title: Design and analysis of slotted antenna array for 5G application

abstract: A compact 1 × 4 slotted antenna array for 5G application is designed and analyzed. The designed antenna consist of a power divider network and a rectangular shaped slot etched at bottom in ground plane. The dimension of this antenna is 29.9 × 28.7 × 0.13 mm3. The substrate is used as Rogers RO3003 having dielectric constant of 3 and loss tangent (tan δ) of 0.013. Performance analysis of the antenna array is carried out by changing the length of the slots. This shows the improvement in values of return loss, bandwidth and gain. The maximum impedance bandwidth is obtained as 1270 MHz at slot length of 4.01 mm. The directivity and gain of the antenna is improved with the changed length of slots at a constant width 0.45 mm. This antenna array can be used for 5G application.


title: Compact Double-Sided Decoupled Coils-Based WPT Systems for High-Power Applications: Analysis, Design, and Experimental Verification

abstract: Wireless power transfer (WPT) system is a practical and promising way for charging electric vehicles due to its security, convenience, and reliability. The requirement for high-power wireless charging is on the rise, but implementing such a WPT system has been a challenge because of the constraints of the power semiconductors and the installation space limitation at the bottom of the vehicle. In this paper, bipolar coils and unipolar coils are integrated into the transmitting side and the receiving side to make the magnetic coupler more compact while delivering high power. The same-side coils are naturally decoupled; therefore, there is no magnetic coupling between the same-side coils. The circuit model of the proposed WPT system using double-sided LCC compensations is presented. Finite-element analysis tool ANSYS MAXWELL is adopted to simulate and design the magnetic coupler. Finally, an experimental setup is constructed to evaluate the proposed WPT system. The proposed WPT system achieved the dc–dc efficiency at 94.07% while delivering 4.73 kW to the load with a vertical air gap of 150 mm.


title: Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

abstract: // Polina Mamoshina 1,2 , Lucy Ojomoko 1 , Yury Yanovich 3 , Alex Ostrovski 3 , Alex Botezatu 3 , Pavel Prikhodko 3 , Eugene Izumchenko 4 , Alexander Aliper 1 , Konstantin Romantsov 1 , Alexander Zhebrak 1 , Iraneus Obioma Ogu 5 and Alex Zhavoronkov 1,6 1 Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA 2 Department of Computer Science, University of Oxford, Oxford, United Kingdom 3 The Bitfury Group, Amsterdam, Netherlands 4 Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA 5 Africa Blockchain Artificial Intelligence for Healthcare Initiative, Insilico Medicine, Inc, Abuja, Nigeria 6 The Biogerontology Research Foundation, London, United Kingdom Correspondence to: Alex Zhavoronkov, email: // Keywords : artificial intelligence; deep learning; data management; blockchain; digital health; health data marketplace Received : October 19, 2017 Accepted : November 02, 2017 Published : November 09, 2017 Abstract The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.


title: Colourful Language: Measuring Word-Colour Associations

abstract: Since many real-world concepts are associated with colour, for example danger with red, linguistic information is often complimented with the use of appropriate colours in information visualization and product marketing. Yet, there is no comprehensive resource that captures concept--colour associations. We present a method to create a large word--colour association lexicon by crowdsourcing. We focus especially on abstract concepts and emotions to show that even though they cannot be physically visualized, they too tend to have strong colour associations. Finally, we show how word--colour associations manifest themselves in language, and quantify usefulness of co-occurrence and polarity cues in automatically detecting colour associations.


title: Learning Deep Spatio-Temporal Dependence for Semantic Video Segmentation

abstract: Semantically labeling every pixel in a video is a very challenging task as video is an information-intensive media with complex spatio-temporal dependence. We present in this paper a novel deep convolutional network architecture, called deep spatio-temporal fully convolutional networks (DST-FCN), which leverages both spatial and temporal dependencies among pixels and voxels by training them in an end-to-end manner. Specifically, we introduce a two-stream network by learning the deep spatio-temporal dependence, in which a 2D FCN followed by the convolutional long short-term memory (ConvLSTM) is employed on the pixel level and a 3-D FCN is exploited on the voxel level. Our model differs from conventional FCN in that it not only extends FCN by adding ConvLSTM on the pixel level for exploring long-term dependence, but also proposes 3-D FCN to enable voxel level prediction. On two benchmarks of A2D and CamVid, our DST-FCN achieves superior results to state-of-the-art techniques. More remarkably, we obtain to-date the best reported results: 45.0% per-label accuracy on A2D and 68.8% mean IoU on CamVid.


title: Automatic detection of ECG wave boundaries using empirical mode decomposition

abstract: Automatic detection of the boundaries of ECG characteristic waves with a reasonable accuracy has been a difficult task. This paper presents an algorithm based on empirical mode decomposition (EMD) for automatically locating the waveform boundaries (the onsets and offsets of P, QRS, and T waves) in generalized single lead ECG signals. First, the R peak of each beat is detected from the first three intrinsic mode functions (IMFs) of the EMD analysis of the filtered ECG signal. Next, the onset and offset of each QRS complex are located. The P wave and T wave, relative to each QRS complex, are then identified using a set of higher order IMFs. Our algorithm is tested using the QT database (reference annotated database) and the MIT-BIH arrhythmia database. Examples of detection of the fiducial points and a comparison with the threshold-based detector are presented for the assessment of performance of the algorithm.


title: VIPLE: Visual IoT/Robotics Programming Language Environment for Computer Science Education

abstract: Microsoft released its Robotics Developer Studio (MSRDS) and Visual Programming Language (VPL) in 2006. Microsoft VPL is service-oriented, uses workflow-based visual programming, and has strong support for parallel computing. It is a milestone and flagship in software engineering and in computer science education. Many universities and high schools have adopted VPL as a tool for teaching computing and engineering concepts and for programming robots. Unfortunately, as part of Microsoft's restructuring plan, the robotics division of Microsoft Research was suspended on September 22, 2014, leaving the Microsoft VPL community without updates and support. Arizona State University (ASU) is among the schools that adopted VPL since its first release in 2006. We started to find a solution to our VPL-based curriculum in 2014. This paper presents our research and development of a new visual programming language and its development environment: ASU VIPLE (Visual IoT/Robotics Programming Language Environment). ASU VIPLE extends the discontinued Microsoft VPL to sustain our curriculum and to help the community with their VPL projects. ASU VIPLE supports LEGO EV3 and all IoT devices based on an open architecture. ASU VIPLE integrates engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming seamlessly into the curriculum. It has been pilot tested at Arizona State University in summer 2015 and in spring 2016, as well as in several other universities. ASU VIPLE software and documents can be freely downloaded at: http://venus.eas.asu.edu/WSRepository/VIPLE/.


title: Connecting a Human Limb to an Exoskeleton

abstract: When developing robotic exoskeletons, the design of physical connections between the device and the human limb to which it is connected is a crucial problem. Indeed, using an embedment at each connection point leads to uncontrollable forces at the interaction port, induced by hyperstaticity. In practice, these forces may be large because in general the human limb kinematics and the exoskeleton kinematics differ. To cope with hyperstaticity, the literature suggests the addition of passive mechanisms inside the mechanism loops. However, empirical solutions that are proposed so far lack proper analysis and generality. In this paper, we study the general problem of connecting two similar kinematic chains through multiple passive mechanisms. We derive a constructive method that allows the determination of all the possible distributions of freed degrees of freedom across different fixation mechanisms. It also provides formal proofs of global isostaticity. Practical usefulness is illustrated through two examples with conclusive experimental results: a preliminary study made on a manikin with an arm exoskeleton controlling the movement (passive mode) and a larger campaign on ten healthy subjects performing pointing tasks with a transparent robot (active mode).


Graph grammars and constraint solving for software architecture styles


title: Bystander Interventions for Sexual Assault and Dating Violence on College Campuses: Are We Putting Bystanders in Harm's Way?.

abstract: ABSTRACTObjective: The present study investigated the well-being of bystanders who witness and intervene in sexual assault and dating violence situations on campus. Participants: Participants were 321 young men and women from a large university in the southeastern United States. Methods: Participants completed a survey at the end of the Spring semester of 2015 about risky situations they had witnessed, with follow-up questions about their responses to the situations (eg, whether they intervened or not) and feelings about their responses. Participants also completed standardized measures of posttraumatic stress disorder and depression. Results: Over 90% of the participants reported witnessing at least 1 of the risky events presented to them, and approximately 50% reported intervening in events. Intervening was associated with positive feelings, but traumatic stress symptoms were related to witnessing events and intervening. Conclusions: Results have direct implications for developing appropriate training p...


Binary Constrained Deep Hashing Network for Image Retrieval without Human Intervention


title: Artefacts in CBCT: a review.

abstract: Artefacts are common in today's cone beam CT (CBCT). They are induced by discrepancies between the mathematical modelling and the actual physical imaging process. Since artefacts may interfere with the diagnostic process performed on CBCT data sets, every user should be aware of their presence. This article aims to discuss the most prominent artefacts identified in the scientific literature and review the existing knowledge on these artefacts. We also briefly review the basic three-dimensional (3D) reconstruction concept applied by today's CBCT scanners, as all artefacts are more or less directly related to it.


title: CoAP Implementation Guidance

abstract: The Constrained Application Protocol (CoAP) is designed for resource- ::: constrained nodes and networks such as sensor nodes in a low-power ::: lossy network (LLN). Yet to implement this Internet protocol on Class ::: 1 devices (as per RFC 7228, ~ 10 KiB of RAM and ~ 100 KiB of ROM) also ::: lightweight implementation techniques are necessary. This document ::: provides lessons learned from implementing CoAP for tiny, battery- ::: operated networked embedded systems. In particular, it provides ::: guidance on correct implementation of the CoAP specification RFC 7252, ::: memory optimizations, and customized protocol parameters.


title: DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking

abstract: Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking, because they require very long training time and a large number of training samples. In this paper, we present an efficient and very robust tracking algorithm using a single convolutional neural network (CNN) for learning effective feature representations of the target object in a purely online manner. Our contributions are multifold. First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation. Second, we enhance the ordinary stochastic gradient descent approach in CNN training with a robust sample selection mechanism. The sampling mechanism randomly generates positive and negative samples from different temporal distributions, which are generated by taking the temporal relations and label noise into account. Finally, a lazy yet effective updating scheme is designed for CNN training. Equipped with this novel updating algorithm, the CNN model is robust to some long-existing difficulties in visual tracking, such as occlusion or incorrect detections, without loss of the effective adaption for significant appearance changes. In the experiment, our CNN tracker outperforms all compared state-of-the-art methods on two recently proposed benchmarks, which in total involve over 60 video sequences. The remarkable performance improvement over the existing trackers illustrates the superiority of the feature representations, which are learned purely online via the proposed deep learning framework.


title: Harnessing human computation cycles for the fpga placement problem

abstract: Harnessing human computation is an approach to find problem solutions. In this paper, we investigate harnessing this human computation for a Field Programmable Gate Array (FPGA) placement problem. We create a game called Plummings. In this game, a player attempts to reduce the critical path of a digital design mapped to an FPGA by swapping clusters on the array, but the problem details are abstracted away, and instead, the game simply presents a challenging problem where paths must be minimised to save the game characters the Plummings. Once players have played a level, the placement is can be evaluated in VPR. Our results show that 4 human players over a set of 5 benchmarks can create placement solutions with comparable critical paths compared to VPR’s solutions. This is not always the case, and we suggest some reasons and further approaches to improving our results.


title: Hidden semi-Markov models

abstract: As an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model (HSMM) allows the underlying stochastic process to be a semi-Markov chain. Each state has variable duration and a number of observations being produced while in the state. This makes it suitable for use in a wider range of applications. Its forward-backward algorithms can be used to estimate/update the model parameters, determine the predicted, filtered and smoothed probabilities, evaluate goodness of an observation sequence fitting to the model, and find the best state sequence of the underlying stochastic process. Since the HSMM was initially introduced in 1980 for machine recognition of speech, it has been applied in thirty scientific and engineering areas, such as speech recognition/synthesis, human activity recognition/prediction, handwriting recognition, functional MRI brain mapping, and network anomaly detection. There are about three hundred papers published in the literature. An overview of HSMMs is presented in this paper, including modelling, inference, estimation, implementation and applications. It first provides a unified description of various HSMMs and discusses the general issues behind them. The boundary conditions of HSMM are extended. Then the conventional models, including the explicit duration, variable transition, and residential time of HSMM, are discussed. Various duration distributions and observation models are presented. Finally, the paper draws an outline of the applications.


title: LiDAR remote sensing of forest structure

abstract: Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.


title: Intramuscular triamcinolone acetonide in chronic severe asthma.

abstract: Seventeen subjects with chronic severe asthma completed a 48 week prospective, double blind study with crossover of treatment at 24 weeks, in which triamcinolone acetonide 80 mg intramuscularly every four weeks was compared with oral prednisolone 10 mg daily. Spirometry, twice daily measurements of peak expiratory flow rate, and self assessment of asthma symptom scores showed significant improvement during triamcinolone treatment; less extra prednisolone was required and there was significant weight loss. Two patients withdrew, one because of dissatisfaction with prednisolone and one because of side effects while taking triamcinolone. Three were withdrawn, one with proximal muscle weakness and two because of intercurrent illness. Adrenal suppression, bruising, and hirsuitism were worse with triamcinolone, other side effects being comparable. On completion of the study 16 of the 17 patients opted to continue taking triamcinolone acetonide. This treatment is an important addition to the therapeutic options available for chronic severe asthma.


title: Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation

abstract: In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.


title: Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text

abstract: A Chinese handwriting database named HIT-MW is presented to facilitate the offline Chinese handwritten text recognition. Both the writers and the texts for handcopying are carefully sampled with a systematic scheme. To collect naturally written handwriting, forms are distributed by postal mail or middleman instead of face to face. The current version of HIT-MW includes 853 forms and 186,444 characters that are produced under an unconstrained condition without preprinted character boxes. The statistics show that the database has an excellent representation of the real handwriting. Many new applications concerning real handwriting recognition can be supported by the database.


title: Selective Data Replication for Online Social Networks with Distributed Datacenters

abstract: Though the new OSN model with many worldwide distributed small datacenters helps reduce service latency, it brings a problem of higher inter-datacenter communication load. In Facebook, each datacenter has a full copy of all data and the master datacenter updates all other datacenters, which obviously generates tremendous load in this new model. Distributed data storage that only stores a user's data to his/her geographically-closest datacenters mitigates the problem. However, frequent interactions between far-away users lead to frequent inter-datacenter communication and hence long service latency. In this paper, we aim to reduce inter-datacenter communications while still achieve low service latency. We first verify the benefits of the new model and present OSN typical properties that lay the basis of our design. We then propose Selective Data replication mechanism in Distributed Datacenters (SD3). In SD3, a datacenter jointly considers update rate and visit rate to select user data for replication, and further atomizes a user's different types of data (e.g., status update, friend post) for replication, making sure that a replica always reduces inter-datacenter communication. The results of trace-driven experiments on the real-world PlanetLab testbed demonstrate the higher efficiency and effectiveness of SD3 in comparison to other replication methods.


title: Learning Rare Word Representations using Semantic Bridging

abstract: We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge. We also perform comparative analysis of the used algorithms in order to identify the best combination for the proposed system. We then apply this to the task of enhancing the coverage of an existing word embedding's vocabulary with rare and unseen words. We show that our technique can provide considerable extra coverage (over 99%), leading to consistent performance gain (around 10% absolute gain is achieved with w2v-gn-500K cf.\S 3.3) on the Rare Word Similarity dataset.


title: A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

abstract: Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.


title: Multi-level Environmental Governance: a concept under stress?

abstract: The past decade has witnessed a change in the world order of environmental policy making. The strongholds of national environmental policy competence gave room for international regimes beginning in the 1980s, a development that reached a peak during the Rio Earth Summit in 1992. Since then the direction of governance patterns has partly been reversed. However, the trend is not back towards nation states, but towards sub-national units that are reforming environmental governance patterns directly with supra-national units, such as the EU, with nation states, with interand non-governmental organisations as well as with other sub-national governments. Additionally, increased networking across public and private actors and shifting responsibilities from the public to the private sector has emerged, leading to new forms of environmental governance regardless of formal hierarchies. This development is especially evident in the Baltic area, including the Nordic countries. The papers in this special issue of Local Environment aim at analysing this development. This editorial will review some of the literature in the field of multi-level governance, with a special focus on that which has earlier appeared in Local Environment. There will also be a brief comment on the way those issues were discussed in a workshop on Multi-level Environmental Governance at the 6th Nordic Conference on Environmental Social Sciences in June 2003 from which the papers in this issue originate.


title: A comparative study of Artificial Bee Colony algorithm

abstract: Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.


title: An efficient search algorithm to find the elementary circuits of a graph

abstract: A theoretically most efficient search algorithm is presented which uses an exhaustive search to find all of the elementary circuits of a graph. The algorithm can be easily modified to find all of the elementary circuits with a particular attribute such as length. A rigorous proof of the algorithm is given as well as an example of its application. Empirical bounds are presented relating the speed of lhe algorithm to the number of vertices and the number of arcs. The speed is also related to the number of circuits inthe graph to give a relation between speed and complexity. Extensions to undirected and s-graphs are discussed.


title: More than a Million Ways to Be Pushed: A High-Fidelity Experimental Data Set of Planar Pushing

abstract: Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects move. ::: In reality, however, robots often rely on approximations which yield models that are computable, but also restricted and inaccurate. Just how close are those models? How reasonable are the assumptions they are based on? To help answer these questions, and to get a better experimental understanding of pushing, we present a comprehensive and high-fidelity dataset of planar pushing experiments. The dataset contains timestamped poses of a circular pusher and a pushed object, as well as forces at the interaction.We vary the push interaction in 6 dimensions: surface material, shape of the pushed object, contact position, pushing direction, pushing speed, and pushing acceleration. An industrial robot automates the data capturing along precisely controlled position-velocity-acceleration trajectories of the pusher, which give dense samples of positions and forces of uniform quality. ::: We finish the paper by characterizing the variability of friction, and evaluating the most common assumptions and simplifications made by models of frictional pushing in robotics.


title: Carbon copy deaths: Carbon monoxide gas chamber

abstract: Abstract The news media can exert a powerful influence over suicidal behaviour. It has been observed that like-minded individuals are able to preplan a group suicide method using modern communication technology in the form of websites and online chatrooms and mobile phone texting. A case of carbon monoxide (CO) poisoning is presented to illustrate the recent phenomenon of cyber suicides by suffocation from a burning barbecue (charcoal burner) in ‘gas chamber’ conversions. Although barbecues (BBQ) are very popular in Britain and widely available, there have been relatively few reported cases of copycat deaths from CO gas suffocation.


title: Grid-Connected Boost-Half-Bridge Photovoltaic Microinverter System Using Repetitive Current Control and Maximum Power Point Tracking

abstract: This paper presents a novel grid-connected boost-half-bridge photovoltaic (PV) microinverter system and its control implementations. In order to achieve low cost, easy control, high efficiency, and high reliability, a boost-half-bridge dc-dc converter using minimal devices is introduced to interface the low-voltage PV module. A full-bridge pulsewidth-modulated inverter is cascaded and injects synchronized sinusoidal current to the grid. Moreover, a plug-in repetitive current controller based on a fourth-order linear-phase IIR filter is proposed to regulate the grid current. High power factor and very low total harmonic distortions are guaranteed under both heavy load and light load conditions. Dynamic stiffness is achieved when load or solar irradiance is changing rapidly. In addition, the dynamic behavior of the boost-half-bridge dc-dc converter is analyzed; a customized maximum power point tracking (MPPT) method, which generates a ramp-changed PV voltage reference is developed accordingly. Variable step size is adopted such that fast tracking speed and high MPPT efficiency are both obtained. A 210 W prototype was fabricated and tested. Simulation and experimental results are provided to verify the validity and performance of the circuit operations, current control, and MPPT algorithm.


title: Blending SQL and NewSQL Approaches: Reference Architectures for Enterprise Big Data Challenges

abstract: As it becomes ever more pervasively engaged in data driven commerce, a modern enterprise becomes increasingly dependent upon reliable and high speed transaction services. At the same time it aspires to capitalize upon large inflows of information to draw timely business insights and improve business results. These two imperatives are frequently in conflict because of the widely divergent strategies that must be pursued: the need to bolster on-line transactional processing generally drives a business towards a small cluster of high-end servers running a mature, ACID compliant, SQL relational database, while high throughput analytics on massive and growing volumes of data favor the selection of very large clusters running non-traditional (NoSQL/NewSQL) databases that employ softer consistency protocols for performance and availability. This paper describes an approach in which the two imperatives are addressed by blending the two types (scale-up and scale-out) of data processing. It breaks down data growth that enterprises experience into three classes-Chronological, Horizontal, and Vertical, and picks out different approaches for blending SQL and NewSQL platforms for each class. To simplify application logic that must comprehend both types of data platforms, the paper describes two new capabilities: (a) a data integrator to quickly sift out updates that happen in an RDBMS and funnel them into a NewSQL database, and (b) extensions to the Hibernate-OGM framework that reduce the programming sophistication required for integrating HBase and Hive back ends with application logic designed for relational front ends. Finally the paper details several instances in which these approaches have been applied in real-world, at a number of software vendors with whom the authors have collaborated on design, implementation and deployment of blended solutions.


title: Living a discrete life in a continuous world: Reference in cross-modal entity tracking

abstract: Reference is the crucial property of language that allows us to connect linguistic expressions to the world. Modeling it requires handling both continuous and discrete aspects of meaning. Data-driven models excel at the former, but struggle with the latter, and the reverse is true for symbolic models. ::: We propose a fully data-driven, end-to-end trainable model that, while operating on continuous multimodal representations, learns to organize them into a discrete-like entity library. We also introduce a referential task to test it, cross-modal tracking. Our model beats standard neural network architectures, but is outperformed by some parametrizations of Memory Networks, another model with external memory.


title: A Survey of Phishing Email Filtering Techniques

abstract: Phishing email is one of the major problems of today's Internet, resulting in financial losses for organizations and annoying individual users. Numerous approaches have been developed to filter phishing emails, yet the problem still lacks a complete solution. In this paper, we present a survey of the state of the art research on such attacks. This is the first comprehensive survey to discuss methods of protection against phishing email attacks in detail. We present an overview of the various techniques presently used to detect phishing email, at the different stages of attack, mostly focusing on machine-learning techniques. A comparative study and evaluation of these filtering methods is carried out. This provides an understanding of the problem, its current solution space, and the future research directions anticipated.


title: Towards aesthetics: a photo quality assessment and photo selection system

abstract: Automatic photo quality assessment and selection systems are helpful for managing the large mount of consumer photos. In this paper, we present such a system based on evaluating the aesthetic quality of consumer photos. The proposed system focuses on photos with faces, which constitute an important part of consumer photo albums. The system has three contributions: 1) We propose an aesthetics-based photo assessment algorithm, by considering different aesthetics-related factors, including the technical characteristics of the photo and the specific features related to faces; 2) Based on the aesthetic measurement, we propose a cropping-based photo editing algorithm, which differs from prior works by eliminating unimportant faces before optimizing photo composition; 3) We also incorporate the aesthetic evaluation with other metrics to select quintessential photos for a large collection of photos. The entire system is delivered by a web interface, which allows users to submit images or albums, and returns promising results for photo evaluation, editing recommendation, and photo selection.


Ghostshell: Secure Biometric Authentication using Integrity-based Homomorphic Evaluations.


title: Bidirectional LSTM Networks for Context-Sensitive Keyword Detection in a Cognitive Virtual Agent Framework

abstract: Robustly detecting keywords in human speech is an important precondition for cognitive systems, which aim at intelligently interacting with users. Conventional techniques for keyword spotting usually show good performance when evaluated on well articulated read speech. However, modeling natural, spontaneous, and emotionally colored speech is challenging for today’s speech recognition systems and thus requires novel approaches with enhanced robustness. In this article, we propose a new architecture for vocabulary independent keyword detection as needed for cognitive virtual agents such as the SEMAINE system. Our word spotting model is composed of a Dynamic Bayesian Network (DBN) and a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net. The BLSTM network uses a self-learned amount of contextual information to provide a discrete phoneme prediction feature for the DBN, which is able to distinguish between keywords and arbitrary speech. We evaluate our Tandem BLSTM-DBN technique on both read speech and spontaneous emotional speech and show that our method significantly outperforms conventional Hidden Markov Model-based approaches for both application scenarios.


title: Relationship between Histological and Clinical Course of Psoriasis: A Pilot Investigation by Reflectance Confocal Microscopy during Goeckerman Treatment

abstract: Alterations of the skin microvasculature are known to play an important role in the development and maintenance of psoriatic skin lesions. In this study, we investigated lesional skin in 11 psoriatic patients during a modified Goeckerman treatment using reflectance confocal microscopy (RCM) to study the relationship between clinical clearance and histological normalization of psoriatic skin and the significance of histological abnormalities on the course of disease. The treatment regimen resulted in a significant reduction of the Psoriasis Area and Severity Index (PASI) as well as capillary and papillary diameters (p < 0.0001). The capillary and papillary diameters were still enlarged when compared to those in normal skin (p < 0.001). Capillary and papillary diameters correlated with each other prior to and after treatment (correlation coefficient = 0.63 and 0.64, p = 0.01 and 0.002, respectively) but not with the PASI. Capillary and papillary diameters after treatment and percentage reduction of the PASI during treatment seemed to be better predictors for the clinical course of relapse than the PASI after treatment. These findings make the subclinical changes of psoriatic skin vessels and dermal papillae a legitimate target for treatment. Further investigations of a large group of patients are needed to evaluate the potential of RCM findings as successor of the PASI in the monitoring of psoriasis.


title: Understanding factors affecting perceived sociability of social software

abstract: Sociability is considered to be important to the success of social software. The goal of the current study is to identify factors that affect the users' perception of the sociability of social software and to examine the impact of sociability on the users' attitude and behavior intentions. In a pilot study, 35 web users were interviewed to gain understanding of how they use social software to supplement their social life and to explore the possible factors that influence the users' utilization of social software. In the first study, a questionnaire was developed, and 163 valid responses were collected. From the factor analysis results, seven important factors for social software design emerged, which accounts for 63.3% of the total variance. In the second study, 246 participants were asked to evaluate one of ten popular social applications with respect to the seven factors, their perceived sociability, and their attitudes and intention regarding the use of the applications. Results show that sociability is influenced by social climate, benefits and purposes, people, interaction richness, self-presentation, and support for formal interaction. System competency is not a sociability factor, but it significantly influences the user's experience. Sociability and system competency, when combined, can predict 43% of users' attitude towards social software and 51% of their intentions to use social software.


title: A sneak into the Devil's Colony - Fake Profiles in Online Social Networks

abstract: Online Social Networks (OSNs) play an important role for internet users to carry out their daily activities like content sharing, news reading, posting messages, product reviews and discussing events etc. At the same time, various kinds of spammers are also equally attracted towards these OSNs. These cyber criminals including sexual predators, online fraudsters, advertising campaigners, catfishes, and social bots etc. exploit the network of trust by various means especially by creating fake profiles to spread their content and carry out scams. All these malicious identities are very harmful for both the users as well as the service providers. From the OSN service provider point of view, fake profiles affect the overall reputation of the network in addition to the loss of bandwidth. To spot out these malicious users, huge manpower effort and more sophisticated automated methods are needed. In this paper, various types of OSN threat generators like compromised profiles, cloned profiles and online bots (spam bots, social bots, like bots and influential bots) have been classified. An attempt is made to present several categories of features that have been used to train classifiers in order to identify a fake profile. Different data crawling approaches along with some existing data sources for fake profile detection have been identified. A refresher on existing cyber laws to curb social media based cyber crimes with their limitations is also presented.


title: Dual Phase-Shifted Modulation Strategy for the Three-Level Dual Active Bridge DC–DC Converter

abstract: In high-voltage dc–dc applications, the switches in the conventional two-level dual active bridge (DAB) dc–dc converter have to bear the whole port voltage, so high voltage switches should be selected. The voltage stress of the switches in the three-level DAB converter can be reduced to half of the port voltage, so it is suitable for high-voltage applications. However, part of the switches still has to bear the entire port voltage under some operation conditions with the single phase-shifted (SPS) modulation strategy. A dual phase-shifted (DPS) modulation strategy is proposed for the converter, compared with SPS modulation strategy, it expands regulating range of transmission power and enhances control flexibility. Moreover, a control algorithm can be developed to ensure that the voltage stress of each switch is half of the port voltage. The detailed operation principle of the three-level DAB converter with DPS control is analyzed in the paper. The characteristics of each operation region and the critical conditions between different operating regions are derived. Finally, the performance of the three-level DAB converter and DPS modulation strategy are verified by the simulation and experimental results of a 125 kW prototype.


title: C-Ku band ultra broadband GaN MMIC amplifier with 20W output power

abstract: This paper reports on a Gallium Nitride High Electron Mobility Transistor (GaN HEMT) Monolithic Microwave Integrated Circuit (MMIC) high power amplifier (HPA), which features high power and high gain over C-Ku band 115 % relative bandwidth. A C-Ku band (6 ∼ 18 GHz) GaN HEMT MMIC amplifier was manufactured and measured. The circuit dimension is 4.8 mm by 4 mm. The fabricated MMIC HPA derived an averaged output power of 20 W with averaged power gain of 9.6 dB over C-Ku band. The output power is state-of-the-art output power for GaN HEMT MMIC amplifiers with more than 100 % relative bandwidth and up to Ku band operation frequency.


title: Comprehensive traffic management system: Real-time traffic data analysis using RFID

abstract: Traffic related problems include not just traffic congestion due to increase in vehicular density, but also difficulty for passage of emergency vehicles, violation of rules such as red signal jumps, vehicle breakdowns and accidents causing blockage of roads and loss of lives. Previously proposed systems for smart traffic management focus on vehicular density based traffic signal control and provision for emergency vehicle passage. The capability of such systems are limited to one or two focus areas and most systems are not cost effective for real time implementation. In order to build a smart city, a comprehensive system for traffic management needs to be built that addresses all traffic related issues, not just traffic congestion. In this paper, a Comprehensive Traffic Management System (CTMS) using Radio Frequency Identification (RFID) and analytics for real time implementation has been proposed. The system is both cost effective and easy to implement. The outcomes are dynamic traffic signal timers that operate based on vehicular density, deviation of vehicles at previous junctions in case of blockage, traffic signal control for passage of emergency vehicles, detecting red signal violations, detecting road accidents, vehicle breakdowns for providing immediate assistance and tracking of vehicles.


title: Building And Evolving Data Warehousing And Business Intelligence Artefacts: The Case Of Sysco

abstract: This paper investigates use of data warehouse and business intelligence capabilities to integrate with customers in the supply chain and improve insights into customer sales. By making internal data warehouse sales information available to customers, additional value to those customers is created, eliminating asymmetries of information in the supply chain. In addition, the evolution of data warehousing into business intelligence is investigated, expanding sales information to include marketing associate performance analysis generated for internal use. Further, a methodology that was used for building a business intelligence system is also examined. Finally, what appears to be a business-intelligence-driven focus on enterprise resource planning systems is analyzed. These issues are illustrated using real-world data warehousing and business intelligence artefacts developed at SYSCO. Copyright © 2012 John Wiley & Sons, Ltd.


title: NicheWorks - Interactive Visualization of Very Large Graphs

abstract: The difference between displaying networks with 100–1000 nodes and displaying ones with 10,000–100,000 nodes is not merely quantitative, it is qualitative. Layout algorithms suitable for the former are too slow for the latter, requiring new algorithms or modified (often relaxed) versions of existing algorithms to be invented. The density of nodes and edges displayed per inch of screen real estate requires special visual techniques to filter the graphs and focus attention. A system for investigating and exploring such large, complex data sets needs to be able to display both graph structure and node and edge attributes so that patterns and information hidden in the data can be seen. We describe a tool that addresses these needs, the NicheWorks tool. We describe and comment on the available layout algorithms and the linked views system, and detail an example of the use of NicheWorks for analyzing web sites.


title: Automatic Facial Expression Recognition

abstract: The face is innervated by two di↵erent brain systems that compete for control of its muscles: a cortical brain system related to voluntary and controllable behavior, and a sub-cortical system responsible for involuntary expressions. The interplay between these two systems generates a wealth of information that humans constantly use to read the emotions, intentions, and interests [25] of others. Given the critical role that facial expressions play in our daily life, technologies that can interpret and respond to facial expressions automatically are likely to find a wide range of applications. For example, in pharmacology, the e↵ect of new anti-depression drugs could be assessed more accurately based on daily records of the patients’ facial expressions than asking the patients to fill out a questionnaire, as it is currently done [7]. Facial expression recognition may enable a new generation of teaching systems to adapt to the expression of their students in the way good teachers do [61]. Expression recognition could be used to assess the fatigue of drivers and air-pilots [58, 59]. Daily-life robots with automatic expression recognition will be able to assess the states and intentions of humans and respond accordingly [41]. Smart phones with expression analysis may help people to prepare for important meetings and job interviews. Thanks to the introduction of machine learning methods, recent years have seen great progress in the field of automatic facial expression recognition. Commercial real-time expression recognition systems are starting to be used in consumer applications, e.g., smile detectors embedded in digital cameras [62]. Nonetheless, considerable progress has yet to be made: Methods for face detection and tracking (the first step of automated face analysis)


title: Twevent: segment-based event detection from tweets

abstract: Event detection from tweets is an important task to understand the current events/topics attracting a large number of common users. However, the unique characteristics of tweets (e.g. short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. Most existing techniques proposed for well written documents (e.g. news articles) cannot be directly adopted. In this paper, we propose a segment-based event detection system for tweets, called Twevent. Twevent first detects bursty tweet segments as event segments and then clusters the event segments into events considering both their frequency distribution and content similarity. More specifically, each tweet is split into non-overlapping segments (i.e. phrases possibly refer to named entities or semantically meaningful information units). The bursty segments are identified within a fixed time window based on their frequency patterns, and each bursty segment is described by the set of tweets containing the segment published within that time window. The similarity between a pair of bursty segments is computed using their associated tweets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified events. We evaluate Twevent and compare it with the state-of-the-art method using 4.3 million tweets published by Singapore-based users in June 2010. In our experiments, Twevent outperforms the state-of-the-art method by a large margin in terms of both precision and recall. More importantly, the events detected by Twevent can be easily interpreted with little background knowledge because of the newsworthy segments. We also show that Twevent is efficient and scalable, leading to a desirable solution for event detection from tweets.


title: Multi-View Stereo for Community Photo Collections

abstract: We present a multi-view stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a per-view and per-pixel level. We show that such adaptive view selection enables robust performance even with dramatic appearance variability. The stereo matching technique takes as input sparse 3D points reconstructed from structure-from-motion methods and iteratively grows surfaces from these points. Optimizing for surface normals within a photoconsistency measure significantly improves the matching results. While the focus of our approach is to estimate high-quality depth maps, we also show examples of merging the resulting depth maps into compelling scene reconstructions. We demonstrate our algorithm on standard multi-view stereo datasets and on casually acquired photo collections of famous scenes gathered from the Internet.


title: V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets

abstract: High-throughput imaging generates massive data sets that are difficult to quantitatively analyze by hand. Peng et al. describe customizable software for visualizing and working with multi-gigabyte three-dimensional images in real time.


Communication and Adolescent Relationships Kaveri Subrahmanyam and


title: Fg-index: towards verification-free query processing on graph databases

abstract: Graphs are prevalently used to model the relationships between objects in various domains. With the increasing usage of graph databases, it has become more and more demanding to efficiently process graph queries. Querying graph databases is costly since it involves subgraph isomorphism testing, which is an NP-complete problem. In recent years, some effective graph indexes have been proposed to first obtain a candidate answer set by filtering part of the false results and then perform verification on each candidate by checking subgraph isomorphism. Query performance is improved since the number of subgraph isomorphism tests is reduced. However, candidate verification is still inevitable, which can be expensive when the size of the candidate answer set is large. In this paper, we propose a novel indexing technique that constructs a nested inverted-index, called FG-index, based on the set of Frequent subGraphs (FGs). Given a graph query that is an FG in the database, FG-index returns the exact set of query answers without performing candidate verification. When the query is an infrequent graph, FG-index produces a candidate answer set which is close to the exact answer set. Since an infrequent graph means the graph occurs in only a small number of graphs in the database, the number of subgraph isomorphism tests is small. To ensure that the index fits into the main memory, we propose a new notion of δ-Tolerance Closed Frequent Graphs (δ-TCFGs), which allows us to flexibly tune the size of the index in a parameterized way. Our extensive experiments verify that query processing using FG-index is orders of magnitude more efficient than using the state-of-the-art graph index.


title: Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?

abstract: Current state-of-the-art approaches to offline Handwritten Text Recognition extensively rely on Multidimensional Long Short-Term Memory networks. However, these architectures come with quite an expensive computational cost, and we observe that they extract features visually similar to those of convolutional layers, which are computationally cheaper. This suggests that the two-dimensional long-term dependencies, which are potentially modeled by multidimensional recurrent layers, may not be essential to achieve a good recognition accuracy, at least in the lower layers of the architecture. In this work, an alternative model is explored that relies only on convolutional and one-dimensional recurrent layers that achieves better or equivalent results than those of the current state-of-the-art architecture, and runs significantly faster. In addition, we observe that using random distortions during training as synthetic data augmentation dramatically improves the accuracy of our model. Thus, are multidimensional recurrent layers really necessary for Handwritten Text Recognition? Probably not.


title: A Write-Friendly and Cache-Optimized Hashing Scheme for Non-Volatile Memory Systems

abstract: Non-volatile memory technologies (NVMs) are promising candidates for building future memory systems, due to their advantages of high density, high scalability, and requiring near-zero standby power, while suffering from the limited endurance and asymmetric properties of reads and writes, compared with traditional memory technologies including DRAM and SRAM. The significant changes of low-level memory devices cause nontrivial challenges to high-level in-memory and in-cache data structure design due to overlooking the NVM device properties. In this paper, we study an important and common data structure, hash table, which is ubiquitous and widely used to construct the index and lookup table in main memory and caches. Based on the observations that existing hashing schemes cause many extra writes to NVMs, we propose a cost-efficient write-friendly hashing scheme, called path hashing, which incurs no extra writes to NVMs while delivers high performance. The basic idea of path hashing is to leverage a novel hash-collision resolution method, i.e., position sharing, which meets the needs of insertion and deletion requests without extra writes to NVMs. By further exploiting double-path hashing and path shortening techniques, path hashing delivers high performance of hash tables in terms of space utilization and request latency. Nevertheless, the original path hashing has low utilization of each cache line for small items, causing low cache efficiency. We hence propose a cache-optimized path hashing to pack multiple cells in the same path together and store them into one cache line, thus improving the cache line utilization to obtain higher performance. We have implemented path hashing and used a gem5 full system simulator with NVMain to evaluate its performance in the context of NVMs. Extensive experimental results demonstrate that path hashing incurs no extra writes to NVMs, and achieves up to 95 percent space utilization ratio as well as low request latency, compared with existing state-of-the-art hashing schemes. We have released the source code of path hashing for public use at github.


title: A comprehensive study of main-memory partitioning and its application to large-scale comparison- and radix-sort

abstract: Analytical database systems can achieve high throughput main-memory query execution by being aware of the dynamics of highly-parallel modern hardware. Such systems rely on partitioning to cluster or divide data into smaller pieces and thus achieve better parallelism and memory locality. This paper considers a comprehensive collection of variants of main-memory partitioning tuned for various layers of the memory hierarchy. We revisit the pitfalls of in-cache partitioning, and utilizing the crucial performance factors, we introduce new variants for partitioning out-of-cache. Besides non-in-place variants where linear extra space is used, we introduce large-scale in-place variants, and propose NUMA-aware partitioning that guarantees locality on multiple processors. Also, we make range partitioning comparably fast with hash or radix, by designing a novel cache-resident index to compute ranges. All variants are combined to build three NUMA-aware sorting algorithms: a stable LSB radix-sort; an in-place MSB radix-sort using different variants across memory layers; and a comparison-sort utilizing wide-fanout range partitioning and SIMD-optimal in-cache sorting. To the best of our knowledge, all three are the fastest to date on billion-scale inputs for both dense and sparse key domains. As shown for sorting, our work can serve as a tool for building other operations (e.g., join, aggregation) by combining the most suitable variants that best meet the design goals.


title: The Power of Optimization from Samples

abstract: We consider the problem of optimization from samples of monotone submodular functions with bounded curvature. In numerous applications, the function optimized is not known a priori, but instead learned from data. What are the guarantees we have when optimizing functions from sampled data? In this paper we show that for any monotone submodular function with curvature c there is a (1 - c)/(1 + c - c^2) approximation algorithm for maximization under cardinality constraints when polynomially-many samples are drawn from the uniform distribution over feasible sets. Moreover, we show that this algorithm is optimal. That is, for any c < 1, there exists a submodular function with curvature c for which no algorithm can achieve a better approximation. The curvature assumption is crucial as for general monotone submodular functions no algorithm can obtain a constant-factor approximation for maximization under a cardinality constraint when observing polynomially-many samples drawn from any distribution over feasible sets, even when the function is statistically learnable.


title: A perceptual visibility metric for banding artifacts

abstract: Banding is a common video artifact caused by compressing low texture regions with coarse quantization. Relatively few previous attempts exist to address banding and none incorporate subjective testing for calibrating the measurement. In this paper, we propose a novel metric that incorporates both edge length and contrast across the edge to measure video banding. We further introduce both reference and non-reference metrics. Our results demonstrate that the new metrics have a very high correlation with subjective assessment and certainly outperforms PSNR, SSIM, and VQM.


title: Age and gender classification using convolutional neural networks

abstract: Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.


title: Deep Communicating Agents for Abstractive Summarization

abstract: We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.


title: IMPALA: matching a protein sequence against a collection of PSI-BLAST-constructed position-specific score matrices

abstract: Motivation: Many studies have shown that database searches using position-specific score matrices (PSSMs) or profiles as queries are more effective at identifying distant protein relationships than are searches that use simple sequences as queries. One popular program for constructing a PSSM and comparing it with a database of sequences is Position-Specific Iterated BLAST (PSIBLAST). Results: This paper describes a new software package, IMPALA, designed for the complementary procedure of comparing a single query sequence with a database of PSI-BLAST-generated PSSMs. We illustrate the use of IMPALA to search a database of PSSMs for protein folds, and one for protein domains involved in signal transduction. IMPALA’s sensitivity to distant biological relationships is very similar to that of PSI-BLAST. However, IMPALA employs a more refined analysis of statistical significance and, unlike PSI-BLAST, guarantees the output of the optimal local alignment by using the rigorous Smith‐Waterman algorithm. Also, it is considerably faster when run with a large database of PSSMs than is BLAST or PSI-BLAST when run against the complete non-redundant protein database. Availability: The IMPALA source code, the wolf1187 database, and the aravind105 database are freely available from the NCBI ftp site ncbi.nlm.nih.gov. The databases may be found in the subdirectory ftp:// ncbi.nlm.nih.gov/ pub/ impala. The source code is in ftp:// ncbi.nlm.nih.gov/ toolbox/ ncbitools. Some IMPALA executables for different implementations of UNIX are in ftp:// ncbi.nlm.nih.gov/ blast/ executables. IMPALA


title: Proposal of a power flow control strategy applied to a hybrid microgrid

abstract: This paper proposes an energy management system designed to control the power flow in a hybrid microgrid composed of a 1 kWp PV array, a 1.2 kWp wind turbine, a 1.2 kW fuel cell and a 31.5 F / 250 V supercapacitor bank. The microgrid provides uninterrupted power supply of critical dc loads and it can operate in both, standalone or grid-connected modes. A power flow control strategy is proposed to provide real time power references for each renewable energy source according to the operational mode (grid-connected or standalone), the availability of energy resources (solar, wind and hydrogen stock) and the priority of dispatch. Experimental results obtained from several arrangements of power generation and load demand are presented and discussed.


title: Recognition of emotions in autism: a formal meta-analysis.

abstract: Determining the integrity of emotion recognition in autistic spectrum disorder is important to our theoretical understanding of autism and to teaching social skills. Previous studies have reported both positive and negative results. Here, we take a formal meta-analytic approach, bringing together data from 48 papers testing over 980 participants with autism. Results show there is an emotion recognition difficulty in autism, with a mean effect size of 0.80 which reduces to 0.41 when a correction for publication bias is applied. Recognition of happiness was only marginally impaired in autism, but recognition of fear was marginally worse than recognition of happiness. This meta-analysis provides an opportunity to survey the state of emotion recognition research in autism and to outline potential future directions.


title: Augmented reality as a new media experience

abstract: The authors discuss their work on applying media theory to the creation of narrative augmented reality (AR) experiences. We summarize the concepts of remediation and media forms as they relate to our work, argue for their importance to the development of a new medium such as AR, and present two examples AR experiences we have designed using these conceptual tools. In particular, we focus on leveraging the interaction between the physical and virtual world, remediating existing media (film, stage and interactive CD-ROM), and building on the cultural expectations of our users.


title: Transcriptional regulation of the stress response by mTOR

abstract: Gloss Cells sense and adapt to various changes in their environment through stress response pathways. The kinase mammalian target of rapamycin (mTOR) is central to the cell’s behavior and plays a main role in enhancing protein synthesis and general biogenesis. mTOR is stimulated by signaling induced by growth factors and nutrients, and it is generally inhibited by cellular stresses, such as insufficient metabolic resources, lack of oxygen, or DNA damage. Intriguingly, mTOR can withstand substantial stress and enhances some of the adaptive mechanisms, yet the contribution of mTOR’s regulation of gene expression to the stress response is largely unexplored. In this review, consisting of 3 figures and 187 references, we discuss how mTOR may contribute to the cellular stress response through its regulation of critical transcription factors, enabling simultaneous sensing of growth-promoting signals and modulation of adaptive signaling pathways.


title: Deep, Skinny Neural Networks are not Universal Approximators

abstract: In order to choose a neural network architecture that will be effective for a particular modeling problem, one must understand the limitations imposed by each of the potential options. These limitations are typically described in terms of information theoretic bounds, or by comparing the relative complexity needed to approximate example functions between different architectures. In this paper, we examine the topological constraints that the architecture of a neural network imposes on the level sets of all the functions that it is able to approximate. This approach is novel for both the nature of the limitations and the fact that they are independent of network depth for a broad family of activation functions.


title: Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving

abstract: Abstract Annotating large-scale datasets to train modern convolutional neural networks is prohibitively expensive and time-consuming for many real tasks. One alternative is to train the model on labeled synthetic datasets and apply it in the real scenes. However, this straightforward method often fails to generalize well mainly due to the domain bias between the synthetic and real datasets. Many unsupervised domain adaptation (UDA) methods were introduced to address this problem but most of them only focused on the simple classification task. This paper presents a novel UDA model which integrates both image and feature level based adaptations to solve the cross-domain object detection problem. We employ objectives of the generative adversarial network and the cycle consistency loss for image translation. Furthermore, region proposal based feature adversarial training and classification are proposed to further minimize the domain shifts and preserve the semantics of the target objects. Extensive experiments are conducted on several different adaptation scenarios, and the results demonstrate the robustness and superiority of the proposed method.


title: Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity

abstract: Social media is continually emerging as a platform of information exchange around health challenges. We study mental health discourse on the popular social media:reddit. Building on findings about health information seeking and sharing practices in online forums, and social media like Twitter, we address three research challenges. First, we present a characterization of self-disclosure inmental illness communities on reddit. We observe individuals discussing a variety of concerns ranging from the daily grind to specific queries about diagnosis and treatment. Second, we build a statistical model to examine the factors that drive social support on mental health reddit communities. We also develop language models to characterize mental health social support, which are observed to bear emotional, informational, instrumental, and prescriptive information. Finally, we study disinhibition in the light of the dissociative anonymity that reddit’s throwaway accounts provide. Apart from promoting open conversations,such anonymity surprisingly is found to gather feedback that is more involving and emotionally engaging. Our findings reveal, for the first time, the kind of unique information needs that a social media like reddit might be fulfilling when it comes to a stigmatic illness. They also expand our understanding of the role of the social web in behavioral therapy.


title: Time is of the Essence: A Review of Electroencephalography (EEG) and Event-Related Brain Potentials (ERPs) in Language Research

abstract: The discovery of electroencephalography (EEG) over a century ago has changed the way we understand brain structure and function, in terms of both clinical and research applications. This paper starts with a short description of EEG and then focuses on the event-related brain potentials (ERPs), and their use in experimental settings. It describes the typical set-up of an ERP experiment. A description of a number of ERP components typically involved in language research is presented. Finally, the advantages and disadvantages of using ERPs in language research are discussed. EEG has an extensive use in today’s world, including medical, psychology, or linguistic research. The excellent temporal resolution of EEG information allows one to track a brain response in milliseconds and therefore makes it uniquely suited to research concerning language processing.


title: Fully homomorphic encryption without bootstrapping

abstract: We present a radically new approach to fully homomorphic encryption (FHE) that dramatically improves performance and bases security on weaker assumptions. A central conceptual contribution in our work is a new way of constructing leveled fully homomorphic encryption schemes (capable of evaluating arbitrary polynomial-size circuits), without Gentry’s bootstrapping procedure. Specifically, we offer a choice of FHE schemes based on the learning with error (LWE) or ring-LWE (RLWE) problems that have 2 security against known attacks. For RLWE, we have: • A leveled FHE scheme that can evaluate L-level arithmetic circuits with O(λ · L) per-gate computation – i.e., computation quasi-linear in the security parameter. Security is based on RLWE for an approximation factor exponential in L. This construction does not use the bootstrapping procedure. • A leveled FHE scheme that uses bootstrapping as an optimization, where the per-gate computation (which includes the bootstrapping procedure) is O(λ), independent of L. Security is based on the hardness of RLWE for quasi-polynomial factors (as opposed to the sub-exponential factors needed in previous schemes). We obtain similar results for LWE, but with worse performance. We introduce a number of further optimizations to our schemes. As an example, for circuits of large width – e.g., where a constant fraction of levels have width at least λ – we can reduce the per-gate computation of the bootstrapped version to O(λ), independent of L, by batching the bootstrapping operation. Previous FHE schemes all required Ω(λ) computation per gate. At the core of our construction is a much more effective approach for managing the noise level of lattice-based ciphertexts as homomorphic operations are performed, using some new techniques recently introduced by Brakerski and Vaikuntanathan (FOCS 2011). ∗Sponsored by the Air Force Research Laboratory (AFRL). Disclaimer: This material is based on research sponsored by DARPA under agreement number FA8750-11-C-0096. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government. Approved for Public Release, Distribution Unlimited. †This material is based on research sponsored by DARPA under agreement number FA8750-11-2-0225. The same disclaimer as above applies. ISSN 1433-8092 Electronic Colloquium on Computational Complexity, Report No. 111 (2011)


title: Cognitive Neuroscience and the Study of Memory

abstract: As Neuron prepares to enter the twenty-first century, the neurosciences, whose six decades of achievement we celebrate in this issue of the journal, have matured. With this maturation, the neurosciences now have moved from the peripheral position they occupied in the 1940s to a central position within the biological sciences. There has been remarkable progress in understanding neuronal and synaptic signaling. These advances now invite a structural approach to visualize the static and dynamic structures of ion channels, receptors, and the molecular machinery for vesicle transport, fusion, and exocytosis.Similarly, an understanding in outline of the development of the nervous system has been achieved by a molecular approach. Specific molecules have been identified as inducers and morphogens, constructs that previously were shrouded in mystery. Progress in this area has in turn made possible a molecular-based neurology, a neurology that will, one hopes, finally be able to address the degenerative diseases of the brain that have for so long eluded our best scientific efforts.The remarkable advances in the cellular understanding of the organization of the somatosensory and visual system by Vernon Mountcastle and Hubel and Wiesel have helped turn our interest to perception and in the broader sense to cognitive psychology. In turn, contact between cognitive psychology and neuroscience has given us a new approach to the classic problems of the mind such as memory—on which we have here focused.But, of all the fields in neuroscience, in fact, of all the fields in all of science, the problems of cognitive neuroscience—the problems of perception, action, memory, attention, and consciousness on an intellectually satisfying biological level—offer the most difficult and the greatest challenge for the next millennium. In the fullness of time, advance in these areas may grant us insight into and perhaps solutions for some of the most debilitating diseases confronting medical science—schizophrenia, depression, and Alzheimer's disease.We have here focused on only one component of cognitive neuroscience, that of memory. As we indicated in the introduction, the problem of memory has two components—the molecular problem of memory and the systems problem of memory. In the last four decades substantial progress has occurred in both areas.On the molecular level, a core signaling pathway has been identified that is used in a variety of nondeclarative and declarative forms of memory to convert short-term to long-term memory. Thus, these two major forms of memory use common elementary mechanisms for storage. Most likely the cAMP–PKA–MAPK–CREB pathway represents only one of what is likely to be several core mechanisms for achieving this transformation. The tasks ahead for a deeper understanding of molecular mechanisms are fairly clear. Although we now know something about the switch from short-term to long-term memory, we know only a small percentage of the downstream genes and proteins, for example, the proteins required for the growth of new synaptic connections. Drosophila, Aplysia, and perhaps C. elegans should contribute importantly to further gene discovery. Because we now know that the molecular mechanisms are at least in part shared across species and forms of memory, it also will be important to direct this molecular analysis to the simpler instances of nondeclarative memory in mice, using fear conditioning and the amygdala and eyeblink conditioning and the vestibulo-ocular reflex, whose modifications occur in the cerebellum and its deep nuclei.The greatest challenges, however, lie in the systems biology of memory and in particular in the biology of declarative, conscious memory. We do not, as yet, understand the functions of the various subdivisions of the medial temporal-lobe system. Analyses of perception indicate that the visual image is deconstructed and processed in the cortex in a series of parallel processing streams. However, despite clear evidence for parallel anatomical pathways in the hippocampus, it has not been possible to delineate their functional significance. By analogy to the visual system, it would seem likely that different regions of the medial temporal-lobe system (the parahippocampal and perirhinal cortices, the entorhinal cortex, the dentate gyrus, the CA1 and CA3 regions of the hippocampus, and the subiculum) have specialized functional roles in memory storage: they might each mediate the store of different aspects of learned information (Figure 9Figure 9). Alternatively, memory itself requires several operations such as encoding, storage, consolidation, and retrieval. Perhaps different regions carry out these different types of operations. To intervene in each of the critical regions and explore each of these component processes, we will need further improvements in genetic methods.It also will be important to understand how acquired representations in the hippocampus act to support memory. For example, how does the spatial map, evident in the hippocampus, relate to spatial memory? How is this spatial map read out? How is the map of space in the hippocampus reflected in our conscious recollection of a space? That of course leads to a still larger question: how does declarative information become available to conscious introspection? How are nonspatial memories that are declarative represented in the hippocampus? How is it transformed from a hippocampal-dependent process to a hippocampal-independent and presumably neocortical-dependent process that is capable of being scanned by conscious attention in ways that we are still very far from understanding? Clearly one Decade of the Brain (and of Neuron) has not been enough. Will a millennium suffice?§To whom correspondence should be addressed.


title: Android-based home door locks application via Bluetooth for disabled people

abstract: This paper discusses about an ongoing project that serves the needs of people with physical disabilities at home. It uses the Bluetooth technology to establish communication between user's Smartphone and controller board. The prototype support manual controlling and microcontroller controlling to lock and unlock home door. By connecting the circuit with a relay board and connection to the Arduino controller board it can be controlled by a Bluetooth available to provide remote access from tablet or smartphone. This paper addresses the development and the functionality of the Android-based application (Android app) to assist disabled people gain control of their living area.


title: How language production shapes language form and comprehension

abstract: Language production processes can provide insight into how language comprehension works and language typology—why languages tend to have certain characteristics more often than others. Drawing on work in memory retrieval, motor planning, and serial order in action planning, the Production-Distribution-Comprehension (PDC) account links work in the fields of language production, typology, and comprehension: (1) faced with substantial computational burdens of planning and producing utterances, language producers implicitly follow three biases in utterance planning that promote word order choices that reduce these burdens, thereby improving production fluency. (2) These choices, repeated over many utterances and individuals, shape the distributions of utterance forms in language. The claim that language form stems in large degree from producers' attempts to mitigate utterance planning difficulty is contrasted with alternative accounts in which form is driven by language use more broadly, language acquisition processes, or producers' attempts to create language forms that are easily understood by comprehenders. (3) Language perceivers implicitly learn the statistical regularities in their linguistic input, and they use this prior experience to guide comprehension of subsequent language. In particular, they learn to predict the sequential structure of linguistic signals, based on the statistics of previously-encountered input. Thus, key aspects of comprehension behavior are tied to lexico-syntactic statistics in the language, which in turn derive from utterance planning biases promoting production of comparatively easy utterance forms over more difficult ones. This approach contrasts with classic theories in which comprehension behaviors are attributed to innate design features of the language comprehension system and associated working memory. The PDC instead links basic features of comprehension to a different source: production processes that shape language form.


title: Large-scale knowledge transfer for object localization in ImageNet

abstract: ImageNet is a large-scale database of object classes with millions of images. Unfortunately only a small fraction of them is manually annotated with bounding-boxes. This prevents useful developments, such as learning reliable object detectors for thousands of classes. In this paper we propose to automatically populate ImageNet with many more bounding-boxes, by leveraging existing manual annotations. The key idea is to localize objects of a target class for which annotations are not available, by transferring knowledge from related source classes with available annotations. We distinguish two kinds of source classes: ancestors and siblings. Each source provides knowledge about the plausible location, appearance and context of the target objects, which induces a probability distribution over windows in images of the target class. We learn to combine these distributions so as to maximize the location accuracy of the most probable window. Finally, we employ the combined distribution in a procedure to jointly localize objects in all images of the target class. Through experiments on 0.5 million images from 219 classes we show that our technique (i) annotates a wide range of classes with bounding-boxes; (ii) effectively exploits the hierarchical structure of ImageNet, since all sources and types of knowledge we propose contribute to the results; (iii) scales efficiently.


title: A Single-Shot Object Detector with Feature Aggregation and Enhancement

abstract: For many real applications, it is equally important to detect objects accurately and quickly. In this paper, we propose an accurate and efficient single shot object detector with fea-ture aggregation and enhancement (FAENet). Our motivation is to enhance and exploit the shallow and deep feature maps of the whole network simultaneously. For achieving this, we introduce a pair of novel feature aggregation modules and two feature enhancement blocks, and integrate them into the original structure of SSD. Extensive experiments on both PASCAL VOC and MS COCO datasets demonstrate that the proposed method achieves much higher accuracy than SSD. In addition, our method performs better than the state-of-the-art one-stage method RefineDet on small objects and can run at a faster speed.


title: Millimeter-Wave Cavity-Backed Patch-Slot Dipole for Circularly Polarized Radiation

abstract: A novel millimeter-wave antenna for circularly polarized (CP) radiation is presented. The proposed antenna is composed of a patch dipole and a slot dipole, and it is realized by a plated through-hole printed technique on a microwave substrate. Measurements show that the antenna can present a bandwidth from 56.2 to 65 GHz for a reflection coefficient S11 ≤ -10 dB as well as for an axial ratio (AR) ≤ 3 dB, and the 10.03-dBi average gain is achieved. This design yields good directional radiation patterns. Studies on several critical parameters are performed for practical designs.


title: PRETO: a high-performance text mining tool for preprocessing Turkish texts

abstract: Text documents are usually unstructured and written in natural language. To apply conventional data mining techniques on text documents, a preprocessing operation is indispensable. In this paper, we introduce PRETO, a cross-platform, powerful and scalable preprocessing tool developed specifically for preprocessing Turkish texts, with a wide range of preprocessing options like stemming, stopword filtering, statistical term filtering, and n-gram generation. We demonstrate the performance and scalability of PRETO with some experiments on large document collections.


title: Human Body Orientation Estimation using Convolutional Neural Network

abstract: Personal robots are expected to interact with the user by recognizing the user's face. However, in most of the service robot applications, the user needs to move himself/herself to allow the robot to see him/her face to face. To overcome such limitations, a method for estimating human body orientation is required. Previous studies used various components such as feature extractors and classification models to classify the orientation which resulted in low performance. For a more robust and accurate approach, we propose the light weight convolutional neural networks, an end to end system, for estimating human body orientation. Our body orientation estimation model achieved 81.58% and 94% accuracy with the benchmark dataset and our own dataset respectively. The proposed method can be used in a wide range of service robot applications which depend on the ability to estimate human body orientation. To show its usefulness in service robot applications, we designed a simple robot application which allows the robot to move towards the user's frontal plane. With this, we demonstrated an improved face detection rate.


title: FOMCON: Fractional-order modeling and control toolbox for MATLAB

abstract: FOMCON is a new fractional-order modeling and control toolbox for MATLAB. It offers a set of tools for researchers in the field of fractional-order control. In this paper we present all the major modules comprising the toolbox and discuss the corresponding mathematical concepts. Fractional-order system analysis, identification and fractional PID controller design, tuning and optimization in the context of the toolbox are presented and discussed.


title: A comparison of regression methods for remote tracking of Parkinson's disease progression

abstract: Remote patient tracking has recently gained increased attention, due to its lower cost and non-invasive nature. In this paper, the performance of Support Vector Machines (SVM), Least Square Support Vector Machines (LS-SVM), Multilayer Perceptron Neural Network (MLPNN), and General Regression Neural Network (GRNN) regression methods is studied in application to remote tracking of Parkinson's disease progression. Results indicate that the LS-SVM provides the best performance among the other three, and its performance is superior to that of the latest proposed regression method published in the literature.


title: Inducing Regular Grammars Using Recurrent Neural Networks

abstract: Grammar induction is the task of learning a grammar from a set of examples. Recently, neural networks have been shown to be powerful learning machines that can identify patterns in streams of data. In this work we investigate their effectiveness in inducing a regular grammar from data, without any assumptions about the grammar. We train a recurrent neural network to distinguish between strings that are in or outside a regular language, and utilize an algorithm for extracting the learned finite-state automaton. We apply this method to several regular languages and find unexpected results regarding the connections between the network's states that may be regarded as evidence for generalization.


On analytical models of gas flow and chemical lasers


title: Predicting Shot Locations in Tennis Using Spatiotemporal Data

abstract: Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.


title: Automatic feature extraction for multiview 3D face recognition

abstract: Current 2D face recognition systems encounter difficulties in recognizing faces with large pose variations. Utilizing the pose-invariant features of 3D face data has the potential to handle multiview face matching. A feature extractor based on the directional maximum is proposed to estimate the nose tip location and the pose angle simultaneously. A nose profile model represented by subspaces is used to select the best candidates for the nose tip. Assisted by a statistical feature location model, a multimodal scheme is presented to extract eye and mouth corners. Using the automatic feature extractor, a fully automatic 3D face recognition system is developed. The system is evaluated on two databases, the MSU database (300 multiview test scans from 100 subjects) and the UND database (953 near frontal scans from 277 subjects). The automatic system provides recognition accuracy that is comparable to the accuracy of a system with manually labeled feature points.


title: Wearable Sensors in Syncope Management

abstract: Syncope is a common disorder with a lifetime prevalence of about 40%. Implantable cardiac electronic devices, including implantable loop recorders (ILR) and implantable cardioverter-defibrillators (ICD), are well established in syncope management. However, despite the successful use of ILR and ICD, diagnosis and therapy still remain challenging in many patients due to the complex hemodynamic interplay of cardiac and vascular adaptations during impending syncopes. Wearable sensors might overcome some limitations, including misdiagnosis and inappropriate defibrillator shocks, because a variety of physiological measures can now be easily acquired by a single non-invasive device at high signal quality. In neurally-mediated syncope (NMS), which is the most common cause of syncope, advanced signal processing methodologies paved the way to develop devices for early syncope detection. In contrast to the relatively benign NMS, in arrhythmia-related syncopes immediate therapeutical intervention, predominantly by electrical defibrillation, is often mandatory. However, in patients with a transient risk of arrhythmia-related syncope, limitations of ICD therapy might outweigh their potential therapeutic benefits. In this context the wearable cardioverter-defibrillator offers alternative therapeutical options for some high-risk patients. Herein, we review recent evidence demonstrating that wearable sensors might be useful to overcome some limitations of implantable devices in syncope management.


title: Job stress and its relationship with the level of secretory IgA in saliva: a comparison between nurses working in emergency wards and hospital clerks.

abstract: BACKGROUND ::: The current study was carried out to evaluate and compare the job stress of female nurses working in emergency wards and female clerks and to analyze the possible relationship between the stress level and level of saliva secretory IgA (SIgA). ::: ::: ::: METHODS ::: Eighty four female nurses in emergency wards and female clerks of hospitals were selected (42 in each group). Their stress level was measured using the Persian short version of generic job stress questionnaire of the National Institute for Occupational Safety and Health (NIOSH). Moreover, the SIgA level was determined using the ELISA method. SPSS software (version 17), independent t-test, variance analysis and partial correlation were used for data analysis. ::: ::: ::: RESULTS ::: The mean score of job stress was 97.30 and 91.85 in nurses and clerks, respectively (p = 0.016). The levels of saliva SIgA in nurses and clerks were 338.04 +/- 380.93 and 706 +/- 354.70, respectively (p < 0.001). The results showed a negative correlation between the levels of saliva SIgA and job stress (p = 0.02, r = -0.203). ::: ::: ::: CONCLUSION ::: Nurses have a higher stress level compared to clerks; while the saliva SIgA level of nurses was much lower than that of the clerks. Considering the negative correlation between the saliva SIgA level and job stress, further study to evaluate the potential uses of saliva SIgA as a biomarker can be performed.


title: An appropriate model predicting pest/diseases of crops using machine learning algorithms

abstract: Wireless Sensor Network is new technology to world and country like India where it can used in Agriculture Sector in India for increasing yield output by providing early prediction of plant diseases and pest. This can be happen by taking raw data from field where WSN network is install and with fitting appropriate machine learning model for this data to get predicted output. This paper gives an idea to how to deploy WSN on field and how Machine learning model is fitted for prediction of pest/diseases using Navie Bayes Kernel Algorithm.


title: AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context

abstract: Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS messages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security-sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security-sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.