furthermore, a new activation function, sigmoid-weighted linear units (SiLUs), is introduced. The proposed frameworks are tested on two LiDAR-DSMs (i.e., Bayview Park and Houston data sets). The MAP-CNNs with SiLU outperform original CNNs by 6.62% and 6.88% in terms of overall accuracy on Bayview Park and Houston data sets, respectively, when the number of training samples of each class is 40.


title: Machine learning approaches to predict basketball game outcome

abstract: Sports prediction has always been a spellbinding research area for sports fans, teams, team managers, team players and a growing number of gamblers. Nowadays, companies are spending more effort in machine learning to predict the sport outcomes. Support Vector Machines (SVMs) are powerful techniques that handle classification problems effectively and efficiently. However, SVM models lack in rule generation. So, this examination leads towards the development of Hybrid Fuzzy-SVM model (HFSVM) by integrating fuzzy approach and SVM technique for prediction of the basketball game outcomes that help the coaches, teams and players to enhance their performance. The HFSVM model combines the advantage of both SVM technique and fuzzy approach, which is a unique strength of SVM and rule generation ability of fuzzy approach using fuzzy membership functions. The HFSVM model is compared with SVM model and the empirical results showed that the HFSVM model not only provides better results than SVM model but also provides relatively satisfactory prediction accuracy. Therefore, promising results can be obtained using HFSVM model when analyzing the outcomes of basketball competitions.


title: Semi-dense SLAM on an FPGA SoC

abstract: Deploying advanced Simultaneous Localisation and Mapping, or SLAM, algorithms in autonomous low-power robotics will enable emerging new applications which require an accurate and information rich reconstruction of the environment. This has not been achieved so far because accuracy and dense 3D reconstruction come with a high computational complexity. This paper discusses custom hardware design on a novel platform for embedded SLAM, an FPGA-SoC, combining an embedded CPU and programmable logic on the same chip. The use of programmable logic, tightly integrated with an efficient multicore embedded CPU stands to provide an effective solution to this problem. In this work an average framerate of more than 4 frames/second for a resolution of 320×240 has been achieved with an estimated power of less than 1 Watt for the custom hardware. In comparison to the software-only version, running on a dual-core ARM processor, an acceleration of 2× has been achieved for LSD-SLAM, without any compromise in the quality of the result.


title: Quantification of food intake using food image analysis

abstract: Measuring free-living peoples’ food intake represents methodological and technical challenges. The Remote Food Photography Method (RFPM) involves participants capturing pictures of their food selection and plate waste and sending these pictures to the research center via a wireless network, where they are analyzed by Registered Dietitians to estimate food intake. Initial tests indicate that the RFPM is reliable and valid, though the efficiency of the method is limited due to the reliance on human raters to estimate food intake. Herein, we describe the development of a semi-automated computer imaging application to estimate food intake based on pictures captured by participants.


title: Knowledge Construction in an E-discussion

abstract: The study is an attempt to describe and discuss knowledge construction on distance and the usage of a web based Learning Management System. The students in the study had no previous experience of a ...


title: Optimizing marketing planning and budgeting using Markov decision processes : An airline case study

abstract: Many companies have no reliable way to determine whether their marketing money has been spent effectively, and their return on investment is often not evaluated in a systematic manner. Thus, a compelling need exists for computational tools that help companies to optimize their marketing strategies. For this purpose, we have developed computational models of customer buying behavior in order to determine and leverage the value generated by a customer within a given time frame. The term "customer value" refers to the revenue generated from a customer's buying behavior in relation to the costs of marketing campaigns. We describe a new tool, the IBM Customer Equity Lifetime Management Solution (CELM), that helps to determine long-term customer value by means of dynamic programming algorithms in order to identify which marketing actions are the most effective in improving customer loyalty and hence increasing revenue. Simulation of marketing scenarios may be performed in order to assess budget requirements and the expected impact of marketing policies. We present a case study of a pilot program with a leading European airline, and we show how this company optimized its frequent flyer program to reduce its marketing budget and increase customer value.


title: Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors

abstract: A key problem of a mobile robot system is how to localize itself and detect objects in the workspace. In this paper, a multiple sensor based robot localization and object pose estimation method is presented. First, optical encoders and odometry model are utilized to determine the pose of the mobile robot in the workspace, with respect to the global coordinate system. Next, a CCD camera is used as a passive sensor to find an object (a box) in the environment, including the specific vertical surfaces of the box. By identifying and tracking color blobs which are attached to the center of each vertical surface of the box, the robot rotates and adjusts its base pose to move the color blob into the center of the camera view in order to make sure that the box is in the range of the laser scanner. Finally, a laser range finder, which is mounted on the top of the mobile robot, is activated to detect and compute the distances and angles between the laser source and laser contact surface on the box. Based on the information acquired in this manner, the global pose of the robot and the box can be represented using the homogeneous transformation matrix. This approach is validated using the Microsoft Robotics Studio simulation environment.


title: Vehicle dynamic state estimation: state of the art schemes and perspectives

abstract: Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.


title: Stressing Out: Bitcoin “Stress Testing”

abstract: In this paper, we present an empirical study of a recent spam campaign (a “stress test”) that resulted in a DoS attack on Bitcoin. The goal of our investigation being to understand the methods spammers used and impact on Bitcoin users. To this end, we used a clustering based method to detect spam transactions. We then validate the clustering results and generate a conservative estimate that 385,256 (23.41 %) out of 1,645,667 total transactions were spam during the 10 day period at the peak of the campaign. We show the impact of increasing non-spam transaction fees from 45 to 68 Satoshis/byte (from $0.11 to $0.17 USD per kilobyte of transaction) on average, and increasing delays in processing non-spam transactions from 0.33 to 2.67 h on average, as well as estimate the cost of this spam attack at 201 BTC (or $49,000 USD). We conclude by pointing out changes that could be made to Bitcoin transaction fees that would mitigate some of the spam techniques used to effectively DoS Bitcoin.


title: Sensory Plasticity in Human Motor Learning

abstract: There is accumulating evidence from behavioral, neurophysiological, and neuroimaging studies that the acquisition of motor skills involves both perceptual and motor learning. Perceptual learning alters movements, motor learning, and motor networks of the brain. Motor learning changes perceptual function and the sensory circuits of the brain. Here, we review studies of both human limb movement and speech that indicate that plasticity in sensory and motor systems is reciprocally linked. Taken together, this points to an approach to motor learning in which perceptual learning and sensory plasticity have a fundamental role.


title: A Self-Cloning Agents Based Model for High-Performance Mobile-Cloud Computing

abstract: The rise of the mobile-cloud computing paradigm in recent years has enabled mobile devices with processing power and battery life limitations to achieve complex tasks in real-time. While mobile-cloud computing is promising to overcome the limitations of mobile devices for real-time computing, the lack of frameworks compatible with standard technologies and techniques for dynamic performance estimation and program component relocation makes it harder to adopt mobile-cloud computing at large. Most of the available frameworks rely on strong assumptions such as the availability of a full clone of the application code and negligible execution time in the cloud. In this paper, we present a dynamic computation offloading model for mobile-cloud computing, based on autonomous agents. Our approach does not impose any requirements on the cloud platform other than providing isolated execution containers, and it alleviates the management burden of offloaded code by the mobile platform using stateful, autonomous application partitions. We also investigate the effects of different cloud runtime environment conditions on the performance of mobile-cloud computing, and present a simple and low-overhead dynamic make span estimation model integrated into autonomous agents to enhance them with self-performance evaluation in addition to self-cloning capabilities. The proposed performance profiling model is used in conjunction with a cloud resource optimization scheme to ensure optimal performance. Experiments with two mobile applications demonstrate the effectiveness of the proposed approach for high-performance mobile-cloud computing.


title: MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study

abstract: This study presents results from a randomized controlled clinical trial of the Mirror Image Movement Enabler (MIME) robotic device for shoulder and elbow neurorehabilitation in subacute stroke patients, including data on the use of its bilateral training mode. MIME incorporates a PUMA 560 robot (Staubli Unimation Inc, Duncan, South Carolina) that applies forces to the paretic limb during unilateral and bilateral movements in three dimensions. Robot-assisted treatment (bilateral, unilateral, and combined bilateral and unilateral) was compared with conventional therapy. Similar to a previous study in chronic stroke, combined unilateral and bilateral robotic training had advantages compared with conventional therapy, producing larger improvements on a motor impairment scale and a measure of abnormal synergies. However, gains in all treatment groups were equivalent at the 6-month follow-up. Combined unilateral and bilateral training yielded functional gains that were similar to the gains from equivalent doses of unilateral-only robotic training, although the combined group had more hypertonia and less movement out of synergy at baseline. Robot-assisted treatment gains exceeded those expected from spontaneous recovery. These results are discussed in light of the need for further device development and continued clinical trials.


title: Recursive bilateral filtering

abstract: This paper proposes a recursive implementation of the bilateral filter. Unlike previous methods, this implementation yields an bilateral filter whose computational complexity is linear in both input size and dimensionality. The proposed implementation demonstrates that the bilateral filter can be as efficient as the recent edge-preserving filtering methods, especially for high-dimensional images. Let the number of pixels contained in the image be N, and the number of channels be D, the computational complexity of the proposed implementation will be O(ND). It is more efficient than the state-of-the-art bilateral filtering methods that have a computational complexity of O(ND2) [1] (linear in the image size but polynomial in dimensionality) or O(Nlog(N)D) [2] (linear in the dimensionality thus faster than [1] for high-dimensional filtering). Specifically, the proposed implementation takes about 43 ms to process a one megapixel color image (and about 14 ms to process a 1 megapixel grayscale image) which is about 18 × faster than [1] and 86× faster than [2]. The experiments were conducted on a MacBook Air laptop computer with a 1.8 GHz Intel Core i7 CPU and 4 GB memory. The memory complexity of the proposed implementation is also low: as few as the image memory will be required (memory for the images before and after filtering is excluded). This paper also derives a new filter named gradient domain bilateral filter from the proposed recursive implementation. Unlike the bilateral filter, it performs bilateral filtering on the gradient domain. It can be used for edge-preserving filtering but avoids sharp edges that are observed to cause visible artifacts in some computer graphics tasks. The proposed implementations were proved to be effective for a number of computer vision and computer graphics applications, including stylization, tone mapping, detail enhancement and stereo matching.


title: Efficient deep web crawling using reinforcement learning

abstract: Deep web refers to the hidden part of the Web that remains unavailable for standard Web crawlers. To obtain content of Deep Web is challenging and has been acknowledged as a significant gap in the coverage of search engines. To this end, the paper proposes a novel deep web crawling framework based on reinforcement learning, in which the crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and selects an action (query) to submit to the environment according to Q-value. The framework not only enables crawlers to learn a promising crawling strategy from its own experience, but also allows for utilizing diverse features of query keywords. Experimental results show that the method outperforms the state of art methods in terms of crawling capability and breaks through the assumption of full-text search implied by existing methods.


title: Secure assisted quantum computation

abstract: Suppose Alice wants to perform some computation that could be done quickly on a quantum computer, but she cannot do universal quantum computation. Bob can do universal quantum computation and claims he is willing to help, but Alice wants to be sure that Bob cannot learn her input, the result of her calculation, or perhaps even the function she is trying to compute. We describe a simple, efficient protocol by which Bob can help Alice perform the computation, but there is no way for him to learn anything about it. We also discuss techniques for Alice to detect whether Bob is honestly helping her or if he is introducing errors.


title: Remote sensing and geographic information systems

abstract: This book describes the remote sensing technique as applied to geological mapping, mineral exploration and mining. This book covers the advances made in remote sensing during the last fifteen years. It addresses a wide range of specialists including explorationists, planners, mining engineers, geologists, geophysicists and conservationists. It introduces basic principles and describes the technique of remote sensing. It is a matter of fact that during last decade there has been considerable progress in the development and practical applications of remote sensing methods. They are now widely used in the industry and by the academic community. This is why I consider it timely to publish a book on this topic which addresses a wide circle of readers who are not yet experienced practitioners of remote sensing. This book serves this task extremely well. It covers a wide range of remote sensing methods and practical applications, starting from basic principles of remote sensing and image processing through geographic information systems (GIS), mineral exploration, mine planning, water supply, waste disposal and land restoration. The author provides detailed information about the electromagnetic spectrum used for remote sensing and about the satellites and their parameters. The chapter describing image processing gives a clear and complete description of image corrections, image enhancements and information extraction. A very important part of the book deals with GIS. This system is now widely used both in industry and in academic research, however, not many people are adequately aware of it. The author discusses principles of GIS, its practical applications and the critical factors governing the incorporation of remotely sensed data into a GIS. Another useful chapter of the book is the one dedicated to modern sensing systems. It includes the classification of different sensors and their characteristics. The author describes different space-borne sensor systems, installed in satellites operating since 1984. For example, the reader can find information about sensor systems installed in the Landsat Multi-Spectral Scanner, Landsat Thematic Mapper, SEASAT, ERS-1 and many others. There are also data about digital optical imagery from the former Soviet Union (including Soyuzkarta Photography). This chapter also describes the different airborne scanners. One of the main reasons for using airborne scanners is the improvement in spatial resolution which is possible from aircraft. The second half of the book contains detailed analysis of the different remote sensing applications in mineral exploration, environmental study and land restoration. This part brings the possibilities of remote sensing for cost-effective solutions to a wide range of tasks. The author demonstrates how useful geological (lithological and structural) information can be extracted from the satellite images. He discusses the advantages of remote sensing in mine planning and natural hazards study, as well as in forestry and agriculture. The book presents the subject in simple and accessible form and doesn't require any previous acquaintance with remote sensing. The compilation of the contents is logical and covers all necessary subjects related to remote sensing. I am sure that this book will be useful to a wide audience of people interested in remote sensing applications for the solution of geographical, geological, geophysical, mineral exploration and environmental problems.


Dungeons and DQNs: Toward Reinforcement Learning Agents that Play Tabletop Roleplaying Games


title: Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning

abstract: We focus on the one-shot learning for video-based person re-Identification (re-ID). Unlabeled tracklets for the person re-ID tasks can be easily obtained by preprocessing, such as pedestrian detection and tracking. In this paper, we propose an approach to exploiting unlabeled tracklets by gradually but steadily improving the discriminative capability of the Convolutional Neural Network (CNN) feature representation via stepwise learning. We first initialize a CNN model using one labeled tracklet for each identity. Then we update the CNN model by the following two steps iteratively: 1. sample a few candidates with most reliable pseudo labels from unlabeled tracklets; 2. update the CNN model according to the selected data. Instead of the static sampling strategy applied in existing works, we propose a progressive sampling method to increase the number of the selected pseudo-labeled candidates step by step. We systematically investigate the way how we should select pseudo-labeled tracklets into the training set to make the best use of them. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 21.46 points (absolute, i.e., 62.67% vs. 41.21%) on the MARS dataset, and 16.53 points on the DukeMTMC-VideoReID dataset1.


title: A novel multiband Psi (Ψ) slotted fractal antenna for S-band applications

abstract: A new design of multiband antenna deploying Psi (Ψ) slotted fractal geometry is introduced in this paper which is relevant for S-band applications. The proposed antenna has been designed up to the second iteration by using substrate thickness 1.6 mm for FR4 material, dielectric constant 4.4 and loss tangent 0.02. The S-parameter for each stage of iteration is compared by using IE3D simulator. Utilization of Rohde and Schwarz ZVL Network Analyzer is done for measuring results of fabricated antenna. Also, the investigation of Voltage Standing Wave Ratio (VSWR) and Directivity has been done. For the frequencies 2.88 GHz, 3.5 GHz and 3.64 GHz, the measured S-parameter (S 11 ) are −15.75 dB, −28.15 dB and −21.84 dB respectively. The designed and fabricated results of antennas are in the account of good agreement with each other.


title: Deep learning classifier for fall detection based on IR distance sensor data

abstract: The goal of research is the fall detection in elderly residents based on infra red depth sensor measurements. Our attention is focused on statistical properties as generalization. The effectiveness of discriminative statistical classifiers (multilayer perceptron) is improved by addition of feature selection block by Gram-Schmidt orthogonalization, which determines the ranking of the features, and NPCA block, which transforms the raw data into a nonlinear manifold and reduces the dimensionality of the data. Performance of our system measured in terms of sensitivity is 92% and precision is 93%, which means it can be used for real life applications.


title: Ni–Cr textured substrates with reduced ferromagnetism for coated conductor applications

abstract: Abstract A series of biaxially textured Ni1−xCrx materials, with compositions x=0, 7, 9, 11, and 13 at.%Cr, have been studied for use as substrate materials in coated conductor applications with high temperature superconductors. The magnetic properties were investigated, including the hysteretic loss in a Ni–7at.%Cr sample that was controllably deformed; for comparison, the loss was also measured in a similarly deformed pure Ni substrate. Complementary X-ray diffraction studies show that thermomechanical processing produces nearly complete {1 0 0}〈1 0 0〉 cube texturing, as desired for applications.


title: A Tunable Three-Pole 1.5–2.2-GHz Bandpass Filter With Bandwidth and Transmission Zero Control

abstract: This paper presents a three-pole tunable combline bandpass filter with center frequency, bandwidth, and zero control. The filter is designed on a Duroid substrate with er=10.2 and h=25 mil . A frequency range of 1.5-2.2 GHz with a 1-dB bandwidth tuning from 50 to 170 MHz (2.2%-11.2% fractional bandwidth) is achieved. The transmission zero can also be controlled, and a zero location of 1.37-1.64 GHz is demonstrated at center frequency fo of 2.05 GHz. The measured third-order intermodulation intercept point and 1-dB power compression point at midband (1.85 GHz) and a bandwidth of 110 MHz are >;15 and 8 dBm, respectively. To our knowledge, this is the first three-pole combline tunable bandpass filter with center frequency, bandwidth, and transmission zero control.


title: Knowledge as a Service Framework for Disaster Data Management

abstract: Each year, a number of natural disasters strike across the globe, killing hundreds and causing billions of dollars in property and infrastructure damage. Minimizing the impact of disasters is imperative in today's society. As the capabilities of software and hardware evolve, so does the role of information and communication technology in disaster mitigation, preparation, response, and recovery. A large quantity of disaster-related data is available, including response plans, records of previous incidents, simulation data, social media data, and Web sites. However, current data management solutions offer few or no integration capabilities. Moreover, recent advances in cloud computing, big data, and NoSQL open the door for new solutions in disaster data management. In this paper, a Knowledge as a Service (KaaS) framework is proposed for disaster cloud data management (Disaster-CDM), with the objectives of 1) storing large amounts of disaster-related data from diverse sources, 2) facilitating search, and 3) supporting their interoperability and integration. Data are stored in a cloud environment using a combination of relational and NoSQL databases. The case study presented in this paper illustrates the use of Disaster-CDM on an example of simulation models.


title: DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.

abstract: Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain unprotected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an application as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for explaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explanations provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, rendering it suitable for checking downloaded applications directly on the device.


title: A hierarchical method for traffic sign classification with support vector machines

abstract: Traffic sign classification is an important function for driver assistance systems. In this paper, we propose a hierarchical method for traffic sign classification. There are two hierarchies in the method: the first one classifies traffic signs into several super classes, while the second one further classifies the signs within their super classes and provides the final results. Two perspective adjustment methods are proposed and performed before the second hierarchy, which significantly improves the classification accuracy. Experimental results show that the proposed method gets an accuracy of 99.52% on the German Traffic Sign Recognition Benchmark (GTSRB), which outperforms the state-of-the-art method. In addition, it takes about 40 ms to process one image, making it suitable for realtime applications.


title: Catching Math Problems Early: Findings From the Number Sense Intervention Project

abstract: This chapter provides an overview of a kindergarten (ie, children 5–6 years of age) number sense intervention that was developed and tested over a 4-year period. The intervention targeted core understandings of number, number relations, and number operations in high-risk learners. Over multiple trials, we obtained moderate to strong effect sizes, relative to controls, on a measure closely aligned to the intervention topics (ie, a number sense brief screener) as well as on a test of mathematics calculation achievement. Many of the gains were still present 2 months after the intervention ended. Catching number sense weaknesses early positions children for later success in formal mathematics.


title: XSEarch: A Semantic Search Engine for XML

abstract: XSEarch, a semantic search engine for XML, is presented. XSEarch has a simple query language, suitable for a naive user. It returns semantically related document fragments that satisfy the user's query. Query answers are ranked using extended information-retrieval techniques and are generated in an order similar to the ranking. Advanced indexing techniques were developed to facilitate efficient implementation of XSEarch. The performance of the different techniques as well as the recall and the precision were measured experimentally. These experiments indicate that XSEarch is efficient, scalable and ranks quality results highly.


Scheduling Live Interactive Narratives with Mixed-Integer Linear Programming.


title: Love and Attachment: The Psychobiology of Social Bonding

abstract: Basic animal studies and human imaging studies have contributed to our understanding of the psychobiology of love and attachment. There are overlaps and distinctions in the neuronal circuitry of maternal love, romantic love, and long-term attachment. In these circuits, important molecules, which have been demonstrated to play a role in the psychobiology of social bonding include dopamine, serotonin, opioids, oxytocin, and vasopressin. Particular genetic and environmental variations contribute to social-bonding phenotypes, consistent with an evolutionary perspective on the value of these behaviors. Advances in the psychobiology of social bonds have led to hypotheses about the pharmacotherapy of disorders of attachment.


title: Fissure sealants: Knowledge and practice of Yemeni dental practitioners

abstract: OBJECTIVES ::: This study was conducted to evaluate Yemeni dental practitioners' knowledge and practices concerning fissure sealants. ::: ::: ::: MATERIALS AND METHODS ::: A modified questionnaire consisted of 25-items was distributed to 500 dentists working in Sana'a City. Descriptive statistics and Chi-square/Fisher's exact tests were used for statistical analyses. ::: ::: ::: RESULTS ::: The response rate was 74%. Most of the respondents were male (61.3%), general practitioners (84.2%), and had <5 years of clinical experience (48.3%). The respondents showed a reasonable level of knowledge about sealants, with the majority (88%) believed that there is strong scientific evidence about fissure sealants effectiveness and around 90% showed a good understanding of sealant placement instructions. On the other hand, respondents showed insufficient knowledge about sealants clinical practice. ::: ::: ::: CONCLUSION ::: Although a high proportion of dental practitioners showed adequate knowledge about dental sealant, following guidelines and standardized procedures in clinical practice is lacking. These emphasize the need for regular continuing education courses for dental professional.


title: The effects of transformational leadership on organizational conditions and student engagement with school

abstract: Most school restucturing initiatives assume significant capacity development on the part of individuals, as well as whole organizations; they also depend on high levels of motivation and commitment to solving the substantial problems associated with the implementation of restructuring initiatives. Transformational approaches to leadership have long been advocated as productive under these conditions, and evidence suggests that transformational practices do contribute to the development of capacity and commitment. Much less evidence is available, however, about whether these socio‐psychological effects actually result in organizational change and enhanced organizational outcomes. Survey data from an achieved sample of 1,762 teachers and 9,941 students in one large school district were used to explore the relative effects of transformational leadership practices on selected organizational conditions and student engagement with school. Results demonstrated strong significant effects of such leadership on organizational conditions, and moderate but still significant total effects on student engagement.


title: Community Detection in Networks using Node Attributes and Modularity

abstract: Community detection in network is of vital importance to find cohesive subgroups. Node attributes can improve the accuracy of community detection when combined with link information in a graph. Community detection using node attributes has not been investigated in detail. To explore the aforementioned idea, we have adopted an approach by modifying the Louvain algorithm. We have proposed Louvain-AND-Attribute (LAA) and Louvain-OR-Attribute (LOA) methods to analyze the effect of using node attributes with modularity. We compared this approach with existing community detection approaches using different datasets. We found the performance of both algorithms better than Newman’s Eigenvector method in achieving modularity and relatively good results of gain in modularity in LAA than LOA. We used density, internal and external edge density for the evaluation of quality of detected communities. LOA provided highly dense partitions in the network as compared to Louvain and Eigenvector algorithms and close values to Clauset. Moreover, LOA achieved few numbers of edges between communities.


title: Origin, Diversity, Expansion History, and Functional Evolution of the Plant Receptor-Like Kinase/Pelle Family

abstract: The RLK/Pelle gene family is one of the largest gene families in plants with several hundred to more than a thousand members, but only a few family members exist in animals. This unbalanced distribution indicates a rather dramatic expansion of this gene family in land plants. In this chapter we review what is known about the RLK/Pelle family’s origin in eukaryotes, its domain content evolution, expansion patterns across plant and animal species, and the duplication mechanisms that contribute to its expansion. We conclude by summarizing current knowledge of plant RLK/Pelle functions for a discussion on the relative importance of neutral evolution and natural selection as the driving forces behind continuous expansion and innovation in this gene family.


title: GenDeR: A Generic Diversified Ranking Algorithm

abstract: Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples. We formulate it as an optimization problem and show that in general it is NP-hard. Then, we show that for a large volume of the parameter space, the proposed objective function enjoys the diminishing returns property, which enables us to design a scalable, greedy algorithm to find the (1 - 1/e) near-optimal solution. Experimental results on real data sets demonstrate the effectiveness of the proposed algorithm.


title: Learning deep neural network policies with continuous memory states

abstract: Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning policies with internal memory for high-dimensional, continuous systems, such as robotic manipulators. Our approach consists of augmenting the state and action space of the system with continuous-valued memory states that the policy can read from and write to. Learning general-purpose policies with this type of memory representation directly is difficult, because the policy must automatically figure out the most salient information to memorize at each time step. We show that, by decomposing this policy search problem into a trajectory optimization phase and a supervised learning phase through a method called guided policy search, we can acquire policies with effective memorization and recall strategies. Intuitively, the trajectory optimization phase chooses the values of the memory states that will make it easier for the policy to produce the right action in future states, while the supervised learning phase encourages the policy to use memorization actions to produce those memory states. We evaluate our method on tasks involving continuous control in manipulation and navigation settings, and show that our method can learn complex policies that successfully complete a range of tasks that require memory.


title: A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition

abstract: Understanding human activity is very challenging even with the recently developed 3D/depth sensors. To solve this problem, this work investigates a novel deep structured model, which adaptively decomposes an activity instance into temporal parts using the convolutional neural networks. Our model advances the traditional deep learning approaches in two aspects. First, we incorporate latent temporal structure into the deep model, accounting for large temporal variations of diverse human activities. In particular, we utilize the latent variables to decompose the input activity into a number of temporally segmented sub-activities, and accordingly feed them into the parts (i.e. sub-networks) of the deep architecture. Second, we incorporate a radius---margin bound as a regularization term into our deep model, which effectively improves the generalization performance for classification. For model training, we propose a principled learning algorithm that iteratively (i) discovers the optimal latent variables (i.e. the ways of activity decomposition) for all training instances, (ii) updates the classifiers based on the generated features, and (iii) updates the parameters of multi-layer neural networks. In the experiments, our approach is validated on several complex scenarios for human activity recognition and demonstrates superior performances over other state-of-the-art approaches.


title: ORIGINAL PAPER Self-kindness when facing stress: The role of self-compassion, goal regulation, and support in college students ’ well-being

abstract: This project brought together the constructs of goal and emotion regulation as a way of understanding college students’ well-being, building on previous work that identified the ability to disengage in goal pursuit and to redirect energy toward alternative goals as an important contributor to well-being. In Study 1, we assessed the amount of variance in well-being accounted for by measures of goal management, adding to the regression measures of student stress and self-compassion, the latter defined as a healthy form of self-acceptance and characterized as a tendency to treat oneself kindly in the face of perceived inadequacy. In Study 2, the stress scale was replaced by measures of perceived need and availability of support. Across studies, although factors such as goal management, stress, and need for and availability of support were important predictors of well-being, self-compassion accounted for a significant amount of additional variance in well-being.


title: Image Generation from Scene Graphs

abstract: To truly understand the visual world our models should be able not only to recognize images but also generate them. To this end, there has been exciting recent progress on generating images from natural language descriptions. These methods give stunning results on limited domains such as descriptions of birds or flowers, but struggle to faithfully reproduce complex sentences with many objects and relationships. To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships. Our model uses graph convolution to process input graphs, computes a scene layout by predicting bounding boxes and segmentation masks for objects, and converts the layout to an image with a cascaded refinement network. The network is trained adversarially against a pair of discriminators to ensure realistic outputs. We validate our approach on Visual Genome and COCO-Stuff, where qualitative results, ablations, and user studies demonstrate our method's ability to generate complex images with multiple objects.


title: MoVEMo: A Structured Approach for Engineering Reward Functions

abstract: Reinforcement learning (RL) is a machine learning technique that has been increasingly used in robotic systems. In reinforcement learning, instead of manually pre-program what action to take at each step, we convey the goal a software agent in terms of reward functions. The agent tries different actions in order to maximize a numerical value, i.e. the reward. A misspecified reward function can cause problems such as reward hacking, where the agent finds out ways that maximize the reward without achieving the intended goal. As RL agents become more general and autonomous, the design of reward functions that elicit the desired behaviour in the agent becomes more important and cumbersome. In this paper, we present a technique to formally express reward functions in a structured way; this stimulates a proper reward function design and as well enables the formal verification of it. We start by defining the reward function using state machines. In this way, we can statically check that the reward function satisfies certain properties, e.g., high-level requirements of the function to learn. Later we automatically generate a runtime monitor — which runs in parallel with the learning agent — that provides the rewards according to the definition of the state machine and based on the behaviour of the agent. We use the UPPAAL model checker to design the reward model and verify the TCTL properties that model high-level requirements of the reward function and LARVA to monitor and enforce the reward model to the RL agent at runtime.


title: Ontology-Based Data Access: Ontop of Databases

abstract: We present the architecture and technologies underpinning the OBDA system Ontop and taking full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop: the tree-witness query rewriting, $\mathcal{T}$ -mappings and optimisations based on database integrity constraints and SQL features. We analyse the performance of Ontop in a series of experiments and demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality.


title: A fixed-point neural network for keyword detection on resource constrained hardware

abstract: Keyword detection is typically used as a front-end to trigger automatic speech recognition and spoken dialog systems. The detection engine needs to be continuously listening, which has strong implications on power and memory consumption. In this paper, we devise a neural network architecture for keyword detection and present a set of techniques for reducing the memory requirements in order to make the architecture suitable for resource constrained hardware. Specifically, a fixed-point implementation is considered; aggressively scaling down the precision of the weights lowers the memory compared to a naive floating-point implementation. For further optimization, a node pruning technique is proposed to identify and remove the least active nodes in a neural network. Experiments are conducted over 10 keywords selected from the Resource Management (RM) database. The trade-off between detection performance and memory is assessed for different weight representations. We show that a neural network with as few as 5 bits per weight yields a marginal and acceptable loss in performance, while requiring only 200 kilobytes (KB) of on-board memory and a latency of 150 ms. A hardware architecture using a single multiplier and a power consumption of less than 10mW is also presented.


title: Holistic Multi-Modal Memory Network for Movie Question Answering

abstract: Answering questions using multi-modal context is a challenging problem, as it requires a deep integration of diverse data sources. Existing approaches only consider a subset of all possible interactions among data sources during one attention hop. In this paper, we present a holistic multi-modal memory network (HMMN) framework that fully considers interactions between different input sources (multi-modal context and question) at each hop. In addition, to hone in on relevant information, our framework takes answer choices into consideration during the context retrieval stage. Our HMMN framework effectively integrates information from the multi-modal context, question, and answer choices, enabling more informative context to be retrieved for question answering. Experimental results on the MovieQA and TVQA datasets validate the effectiveness of our HMMN framework. Extensive ablation studies show the importance of holistic reasoning and reveal the contributions of different attention strategies to model performance.


title: A Branch-and-Cut Algorithm for the Dial-a-Ride Problem

abstract: In the dial-a-ride problem, users formulate requests for transportation from a specific origin to a specific destination. Transportation is carried out by vehicles providing a shared service. The problem consists of designing a set of minimum-cost vehicle routes satisfying capacity, duration, time window, pairing, precedence, and ride-time constraints. This paper introduces a mixed-integer programming formulation of the problem and a branch-and-cut algorithm. The algorithm uses new valid inequalities for the dial-a-ride problem as well as known valid inequalities for the traveling salesman, the vehicle routing, and the pick-up and delivery problems. Computational experiments performed on randomly generated instances show that the proposed approach can be used to solve small to medium-size instances.


title: A New Path Planning Method Based on Firefly Algorithm

abstract: Firefly algorithm is a new nature-inspired algorithm and has been used gradually in solving optimization problems. a path planning method based on firefly algorithm was proposed. After the detailed analysis of the basic algorithm, random parameter and absorption parameter were designed to be adaptive, and this can improve the solution quality and convergence speed of the firefly algorithm. According to the characteristics of path planning, the coding of firefly algorithm, the design of evaluation function and path planning method were completed. Simulation tests verified the effectiveness of the improved algorithm and the feasibility of path planning method based on firefly algorithm.


title: Modeling and simulation of inverse time overcurrent relay using Matlab/Simulink

abstract: Overcurrent relay is an important relay used to protect transmission and distribution feeders, transformer, bus coupler etc. It can be used as main or backup protection relays. The modeling of overcurrent relay and other protection relays is important in the research on the effects of network parameters and configurations on the operation of these relays. This research deal with modeling and simulation of inverse time overcurrent relay using commonly used Matlab/Simulink software. The developed model was tested with single line to ground fault and phase to phase fault with various fault locations and fault resistances to evaluate phase and earth detectors in sensing the fault current. Simulations results show the feasibility of the software to be used to develop the relay.


Curriculum Learning Based Probabilistic Linear Discriminant Analysis for Noise Robust Speaker Recognition.


title: Enhancing Students' Engagement by Increasing Teachers' Autonomy Support

abstract: Engagement refers to the behavioral intensity and emotional quality of a person’s active involvement during a task. Recognizing the benefits highly engaged people experience, we tested whether classroom teachers could incorporate the relatively foreign concept of autonomy support into their motivating styles as a way to promote their students’ engagement during instruction. Teachers in an experimental group and teachers in a delayed-treatment control group received information and guidance consistent with self-determination theory on how to support students’ autonomy. Over a series of 3 classroom observations, trained raters scored each teacher’s autonomy support and 2 measures of their students’ engagement–task involvement and influence attempts. Trained teachers displayed significantly more autonomy-supportive behaviors than did nontrained teachers. Further, the more teachers used autonomy support during instruction, the more engaged were their students. This was true on both measures of engagement.


title: The Artificial Leaf

abstract: To convert the energy of sunlight into chemical energy, the leaf splits water via the photosynthetic process to produce molecular oxygen and hydrogen, which is in a form of separated protons and electrons. The primary steps of natural photosynthesis involve the absorption of sunlight and its conversion into spatially separated electron–hole pairs. The holes of this wireless current are captured by the oxygen evolving complex (OEC) of photosystem II (PSII) to oxidize water to oxygen. The electrons and protons produced as a byproduct of the OEC reaction are captured by ferrodoxin of photosystem I. With the aid of ferrodoxin–NADP+ reductase, they are used to produce hydrogen in the form of NADPH. For a synthetic material to realize the solar energy conversion function of the leaf, the light-absorbing material must capture a solar photon to generate a wireless current that is harnessed by catalysts, which drive the four electron/hole fuel-forming water-splitting reaction under benign conditions and under 1 su...


title: Robust nonnegative matrix factorization using L21-norm

abstract: Nonnegative matrix factorization (NMF) is widely used in data mining and machine learning fields. However, many data contain noises and outliers. Thus a robust version of NMF is needed. In this paper, we propose a robust formulation of NMF using L21 norm loss function. We also derive a computational algorithm with rigorous convergence analysis. Our robust NMF approach, (1) can handle noises and outliers; (2) provides very efficient and elegant updating rules; (3) incurs almost the same computational cost as standard NMF, thus potentially to be used in more real world application tasks. Experiments on 10 datasets show that the robust NMF provides more faithful basis factors and consistently better clustering results as compared to standard NMF.


title: Tunable CMOS Delay Gate With Improved Matching Properties

abstract: This paper presents the analysis and design of a tun- able CMOS delay gate with improved matching properties as com- pared with the commonly used "current starved inverter" (CSI). The main difference between these structures lies in the location of the current limiting transistor on the inverter's output rather than on the side of the power rail. This improves the dynamic per- formance of the proposed "output split inverter" (OSI) circuit re- ducing the influence of the MOS transistor mismatch on the gen- erated delay time variability. A test chip including two arrays con- sisting of 512 16-stage delay lines employing the CSI and OSI gates hasbeen designed andfabr icated inas tandard 90nmCMOSt ech- nology. The experimental results show that the proposed OSI cir- cuit is about 10-50% more accurate than the conventional current starved inverter with no penalty in terms of the increased area, power consumption or complexity. Applications of the proposed circuit are in the design of time-to-digital converters (TDCs), delay locked loops,readout circuits for particle detection and time-based asynchronous computation systems.


title: Is there chaos in the brain? II. Experimental evidence and related models

abstract: The search for chaotic patterns has occupied numerous investigators in neuroscience, as in many other fields of science. Their results and main conclusions are reviewed in the light of the most recent criteria that need to be satisfied since the first descriptions of the surrogate strategy. The methods used in each of these studies have almost invariably combined the analysis of experimental data with simulations using formal models, often based on modified Huxley and Hodgkin equations and/or of the Hindmarsh and Rose models of bursting neurons. Due to technical limitations, the results of these simulations have prevailed over experimental ones in studies on the nonlinear properties of large cortical networks and higher brain functions. Yet, and although a convincing proof of chaos (as defined mathematically) has only been obtained at the level of axons, of single and coupled cells, convergent results can be interpreted as compatible with the notion that signals in the brain are distributed according to chaotic patterns at all levels of its various forms of hierarchy. This chronological account of the main landmarks of nonlinear neurosciences follows an earlier publication [Faure, Korn, C. R. Acad. Sci. Paris, Ser. III 324 (2001) 773-793] that was focused on the basic concepts of nonlinear dynamics and methods of investigations which allow chaotic processes to be distinguished from stochastic ones and on the rationale for envisioning their control using external perturbations. Here we present the data and main arguments that support the existence of chaos at all levels from the simplest to the most complex forms of organization of the nervous system. We first provide a short mathematical description of the models of excitable cells and of the different modes of firing of bursting neurons (Section 1). The deterministic behavior reported in giant axons (principally squid), in pacemaker cells, in isolated or in paired neurons of Invertebrates acting as coupled oscillators is then described (Section 2). We also consider chaotic processes exhibited by coupled Vertebrate neurons and of several components of Central Pattern Generators (Section 3). It is then shown that as indicated by studies of synaptic noise, deterministic patterns of firing in presynaptic interneurons are reliably transmitted, to their postsynaptic targets, via probabilistic synapses (Section 4). This raises the more general issue of chaos as a possible neuronal code and of the emerging concept of stochastic resonance Considerations on cortical dynamics and of EEGs are divided in two parts. The first concerns the early attempts by several pioneer authors to demonstrate chaos in experimental material such as the olfactory system or in human recordings during various forms of epilepsies, and the belief in 'dynamical diseases' (Section 5). The second part explores the more recent period during which surrogate-testing, definition of unstable periodic orbits and period-doubling bifurcations have been used to establish more firmly the nonlinear features of retinal and cortical activities and to define predictors of epileptic seizures (Section 6). Finally studies of multidimensional systems have founded radical hypothesis on the role of neuronal attractors in information processing, perception and memory and two elaborate models of the internal states of the brain (i.e. 'winnerless competition' and 'chaotic itinerancy'). Their modifications during cognitive functions are given special attention due to their functional and adaptive capabilities (Section 7) and despite the difficulties that still exist in the practical use of topological profiles in a state space to identify the physical underlying correlates. The reality of 'neurochaos' and its relations with information theory are discussed in the conclusion (Section 8) where are also emphasized the similarities between the theory of chaos and that of dynamical systems. Both theories strongly challenge computationalism and suggest that new models are needed to describe how the external world is represented in the brain.


title: From proxy encryption primitives to a deployable secure-mailing-list solution

abstract: Proxy encryption schemes transform cipher-text from one key to another without revealing the plain-text. Agents that execute such transformations are therefore minimally trusted in distributed systems leading to their usefulness in many applications. However, till date no application of proxy encryption has been deployed and used in practice. In this work we describe our efforts in developing a deployable secure mailing list solution based on proxy encryption techniques. Securing emails exchanged on mailing lists requires that confidentiality, integrity, and authentication of the emails be provided. This includes ensuring their confidentiality while in transit at the list server; a functionality that is uniquely supported by proxy encryption. In developing this solution we addressed the challenges of identifying requirements for deployability, defining a component architecture that maximizes the use of COTS components to help in deployment, developing the proxy encryption protocol to satisfy requirements and to fit within the component architecture, implementing and testing the solution, and packaging the release. As evidence of its deployability, the resulting secure mailing list solution is compatible with common email clients including Outlook, Thunderbird, Mac Mail, Emacs, and Mutt.


title: Cardiovascular disease in the Eastern Mediterranean region: epidemiology and risk factor burden

abstract: The Eastern Mediterranean region (EMR) comprises 22 countries or territories spanning from Morocco in the west to Pakistan in the east, and contains a population of almost 600 million people. Like many other developing regions, the burden of disease in the EMR has shifted in the past 30 years from primarily communicable diseases to noncommunicable diseases such as cardiovascular disease (CVD). Cardiovascular mortality in the EMR, mostly attributable to ischaemic heart disease, is expected to increase more dramatically in the next decade than in any other region except Africa. The most prominent CVD risk factors in this region include tobacco consumption, physical inactivity, depression, obesity, hypertension, and diabetes mellitus. Many individuals living in the EMR are unaware of their risk factor status, and even if treated, these risk factors are often poorly controlled. Furthermore, infrequent use of emergency medical services, delays in access to care, and lack of access to cardiac catheterization affects the timely diagnosis of CVD. Treatment of CVD is also suboptimal in this region, consisting primarily of thrombolysis, with insufficient provision of timely revascularization. In this Review, we summarize what is known about CVD burden, risk factors, and treatment strategies for individuals living in the EMR. This information will hopefully aid decision-makers when devising strategies on how to improve CVD prevention and management in this region.


title: Automatic detection of learning styles: state of the art

abstract: A learning style describes the attitudes and behaviors, which determine an individual's preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students' lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.


title: Toward Motion Coordination Control and Design Optimization for Dual-Arm Concentric Tube Continuum Robots

abstract: Dual-arm continuum robots have been considered mainly for teleoperation, where human perception and cognition permitted coordination, and collision-free motions. This letter describes theoretical investigations on automation of dual-arm robots constituted of two concentric tube continuum manipulators. An optimization algorithm is developed in order to improve triangulation ability of the robot and thus enhance the arms’ collaborative operation. This a priori knowledge provides design directives in order to fulfill integration, reachability, and collaboration requirements. Further, automatic control is assigned to perform online safe collaboration tasks. Our initial exploration is validated with numerical simulations using robot designs based on the optimization algorithm output. The control algorithm—based on the relative Jacobian and Cosserat rod modeling—performs simultaneously with less than 1% of the total robot's length of accuracy for both relative end-effector distance control and trajectory tracking.


title: How text and audio chat change the online video experience

abstract: Many online video sites provide a text chat feature so viewers can chat with others while watching videos. How does chatting affect their experience? Would audio chat be more fun or would it be too distracting? The richer medium of audio may more closely approximate the living room or club experience, but human factors research suggests that audio chat could increase distraction and detract from the viewing experience. This paper presents the results of an experiment comparing text with audio chat when the video does or does not have dialogue, and when viewers are watching the videos in the same or a different order. A control group watched videos without chat. Overall, audio chat and text chat were equally distracting, and chat was more distracting when the video contained dialogue. Despite the presence of distraction, viewers found both text and audio chat enjoyable. Those who used audio chat preferred it to text chat for talking with others while watching videos with their friends.


title: Understanding the Privacy Goal Intervenability

abstract: Privacy is gaining more and more attention in society and hence, gains more importance as a software quality that has to be considered during software development. A privacy goal that has not yet been deeply studied is the empowerment of end-users to have control over how their personal data is processed by information systems. This privacy goal is called intervenability. Several surveys have shown that one of end-users’ main privacy concerns is the lack of intervenability options in information systems. In this paper, we refine the privacy goal intervenability into a software requirements taxonomy and relate it to a taxonomy of transparency requirements because transparency can be regarded as a prerequisite for intervenability. The combined taxonomy of intervenability and transparency requirements shall guide requirements engineers to identify the intervenability requirements relevant for the system they consider. We validated the completeness of our taxonomy by comparing it to the relevant literature that we derived based on a systematic literature review.


title: The Hitman Study Violent Video Game Exposure Effects on Aggressive Behavior , Hostile Feelings , and Depression

abstract: This article explores commonly discussed theories of violent video game effects: the social learning, mood management, and catharsis hypotheses. An experimental study was carried out to examine violent video game effects. In this study, 103 young adults were given a frustration task and then randomized to play no game, a nonviolent game, a violent game with good versus evil theme (i.e., playing as a good character taking on evil), or a violent game in which they played as a ''bad guy.'' Results indicated that randomized video game play had no effect on aggressive behavior; real-life violent video game-playing history, however, was predictive of decreased hostile feelings and decreased depression following the frustration task. Results do not support a link between violent video games and aggressive behavior, but do suggest that violent games reduce depression and hostile feelings in players through mood management.


title: Programming cells by multiplex genome engineering and accelerated evolution

abstract: The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments. However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales. Although in vitro and directed evolution methods have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-D-xylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.