title: Do habits always override intentions? Pitting unhealthy snacking habits against snack-avoidance intentions

abstract: Background: Habit is defined as a process whereby an impulse towards behaviour is automatically initiated upon encountering a setting in which the behaviour has been performed in the past. A central tenet of habit theory is that habit overrides intentional tendencies in directing behaviour, such that as habit strength increases, intention becomes less predictive of behaviour. Yet, evidence of this effect has been methodologically limited by modelling the impact of positively-correlated habits and intentions. This study sought to test the effect of habits for unhealthy snacking on the relationship between intentions to avoid unhealthy snacks and snack intake. Methods: Methods were chosen to match those used in studies that have shown habit-intention interactions. 239 adults completed valid and reliable measures of habitual snacking and intention to avoid snacking at baseline, and a self-report measure of snack intake two weeks later. Data were analysed using multiple regression. Results: While both habit and intention independently predicted snack intake, no interaction between habit and intention was found. Conclusions: No support was found for the expected moderating impact of habit on the intention-behaviour relationship, indicating that individuals with intentions can act on those intentions despite having habits. Previous evidence of a habit-intention interaction effect may be unreliable. A growing literature indicates that habitual tendencies can be inhibited, albeit with difficulty. Habits and intentions may vary in the influence they exert over discrete behaviour instances. While the aggregation of behaviours across instances and individuals used in our study reflects the dominant methodology in habit research, it precludes examination of effects of in-situ habits and intentions. More sophisticated data collection and analysis methods may be needed to better understand potential habit-intention interactions.


title: A Novel Active Gate Driver for Improving SiC MOSFET Switching Trajectory

abstract: The trend in power electronic applications is to reach higher power density and higher efficiency. Currently, the wide band-gap devices such as silicon carbide MOSFET (SiC MOSFET) are of great interest because they can work at higher switching frequency with low losses. The increase of the switching speed in power devices leads to high power density systems. However, this can generate problems such as overshoots, oscillations, additional losses, and electromagnetic interference (EMI). In this paper, a novel active gate driver (AGD) for improving the SiC MOSFET switching trajectory with high performance is presented. The AGD is an open-loop control system and its principle is based on gate energy decrease with a gate resistance increment during the Miller plateau effect on gate–source voltage. The proposed AGD has been designed and validated through experimental tests for high-frequency operation. Moreover, an EMI discussion and a performance analysis were realized for the AGD. The results show that the AGD can reduce the overshoots, oscillations, and losses without compromising the EMI. In addition, the AGD can control the turn-on and turn-off transitions separately, and it is suitable for working with asymmetrical supplies required by SiC MOSFETs.


title: 3-D Printed Metal-Pipe Rectangular Waveguides

abstract: This paper first reviews manufacturing technologies for realizing air-filled metal-pipe rectangular waveguides (MPRWGs) and 3-D printing for microwave and millimeter-wave applications. Then, 3-D printed MPRWGs are investigated in detail. Two very different 3-D printing technologies have been considered: low-cost lower-resolution fused deposition modeling for microwave applications and higher-cost high-resolution stereolithography for millimeter-wave applications. Measurements against traceable standards in MPRWGs were performed by the U.K.’s National Physical Laboratory. It was found that the performance of the 3-D printed MPRWGs were comparable with those of standard waveguides. For example, across X-band (8–12 GHz), the dissipative attenuation ranges between 0.2 and 0.6 dB/m, with a worst case return loss of 32 dB; at W-band (75–110 GHz), the dissipative attenuation was 11 dB/m at the band edges, with a worst case return loss of 19 dB. Finally, a high-performance W-band sixth-order inductive iris bandpass filter, having a center frequency of 107.2 GHz and a 6.8-GHz bandwidth, was demonstrated. The measured insertion loss of the complete structure (filter, feed sections, and flanges) was only 0.95 dB at center frequency, giving an unloaded quality factor of 152—clearly demonstrating the potential of this low-cost manufacturing technology, offering the advantages of lightweight rapid prototyping/manufacturing and relatively very low cost when compared with traditional (micro)machining.


title: Global and efficient self-similarity for object classification and detection

abstract: Self-similarity is an attractive image property which has recently found its way into object recognition in the form of local self-similarity descriptors [5, 6, 14, 18, 23, 27] In this paper we explore global self-similarity (GSS) and its advantages over local self-similarity (LSS). We make three contributions: (a) we propose computationally efficient algorithms to extract GSS descriptors for classification. These capture the spatial arrangements of self-similarities within the entire image; (b) we show how to use these descriptors efficiently for detection in a sliding-window framework and in a branch-and-bound framework; (c) we experimentally demonstrate on Pascal VOC 2007 and on ETHZ Shape Classes that GSS outperforms LSS for both classification and detection, and that GSS descriptors are complementary to conventional descriptors such as gradients or color.


title: Combined optic-flow and stereo-based navigation of urban canyons for a UAV

abstract: We present a novel vision-based technique for navigating an unmanned aerial vehicle (UAV) through urban canyons. Our technique relies on both optic flow and stereo vision information. We show that the combination of stereo and optic flow (stereo flow) is more effective at navigating urban canyons than either technique alone. Optic flow from a pair of sideways looking cameras is used to stay centered in a canyon and initiate turns at junctions, while stereo vision from a forward facing stereo head is used to avoid obstacles to the front. The technique was tested in full on an autonomous tractor at CSIRO and in part on the USC autonomous helicopter. Experimental results are presented from these two robotic platforms operating in outdoor environments. We show that the autonomous tractor can navigate urban canyons using stereo-flow, and that the autonomous helicopter can turn away from obstacles to the side using optic flow. In addition, preliminary results show that a single pair of forward facing fisheye cameras can be used for both stereo and optic flow. The center portions of the fisheye images are used for stereo, while flow is measured in the periphery of the images.


title: Computer Vision Applied to Super Resolution

abstract: Super-resolution (SR) restoration aims to solve the following problem: given a set of observed images, estimate an image at a higher resolution than is present in any of the individual images. Where the application of this technique differs in computer vision from other fields is in the variety and severity of the registration transformation between the images. In particular this transformation is generally unknown, and a significant component of solving the SR problem in computer vision is the estimation of the transformation. The transformation may have a simple parametric form, or it may be scene dependent and have to be estimated for every point. In either case the transformation is estimated directly and automatically from the images. We describe the two key components that are necessary for successful SR restoration: the accurate alignment or registration of the LR images and the formulation of an SR estimator that uses a generative image model together with a prior model of the super-resolved image itself. As with many other problems in computer vision, these different aspects are tackled in a robust, statistical framework.


title: Accident Detection System using Image Processing and MDR

abstract: Summary In this research, we suggested a vision-based traffic accident detection system for automatically detecting, recording, and reporting traffic accidents at intersections. This model first extracts the vehicles from the video image of CCD camera, tracks the moving vehicles, and extracts features such as the variation rate of the velocity, position, area, and direction of moving vehicles. The model then makes decisions on the traffic accident based on the extracted features. And we suggested and designed the metadata registry for the system to improve the interoperability. In the field test, 4 traffic accidents were detected and recorded by the system. The video clips are invaluable for intersection safety analysis.


title: Knowledge Graph Error detection and Completion

abstract: In the era of big data, people face enormous challenges in acquiring information and knowledge. A knowledge graph (KG) lays the foundation for the knowledge-based organization and intelligent application in the Internet age with its powerful semantic processing capabilities and open organization capabilities. In recent years, the research and applications of large-scale knowledge graph libraries have attracted increasing attention in academic and industrial circles. The knowledge graph aims to describe the various entities or concepts and their relationships existing in the objective world, which constitutes a huge semantic network map. It usually stores knowledge in the form of triples (head entity, relationship, tail entity), which can be simplified to $(h, r, t)$.


title: Evaluation of Algorithms Using Games: The Case of Music Tagging

abstract: Search by keyword is an extremely popular method for retrieving music. To support this, novel algorithms that automatically tag music are being developed. The conventional way to evaluate audio tagging algorithms is to compute measures of agreement between the output and the ground truth set. In this work, we introduce a new method for evaluating audio tagging algorithms on a large scale by collecting set-level judgments from players of a human computation game called TagATune. We present the design and preliminary results of an experiment comparing five algorithms using this new evaluation metric, and contrast the results with those obtained by applying several conventional agreement-based evaluation metrics.


title: The Factors Affecting Cross-border E-commerce Development of SMEs ---An Empirical Study

abstract: Recently, there are two mainly methods for SMEs operating cross-border e-commerce in China. One is online store of firms via the cross-border third party platform. The other is the online store of firms built by themselves for export markets expansion. Based on the analysis of cross-border e-commerce process, we explore four factors which may affect the mode selection of cross-border e-commerce of SMEs, namely E-marketing, electronic payment, electronic customs and international logistics. For the methodology, we use Probit model of Logit modeling and then have the finding that the three factors E-marketing, electronic customs and international logistics are the impact of SMEs cross-border e-commerce mode selection, and further find that most of SMEs who are weak at cross-border e-commerce operation stay on third party cross-border e-commerce service platform. Based on the finding, we also have some conclusion. In order to assist SMEs development, the government should establish and improve the trade informational platform. Meanwhile, they should encourage the cross-border e-commerce third-party platform to become bigger and stronger and improve electronic customs clearance continuously to raise large scale cross-border logistic firms and third parties with good service, strong competition and high technologies. For the SMEs, they should adopt the third party cross-border e-commerce platform actively. Some firms should have their own cross-border websites for opening cross-border e-commerce channel actively.


title: Leaf image based cucumber disease recognition using sparse representation classification

abstract: The log frequency histogram features are extracted from each lesion image.The lesion shape features are combined with color features.The sparse coefficients can explain the importance of each disease leaf image.The cucumber disease is recognized by SRC. Most existing image-based crop disease recognition algorithms rely on extracting various kinds of features from leaf images of diseased plants. They have a common limitation as the features selected for discriminating leaf images are usually treated as equally important in the classification process. We propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR). A major advantage of this approach is that the classification in the SR space is able to effectively reduce the computation cost and improve the recognition performance. We perform a comparison with four other feature extraction based methods using a leaf image dataset on cucumber diseases. The proposed approach is shown to be effective in recognizing seven major cucumber diseases with an overall recognition rate of 85.7%, higher than those of the other methods.


IoT Based Precision Agriculture using Agribot


title: Vehicle Color Recognition using Convolutional Neural Network

abstract: Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the input image to two different color spaces, HSV and CIE Lab, and run it to some CNN architecture. The training process follow procedure introduce by Krizhevsky, that learning rate is decreasing by factor of 10 after some iterations. To test our method, we use publicly vehicle color recognition dataset provided by Chen. The results, our model outperform the original system provide by Chen with 2% higher overall accuracy.


title: NEURAL IMAGE RESTORATION FOR DECODING 1-D BARCODES USING COMMON CAMERA PHONES

abstract: The existing open-source libraries for 1-D barcodes recognition are not able to recognize the codes from images acquired using simple devices without autofocus or macro function. In this article we present an improvement of an existing algorithm for recognizing 1-D barcodes using camera phones with and without autofocus. The multilayer feedforward neural network based on backpropagation algorithm is used for image restoration in order to improve the selected algorithm. Performances of the proposed algorithm were compared with those obtained from available open-source libraries. The results show that our method makes possible the decoding of barcodes from images captured by mobile phones without autofocus.


title: RWC Music Database: Popular, Classical and Jazz Music Databases

abstract: paper describes the design policy and specifications of the RWC Music Database , a music database (DB) that is available to researchers for common use and research purposes. Various com- monly available DBs have been built in other research fields and have made a significant contribution to the research in those fields. The field of musical information processing, however, has lacked a commonly available music DB. We therefore built the RWC Mu- sic Database which contains four original DBs: the Popular Music Database (100 pieces), Royalty-Free Music Database(15 pieces), Classical Music Database(50 pieces), and Jazz Music Database (50 pieces). Each consists of originally-recorded music compact discs, standard MIDI files, and text files of lyrics. These DBs are now available in Japan at a cost equal to only duplication, shipping, and handling charges (virtually for free), and we plan to make them available outside Japan. We hope that our DB will encourage further advances in musical information processing research.


title: Nanofibers and their applications in tissue engineering

abstract: Developing scaffolds that mimic the architecture of tissue at the nanoscale is one of the major challenges in the field of tissue engineering. The development of nanofibers has greatly enhanced the scope for fabricating scaffolds that can potentially meet this challenge. Currently, there are three techniques available for the synthesis of nanofibers: electrospinning, self-assembly, and phase separation. Of these techniques, electrospinning is the most widely studied technique and has also demonstrated the most promising results in terms of tissue engineering applications. The availability of a wide range of natural and synthetic biomaterials has broadened the scope for development of nanofibrous scaffolds, especially using the electrospinning technique. The three dimensional synthetic biodegradable scaffolds designed using nanofibers serve as an excellent framework for cell adhesion, proliferation, and differentiation. Therefore, nanofibers, irrespective of their method of synthesis, have been used as scaffolds for musculoskeletal tissue engineering (including bone, cartilage, ligament, and skeletal muscle), skin tissue engineering, vascular tissue engineering, neural tissue engineering, and as carriers for the controlled delivery of drugs, proteins, and DNA. This review summarizes the currently available techniques for nanofiber synthesis and discusses the use of nanofibers in tissue engineering and drug delivery applications.


Time – based strategy in distribution logistics Gaining competitive advantages in IKEA


title: Towards Ontology Learning from Folksonomies

abstract: A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.


title: Exact Matrix Completion via Convex Optimization

abstract: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? ::: ::: We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys $$m\ge C\,n^{1.2}r\log n$$ for some positive numerical constant C, then with very high probability, most n×n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.


title: Efficient manifold learning for speech recognition using locality sensitive hashing

abstract: This paper considers the application of a random projections based hashing scheme, known as locality sensitive hashing (LSH), for fast computation of neighborhood graphs in manifold learning based feature space transformations in automatic speech recognition (ASR). Discriminative manifold learning based feature transformations have already been found to provide significant improvements in ASR performance. The motivation of this work is the fact that the high computational complexity of these techniques has prevented their application to very large speech corpora. The performance of this integrated system is evaluated both in terms of computational complexity and ASR word recognition accuracy. Further comparisons of ASR performance with the well-known linear discriminant analysis are provided. These results demonstrate that LSH provides the much needed speed boost to manifold learning techniques with minimal impact on their ASR performance, thus enabling the application of these algorithms to large speech databases.


title: Inferring human mobility patterns from taxicab location traces

abstract: Taxicabs equipped with real-time location sensing devices are increasingly becoming popular. Such location traces are a rich source of information and can be used for congestion pricing, taxicab placement, and improved city planning. An important problem to enable these application is to identify human mobility patterns from the taxicab traces, which translates to being able to identify pickup and dropoff points for a particular trip. In this paper, we show that while past approaches are effective in detecting hotspots using location traces, they are largely ineffective in identifying trips (pairs of pickup and dropoff points). We propose the use of a graph theory concept - stretch factor in a novel manner to identify trip(s) made by a taxicab and show that a Hidden Markov Model based algorithm can identify trips (using real datasets from taxicab deployments in Shanghai and partially simulated datasets from Stockholm) with precision and recall of 90-94%, a significant improvement over past approaches that result in a precision and recall of about 50-60%.


title: Low voltage analog circuit design techniques

abstract: Analog signal processing is fast and can address real world problems. The applications of battery powered analog and mixed mode electronic devices require designing analog circuits to operate at low voltage levels. In this paper, some of the issues facing analog designers in implementing low voltage circuits are discussed, and possible low voltage design techniques are examined. The authors describe briefly almost all low voltage design techniques suitable for analog circuit structures along with their merits and demerits.


title: Better matching with fewer features: The selection of useful features in large database recognition problems

abstract: There has been recent progress on the problem of recognizing specific objects in very large datasets. The most common approach has been based on the bag-of-words (BOW) method, in which local image features are clustered into visual words. This can provide significant savings in memory compared to storing and matching each feature independently. In this paper we take an additional step to reducing memory requirements by selecting only a small subset of the training features to use for recognition. This is based on the observation that many local features are unreliable or represent irrelevant clutter. We are able to select “useful” features, which are both robust and distinctive, by an unsupervised preprocessing step that identifies correctly matching features among the training images. We demonstrate that this selection approach allows an average of 4% of the original features per image to provide matching performance that is as accurate as the full set. In addition, we employ a graph to represent the matching relationships between images. Doing so enables us to effectively augment the feature set for each image through merging of useful features of neighboring images. We demonstrate adjacent and 2-adjacent augmentation, both of which give a substantial boost in performance.


title: Location Diversity: Enhanced Privacy Protection in Location Based Services

abstract: Location-based Services are emerging as popular applications in pervasive computing. Spatial k -anonymity is used in Location-based Services to protect privacy, by hiding the association of a specific query with a specific user. Unfortunately, this approach fails in many practical cases such as: (i) personalized services, where the user identity is required, or (ii) applications involving groups of users (e.g., employees of the same company); in this case, associating a query to any member of the group, violates privacy. ::: ::: In this paper, we introduce the concept of Location Diversity , which solves the above-mentioned problems. Location Diversity improves Spatial k -anonymity by ensuring that each query can be associated with at least *** different semantic locations (e.g., school, shop, hospital, etc). We present an attack model that maps each observed query to a linear equation involving semantic locations, and we show that a necessary condition to preserve privacy is the existence of infinite solutions in the resulting system of linear equations. Based on this observation, we develop algorithms that generate groups of semantic locations, which preserve privacy and minimize the expected query processing and communication cost. The experimental evaluation demonstrates that our approach reduces significantly the privacy threats, while incurring minimal overhead.


title: Scalable DC Microgrids for Rural Electrification in Emerging Regions

abstract: We present the design and experimental validation of a scalable dc microgrid for rural electrification in emerging regions. A salient property of the dc microgrid architecture is the distributed control of the grid voltage, which enables both instantaneous power sharing and a metric for determining the available grid power. A droop-voltage power-sharing scheme is implemented wherein the bus voltage droops in response to low supply/high demand. In addition, the architecture of the dc microgrid aims to minimize the losses associated with stored energy by distributing storage to individual households. In this way, the number of conversion steps and line losses are reduced. We calculate that the levelized cost of electricity of the proposed dc microgrid over a 15-year time horizon is $0.35/kWh. We also present the experimental results from a scaled-down experimental prototype that demonstrates the steady-state behavior, the perturbation response, and the overall efficiency of the system. Moreover, we present fault mitigation strategies for various faults that can be expected to occur in a microgrid distribution system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost-effective method for electrifying emerging regions.


Cayuga : A High-Performance Event Processing Engine ∗ [ Demonstration Paper ]


title: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form

abstract: In this paper, adaptive dynamic surface control (DSC) is developed for a class of pure-feedback nonlinear systems with unknown dead zone and perturbed uncertainties using neural networks. The explosion of complexity in traditional backstepping design is avoided by utilizing dynamic surface control and introducing integral-type Lyapunov function. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants. Simulation results demonstrate the effectiveness of the proposed approach.


title: [Bacterial cyclostatin, or how do bacteria manipulate the eukaryotic cell cycle].

abstract: Several bacterial proteins have been recently described that share the ability to inhibit the proliferation of cells in culture without causing early signs of cytotoxicity. Such observations suggest the existence of bacterial mechanisms of control of the eukaryotic cell cycle contributing to pathogenicity or adaptation to the host. This emerging concept of cellular microbiology is critically analyzed considering as a model the cytolethal distending toxins (CDT), a family of toxins whose mode of action on the cell cycle has been thoroughly studied over the last few years. CDTs activate a physiological G2 checkpoint in exposed cells, probably from an initial DNA alteration whose precise molecular nature has not yet been determined. Experimental data are lacking to extrapolate in vivo the antiproliferative effect of these bacterial proteins that we tentatively propose to call cyclostatins.


title: Improving multi-class text classification with naive bayes

abstract: We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM’s lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties.


title: Augmented Reality Oculus Rift Developement Of An Augmented Reality Stereoscopic-Render-Engine To Extend Reality With 3D Holograms

abstract: This paper covers the whole process of developing an Augmented Reality Stereoscopig Render Engine for the Oculus Rift. To capture the real world in form of a camera stream, two cameras with fish-eye lenses had to be installed on the Oculus Rift DK1 hardware. The idea was inspired by Steptoe \cite{steptoe2014presence}. After the introduction, a theoretical part covers all the most neccessary elements to achieve an AR System for the Oculus Rift, following the implementation part where the code from the AR Stereo Engine is explained in more detail. A short conclusion section shows some results, reflects some experiences and in the final chapter some future works will be discussed. The project can be accessed via the git repository this https URL


title: Promises and Partnership

abstract: We examine experimentally the impact of communication on trust and cooperation. Our design admits observation of promises, lies, and beliefs. The evidence is consistent with people striving to live up to others' expectations so as to avoid guilt, as can be modeled using psychological game theory. When players exhibit such guilt aversion, communication may influence motivation and behavior by influencing beliefs about beliefs. Promises may enhance trustworthy behavior, which is what we observe. We argue that guilt aversion may be relevant for understanding strategic interaction in a variety of settings, and that it may shed light on the role of language, discussions, agreements, and social norms in these contexts. Copyright The Econometric Society 2006. (This abstract was borrowed from another version of this item.)


title: Superimposed Adaptive Sequence Current Based Microgrid Protection: A New Technique

abstract: In this paper, an adaptive directional overcurrent relaying technique based on the positive-sequence (PSQ) and negative-sequence (NSQ) superimposed currents is proposed for microgrid protection. Due to the change in mode of operation of microgrid, the level and direction of prefault current are altered. On occurrence of a fault in any line section of the microgrid, coordination between the primary and backup overcurrent relays is lost due to variations in level and direction of fault current. With inception of faults, the current contributed by the inverter-based distributed energy resource is in the range of 2 p.u. To solve protection coordination issue, this paper presents an adaptive overcurrent relay settings using PSQ and NSQ superimposed currents those are contributed during fault conditions. Further, the direction of the fault is obtained using the new phase change between prefault and superimposed sequence fault currents. The proposed technique is implemented on a dSPACE processor board, which is connected to a real-time digital simulator to carry out the hardware-in-the-loop test. Extensive simulation and hardware results obtained for several microgrid operating modes indicate the effectiveness of the proposed technique. This proposed approach is independent of the voltage information.


title: DroidJust: automated functionality-aware privacy leakage analysis for Android applications

abstract: Android applications (apps for short) can send out users' sensitive information against users' intention. Based on the stats from Genome and Mobile-Sandboxing, 55.8% and 59.7% Android malware families feature privacy leakage. Prior approaches to detecting privacy leakage on smartphones primarily focused on the discovery of sensitive information flows. However, Android apps also send out users' sensitive information for legitimate functions. Due to the fuzzy nature of the privacy leakage detection problem, we formulate it as a justification problem, which aims to justify if a sensitive information transmission in an app serves any purpose, either for intended functions of the app itself or for other related functions. This formulation makes the problem more distinct and objective, and therefore more feasible to solve than before. We propose D roid J ust , an automated approach to justifying an app's sensitive information transmission by bridging the gap between the sensitive information transmission and application functions. We also implement a prototype of D roid J ust and evaluate it with over 6000 Google Play apps and over 300 known malware collected from VirusTotal. Our experiments show that our tool can effectively and efficiently analyze Android apps w.r.t their sensitive information flows and functionalities, and can greatly assist in detecting privacy leakage.


title: The Impact of the New Web 2.0 Technologies in Communication, Development, and Revolutions of Societies

abstract: In lase years with all of the attention paid to social networks (SN) and Web 2.0 tools these days, it is important to both explore their uses and evaluate their effectiveness in supporting communication, developing, and revolutions of countries, are rapidly evolving technology and play an important role in every daily life activities in societies. This technology includes wikis(Wikipedia, Seedwiki), blog, micro blogging (Twitter), YouTube, social book marking, podcasts, Second Life (virtual communities), and RSS.This paper will illustrate how Web 2.0 technology has been successfully used as a supplement for communicative practice in societies. Moreover, this paper explores the impact of communication SNs, Web 2.0 technologies, and the Internet in particular, has been widely credited as contributor to the democracy and freedom of countries. Some challenges of SNs and Web 2.0 have been overviewed. In addition to, the role of such technologies has been influenced by the Tunisian and Egyptian revolutions in 2011 which have been explained and highlighted in this work.


title: Q-DNN: A quality-aware deep neural network for blind assessment of enhanced images

abstract: Image enhancement is widely popular due to its capability of producing "better" visual quality for specific applications. Although many enhancement algorithms have been developed in recent years, the studies towards blind assessment of enhanced images are still very lacking. In this paper, we propose a data-driven blind image quality assessment (BIQA) method based on the quality-aware deep neural network (Q-DNN). Unlike the conventional hand-crafted features designed for measuring the degradation level of specific distortion types, a supervised learning model is utilized in our Q-DNN, which is capable of adaptively updating the feature extractor and quality regressor for describing the visual artifacts caused by different image enhancement tasks. Experimental results on two challenging enhanced image databases show that the proposed method is significantly superior to the state-of-the-art BIQA metrics.


Ensemble Integrations of Telemac-Mascaret for the optimal model calibration


title: Procedural content generation for games: A survey

abstract: Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over the last decade of both the gamer population and the production costs. Procedural Content Generation for Games (PCG-G) may address these challenges by automating, or aiding in, game content generation. PCG-G is difficult, since the generator has to create the content, satisfy constraints imposed by the artist, and return interesting instances for gamers. Despite a large body of research focusing on PCG-G, particularly over the past decade, ours is the first comprehensive survey of the field of PCG-G. We first introduce a comprehensive, six-layered taxonomy of game content: bits, space, systems, scenarios, design, and derived. Second, we survey the methods used across the whole field of PCG-G from a large research body. Third, we map PCG-G methods to game content layers; it turns out that many of the methods used to generate game content from one layer can be used to generate content from another. We also survey the use of methods in practice, that is, in commercial or prototype games. Fourth and last, we discuss several directions for future research in PCG-G, which we believe deserve close attention in the near future.


title: Community Detection in Multi-Dimensional Networks

abstract: Abstract : The pervasiveness of Web 2.0 and social networking sites has enabled people to interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment on shared content (bookmarks, photos, videos), and users can tag their favorite content. Users can also connect with one another, and subscribe to or become a fan or a follower of others. These diverse activities result in a multi-dimensional network among actors, forming group structures with group members sharing similar interests or a liations. This work systematically addresses two challenges. First, it is challenging to e ectively integrate interactions over multiple dimensions to discover hidden community structures shared by heterogeneous interactions. We show that representative community detection methods for single-dimensional networks can be presented in a uni- ed view. Based on this uni ed view, we present and analyze four possible integration strategies to extend community detection from single-dimensional to multi-dimensional networks. In particular, we propose a novel integration scheme based on structural features. Another challenge is the evaluation of different methods without ground truth information about community membership. We employ a novel cross-dimension network validation procedure to compare the performance of di erent methods. We use synthetic data to deepen our understanding, and real-world data to compare integration strategies as well as baseline methods in a large scale. We study further the computational time of di erent methods, normalization e ect during integration, sensitivity to related parameters, and alternative community detection methods for integration.


title: A Tutorial on Interactive Sensing in Social Networks

abstract: This paper considers models and algorithms for interactive sensing in social networks in which individuals act as sensors and the information exchange between individuals is exploited to optimize sensing. Social learning is used to model the interaction between individuals that aim to estimate an underlying state of nature. In this context, the following questions are addressed: how can self-interested agents that interact via social learning achieve a tradeoff between individual privacy and reputation of the social group? How can protocols be designed to prevent data incest in online reputation blogs where individuals make recommendations? How can sensing by individuals that interact with each other be used by a global decision maker to detect changes in the underlying state of nature? When individual agents possess limited sensing, computation, and communication capabilities, can a network of agents achieve sophisticated global behavior? Social and game-theoretic learning are natural settings for addressing these questions. This article presents an overview, insights, and discussion of social learning models in the context of data incest propagation, change detection, and coordination of decision-making.


title: A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography

abstract: Brain CT has become a standard imaging tool for emergent evaluation of brain condition, and measurement of midline shift (MLS) is one of the most important features to address for brain CT assessment. We present a simple method to estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons and our automated system were asked to measure MLS and MLS/ICWMAX in the same sets of axial CT images obtained from 41 patients admitted to ICU under neurosurgical service. A weighted midline (WML) was plotted based on individual pixel intensities, with higher weighted given to the darker portions. The MLS could then be measured as the distance between the WML and ideal midline (IML) near the foramen of Monro. The average processing time to output an automatic MLS measurement was around 10 seconds. Our automated system achieved an overall accuracy of 90.24% when the CT images were calibrated automatically, and performed better when the calibrations of head rotation were done manually (accuracy: 92.68%). MLS/ICWMAX and MLS both gave results in same confusion matrices and produced similar ROC curve results. We demonstrated a simple, fast and accurate automated system of MLS measurement and introduced a new parameter (MLS/ICWMAX) as a good alternative to MLS in terms of estimating the degree of brain deformation, especially when non-DICOM images (e.g. JPEG) are more easily accessed.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Optimism in Close Relationships: How Seeing Things in a Positive Light Makes Them So

abstract: Does expecting positive outcomes—especially in important life domains such as relationships—make these positive outcomes more likely? In a longitudinal study of dating couples, the authors tested whether optimists (who have a cognitive disposition to expect positive outcomes) and their romantic partners are more satisfied in their relationships, and if so, whether this is due to optimists perceiving greater support from their partners. In cross-sectional analyses, both optimists and their partners indicated greater relationship satisfaction, an effect that was mediated by optimists’ greater perceived support. When the couples engaged in a conflict conversation, optimists and their partners saw each other as engaging more constructively during the conflict, which in turn led both partners to feel that the conflict was better resolved 1 week later. In a 1-year follow-up, men’s optimism predicted relationship status. Effects of optimism were mediated by the optimists’ perceived support, which appears to promote a variety of beneficial processes in romantic relationships.


title: Scaling distributed transaction processing and recovery based on dependency logging

abstract: Dependency graph-based concurrency control (DGCC) protocol has been shown to achieve good performance on multi-core in-memory system. DGCCseparates contention resolution from the transaction execution and employs dependency graphs to derive serializable transaction schedules. However, distributed transactions complicate the dependency resolution, and therefore, an effective transaction partitioning strategy is essential to reduce expensive multi-node distributed transactions. During failure recovery, log must be examined from the last checkpoint onward and the affected transactions are re-executed based on the way they are partitioned and executed. Existing approaches treat both transaction management and recovery as two separate problems, even though recovery is dependent on the sequence in which transactions are executed. In this paper, we propose to treat the transaction management and recovery problems as one. We first propose an efficient distributed dependency graph-based concurrency control (DistDGCC) protocol for handling transactions spanning multiple nodes and propose a new novel and efficient logging protocol called dependency logging that also makes use of dependency graphs for efficient logging and recovery. DistDGCCoptimizes the average cost for each distributed transaction by processing transactions in batches. Moreover, it also reduces the effects of thread blocking caused by distributed transactions and consequently improves the runtime performance. Further, dependency logging exploits the same data structure that is used by DistDGCCto reduce the logging overhead, as well as the logical dependency information to improve the recovery parallelism. Extensive experiments are conducted to evaluate the performance of our proposed technique against state-of-the-art techniques. Experimental results show that DistDGCCis efficient and scalable, and dependency logging supports fast recovery with marginal runtime overhead. Hence, the overall system performance is significantly improved as a result.


Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover's Distance Regularization


title: Amygdala activation at 3T in response to human and avatar facial expressions of emotions

abstract: Facial expressions of emotions are important in nonverbal communication. Although numerous neural structures have been identified to be involved in emotional face processing, the amygdala is thought to be a core moderator. While previous studies have relied on facial images of humans, the present study is concerned with the effect of computer-generated (avatar) emotional faces on amygdala activation. Moreover, elicited activation patterns in response to viewing avatar faces are compared with the neuronal responses to human facial expressions of emotions. Twelve healthy subjects (five females) performed facial emotion recognition tasks with optimized 3T event-related fMRI. Robust amygdala activation was apparent in response to both human and avatar emotional faces, but the response was significantly stronger to human faces in face-sensitive structures, i.e. fusiform gyri. We suggest that avatars could be a useful tool in neuroimaging studies of facial expression processing because they elicit amygdala activation similarly to human faces, yet have the advantage of being highly manipulable and fully controllable. However, the finding of differences between human and avatar faces in face-sensitive regions indicates the presence of mechanisms by which human brains can differentiate between them. This mechanism merits further investigation.


title: On the Equivalence of Holographic and Complex Embeddings for Link Prediction

abstract: We show the equivalence of two state-of-the-art link prediction/knowledge graph completion methods: Nickel et al's holographic embedding and Trouillon et al.'s complex embedding. We first consider a spectral version of the holographic embedding, exploiting the frequency domain in the Fourier transform for efficient computation. The analysis of the resulting method reveals that it can be viewed as an instance of the complex embedding with certain constraints cast on the initial vectors upon training. Conversely, any complex embedding can be converted to an equivalent holographic embedding.


title: BAP: A Binary Analysis Platform

abstract: BAP is a publicly available infrastructure for performing program verification and analysis tasks on binary (i.e., executable) code. In this paper, we describe BAP as well as lessons learned from previous incarnations of binary analysis platforms. BAP explicitly represents all side effects of instructions in an intermediate language (IL), making syntaxdirected analysis possible. We have used BAP to routinely generate and solve verification conditions that are hundreds of megabytes in size and encompass 100,000's of assembly instructions.


title: Concolic Execution on Small-Size Binaries: Challenges and Empirical Study

abstract: Concolic execution has achieved great success in many binary analysis tasks. However, it is still not a primary option for industrial usage. A well-known reason is that concolic execution cannot scale up to large-size programs. Many research efforts have focused on improving its scalability. Nonetheless, we find that, even when processing small-size programs, concolic execution suffers a great deal from the accuracy and scalability issues. This paper systematically investigates the challenges that can be introduced even by small-size programs, such as symbolic array and symbolic jump. We further verify that the proposed challenges are non-trivial via real-world experiments with three most popular concolic execution tools: BAP, Triton, and Angr. Among a set of 22 logic bombs we designed, Angr can solve only four cases correctly, while BAP and Triton perform much worse. The results imply that current tools are still primitive for practical industrial usage. We summarize the reasons and release the bombs as open source to facilitate further study.


title: Optimizing the Matrix Multiplication Using Strassen and Winograd Algorithms with Limited Recursions on Many-Core

abstract: Many-core systems are basically designed for applications having large data parallelism. We propose an efficient hybrid matrix multiplication implementation based on Strassen and Winograd algorithms (S-MM and W-MM) on many-core. A depth first (DFS) traversal of a recursion tree is used where all cores work in parallel on computing each of the $$N \times N$$N×N sub-matrices, which are computed in sequence. DFS reduces the storage to the detriment of large data motion to gather and aggregate the results. The proposed approach uses three optimizations: (1) a small set of basic algebra functions to reduce overhead, (2) invoking efficient library (CUBLAS 5.5) for basic functions, and (3) using parameter-tuning of parametric kernel to improve resource occupancy. Evaluation of S-MM and W-MM is carried out on GPU and MIC (Xeon Phi). For GPU, W-MM and S-MM with one recursion level outperform CUBLAS 5.5 Library with up to twice as fast for arrays satisfying $$N \ge 2048$$Nź2048 and $$N \ge 3072$$Nź3072, respectively. Similar trends are observed for S-MM with reordering (R-S-MM), which is used to save storage. Compared to NVIDIA SDK library, S-MM and W-MM achieved a speedup between 20$$\times $$× and 80$$\times $$× for the above arrays. For MIC, two-recursion S-MM with reordering is faster than MKL library by 14---26 % for $$N \ge 1024$$Nź1024. Proposed implementations achieve 2.35 TFLOPS (67 % of peak) on GPU and 0.5 TFLOPS (21 % of peak) on MIC. Similar encouraging results are obtained for a 16-core Xeon-E5 server. We conclude that S-MM and W-MM implementations with a few recursion levels can be used to further optimize the performance of basic algebra libraries.


title: Immersive Interactive Virtual Reality in the Museum

abstract: The use of immersive Virtual Reality (VR) technology is a relatively recent trend enjoyed almost exclusively by the academic, military, and industrial research and development communities. However, as VR technologies mature, research is expanding from the military and scientific visualisation realm into more multidisciplinary areas, such as education, art, culture, and the humanities. As representative institutions involved in the research and presentation of these fields, museums, cultural centres, and entertainment venues may be in a better position to make use of advanced virtual reality technologies in order to investigate their educational potential while effectively shaping how they deliver public education and recreation. This paper will discuss the issues involved in using state-of-the-art interactive virtual environments in cultural public spaces by presenting the virtual environments developed for learners of all ages at the Foundation of the Hellenic World (FHW), a cultural heritage institution of informal education located in Athens. Virtual Reality in the Museum The development of new interactive technologies has inevitably impacted the more traditional sciences and arts. This is more evident in the case of novel interactive technologies that fascinate the broad public, as has always been the case with virtual reality. The blending of disciplines and the evolution of techniques has brought forth the need for better modes of communication. Consequently, virtual reality interfaces, interaction techniques, and devices have improved greatly in order to provide more natural and obvious modes of interaction and motivational elements. Nevertheless, the prohibitive costs and inaccessibility of VR technologies, coupled with issues of usability, staff training, operation, and maintenance, present important drawbacks for the use of VR in public spaces, making it difficult to incorporate in dwindling museum or school budgets. In spite of these concerns and objections regarding the appropriateness and educational efficacy of virtual reality, there remain compelling reasons for believing that VR environments for the broad public warrant serious investigation and can provide strong tools for learning. Institutions of informal education, such as museums, research, and cultural centres are in a better position to make use of such advanced systems and investigate their educational potential while effectively shaping how they deliver public entertainment and education. Of particular interest to museums in the use of virtual reality displays and computergenerated interactive experiences is the fact that they can allow visitors to travel through space and time without stepping out of the museum building [5]. The potential to transcend the physical location of the built environment and the growing sense of the educative function of the museum juxtaposed with the commercial pressure has lead museums to consider virtual reality as a necessary component in the arsenal of tools to educate, entertain, and dazzle [8]. When used for cultural heritage purposes, a virtual reality exhibit may give scholars a completely new way of communicating the scientific results of archaeological investigation within the scientific community, improving also the way in which these results are communicated to the public [3]. In addition to the fairly photorealistic representation of places, people, and sites that do not exist, never existed, or may not be easily experienced, there are two basic advantages offered by virtual reality programs: the immersive experience and interactive capabilities that characterize this medium. Immersion is the illusion of being in the projected world, being surrounded by the image and sound in a way, which makes you believe that you are really there. It offers a "better than real life" or "better than being there" experience. Interaction refers to the fact that members of the audience are not merely a viewer of the realistic scenery, but can actively participate in the program and determine what their experience will be. Since the content displayed in VR exhibits is not predetermined or pre-recorded, but generated in "real-time", the audience is able to interact with the programs and define their behavior. If, for example, the program exhibited is the journey through an ancient city, museum visitors can choose the path through the city just as in a real tour. They may also "knock" on doors to enter buildings, or fly up high to view the city from above with the use of simple devices such as a joystick. Although virtual reality suffers immensely from media hyperbole and thus has not lived up to its promises, the development of VR systems is evolving and gaining territory in the museum world for many of the reasons mentioned above.


title: A planning framework for community empowerment goals within health promotion.

abstract: Health promotion often comprises a tension between 'bottom-up' and 'top-down' programming. The former, more associated with concepts of community empowerment, begins on issues of concern to particular groups or individuals, and regards some improvement in their overall power or capacity as the important health outcome. The latter, more associated with disease prevention efforts, begins by seeking to involve particular groups or individuals in issues and activities largely defined by health agencies, and regards improvement in particular behaviours as the important health outcome. Community empowerment is viewed more instrumentally as a means to the end of health behaviour change. The tension between these two approaches is not unresolvable, but this requires a different orientation on the part of those responsible for planning more conventional, top-down programmes. This article presents a framework intended to assist planners, implementers and evaluators to systematically consider community empowerment goals within top-down health promotion programming. The framework 'unpacks' the tensions in health promotion at each stage of the more conventional, top-down programme cycle, by presenting a parallel 'empowerment' track. The framework also presents a new technology for the assessment and strategic planning of nine identified 'domains' that represent the organizational influences on the process of community empowerment. Future papers analyze the design of this assessment and planning methodology, and discuss the findings of its field-testing in rural communities in Fiji.


title: Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts

abstract: We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country i took action a toward country j at time t" - known as dyadic events - in order to form and test theories of international relations. We represent these event data as a tensor of counts and develop Bayesian Poisson tensor factorization to infer a low-dimensional, interpretable representation of their salient patterns. We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods. We also provide a comparison of our variational updates to their maximum likelihood counterparts. In doing so, we identify a better way to form point estimates of the latent factors than that typically used in Bayesian Poisson matrix factorization. Finally, we showcase our model as an exploratory analysis tool for political scientists. We show that the inferred latent factor matrices capture interpretable multilateral relations that both conform to and inform our knowledge of international a airs.


title: Analysis of sequence to sequence neural networks on grapheme to phoneme conversion task

abstract: In this paper, we analyze the performance of various sequence to sequence neural networks on the task of grapheme to phoneme (G2P) conversion. G2P is a very important component in applications like text-to-speech, automatic speech recognition etc,. Because the number of graphemes that a word consists of and the corresponding number of phonemes are different, they are first aligned and then mapped. With the recent advent of sequence to sequence neural networks, the alignment step can be skipped allowing us to directly map the input and output sequences. Although the sequence to sequence neural nets have been applied for this task very recently, there are some questions concerning the architecture that need to be addressed. We show in this paper that, complex recurrent neural network units (like long-short term memory cells) may not be required to achieve good performance on this task. Instead simple recurrent neural networks (RNN) will suffice. We also show that the encoder can be a uni-directional RNN as opposed to the usually preferred bi-directional RNN. Further, our experiments reveal that encoder-decoder models with soft-alignment outperforms fixed vector context counterpart. The results demonstrate that with very few parameters we can indeed achieve comparable performance to much more complicated architectures.


title: Extracting domain models from natural-language requirements: approach and industrial evaluation

abstract: Domain modeling is an important step in the transition from natural-language requirements to precise specifications. For large systems, building a domain model manually is a laborious task. Several approaches exist to assist engineers with this task, whereby candidate domain model elements are automatically extracted using Natural Language Processing (NLP). Despite the existing work on domain model extraction, important facets remain under-explored: (1) there is limited empirical evidence about the usefulness of existing extraction rules (heuristics) when applied in industrial settings; (2) existing extraction rules do not adequately exploit the natural-language dependencies detected by modern NLP technologies; and (3) an important class of rules developed by the information retrieval community for information extraction remains unutilized for building domain models. Motivated by addressing the above limitations, we develop a domain model extractor by bringing together existing extraction rules in the software engineering literature, extending these rules with complementary rules from the information retrieval literature, and proposing new rules to better exploit results obtained from modern NLP dependency parsers. We apply our model extractor to four industrial requirements documents, reporting on the frequency of different extraction rules being applied. We conduct an expert study over one of these documents, investigating the accuracy and overall effectiveness of our domain model extractor.


EXPLORATION OF URBAN HERITAGE IN THE HISTORIC CORE OF LONDON: A SPATIAL NETWORK APPROACH


title: Visual-VM: A Social Network Visualization Tool for Viral Marketing

abstract: The paper presents Visual-VM, a social network visualization tool, where the main focus is to provide utilities for viral marketing (e.g., Influence maximization). Besides, Visual-VM utilizes the location information of each user (which could be estimated from the user's profile) for social network visualization, which is not used in existing social network visualization tools. Visual-VM also supports common utilities for social network exploration.


title: MPPT techniques for photovoltaic system under uniform insolation and partial shading conditions

abstract: The photovoltaic energy is one of the renewable energies that has attracted the attention of researchers in the recent decades. The photovoltaic generators exhibit nonlinear I-V and P-V characteristics. The maximum power produced varies with solar insolation and temperature. It requires maximum power point tracking (MPPT) control techniques to extract the maximum available power from PV arrays. Due to partial shading condition, the characteristics of a PV system considerably change and often exhibit several local maxima with one global maxima. Conventional Maximum Power Point Tracking techniques can easily be trapped at local maxima under partial shading. This significantly reduced the energy yield of the PV systems. In this paper, various MPPT algorithms for uniform insolation and partial shading conditions are reviewed with their merits and demerits. Also, some new algorithms are proposed which are shown to be more efficient than the existing ones.


title: A Fully-Integrated Switched-Capacitor Step-Down DC-DC Converter With Digital Capacitance Modulation in 45 nm CMOS

abstract: Implementing efficient and cost-effective power regulation schemes for battery-powered mixed-signal SoCs is a key focus in integrated circuit design. This paper presents a fully-integrated switched-capacitor DC-DC converter in 45 nm digital CMOS technology. The proposed implementation uses digital capacitance modulation instead of traditional PFM and PWM control methods to maintain regulation against load current changes. This technique preserves constant frequency switching while also scaling switching and bottom-plate losses with changes in load current. Therefore, high efficiency can be achieved across different load current levels while maintaining a predictable switching noise behavior. The converter occupies only 0.16 mm2, and operates from 1.8 V input. It delivers a programmable sub-1 V power supply with efficiency as high as 69% and load current between 100 μA and 8 mA. Measurement results confirm the theoretical basis of the proposed design.


title: Chapter 13: Mining Electronic Health Records in the Genomics Era

abstract: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.


title: A tangible AR desktop environment

abstract: Abstract We present an AR desktop environment which integrates the standard 2D computer desktop into an augmented 3D space. The underlying physical space is given by the standard office desk used in everyday work. Instead of sitting only in front of a computer screen, the user wears a video-through head mounted displays and interacts with the environment in both tangible and virtual ways. In this augmented environment, standard 2D application windows are attached to physical clipboards and can be freely positioned in the working space by simply moving the clipboard. Additionally, 3D content can be brought into and manipulated within the same space. To support interaction with 3D content we have experimented with a circular platform, on which a 3D model can be placed, and which the user can turn in a natural tangible way. By fusing familiar desktop utensils, two-dimensional computer desktop applications, and three-dimensional models, we provide a seamless transition from the traditional 2D computer desktop to a 3D augmented working environment. The paper describes the concept and implementation of the system and illustrates some interfaces and interaction techniques used in the context of CAD engineering.


Edmodo E-portfolios in EFL – A Case Study


title: Design a personalized e-learning system based on item response theory and artificial neural network approach

abstract: In web-based educational systems the structure of learning domain and content are usually presented in the static way, without taking into account the learners' goals, their experiences, their existing knowledge, their ability (known as insufficient flexibility), and without interactivity (means there is less opportunity for receiving instant responses or feedbacks from the instructor when learners need support). Therefore, considering personalization and interactivity will increase the quality of learning. In the other side, among numerous components of e-learning, assessment is an important part. Generally, the process of instruction completes with the assessment and it is used to evaluate learners' learning efficiency, skill and knowledge. But in web-based educational systems there is less attention on adaptive and personalized assessment. Having considered the importance of tests, this paper proposes a personalized multi-agent e-learning system based on item response theory (IRT) and artificial neural network (ANN) which presents adaptive tests (based on IRT) and personalized recommendations (based on ANN). These agents add adaptivity and interactivity to the learning environment and act as a human instructor which guides the learners in a friendly and personalized teaching environment.


title: Forensic Zero-Knowledge Event Reconstruction on Filesystem Metadata.

abstract: Criminal investigations today can hardly be imagined without the forensic analysis of digital devices, regardless of whether it is a desktop computer, a mobile phone, or a navigation system. This not only holds true for cases of cybercrime, but also for traditional delicts such as murder or blackmail, and also private corporate investigations rely on digital forensics. This leads to an increasing number of cases with an ever-growing amount of data, that exceeds the capacity of the forensic experts. To support investigators to work more efficiently, we introduce a novel approach to automatically reconstruct events that previously occurred on the examined system and to provide a quick overview to the investigator as a starting point for further investigation. In contrast to the few existing approaches, our solution does not rely on any previously profiled system behavior or knowledge about specific applications, log files, or file formats. We further present a prototype implementation of our so-called zero knowledge event reconstruction approach, that solely tries to make sense of characteristic structures in file system metadata such as fileand folder-names and timestamps.


title: Multi-armed bandit algorithms and empirical evaluation

abstract: The multi-armed bandit problem for a gambler is to decide which arm of a K-slot machine to pull to maximize his total reward in a series of trials. Many real-world learning and optimization problems can be modeled in this way. Several strategies or algorithms have been proposed as a solution to this problem in the last two decades, but, to our knowledge, there has been no common evaluation of these algorithms. ::: ::: This paper provides a preliminary empirical evaluation of several multi-armed bandit algorithms. It also describes and analyzes a new algorithm, Poker (Price Of Knowledge and Estimated Reward) whose performance compares favorably to that of other existing algorithms in several experiments. One remarkable outcome of our experiments is that the most naive approach, the e-greedy strategy, proves to be often hard to beat.


title: A region-based compilation technique for dynamic compilers

abstract: Method inlining and data flow analysis are two major optimization components for effective program transformations, but they often suffer from the existence of rarely or never executed code contained in the target method. One major problem lies in the assumption that the compilation unit is partitioned at method boundaries. This article describes the design and implementation of a region-based compilation technique in our dynamic optimization framework, in which the compiled regions are selected as code portions without rarely executed code. The key parts of this technique are the region selection, partial inlining, and region exit handling. For region selection, we employ both static heuristics and dynamic profiles to identify and eliminate rare sections of code. The region selection process and method inlining decisions are interwoven, so that method inlining exposes other targets for region selection, while the region selection in the inline target conserves the inlining budget, allowing more method inlining to be performed. The inlining process can be performed for parts of a method, not just for the entire body of the method. When the program attempts to exit from a region boundary, we trigger recompilation and then use on-stack replacement to continue the execution from the corresponding entry point in the recompiled code. We have implemented these techniques in our Java JIT compiler, and conducted a comprehensive evaluation. The experimental results show that our region-based compilation approach achieves approximately 4p performance improvement on average, while reducing the compilation overhead by 10p to 30p, in comparison to the traditional method-based compilation techniques.


title: An improved wide input voltage buck-boost + LLC cascaded converter

abstract: An improved buck-boost + LLC (IBB LLC) cascaded converter appropriate for wide input voltage situation is presented in this paper, which can be used as a constant voltage source or a constant current source charger for battery. The control strategy of dual frequency PWM (DFPWM) is introduced. For the LLC converter, ZVS for MOSFETs and ZCS for diode are achieved. As a result, the switching loss is reduced and the converter can operate at higher switching frequency with high efficiency. The operation principle and characteristics of the presented converter are verified on a 200V-450V input 14V/80A output experimental prototype.


title: Millimeter-wave EBG-based antenna pattern diversity

abstract: Millimeter-wave (MMW) electromagnetic bandgap (EBG)-based dielectric resonator (DR) antenna with beam steering capability is presented. Six EBG sectors of several cells are placed symmetrically around the DR antenna to achieve beam steering in the azimuth plane. Each sector is composed of twenty one circular-patch mushroom EBG unit cell. The vias in each sector are connected through thin metallic veins printed on a conductor-backed dielectric slab. The metallic veins are then connected to the ground plane through a switching diode. By switching the diode on and off, the EBG sector band-stop and band-pass properties are changed, yielding beam steering into that sector. Results show flexible and effective beam steering capability in the azimuth plane using minimal number of diodes.


title: Stochastic choice of basis functions in adaptive function approximation and the functional-link net

abstract: A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on a general approach to adaptive function approximation. The approach consists of formulating a limit-integral representation of the function to be approximated and subsequently evaluating that integral with the Monte-Carlo method. Two main results are: (1) the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets, and (2) the RVFL is an efficient universal approximator with the rate of approximation error convergence to zero of order O(C/ radicaln), where n is number of basis functions and with C independent of n. Similar results are also obtained for neural nets with hidden nodes implemented as products of univariate functions or radial basis functions. Some possible ways of enhancing the accuracy of multivariate function approximations are discussed.


title: Feasibility of a Humor Training to Promote Humor and Decrease Stress in a Subclinical Sample: A Single-Arm Pilot Study

abstract: The present study investigates feasibility of a humor training for a subclinical sample suffering from increased stress, depressiveness, or anxiety. Based on diagnostic interviews, 35 people were invited to participate in a seven-week humor training. Evaluation measures were filled in prior training, after training, and at a one-month follow-up including humor related outcomes (coping humor and cheerfulness) and mental health related outcomes (perceived stress, depressiveness, anxiety, and well-being). Outcomes were analyzed using repeated-measures ANOVAs. Within-group comparisons of intention-to-treat analysis showed main effects of time with large effect sizes on all outcomes. Post hoc tests showed medium to large effect sizes on all outcomes from pre to post and results remained stable until follow-up. Satisfaction with the training was high, attrition rate low (17.1%), and participants would highly recommend the training. Summarizing the results, the pilot study showed promising effects for people suffering from subclinical symptoms. All outcomes were positively influenced and showed stability over time. Humor trainings could be integrated more into mental health care as an innovative program to reduce stress whilst promoting also positive emotions. However as this study was a single-arm pilot study, further research (including also randomized controlled trials) is still needed to evaluate the effects more profound.


title: Xavier: Experience with a layered robot architecture

abstract: Office delivery robots have to perform many tasks such as picking up and delivering mail or faxes, returning library books, and getting coffee. They have to determine the order in which to visit locations, plan paths to those locations, follow paths reliably, and avoid static and dynamic obstacles in the process. Reliability and efficiency are key issues in the design of such autonomous robot systems. They must deal reliably with noisy sensors and actuators and with incomplete knowledge of the environment. They must also act efficiently, in real time, to deal with dynamic situations. To achieve these objectives, we have developed a robot architecture that is composed of four layers: obstacle avoidance, navigation, path planning, and task planning. The layers are independent, communicating processes that are always active, processing sensory data and status information to update their decisions and actions. A version of our robot architecture has been in nearly daily use in our building since December 1995. As of January 1997, the robot has traveled more than 110 kilometers (65 miles) in service of over 2500 navigation requests that were specified using our World Wide Web interface.


title: Leakproofing the Singularity Artificial Intelligence Confinement Problem

abstract: This paper attempts to formalize and to address the 'leakproofing' of the Singularity problem presented by David Chalmers. The paper begins with the definition of the Artificial Intelli- gence Confinement Problem. After analysis of existing solutions and their shortcomings, a protocol is proposed aimed at making a more secure confinement environment which might delay potential negative effect from the technological singularity while allowing humanity to benefit from the superintelligence.


title: NoiseOut: A Simple Way to Prune Neural Networks

abstract: Neural networks are usually over-parameterized with significant redundancy in the number of required neurons which results in unnecessary computation and memory usage at inference time. One common approach to address this issue is to prune these big networks by removing extra neurons and parameters while maintaining the accuracy. In this paper, we propose NoiseOut, a fully automated pruning algorithm based on the correlation between activations of neurons in the hidden layers. We prove that adding additional output neurons with entirely random targets results into a higher correlation between neurons which makes pruning by NoiseOut even more efficient. Finally, we test our method on various networks and datasets. These experiments exhibit high pruning rates while maintaining the accuracy of the original network.


title: Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns

abstract: Local binary patterns LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well.


title: POSTER: Towards Privacy-Preserving Biometric Identification in Cloud Computing

abstract: Wang et al. recently proposed a privacy-preserving biometric identification scheme. However, the security assumption of the scheme does not capture practical aspects of real world attacks. In this paper, we consider a practical attack model which results in the leakage of biometric data in Wang et al.'s scheme. We first show the feasibility of our attack model and demonstrate how an attacker is able to recover the biometric data. Then, we propose a new biometric identification scheme that is secure against the attack model.


title: Towards Universal Representation for Unseen Action Recognition

abstract: Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples. While previous methods focus on inner-dataset seen/unseen splits, this paper proposes a pipeline using a large-scale training source to achieve a Universal Representation (UR) that can generalise to a more realistic Cross-Dataset UAR (CDUAR) scenario. We first address UAR as a Generalised Multiple-Instance Learning (GMIL) problem and discover 'building-blocks' from the large-scale ActivityNet dataset using distribution kernels. Essential visual and semantic components are preserved in a shared space to achieve the UR that can efficiently generalise to new datasets. Predicted UR exemplars can be improved by a simple semantic adaptation, and then an unseen action can be directly recognised using UR during the test. Without further training, extensive experiments manifest significant improvements over the UCF101 and HMDB51 benchmarks.


title: Using Trusted Execution Environments in Two-factor Authentication: comparing approaches

abstract: Classic two-factor authentication has been around for a long time and has enjoyed success in certain markets (such as the corporate and the banking environ- ment). A reason for this success are the strong security properties, particularly where user interaction is concerned. These properties hinge on a security token being a physi- cally separate device. This paper investigates whether Trusted Execution Environments (TEE) can be used to achieve a comparable level of security without the need to have a separate device. To do this, we introduce a model that shows the security properties of user interaction in two-factor authentication. The model is used to examine two TEE technologies, Intel’s IPT and ARM TrustZone, revealing that, although it is possible to get close to classic two-factor authentication in terms of user interaction security, both technologies have distinct drawbacks. The model also clearly shows an open problem shared by many TEEs: how to prove to the user that they are dealing with a trusted application when trusted and untrusted applications share the same display.


title: I/O characteristics of MongoDB and trim-based optimization in flash SSDs

abstract: NoSQL solutions become emerging for large scaled, high performance, schema-flexible applications. WiredTiger is cost effective, non-locking, no-overwrite storage used as default storage engine in MongoDB. Understanding I/O characteristics of storage engine is important not only for choosing suitable solution with an application but also opening opportunities for researchers optimizing current working system, especially building more flash-awareness NoSQL DBMS. This paper explores background of MongoDB internals then analyze I/O characteristics of WiredTiger storage engine in detail. We also exploit space management mechanism in WiredTiger by using TRIM command.


title: Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques

abstract: The present paper aims in investigating the performance of state-of-the-art machine learning techniques in trading with the EUR/USD exchange rate at the ECB fixing. For this purpose, five supervised learning classification techniques (K-Nearest Neighbors algorithm, Naive Bayesian Classifier, Artificial Neural Networks, Support Vector Machines and Random Forests) were applied in the problem of the one day ahead movement prediction of the EUR/USD exchange rate with only autoregressive terms as inputs. For comparison reasons, the performance of all machine learning techniques was benchmarked by two traditional techniques (Naive Strategy and moving average convergence/divergence model). Trading strategies produced by the machine learning techniques of Support Vector Machines and Random Forests clearly outperformed all other strategies in terms of annualized return and sharp ratio. To the best of our knowledge, this is the first application of Random Forests in the problem of trading with the EUR/USD exchange rate providing extremely satisfactory results.


title: Combining KLD-sampling with Gmapping proposal for grid-based Monte Carlo localization of a moving robot

abstract: Particle filters using Gmapping proposal distribution has demonstrated their effectiveness in target tracking and robot self-localization. Due to the number of particles required in this approach, the computational demand is an issue associated with the Gmapping proposal distribution. The traditional approach is often ad hoc by setting a threshold for acceptance/rejection sampling to reduce the number of particles. However, the number of particles required in this approach is fixed and needs to be selected in advance which can be subjective and inefficient in representing a posterior distribution of various complexity. In parallel, the KLD-MCL algorithm has the capability to adaptively change the sample size of particles with an arbitrarily chosen proposal distribution. This paper combines the Gmapping proposal distribution with the KLD-MCL algorithm, resulting in an efficient particle filter which systematically adapts the number of particles. Simulation results demonstrate that the proposed approach has higher self-localization accuracy and requires a lower number of particles than the standard KLD-MCL algorithm.


title: Perceptually-based color difference for complex images

abstract: Faithful color reproduction of digital images requires a reliable measure to compare such images in order to evaluate the reproduction performance. The conventional methods attempt to apply the CIE Colorimetry based color difference equations, s uch as CIELAB, CMC, CIE94 and CIEDE2000, to complex images on a pixel-by-pixel basis, and calculates the overall color difference as the averaged difference of each pixel in the image. This method is simple and straightforward but often does not represent the color difference perceived by human visual system. This paper proposes a new algorithm for calculating the overall color difference between complex images. The results show that this new metric corresponds more closely to the color difference perceived by human visual system.


title: A Review of Machine Learning Algorithms for Text-Documents Classification

abstract: With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and know- ledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing tech- niques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing litera- ture.


title: Kanzi: A Distributed, In-memory Key-Value Store

abstract: Traditional database systems either sacrifice availability or partitionability at the cost of offering strict consistency guarantee of data. However, the significant growth of Web-scale applications and the wider array of emerging workloads demand revisiting the need for full transactional consistency. One new dominant class of workload is the ability to efficiently support single statement transaction consisting of either Get or Put operation; thus, simplifying the consistency model. These simple workloads have given rise to decade-long efforts for building efficient key-value stores that often rely on disk-resident and log-structured storage model that is distributed across many machines. To further expand the scope of key-value stores, in this paper, we introduce Kanzi, a distributed, in-memory key-value stored over shared-memory architecture enabled by remote direct memory access (RDMA) technology. The simple data and transaction model of our proposed Kanzi additionally may serve as a generic (embedded) caching layer to speed up any disk-resident data-intensive workloads.


Directly Revocable and Verifiable Key-Policy Attribute-based Encryption for Large Universe


title: Decoupled control of posture and trajectory of the hybrid wheel-legged robot hylos

abstract: This paper addresses the control of a hybrid wheel-legged system evolving on rough terrain. First, the posture and trajectory parameters are introduced. Then, a decoupled posture and trajectory control algorithm based on the velocity model of the robot is proposed. Last, the performance and feasibility of the control algorithm are evaluated through simulations and experiments with the Hylos robot.


title: Real-time 3D head pose and facial landmark estimation from depth images using triangular surface patch features

abstract: We present a real-time system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. We introduce a novel triangular surface patch (TSP) descriptor, which encodes the shape of the 3D surface of the face within a triangular area. The proposed descriptor is viewpoint invariant, and it is robust to noise and to variations in the data resolution. Using a fast nearest neighbor lookup, TSP descriptors from an input depth map are matched to the most similar ones that were computed from synthetic head models in a training phase. The matched triangular surface patches in the training set are used to compute estimates of the 3D head pose and facial landmark positions in the input depth map. By sampling many TSP descriptors, many votes for pose and landmark positions are generated which together yield robust final estimates. We evaluate our approach on the publicly available Biwi Kinect Head Pose Database to compare it against state-of-the-art methods. Our results show a significant improvement in the accuracy of both pose and landmark location estimates while maintaining real-time speed.


title: DIXDC1 Phosphorylation and Control of Dendritic Morphology Are Impaired by Rare Genetic Variants.

abstract: The development of neural connectivity is essential for brain function, and disruption of this process is associated with autism spectrum disorders (ASDs). DIX domain containing 1 (DIXDC1) has previously been implicated in neurodevelopmental disorders, but its role in postnatal brain function remains unknown. Using a knockout mouse model, we determined that DIXDC1 is a regulator of excitatory neuron dendrite development and synapse function in the cortex. We discovered that MARK1, previously linked to ASDs, phosphorylates DIXDC1 to regulate dendrite and spine development through modulation of the cytoskeletal network in an isoform-specific manner. Finally, rare missense variants in DIXDC1 were identified in ASD patient cohorts via genetic sequencing. Interestingly, the variants inhibit DIXDC1 isoform 1 phosphorylation, causing impairment to dendrite and spine growth. These data reveal that DIXDC1 is a regulator of cortical dendrite and synaptic development and provide mechanistic insight into morphological defects associated with neurodevelopmental disorders.


title: Measuring thin-client performance using slow-motion benchmarking

abstract: Modern thin-client systems are designed to provide the same graphical interfaces and applications available on traditional desktop computers while centralizing administration and allowing more efficient use of computing resources. Despite the rapidly increasing popularity of these client-server systems, there are few reliable analyses of their performance. Industry standard benchmark techniques commonly used for measuring desktop system performance are ill-suited for measuring the performance of thin-client systems because these benchmarks only measure application performance on the server, not the actual user-perceived performance on the client. To address this problem, we have developed slow-motion benchmarking, a new measurement technique for evaluating thin-client systems. In slow-motion benchmarking, performance is measured by capturing network packet traces between a thin client and its respective server during the execution of a slow-motion version of a conventional benchmark application. These results can then be used either independently or in conjunction with conventional benchmark results to yield an accurate and objective measure of the performance of thin-client systems. We have demonstrated the effectiveness of slow-motion benchmarking by using this technique to measure the performance of several popular thin-client systems in various network environments on Web and multimedia workloads. Our results show that slow-motion benchmarking solves the problems with using conventional benchmarks on thin-client systems and is an accurate tool for analyzing the performance of these systems.


title: MapReduce Performance Evaluation on a Private HPC Cloud

abstract: The convergence of accessible cloud computing resources and big data trends have introduced unprecedented opportunities for scientific computing and discovery. However, HPC cloud users face many challenges when selecting valid HPC configurations. In this paper, we report a set of performance evaluations of data intensive benchmarks on a private HPC cloud to help with the selection of such configurations. More precisely, we study the effect of virtual machines core-count on the performance of 3 benchmarks widely used by the MapReduce community. We notice that depending on the computation to communication ratios of the studied applications, using higher core-counts virtual machines do not always lead to higher performance for data-intensive applications.


title: Synonym Extraction of Medical Terms from Clinical Text Using Combinations of Word Space Models

abstract: In information extraction, it is useful to know if two signifiers have the same or very similar semantic content. Maintaining such information in a controlled vocabulary is, however, costly. Here it is demonstrated how synonyms of medical terms can be extracted automatically from a large corpus of clinical text using distributional semantics. By combining Random Indexing and Random Permutation, different lexical semantic aspects are captured, effectively increasing our ability to identify synonymic relations between terms. 44% of 340 synonym pairs from MeSH are successfully extracted in a list of ten suggestions. The models can also be used to map abbreviations to their full-length forms; simple pattern-based filtering of the suggestions yields substantial improvements.