title: Wafer Classification Using Support Vector Machines

abstract: Increasing yield is a primary concern to integrated circuit manufacturing companies as it dictates the readiness of a new process for high volume manufacturing. In order to expedite the process of discovering yield issues, companies have started looking for ways to perform early prediction for such issues. This paper suggests the use of the support vector machines (SVMs) for early wafer classification. The choice of SVM is motivated by the model's ability to effectively classify multivariate, multimodal, and inseparable data points. This model uses multidimensional hyperplanes to separate and classify wafers into low-yield and high-yield classes. This paper includes a proposal for how the classification model can be applied for yield classification and how it can be adaptively updated in a manufacturing environment. We show how the values for the SVM parameters can be selected for best yield classification. Furthermore, performance evaluation is conducted on real manufacturing data, comparing the proposed SVM classifier to state of the art. Results show that in all cases, SVM consistently outperforms other methods with and without adaptive model updates. The experiments also show that all classifiers' performances depend on yield thresholds. It is also shown that the classification model can be built and executed using a reduced set without compromising its accuracy.


Are solar gamma-ray-line flares different from other large flares?


title: Clinical Trials Directive: better regulation required now

abstract: 1 Department of Health. A policy framework for commissioning cancer services: a report by the Expert Advisory Group on Cancer to the Chief Medical Offi cers of England and Wales. London: HM Stationery Offi ce, 1995. 2 Department of Health. The NHS cancer plan: a plan for investment, a plan for reform. London: Stationery Offi ce, 2000. http://www. dh.gov.uk/en/Publicationsandstatistics/ Publications/PublicationsPolicyAndGuidance/ DH_4009609 (accessed July 7, 2010). 3 Birgisson H, Pahlman L, Glimelius B. Adverse eff ects of preoperative radiation therapy for rectal cancer: long-term follow-up of the Swedish Rectal Cancer Trial. J Clin Oncol 2005; 23: 8697–705. 4 Krook JE, Moertel CG, Gunderson LL, et al. Eff ective surgical adjuvant therapy for highrisk rectal carcinoma. N Engl J Med 1991; 324: 709–15. 5 Andre T, Boni C, Mounedji-Boudiaf L, et al. Oxaliplatin, fl uorouracil, and leucovorin as adjuvant treatment for colon cancer. N Engl J Med 2004; 350: 2343–51.


title: Study of microorganisms coming into the port of Vladivostok with ballast water of ships

abstract: The number and variety of heterotrophic and sanitary indicative bacteria in the ballast water of the Minotaur and Sunrise Wisteria tankers arriving from the ports of China and Japan are studied. It is found that ballast water coming into the port of Vladivostok from Japanese ports is less polluted than that from Chinese ports, which is defined as dirty and very dirty in the summer period. According to the pathogenicity of microflora, the donor ports can be arranged in the following series: Nantong > Longkou > Yangzhou > Laizhou > Mizushima > Iwakuni > Kawasaki. In ballast water of the Minotaur (from the Chinese ports), enterobacteria, the number of which exceeded the sanitary norms (by 40 or more times), and gram-negative mobile rod-shaped bacteria with an enzymatic type of metabolism resistant to heavy metals and antibiotics are singled out. For this reason, the habitats of these bacteria are characterized as insufficiently sated with oxygen and polluted by industrial and household water wastes. The sanitary indicator microorganisms are represented mainly by the following enterobacteria: Hafnia (37%), Enterobacter liqueface (22%), Citrobacter (15%), and Shigella (7%). According to data from a cytolytic test, the highest pathogenicity is found in Shigella, Hafnia, Citrobacter, and Salmonella.


title: Computational models of reading: a primer

abstract: This article briefly reviews four computational models of reading and the types of empirical findings that they are designed to explain: (1) the Dual-Route model of word identification (Coltheart et al., 2001); (2) the Simple-Recurrent Network model of sentence processing (Elman, 1990); (3) the Construction–Integration model of discourse representation (Kintsch, 1988); and (4) the E-Z Reader model of eye-movement control in reading (Reichle et al., 2012). These particular models are reviewed because they provide comprehensive accounts of a large number of empirical findings in each of their respective domains, and because they have advanced our understanding of the cognitive processes involved in reading by motivating new empirical studies. Future models of reading will build upon the success of their predecessors by integrating models from two or more of the aforementioned domains, thereby providing a means to examine the compatibility of their theoretical assumptions and more comprehensive accounts of the cognitive processes involved in reading.


title: Real Time Prediction of Drive by Download Attacks on Twitter

abstract: The popularity of Twitter for information discovery, coupled with the automatic shortening of URLs to save space, given the 140 character limit, provides cyber criminals with an opportunity to obfuscate the URL of a malicious Web page within a tweet. Once the URL is obfuscated the cyber criminal can lure a user to click on it with enticing text and images before carrying out a cyber attack using a malicious Web server. This is known as a drive-by- download. In a drive-by-download a user's computer system is infected while interacting with the malicious endpoint, often without them being made aware, the attack has taken place. An attacker can gain control of the system by exploiting unpatched system vulnerabilities and this form of attack currently represents one of the most common methods employed. In this paper, we build a machine learning model using machine activity data and tweet meta data to move beyond post-execution classification of such URLs as malicious, to predict a URL will be malicious with 99.2% F-measure (using 10-fold cross validation) and 83.98% (using an unseen test set) at 1 second into the interaction with the URL. Thus providing a basis from which to kill the connection to the server before an attack has completed and proactively blocking and preventing an attack, rather than reacting and repairing at a later date.


title: Deep Image: Scaling up Image Recognition

abstract: We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep learning, a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolution images. Our method achieves excellent results on multiple challenging computer vision benchmarks.


title: Modeling and Decoupled Control of Series-Connected Split-Phase Synchronous Machines With Open-Circuit Fault

abstract: Split-phase motor drives have advantages such as lower torque ripple, enhanced fault tolerance, higher power-to-weight ratio, and lower dc-link voltage requirement, making it an attractive alternative to the conventional three-phase drives. In the proposed series-connected topology, both the motors are connected in different phase sequences, allowing the fundamental component of the winding current to produce torque in the first machine, and sixth-order harmonic currents to produce torque in the other machine. Both the motors used are synchronous machines, which have advantages such as higher power density and easier control. The proposed method can be easily extended to other synchronous machines such as permanent magnet machines and reluctance machines. In this article, a mathematical model for series-connected split-phase synchronous motors is developed and validated experimentally for decoupled field-oriented control of dual split-phase synchronous machines. Furthermore, the developed model (series-connected system) is extended for open-circuit fault operation, and experimental results are presented for the first time. Simulation results as well experimental results for steady state, transient conditions for normal, and fault operation are presented to validate the proposed scheme.


title: A Comparative Study of Fuel-Cell–Battery, Fuel-Cell–Ultracapacitor, and Fuel-Cell–Battery–Ultracapacitor Vehicles

abstract: Although many researchers have investigated the use of different powertrain topologies, component sizes, and control strategies in fuel-cell vehicles, a detailed parametric study of the vehicle types must be conducted before a fair comparison of fuel-cell vehicle types can be performed. This paper compares the near-optimal configurations for three topologies of vehicles: fuel-cell-battery, fuel-cell-ultracapacitor, and fuel-cell-battery-ultracapacitor. The objective function includes performance, fuel economy, and powertrain cost. The vehicle models, including detailed dc/dc converter models, are programmed in Matlab/Simulink for the customized parametric study. A controller variable for each vehicle type is varied in the optimization.


title: Municipal web sites accessibility and usability for blind users: preliminary results from a pilot study

abstract: Despite the existing regulations and standards at national and international level, web content is still difficult to use, if not completely unusable, for visually impaired people. This paper presents the evaluation results for three municipal web sites. A combined method, based on conformance review and expert review, was employed. Overall, the results reveal a low conformance to WCAG2 and many accessibility and usability problems. The content is not properly structured, and this reduces usability even for sighted user. The analysis of the evaluation data suggests that many accessibility barriers could be avoided by adopting a user-centered approach during web design.


title: Search result diversification

abstract: Result diversification has recently attracted much attention as a means of increasing user satisfaction in recommender systems and web search. Many different approaches have been proposed in the related literature for the diversification problem. In this paper, we survey, classify and comparatively study the various definitions, algorithms and metrics for result diversification.


title: A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems

abstract: Abstract: Artificial Bee Colony (ABC) algorithm was firstly proposed for unconstrained optimization problems on where that ABC algorithm showed superior performance. This paper describes a modified ABC algorithm for constrained optimization problems and compares the performance of the modified ABC algorithm against those of state-of-the-art algorithms for a set of constrained test problems. For constraint handling, ABC algorithm uses Deb's rules consisting of three simple heuristic rules and a probabilistic selection scheme for feasible solutions based on their fitness values and infeasible solutions based on their violation values. ABC algorithm is tested on thirteen well-known test problems and the results obtained are compared to those of the state-of-the-art algorithms and discussed. Moreover, a statistical parameter analysis of the modified ABC algorithm is conducted and appropriate values for each control parameter are obtained using analysis of the variance (ANOVA) and analysis of mean (ANOM) statistics.


title: Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction

abstract: State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors usually reduce drift in camera tracking by globally optimizing the estimated camera poses in real-time without simultaneously updating the reconstructed surface on pose changes. We propose an efficient on-the-fly surface correction method for globally consistent dense 3D reconstruction of large-scale scenes. Our approach uses a dense Visual RGB-D SLAM system that estimates the camera motion in real-time on a CPU and refines it in a global pose graph optimization. Consecutive RGB-D frames are locally fused into keyframes, which are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a novel keyframe re-integration strategy with reduced GPU-host streaming. We demonstrate in an extensive quantitative evaluation that our method is up to 93% more runtime efficient compared to the state-of-the-art and requires significantly less memory, with only negligible loss of surface quality. Overall, our system requires only a single GPU and allows for real-time surface correction of large environments.


Lane Marking Detection in Various Lighting Conditions using Robust Feature Extraction


title: Current status of midinfrared quantum and interband cascade lasers for clinical breath analysis

abstract: State-of-the-art quantum- and interband-cascade-based chemical sensors may be effective new tools for the identification and quantification of trace gases in human breath for clinical uses. Increased or decreased concentrations of these molecules are associated with the pathogenesis of a large number of diseases. Current technologies enable breath analyses to be performed on a single breath and the results are available in real time. Critical parameters including sensor sensitivity, selectivity, real-time monitoring capability, robustness, cost, size, and weight determine the progress made toward the development and availability of commercial diagnostic material.


Independent coordinates for strange attractors from mutual information.


title: SYSTRAN's Pure Neural Machine Translation Systems

abstract: Since the first online demonstration of Neural Machine Translation (NMT) by LISA, NMT development has recently moved from laboratory to production systems as demonstrated by several entities announcing roll-out of NMT engines to replace their existing technologies. NMT systems have a large number of training configurations and the training process of such systems is usually very long, often a few weeks, so role of experimentation is critical and important to share. In this work, we present our approach to production-ready systems simultaneously with release of online demonstrators covering a large variety of languages (12 languages, for 32 language pairs). We explore different practical choices: an efficient and evolutive open-source framework; data preparation; network architecture; additional implemented features; tuning for production; etc. We discuss about evaluation methodology, present our first findings and we finally outline further work. ::: Our ultimate goal is to share our expertise to build competitive production systems for "generic" translation. We aim at contributing to set up a collaborative framework to speed-up adoption of the technology, foster further research efforts and enable the delivery and adoption to/by industry of use-case specific engines integrated in real production workflows. Mastering of the technology would allow us to build translation engines suited for particular needs, outperforming current simplest/uniform systems.


title: Annotating Multi-media/Multi-modal Resources with ELAN

abstract: This paper shows the actual state of development of the manual annotation tool ELAN. It presents usage requirements from three different groups of users and how one annotation model and a number of generic design principles guided the choices made during the development process of ELAN. Introduction At the Max-Planck-Institute for Psycholinguistics (MPI) software development on annotation tools for the manual annotation of multimedia data has been going on since the early 90’s. Over this decade there have been large changes in enabling technology and insights in the nature of linguistic annotation. Media frameworks for the handling of digital audio and especially digital video files have matured, as has media streaming technology. XML has come to existence and has become highly relevant in a short time. Rendering and input of Unicode characters is now commonplace. Simultaneously, users made experiences with the first generation of video annotation tools and became aware of and got used to these new technologies. From this a new set of requirements arose. Finally, annotation tool builders are better aware of each other’s approaches, annotation models and annotation document formats. Clearly convergence is going on, leading to easier exchange of data between annotation tools. An important role in this process was played by the paper by (Bird & Liberman, 2001) that introduced Annotation Graphs. We are closely watching and trying to participate in standards initiatives, as for example ISO TC37/SC4. The first video annotation tool developed at the MPI was MediaTagger, a QuickTime based application that runs only on pre-OS X Macintoshes. It started as a first attempt to exploit the QuickTime Movie data structure, and especially it’s text tracks, as an informal model for linguistic annotation. Since then several new formal models where made, each one building on the experiences of the previous ones and considering new user requirements. The formal modeling languages that were used are Entity-Relationship diagrams and UML. A detailed presentation and evaluation of these models can be found in (Brugman & Wittenburg, 2001). The next chapters will discuss the requirements of several different groups of users and describe the latest state of ELAN functionality. We will then present our model for annotation in some detail and show how we can cover the needs of very different user groups with one relatively simple model. In the discussions plans for future development will presented. 1 http://www.mpi.nl User requirements ELAN is developed with a number of different user groups in mind. These users are situated both within the MPI and, in an increasing number of cases, outside the MPI. Often they are participating in externally funded projects (DoBeS, ECHO). We will discuss the main requirements per group, although there is of course a substantial overlap between each group’s needs.


title: An ultrasonic sensor based low-power acoustic modem for underwater communication in underwater wireless sensor networks

abstract: Applications of underwater sensor networks involve environmental monitoring, disaster prevention, and resource detection. As the importance of these applications has recently grown, underwater sensor networks made up of sensor nodes have to be further investigated. However, little research has been performed to develop an underwater sensor node with communication functionality. In an underwater environment, typical RF-based communication is not appropriate because of two facts. One fact is that radio waves require large antennae and high transmission power. The other fact is that the Berkeley Mica 2 Motes have been reported to have a transmission range of 120cm underwater. Consequently, we have concluded that underwater communication has to use an acoustic or ultrasonic wave rather than a radio wave. The objective of our work is to develop an acoustic modem for underwater communication, where we have to consider an energy-aware acoustic modem. This is because the battery can not easily be recharged underwater as well as in a terrestrial environment. As a consequence, an acoustic modem has to be designed with low-power. In this paper, we describe our implementation of an energy-aware acoustic modem and its performance in underwater experiments.


title: Graphene–Ferroelectric Hybrid Structure for Flexible Transparent Electrodes

abstract: Graphene has exceptional optical, mechanical, and electrical properties, making it an emerging material for novel optoelectronics, photonics, and flexible transparent electrode applications. However, the relatively high sheet resistance of graphene is a major constraint for many of these applications. Here we propose a new approach to achieve low sheet resistance in large-scale CVD monolayer graphene using nonvolatile ferroelectric polymer gating. In this hybrid structure, large-scale graphene is heavily doped up to 3 × 1013 cm–2 by nonvolatile ferroelectric dipoles, yielding a low sheet resistance of 120 Ω/□ at ambient conditions. The graphene–ferroelectric transparent conductors (GFeTCs) exhibit more than 95% transmittance from the visible to the near-infrared range owing to the highly transparent nature of the ferroelectric polymer. Together with its excellent mechanical flexibility, chemical inertness, and the simple fabrication process of ferroelectric polymers, the proposed GFeTCs represent a new ro...


title: Supporting Everyday Philanthropy: Care Work In Situ and at Scale

abstract: Prior research has drawn attention to numerous problems with ICTs designed for philanthropic contexts. Yet, little is known about how to support philanthropic work in its own right, especially as it transcends formal engagements with nonprofit organizations and suffuses everyday life. In this research, we draw on data from a 33-day mobile diary study augmented by follow-up interviews to examine the work of everyday philanthropy. By taking individuals' philanthropic work-rather than a specific technology or its users-as our unit of analysis, we establish an empirical foundation for a new design space for philanthropic informatics. Our research shows that philanthropic work is not just about transactions of money, goods, or services. Instead, we argue that a lens of care work is a more useful framing of everyday philanthropy. We also draw renewed attention to the myriad roles of institutions beyond that of a resource intermediary.


title: Effects of Sedative Interruption in Critically Ill, Mechanically Ventilated Patients Receiving Midazolam or Propofol

abstract: • Objective: To assess the impact of daily interruption of sedative infusions on duration of mechanical ventilation and length of stay (LOS) in patients receiving either midazolam and morphine or propofol and morphine. • Design: Randomized controlled trial. • Setting and participants: 128 mechanically ventilated adult patients receiving continuous sedative infusions in a medical intensive care unit (ICU). • Methods: Patients were randomized to receive either midazolam or propofol along with morphine infusions. Half the patients in each group had their sedatives discontinued every day until they were awake, while the other half had their sedatives discontinued at the discretion of the ICU team. • Outcome measures: Primary outcomes were duration of mechanical ventilation, ICU LOS, and hospital LOS. Other measures included total dose of sedative and opiate administered, neurologic tests performed to assess unexplained mental status changes, and adverse events. • Results: Median duration of mechanical ventilation was 5.5 days in the midazolam group versus 5.6 days in the propofol group (P = 0.45). Median ICU LOS was 6.7 days in the midazolam group versus 7.1 days in the propofol group (P = 0.60). Neurologic tests were used in 18% of midazolam patients compared with 16% in the propofol group (P = 0.94). Adverse events occurred in 7.6% of the midazolam group and 3.2% of the propofol group (P = 0.49). Similar results were seen when propofol and midazolam were compared in subgroups assigned to daily sedative interruption and to interruption only at the discretion of the ICU team. • Conclusion: Daily interruption of sedative infusions is a safe and practical approach to managing mechanically ventilated patients receiving midazolam or propofol. The method of drug administration may be more important than the particular drug used for sedation in the ICU. Critically ill patients needing mechanical ventilation typically are given intravenous infusions of sedatives for pain, anxiety, and agitation. Opiates are most commonly used as analgesics, while benzodiazepines and propofol are typically used to treat anxiety and agitation. Midazolam, a short-acting benzodiazepine, and propofol have been evaluated in several clinical trials, and in the growing body of literature on sedation in the intensive care unit (ICU), they are the most commonly compared drugs in head-to-head trials [1,2]. Several authors have reported that time for recovery from sedation is shorter with propofol compared with midazolam [3–5]. Propofol’s pharmacokinetic profile has been proposed as the mechanism behind patients’ rapid clinical recovery from the drug [6]. Because propofol is lipid soluble, it has a very high volume of distribution, resulting in short-lived sedative effects as the drug is redistributed from plasma to tissue. However, the rapid recovery seen with propofol may be attenuated by concomitant opiate administration [6,7]. The increased time for recovery seen with midazolam may be explained by the drug’s pharmacokinetics, which are significantly altered in critical illness. A number of reports have described impaired midazolam metabolism in critically ill patients, with accumulation of both parent drug and active metabolites that may prolong altered mental status [8–12]. Sedatives such as midazolam and propofol are often administered to critically ill patients by continuous infusion [13–15]. However, there is increasing evidence that this practice may extend the duration of mechanical ventilation and length of stay (LOS) [15,16]. We have recently described a strategy of daily interruption of sedative infusions as a method for minimizing these adverse effects. Interrupting sedative infusions to


title: Expressing Facial Structure and Appearance Information in Frequency Domain for Face Recognition

abstract: Beneath the uncertain primitive visual features of face images are the primitive intrinsic structural patterns (PISP) essential for characterizing a sample face discriminative attributes. It is on this basis that this paper presents a simple yet effective facial descriptor formed from derivatives of Gaussian and Gabor Wavelets. The new descriptor is coined local edge gradient Gabor magnitude (LEGGM) pattern. LEGGM first uncovers the PISP locked in every pixel through determining the pixel gradient in relation to its neighbors using the Derivatives of Gaussians. Then, the resulting output is embedded into the global appearance of the face which are further processed using Gabor wavelets in order to express its frequency characteristics. Additionally, we adopted various subspace models for dimensionality reduction in order to ascertain the best fit model for reporting a more effective representation of the LEGGM patterns. The proposed descriptor-based face recognition method is evaluated on three databases: Plastic surgery, LFW, and GT face databases. Through experiments, using a base classifier, the efficacy of the proposed method is demonstrated, especially in the case of plastic surgery database. The heterogeneous database, which we created to typify real-world scenario, show that the proposed method is to an extent insensitive to image formation factors with impressive recognition performances.


title: Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

abstract: It is common in the study of networks to investigate meso-scale features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify"communities,"which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that"communities"are associated with bottlenecks of locally-biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for ``size-resolved community structure'' that can arise in real (and realistic) networks. Depending on which scenario holds, one may or may not be able to successfully identify ``good'' communities in a given network, the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics.In addition, our results suggest that, for many large realistic networks, the output of locally-biased methods that focus on communities that are centered around a given seed node might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate subtler structural properties that are important to consider in the development of better benchmark networks to test methods for community detection. [Note: Because of space limitations in the arXiv's abstract field, this is an abridged version of the paper's abstract.]


title: How the Great Firewall of China is Blocking Tor

abstract: Internet censorship in China is not just limited to the web: the Great Firewall of China prevents thousands of potential Tor users from accessing the network. In this paper, we investigate how the ...


title: Big five factors and facets and the prediction of behavior.

abstract: The authors compared the Big 5 factors of personality with the facets or traits of personality that constitute those factors on their ability to predict 40 behavior criteria. Both the broad factors and the narrow facets predicted substantial numbers of criteria, but the latter did noticeably better in that regard, even when the number of facet predictors was limited to the number of factor predictors. Moreover, the criterion variance accounted for by the personality facets often included large portions not predicted by the personality factors. The narrow facets, therefore, were able to substantially increase the maximum prediction achieved by the broad factors. The results of this study are interpreted as supporting a more detailed approach to personality assessment, one that goes beyond the measurement of the Big 5 factors alone.


title: Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection

abstract: A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled. Following the intuitive idea of detecting changes by directly comparing dissimilarities between a pair of features, we propose a novel fully Convolutional siamese metric Network(CosimNet) to measure changes by customizing implicit metrics. To learn more discriminative metrics, we utilize contrastive loss to reduce the distance between the unchanged feature pairs and to enlarge the distance between the changed feature pairs. Specifically, to address the issue of large viewpoint differences, we propose Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish noisy changes. We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our approach is robust to lots of challenging conditions, such as illumination changes, large viewpoint difference caused by camera motion and zooming. In addition, we incorporate the distance metric into the segmentation framework and validate the effectiveness through visualization of change maps and feature distribution. The source code is available at this https URL.


Climate contributes to the evolution of pesticide resistance


Convolutional Interaction Network for Natural Language Inference


title: Clustrophile: A Tool for Visual Clustering Analysis

abstract: While clustering is one of the most popular methods for data mining, analysts lack adequate tools for quick, iterative clustering analysis, which is essential for hypothesis generation and data reasoning. We introduce Clustrophile, an interactive tool for iteratively computing discrete and continuous data clusters, rapidly exploring different choices of clustering parameters, and reasoning about clustering instances in relation to data dimensions. Clustrophile combines three basic visualizations -- a table of raw datasets, a scatter plot of planar projections, and a matrix diagram (heatmap) of discrete clusterings -- through interaction and intermediate visual encoding. Clustrophile also contributes two spatial interaction techniques, $\textit{forward projection}$ and $\textit{backward projection}$, and a visualization method, $\textit{prolines}$, for reasoning about two-dimensional projections obtained through dimensionality reductions.


title: A Comprehensive Survey of Pilot Contamination in Massive MIMO—5G System

abstract: Massive MIMO has been recognized as a promising technology to meet the demand for higher data capacity for mobile networks in 2020 and beyond. Although promising, each base station needs accurate estimation of the channel state information (CSI), either through feedback or channel reciprocity schemes in order to achieve the benefits of massive MIMO in practice. Time division duplex (TDD) has been suggested as a better mode to acquire timely CSI in massive MIMO systems. The use of non-orthogonal pilot schemes, proposed for channel estimation in multi-cell TDD networks, is considered as a major source of pilot contamination in the literature due to the limitations of coherence time. Given the importance of pilot contamination in massive MIMO systems, we provide an extensive survey on pilot contamination, and identify other possible sources of pilot contamination, which include hardware impairment and non-reciprocal transceivers. We review established theories that have analyzed the effect of pilot contamination on the performance of massive MIMO systems, particularly on achievable rates. Next, we categorize the different proposed mitigation techniques for pilot contamination using the following taxonomy: pilot-based approach and subspace-based approach. Finally, we highlight the open issues, such as training overhead, deployment scenario, computational complexity, use of channel reciprocity, and conclude with broader perspective and a look at future trends in pilot contamination in massive MIMO systems.


title: Fully Homomorphic Encryption for Classification in Machine Learning

abstract: Using fully homomorphic encryption scheme, we construct fully homomorphic encryption scheme FHE4GT that can homomorphically compute an encryption of the greater-than bit that indicates x > x' or not, given two ciphertexts c and c' of x and x', respectively, without knowing the secret key. Then, we construct homomorphic classifier homClassify that can homomorphically classify a given encrypted data without decrypting it, using machine learned parameters.


title: A 1.2V 246uW CMOS latched comparator with neutralization technique for reducing Kickback Noise

abstract: A low power CMOS latched comparator has been designed in TSMC 0.18um employ neutralization technique for reducing Kickback Noise. The simulation results demonstrate that it can work at 1GHz suitable for high speed applications. Measurement results prove that the latched comparator consumes 246uW with a power supply of 1.2v at 10MHz. A simulation method for accurately determining dynamic offset in latched comparator is presented.


title: Deep content-based music recommendation

abstract: Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.


title: Leveraging personal metadata for Desktop search: The Beagle++ system

abstract: Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results. We also present a practical application of this approach-the extensible Beagle^+^+ toolbox. To prove the validity of our approach, we conducted a series of experiments. By comparing our results against the ones of a regular Desktop search solution - Beagle - we show an improved quality in search and overall performance.


title: A Nephelometric Turbidity System for Monitoring Residential Drinking Water Quality

abstract: In this paper the design and development of a turbidity system for monitoring drinking water quality in households is presented. Its operation is based on the principle that the intensity of the light scattered by the suspended matter is proportional to its concentration. Unlike the commercially available turbidity meters, which are relatively expensive and bulky, the proposed device is small-sized, low power, lightweight, easy to use and inexpensive. Laboratory tests of the device have yielded satisfactory repeatability and precision. This sensor can be used as a part of a low cost sensor network consisting of different types of sensors (pH, temperature, chloride, etc) to provide water quality information to consumers. Fusing on-line multi sensor measurements, the system can provide useful information regarding hazardous agents and waterborne pathogens contaminants of household drinking water raising awareness and encourage better water-handling.


title: Event Detection in Social Streams

abstract: Social networks generate a large amount of text content over time because of continuous interaction between participants. The mining of such social streams is more challenging than traditional text streams, because of the presence of both text content and implicit network structure within the stream. The problem of event detection is also closely related to clustering, because the events can only be inferred from aggregate trend changes in the stream. In this paper, we will study the two related problems of clustering and event detection in social streams. We will study both the supervised and unsupervised case for the event detection problem. We present experimental results illustrating the effectiveness of incorporating network structure in event discovery over purely content-based


title: Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals

abstract: Prediction intervals (PIs) have been proposed in the literature to provide more information by quantifying the level of uncertainty associated to the point forecasts. Traditional methods for construction of neural network (NN) based PIs suffer from restrictive assumptions about data distribution and massive computational loads. In this paper, we propose a new, fast, yet reliable method for the construction of PIs for NN predictions. The proposed lower upper bound estimation (LUBE) method constructs an NN with two outputs for estimating the prediction interval bounds. NN training is achieved through the minimization of a proposed PI-based objective function, which covers both interval width and coverage probability. The method does not require any information about the upper and lower bounds of PIs for training the NN. The simulated annealing method is applied for minimization of the cost function and adjustment of NN parameters. The demonstrated results for 10 benchmark regression case studies clearly show the LUBE method to be capable of generating high-quality PIs in a short time. Also, the quantitative comparison with three traditional techniques for prediction interval construction reveals that the LUBE method is simpler, faster, and more reliable.


Cognition, Religious Ritual, and Archaeology by


title: Identity and Origin of the ATPase activity associated with neuronal microtubules. I. The ATPase activity is associated with membrane vesicles.

abstract: Microtubule protein purified from brain tissue by cycles of in vitro assembly-disassembly contains ATPase activity that has been postulated to be associated with microtubule-associated proteins (MAPs) and therefore significant for studies of microtubule-dependent motility. In this paper we demonstrate that greater than 90% of the ATPase activity is particulate in nature and may be derived from contaminating membrane vesicles. We also show that the MAPs (MAP-1, MAP-2, and tau factors) and other high molecular weight polypeptides do not contain significant amounts of ATPase activity. These findings do not support the concept of "brain dynein" or of MAPs with ATPase activity.


title: Wireless Non-contact Cardiac and Neural Monitoring

abstract: Ubiquitous physiological monitoring will be a key driving force in the upcoming wireless health revolution. Cardiac and brain signals in the form of ECG and EEG are two critical health indicators that directly benefit from long-term monitoring. Despite advancements in wireless technology and electronics miniaturization, however, the use of wireless home ECG/EEG monitoring is still limited by the inconvenience and discomfort of wet adhesive electrodes. We have developed a wireless biopotential instrumentation system using non-contact capacitive electrodes that operate without skin contact. The sensors can be embedded within comfortable layers of fabric for unobtrusive use. All of the issues relating to the design of low noise, high performance capacitive sensors are discussed along with full technical details, circuit schematics and construction techniques. The non-contact electrode has been integrated into both a wearable ECG chest harness as well a EEG headband. We have also designed a compact, battery-powered, wireless data acquisition system to interface with multiple electrodes and monitor patient cardiac and neural signals in real time. Experimental data shows that the non-contact capacitive electrode perform comparable to Ag/AgCl electrodes using our special chest harness and head bands to ensure tight, movement-free electrode positioning.


title: Application impersonation: problems of OAuth and API design in online social networks

abstract: OAuth 2.0 protocol has enjoyed wide adoption by Online Social Network (OSN) providers since its inception. Although the security guideline of OAuth 2.0 is well discussed in RFC6749 and RFC6819, many real-world attacks due to the implementation specifics of OAuth 2.0 in various OSNs have been discovered. To our knowledge, previously discovered loopholes are all based on the misuse of OAuth and many of them rely on provider side or application side vulnerabilities/ faults beyond the scope of the OAuth protocol. It was generally believed that correct use of OAuth 2.0 is secure. In this paper, we show that OAuth 2.0 is intrinsically vulnerable to App impersonation attack due to its provision of multiple authorization flows and token types. We start by reviewing and analyzing the OAuth 2.0 protocol and some common API design problems found in many 1st tiered OSNs. We then propose the App impersonation attack and investigate its impact on 12 major OSN providers. We demonstrate that, App impersonation via OAuth 2.0, when combined with additional API design features/ deficiencies, make large-scale exploit and privacy-leak possible. For example, it becomes possible for an attacker to completely crawl a 200-million-user OSN within just one week and harvest data objects like the status list and friend list which are expected, by its users, to be private among only friends. We also propose fixes that can be readily deployed to tackle the OAuth2.0-based App impersonation problem.


title: Contextual Cueing : Implicit Learning and Memory of Visual Context Guides Spatial Attention

abstract: Abstract Global context plays an important, but poorly understood, role in visual tasks. This study demonstrates that a robust memory for visual context exists to guide spatial attention. Global context was operationalized as the spatial layout of objects in visual search displays. Half of the configurations were repeated across blocks throughout the entire session, and targets appeared within consistent locations in these arrays. Targets appearing in learned configurations were detected more quickly. This newly discovered form of search facilitation is termed contextual cueing. Contextual cueing is driven by incidentally learned associations between spatial configurations (context) and target locations. This benefit was obtained despite chance performance for recognizing the configurations, suggesting that the memory for context was implicit. The results show how implicit learning and memory of visual context can guide spatial attention towards task-relevant aspects of a scene.


title: A centralized reinforcement learning method for multi-agent job scheduling in Grid

abstract: One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. Moreover, decentralized methods require a coordination mechanism with limited communications. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources. In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level. We evaluated the efficiency of the CLDS method by designing and performing a set of experiments on a simulated Grid system under different system scales and loads. The results show that the CLDS can effectively balance the load of system even in large scale and heavy loaded Grids, while maintains its adaptive performance and scalability.


title: Integrating information retrieval, execution and link analysis algorithms to improve feature location in software

abstract: Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining or link analyses algorithms applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation on three Java systems indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 87%.


title: Caloric restriction, the traditional Okinawan diet, and healthy aging: the diet of the world's longest-lived people and its potential impact on morbidity and life span.

abstract: Long-term caloric restriction (CR) is a robust means of reducing age-related diseases and extending life span in multiple species, but the effects in humans are unknown. The low caloric intake, long life expectancy, and the high prevalence of centenarians in Okinawa have been used as an argument to support the CR hypothesis in humans. However, no long-term, epidemiologic analysis has been conducted on traditional dietary patterns, energy balance, and potential CR phenotypes for the specific cohort of Okinawans who are purported to have had a calorically restricted diet. Nor has this cohort's subsequent mortality experience been rigorously studied. Therefore, we investigated six decades of archived population data on the elderly cohort of Okinawans (aged 65-plus) for evidence of CR. Analyses included traditional diet composition, energy intake, energy expenditure, anthropometry, plasma DHEA, mortality from age-related diseases, and current survival patterns. Findings include low caloric intake and negative energy balance at younger ages, little weight gain with age, life-long low BMI, relatively high plasma DHEA levels at older ages, low risk for mortality from age-related diseases, and survival patterns consistent with extended mean and maximum life span. This study lends epidemiologic support for phenotypic benefits of CR in humans and is consistent with the well-known literature on animals with regard to CR phenotypes and healthy aging.


title: Detecting Qualia in Natural and Artificial Agents

abstract: The Hard Problem of consciousness has been dismissed as an illusion. By showing that computers are capable of experiencing, we show that they are at least rudimentarily conscious with potential to eventually reach superconsciousness. The main contribution of the paper is a test for confirming certain subjective experiences in a tested agent. We follow with analysis of benefits and problems with conscious machines and implications of such capability on future of computing, machine rights and artificial intelligence safety.


title: First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs

abstract: We present a method to perform first-pass large vocabulary co ntinuous speech recognition using only a neural network and language model. Deep neural network acoustic models are now commonplace in HMM-based speech recognition systems, but building such systems is a complex, domain-specific task. Recent work demonstrated the feasibility of discarding the HMM sequence modeling framework by directly predicting transcript text from audio. This paper extends this approach in two ways. First, we demonstrate that a straightforward recurrent neural network architecture can achieve a high level of accuracy. Second, we propose and evaluate a modified prefix-search decoding algorith m. This approach to decoding enables first-pass speech recognition with a langu age model, completely unaided by the cumbersome infrastructure of HMM-based systems. Experiments on the Wall Street Journal corpus demonstrate fairly competitive word error rates, and the importance of bi-directional network recurrence.


title: Image thresholding based on swarm intelligence technique for image segmentation

abstract: Image thresholding is a critical task of image segmentation. Selection of the optimal value of the threshold is the most important task for image thresholding. The better the value of threshold better is the quality of segmentation. In this paper, recent swarm intelligence technique (fireworks algorithm) has been used for image thresholding. Fireworks algorithm is used to maximize two functions, namely Kapur and Otsu. Results show that quality of segmentation is better in case of Firewok-Otsu than Firework-Kapur. Comparison of results has been done on the basis of PSNR value.


title: Towards mobile cloud authentication and gait based security using time warping technique

abstract: In this paper, we propose an effective algorithm called multi-cross template matching using fast dynamic time warping (MuCFdTW) and is used in authentication and data security through gait based encryption, in mobile cloud services. MuCFdTW improves the authentication of the user in lesser time with an accuracy of more than 99%. The data flow between the mobile device and cloud server is secured through encryption using Organization ID and private key. These keys are derived from the parameters of Gait cyclic patterns, which enhances the security by double fold. This framework effectively addresses the efficiency limitations of public key generation and computational overhead. It also removes the revocation issues as the access of the users will be automatically revoked as they move out of the organization sites. Experimental results and comparison reveals that false positive rate is better compared to other existing identity based systems with encryption and decryption time ranging from 3 to 5 ms and the time taken is comparatively less ranging from 3.4 \({\pm }\) 0.5%.


title: Alleviating Solar Energy Congestion in the Distribution Grid via Smart Metering Communications

abstract: The operation and control of the existing power grid system, which is challenged with rising demands and peak loads, has been considered passive. Congestion is often discovered in high-demand regions, and at locations where abundant renewable energy is generated and injected into the grid; this is attributed to a lack of transmission lines, transfer capability, and transmission capacity. While developing distributed generation (DG) tends to alleviate the traditional congestion problem, employing information and communications technology (ICT) helps manage DG more effectively. ICT involves a vast amount of data to facilitate a broader knowledge of the network status. Data computation and communications are critical elements that can impact the system performance. In this paper, we consider congestion caused by power surpluses produced from households' solar units on rooftops or on ground. Disconnecting some solar units is required to maintain the reliability of the distribution grid. We propose a model for the disconnection process via smart metering communications between smart meters and the utility control center. By modeling the surplus congestion issue as a knapsack problem, we can solve it by proposed greedy solutions. Reduced computation time and data traffic in the network can be achieved.


title: Efficient Utilization of Rare Earth Permanent-Magnet Materials and Torque Ripple Reduction in Interior Permanent-Magnet Machines

abstract: This paper proposes a new interior permanent-magnet machine (IPMM) design in which the rotor consists of several rotor segments arranged in the axial direction, aiming to improve the utilization of rare earth permanent-magnet (PM) materials and reduce the torque ripple. The proposed design is optimized to feature a ${\text{21}}\%$ reduction of rare earth PM materials and a ${\text{50}}\%$ reduction of ripple torque ratio compared with the Camry 2007 design, which uses the conventional pole-shaping technique to suppress its torque ripple. Although the torque is reduced by ${\text{9}}\%$ , the torque per magnet weight is improved by ${\text{15}}\%$ , indicating the PMs are more efficiently used in the proposed design. The designs are verified by 3-D finite element. Despite the small torque reduction, the efficiency of the proposed design is still about the same as the Camry design.


title: Typical Children's Responsivity Patterns of the Tactile and Vestibular Systems

abstract: OBJECTIVES. The purposes of this study were to investigate the responsivity patterns of typical 3- and 4year-old Israeli children to tactile or vestibular stimulation, or both, and to examine whether differences in these patterns exist between them with respect to age and gender. METHOD. The study sample consisted of one hundred seventeen 3-year-old and one hundred forty-three 4year-old healthy Israeli children (N = 260). Mothers of these children completed a comprehensive tactile and vestibular responsivity questionnaire. RESULTS. The subjects’ tactile and vestibular responsivity scores were neither hyperresponsive nor hyporesponsive. In addition, neither age nor gender was found to significantly differentiate between respective participant groups for hypo- or hyperresponsive behaviors. CONCLUSION. Typical Israeli children can be characterized by moderate responsivities to tactile and vestibular stimulation. Responsivity to tactile and vestibular input was similar for 3- and 4-year-olds, across genders.


title: A Hybrid Algorithm For Robot Path Planning

abstract: This paper uses a gradient-based optimization algorithm to reduce the oscillation of the path planned by artificial potential field method in presence of obstacles and in narrow passages, then this improved method is combined with A * algorithm to plan a path. This hybrid algorithm takes the best of both methods to reduces the overall path cost by using A * as well as time complexity by adapting the advantage of artificial potential field. Experimental results prove the effectiveness of the hybrid algorithm.


title: Semi-supervised Feature Learning For Improving Writer Identification

abstract: Data augmentation is typically used by supervised feature learning approaches for offline writer identification, but such approaches require a mass of additional training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline is proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we propose a weighted label smoothing regularization (WLSR) method for data augmentation, which assigns a weighted uniform label distribution to the extra unlabeled data. The WLSR method regularizes the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning approach significantly improves the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline writer identification.


title: Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A PSO Approach

abstract: Multiple photovoltaic (PV) modules feeding a common load is the most common form of power distribution used in solar PV systems. In such systems, providing individual maximum power point tracking (MPPT) schemes for each of the PV modules increases the cost. Furthermore, its v-i characteristic exhibits multiple local maximum power points (MPPs) during partial shading, making it difficult to find the global MPP using conventional single-stage (CSS) tracking. To overcome this difficulty, the authors propose a novel MPPT algorithm by introducing a particle swarm optimization (PSO) technique. The proposed algorithm uses only one pair of sensors to control multiple PV arrays, thereby resulting in lower cost, higher overall efficiency, and simplicity with respect to its implementation. The validity of the proposed algorithm is demonstrated through experimental studies. In addition, a detailed performance comparison with conventional fixed voltage, hill climbing, and Fibonacci search MPPT schemes are presented. Algorithm robustness was verified for several complicated partial shading conditions, and in all cases this method took about 2 s to find the global MPP.


title: Lists2Networks: Integrated analysis of gene/protein lists

abstract: BackgroundSystems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses.ResultsWe present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system.ConclusionsLists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org.


title: Psychometric Properties of the Perceived Diabetes Self-Management Scale (PDSMS)

abstract: Patients with chronic medical conditions such as diabetes mellitus need to self-manage their condition for optimal outcomes. The degree to which patients with diabetes feel competent or self-efficacious in managing their diabetes has been related to behaviors such as dieting or exercise as well as to indicators of glycemic control. A diabetes-specific adaptation of the Perceived Medical-Condition Self-Management Scale was administered to 398 patients with either type 1 (n = 57) or type 2 (n = 341) diabetes. Cronbach’s alpha of the 8-item Perceived Diabetes Self-Management Scale (PDSMS) was .83 indicating internal consistency. PDSMS scores were uncorrelated with age, years since diagnosis, and years of schooling, but were negatively correlated with body mass index. Those with type 1 DM had higher PDSMS scores than those with type 2 DM. PDSMS scores were positively correlated with a number of self-reported self-care activities and negatively related to measures of glycemic control. The PDSMS is a valid measure of diabetes self-efficacy, and the more generic template from which it was adapted can easily be altered for use with other chronic medical conditions.


Automatic Item Generation


title: A Fast Multiport Memory Based on Single-Port Memory Cells

abstract: Abstract : We present a new design for dual-port memories that uses single-port memory cells but guarantees fast deterministic read/write access. The basic unit of storage is the word, rather than the bit, and addressing conflicts result in bit errors that are removed by correction circuitry. The addressing scheme uses Galois field arithmetic to guarantee that the maximum number of bit errors in any word accessed is one. These errors can be corrected every time with a simple correction scheme. The scheme can be generalized to an arbitrary number of ports.


title: Star diamond: an efficient algorithm for fast block matching motion estimation in H264/AVC video codec

abstract: Block based motion estimation is usually used to reduce the computational complexity while preserving a good quality of the images used in real-time video coding. A new block matching motion estimation algorithm called star diamond search is proposed in this paper. This one proceeds in two stages: the first one consists in carrying out a search having star shape. This one aims a coarse search for the solution, the second phase consists in refining the search for the solution. A comparative study with the most known methods is given showing the interest of the proposed algorithm.


title: Effect of Amiodarone on the Serum Concentration/Dose Ratio of Metoprolol in Patients with Cardiac Arrhythmia

abstract: Amiodarone has pharmacokinetic interactions with a number of therapeutic drugs, including warfarin, phenytoin, flecainide, and cyclosporine. Metoprolol is mainly metabolized by CYP2D6, and desethylamiodarone, a metabolite of amiodarone, has a markedly greater inhibitory effect on CYP2D6 than amiodarone. Therefore, the goal of this study was to evaluate the effect of amiodarone and desethylamiodarone on the serum concentration/dose ratio (C/D) of metoprolol in 120 inpatients with cardiac arrhythmias that received either metoprolol and amiodarone (MET+AMD group, n=30) or metoprolol alone (MET group, n=90). The ratio of administered metoprolol was compared between the MET and the MET+AMD groups. The dose of metoprolol and patient age were significantly higher in the MET group when compared with the MET+AMD group (1.00+/-0.480 versus 0.767+/-0.418 mg/kg/day, p<0.050; 68.6+/-10.6 versus 57.6+/-14.1 years, p<0.001, respectively), but the C/D ratio was significantly lower in the MET group than in the MET+AMD group (90.8+/-64.0 versus 136+/-97.8, p<0.01). Furthermore, a significant correlation was found between the C/D ratio and desethylamiodarone concentration (n=30, r=0.371, p<0.01). The results suggest that there is a significant interaction between amiodarone and metoprolol via desethylamiodarone-induced inhibition of CYP2D6. Therefore, careful monitoring of metoprolol concentrations/bioactivity of CYP2D6 is required in the context of co-administration of amiodarone and metoprolol.


title: A fuzzy detection approach toward different speed port scan attacks based on Dempster-Shafer evidence theory

abstract: Port scan detection is one of the important topics in network security and has received lots of attention by researchers; however a slow port scan attack can deceive most of the existing IDS. Besides, it is unreasonable in typical detection to decide whether it is a probe based on the precise threshold especially when the feature values are around the threshold without taking the uncertainty into consideration. To address these problems, a novel approach was proposed by collecting traffic statistics information called access port set APS for each IP address in time windows; several traffic features are extracted from APS which are considered as multiple evidences to indicate a probe. For each evidence, three probabilities are concerned to evaluate the likelihood of the probe occurring which include the probabilities of support, nonsupport and uncertainty. The comprehensive evidence can be obtained from the combination of multiple evidences to evaluate the probe threaten. Several experiments were performed to evaluate the approach with DARPA/MIT datasets and our own generated attack datasets; the experimental results show the feasibility of our approach in terms of detection accuracy and effectiveness. The mechanism can be applied not only in port scan detection but also other precise threshold based situations such as traffic abnormal analysis and intrusion detection. Copyright © 2016 John Wiley & Sons, Ltd.


title: Box-trees and R-trees with near-optimal query time

abstract: A box-tree is a \ifasci so-called \emph{bounding-volume hierarchy} \else bounding-volume hierarchy \fi that uses axis-aligned boxes as bounding volumes. The query complexity of a box-tree with respect to a given type of query is the maximum number of nodes visited when answering such a query. We describe several new algorithms for constructing box-trees with small worst-case query complexity with respect to queries with axis-parallel boxes and with points. We also prove lower bounds on the worst-case query complexity for box-trees, which show that our results are optimal or close to optimal. Finally, we present algorithms to convert box-trees to R-trees, resulting in R-trees with (almost) optimal query complexity.


Q methodology as a constructivist method


title: Modeling mention, context and entity with neural networks for entity disambiguation

abstract: Given a query consisting of a mention (name string) and a background document, entity disambiguation calls for linking the mention to an entity from reference knowledge base like Wikipedia. Existing studies typically use hand-crafted features to represent mention, context and entity, which is laborintensive and weak to discover explanatory factors of data. In this paper, we address this problem by presenting a new neural network approach. The model takes consideration of the semantic representations of mention, context and entity, encodes them in continuous vector space and effectively leverages them for entity disambiguation. Specifically, we model variable-sized contexts with convolutional neural network, and embed the positions of context words to factor in the distance between context word and mention. Furthermore, we employ neural tensor network to model the semantic interactions between context and mention. We conduct experiments for entity disambiguation on two benchmark datasets from TAC-KBP 2009 and 2010. Experimental results show that our method yields state-of-the-art performances on both datasets.


Rule Based Machine Translation Combined with Statistical Post Editor for Japanese to English Patent Translation


title: Neural network-based adaptive tracking control of mobile robots in the presence of wheel slip and external disturbance force

abstract: Abstract In this paper, a novel adaptive tracking controller is proposed for mobile robots in presence of wheel slip and external disturbance force based on neural networks with online weight updating laws. The uncertainties due to the wheel slip and external force are compensated online by neural networks in order to achieve the desired tracking performance. The online weight updating laws are modified versions of the backpropagation with an e-modification term added for robustness. The global uniformly ultimately bounded stability of the system to an arbitrarily small neighborhood of the origin is proven using Lyapunov method. The validity of the proposed controller is confirmed by two simulation examples of tracking a straight line and a U-shape trajectory.


title: Anterior Ethmoidal Artery Emerging Anterior to Bulla Ethmoidalis: An Abnormal Anatomical Variation in Waardenburg's Syndrome

abstract: In endoscopic sinus surgery, the anterior ethmoidal artery (AEA) is usually identified as it traverses obliquely across the fovea ethmoidalis, posterior to the bulla ethmoidalis and anterior to or within the ground lamella's attachment to the skull base. Injury to the AEA may result in hemorrhage, retraction of the AEA into the orbit, and a retrobulbar hematoma. The resulting increase in intraorbital pressure may threaten vision. Waardenburg's syndrome (WS) is a rare congenital, autosomal dominantly inherited disorder, distinguished by characteristic facial features, pigmentation abnormalities, and profound, congenital, sensorineural hearing loss. We present a case of AEAs located anterior to the bulla ethmoidalis in a 36-year-old male with WS and chronic rhinosinusitis. The anatomic abnormality was not obvious on a preoperative computed tomography scan and was discovered intraoperatively when the left AEA was injured, resulting in a retrobulbar hematoma. The hematoma was immediately identified and decompressed endoscopically without lasting complications. The AEA on the right was identified intraoperatively and preserved. The characteristic craniofacial features in WS were probably associated with the abnormal vascular anatomy. Endoscopic sinus surgeons should be aware of these potential anatomic anomalies in patients with abnormal craniofacial development.


title: Evaluating Bluetooth Low Energy in realistic wireless environments

abstract: The 2.4 GHz ISM band is crowded with a wide variety of wireless devices operating under various access protocols, Bluetooth Low Energy (BLE) among them. Low power requirements, low cost, and ease of integration have promoted BLE's rapidly growing popularity. BLE applications range from providing wireless interface for monitoring household equipment status to reporting critical information from medical devices that might have less tolerance for transmission errors to function properly. In this paper, we identify risks in a real world wireless environment that adversely affect BLE system functionality. We also propose a methodology utilizing spectrum surveys to quantify probability of transmission failure relative to the system's interference detection threshold. Spectrum surveys were conducted in a basketball sport facility, a university student union and a hospital intensive care unit (ICU). Results demonstrate how a BLE system selects data transmission channels in the presence of interference. Moreover, findings of this study confirm that a BLE system is able to maintain a low probability of failed transmission while operating in the presence of high interference unless the environment noise floor is close to the employed interference detection threshold.


title: Advanced Gate Drive Unit With Closed-Loop $di_{{C}}/dt$ Control

abstract: This paper describes the design and the experimental investigation of a gate drive unit with closed-loop control of the collector current slope diC/dt for multichip insulated-gate bipolar transistors (IGBTs). Compared to a pure resistive gate drive, the proposed diC/dt control offers the ability to adjust the collector current slope freely which helps to find an optimized relation between switching losses and secure operation of the freewheeling diode for every type of IGBT. Based on the description of IGBT's switching behavior, the design and the realization of the gate drive are presented. The test setup and the comparison of switching tests with and without the proposed diC/dt control are discussed.


title: Identification of important citations by exploiting research articles’ metadata and cue-terms from content

abstract: Citations play a pivotal role in indicating various aspects of scientific literature. Quantitative citation analysis approaches have been used over the decades to measure the impact factor of journals, to rank researchers or institutions, to discover evolving research topics etc. Researchers doubted the pure quantitative citation analysis approaches and argued that all citations are not equally important; citation reasons must be considered while counting. In the recent past, researchers have focused on identifying important citation reasons by classifying them into important and non-important classes rather than individually classifying each reason. Most of contemporary citation classification techniques either rely on full content of articles, or they are dominated by content based features. However, most of the time content is not freely available as various journal publishers do not provide open access to articles. This paper presents a binary citation classification scheme, which is dominated by metadata based parameters. The study demonstrates the significance of metadata and content based parameters in varying scenarios. The experiments are performed on two annotated data sets, which are evaluated by employing SVM, KLR, Random Forest machine learning classifiers. The results are compared with the contemporary study that has performed similar classification employing rich list of content-based features. The results of comparisons revealed that the proposed model has attained improved value of precision (i.e., 0.68) just by relying on freely available metadata. We claim that the proposed approach can serve as the best alternative in the scenarios wherein content in unavailable.


title: Design of Incoherent Frames via Convex Optimization

abstract: This paper describes a new procedure for the design of incoherent frames used in the field of sparse representations. We present an efficient algorithm for the design of incoherent frames that works well even when applied to the construction of relatively large frames. The main advantage of the proposed method is that it uses a convex optimization formulation that operates directly on the frame, and not on its Gram matrix. Solving a sequence of convex optimization problems allows for the introduction of constraints on the frame that were previously considered impossible or very hard to include, such as non-negativity. Numerous experimental results validate the approach.


title: Hyperparameters: optimize, or integrate out?

abstract: I examine two approximate methods for computational implementation of Bayesian hierarchical models, that is, models which include unknown hyperparameters such as regularization constants. In the ‘evidence framework’ the model parameters are integrated over, and the resulting evidence is maximized over the hyperparameters. The optimized hyperparameters are used to define a Gaussian approximation to the posterior distribution. In the alternative ‘MAP’ method, the true posterior probability is found by integrating over the hyperparameters. The true posterior is then maximized over the model parameters, and a Gaussian approximation is made. The similarities of the two approaches, and their relative merits, are discussed, and comparisons are made with the ideal hierarchical Bayesian solution.


title: New consumer behavior: A review of research on eWOM and hotels

abstract: Abstract This study aims to gather and analyze published articles regarding the influence of electronic word-of-mouth (eWOM) on the hotel industry. Articles published in the last five years appearing in six different academically recognized journals of tourism have been reviewed in the present study. Analysis of these articles has identified two main lines of research: review-generating factors (previous factors that cause consumers to write reviews) and impacts of eWOM (impacts caused by online reviews) from consumer perspective and company perspective. A summary of each study's description, methodology and main results are outlined below, as well as an analysis of findings. This study also seeks to facilitate understanding and provide baseline information for future articles related to eWOM and hotels with the intention that researchers have a “snapshot” of previous research and the results achieved to date.


title: Crawling AJAX by Inferring User Interface State Changes

abstract: AJAX is a very promising approach for improving rich interactivity and responsiveness of web applications. At the same time, AJAX techniques shatter the metaphor of a web "page" upon which general search crawlers are based. This paper describes a novel technique for crawling AJAX applications through dynamic analysis and reconstruction of user interface state changes. Our method dynamically infers a state-flow graph modeling the various navigation paths and states within an AJAX application. This reconstructed model can be used to generate linked static pages. These pages could be used to expose AJAX sites to general search engines. Moreover, we believe that the crawling techniques that are part of our solution have other applications, such as within general search engines, accessibility improvements, or in automatically exercising all user interface elements and conducting state-based testing of AJAX applications. We present our open source tool called CRAWLJAX which implements the concepts discussed in this paper. Additionally, we report a case study in which we apply our approach to a number of representative AJAX applications and elaborate on the obtained results.


Are these Ads Safe: Detecting Hidden Attacks through the Mobile App-Web Interfaces


title: THE ANALYSIS OF POTATO CHIPS PRODUCT DAMAGED RISK IN WIDURI JOINT VENTURE GROUP

abstract: ABSTRACT The purpose of this research is to identify the risks of potato chips product damaged, to measure and map the risk and formulating an effective risk mitigation strategy for Widuri joint venture group. This research uses qualitative approach. The method of risk management with Enterprise Risk Management (ERM). The location of the research is in Kijang City, Bintan, Kepulauan Riau. Data research obtained by interview, observation and documentation. The results showed that there are several factors that can cause the failure of the production process and the product itself which was an error in the selection of raw materials, passing the process of soaking potatoes, a mistake in the provision of lime paste, errors at uncontrolled fire, and neglect of human resources that have impact for entity. The impact of arising from risk was product destroyed, potatoes become scorched, and the potato chips would be easy to catch cold. The amount of risk experienced by Widuri joint venture group were reflected in the risk map formed on the measurement of risk based on probability and impact. Widuri joint venture group certainly have to develop a strategy for an effective risk mitigation were prioritized addressing the highest risk first to a lower risk so that the risk of product damaged can be minimized. KeyWords: ERM, Product Damaged, Joint Venture


title: Microplastics Generation: Onset of Fragmentation of Polyethylene Films in Marine Environment Mesocosms

abstract: The fragmentation of high-density polyethylene (HDPE) films from single-use supermarket plastic bags to microplastics under laboratory-simulated onshore and nearshore conditions was investigated for a period of 6 months. The weathering process of the plastic strips either on beach sand or in seawater under direct natural sunlight was monitored by tensile strength, molecular weight measurements, FTIR, weight loss and image processing of photographs of the plastic strips before and after mild mechanical stress was applied. The latter represents a novel method proposed for determining the onset of fragmentation through the application of mild mechanical stress on the weathered plastic samples emulating the action of sand and wind on a beach. It was found that 12 h of application of mild mechanical stress in rotating glass bottles filled partially with sand was sufficient time to reach the maximum degree of fragmentation that could occur for the weathered plastics samples being tested. For example, applied mechanical stress yielded an area loss of almost 14% for samples weathered for a period of 5 months and about 16.7% after 5.5 months. While tensile strength tests and molecular weight measurements were rather inconclusive till the very last month when the onset of fragmentation was identified; FTIR measurements revealed that samples under ultraviolet irradiation were gradually modified chemically until fragmentation commenced. After 6 months of weathering, molecular weight measurements showed a 60% reduction for sample SMB-1 whereas for sample SMB-2 the measurement was not possible due to extensive fragmentation. The onset of fragmentation for SMB-1 and SMB-2 samples occurred at a cumulative luminance of 5.3x106 lux•d and in the presence of atmospheric oxygen whereby the polymer films broke down partially to microplastics. When the UV exposure reached 7.2x106 lux•d the weathered plastic strips broke down fully to microplastics with the application of a mild mechanical stress. Samples placed in seawater proved to be resistant to fragmentation compared to those on sand over the six-month period of the weathering experiment. The direct implication of this work is that beached macroplastic debris should be regularly collected from the seashore before they are weathered by sunlight and returned to the


title: Development of Support Apparatuses for Elderly to Keep Vital Life

abstract: This paper presents the support apparatuses, we have developed, from the viewpoint of engineering for elderly people to keep vital life. The apparatuses are introduced in this paper and are many kinds of support technologies, we especially focused on the knowledge and the exercise activity. From this, the six items as enhancing those activities have been considered.


title: Analysis of Electrostatic MEMS Using Energy-Charge Landscape

abstract: A common way to analyze electrostatic Micro Electro Mechanical Systems (MEMS) actuators is to use their energy-displacement landscape. Here, we introduce an alternative approach to analyze electrostatic MEMS actuators using their energy-charge landscape. This technique involves coordinate transformation from displacement to charge, thereby formulating the Hamiltonian of electrostatic MEMS actuators in terms of charge. We present the first investigation on using the energy-charge landscape to analyze static pull-in, dynamic pull-in and pull-out phenomena in a unified manner. The voltage expressions derived using this method are identical with those derived using the conventional energy-displacement landscape. In addition, we also obtain the expressions for charge under static and dynamic pull-in conditions. This work can aid in the design and analysis of electrostatic MEMS devices.


title: Feature Learning with Rank-Based Candidate Selection for Product Search

abstract: Nowadays, more and more people buy products via e-commerce websites. We can not only compare prices from different online retailers but also obtain useful review comments from other customers. Especially, people tend to search for visually similar products when they are looking for possible candidates. The need for product search is emerging. To tackle the problem, recent works integrate different additional information (e.g., attributes, image pairs, category) with deep convolutional neural networks (CNNs) for solving cross-domain image retrieval and product search. Based on the state-of-the-art approaches, we propose a rank-based candidate selection for feature learning. Given a query image, we attempt to push hard negative (irrelevant) images away from queries and make ambiguous positive (relevant) images close to queries. We investigate the effects of global and attention-based local features on the proposed method, and achieve 15.8% relative gain for product search.


title: Design and Fabrication of a Six Degree-of-Freedom Open Source Hand

abstract: Currently, most externally powered prostheses are controlled using electromyography (or EMG), which is the measure of the electrical signals that are produced when voluntary muscle is contracted. One of the major problems is that there are a limited number of muscular control sites that can be used, which limits the complexity of the hands that are controllable. Many upper-limb prosthetics researchers are searching for methods to simply and effectively control complex prosthetic hands, and a significant number of these researchers have utilized virtual hands and other simulations to perform testing of these control algorithms (oftentimes on able bodied subjects). Thus, these control techniques remain firmly planted in the virtual realm, and the authors postulate that the development of a physical hand would help to validate results obtained through use of virtual hands and could help establish whether or not a given control scheme is realistically applicable for use by amputees. The development of such a hand would be beneficial for researchers in the field. A six degree-of-freedom hand was developed with such a purpose in mind, and two of the major goals of the project were that the hand be inexpensive and open source. The hand design is being shared on www.opensourcehand.wordpress.com .


title: Consumer Attitudes Toward Online Mass Customization: An Application of Extended Technology Acceptance Model

abstract: This study investigates consumer attitudes toward the online codesign process in mass customization among Korean consumers. The extended Technology Acceptance Model (TAM) was our theoretical framework, along with the constructs of perceived control and enjoyment. Web skill and fashion involvement were integrated as individual characteristics variables in the model. Data from 749 self-administered surveys collected in South Korea were used in this study. Findings indicated that TAM was an effective conceptual framework in explaining consumer response to the online codesign process. The statistically significant results of the perceived enjoyment and control constructs in the extended TAM suggest the importance of their addition to building a model to explain consumer attitudes toward the online codesign process.


title: Memory-Based Methods for Regression and Classification

abstract: Memory-based learning methods operate by storing all (or most) of the training data and deferring analysis of that data until "run time" (i.e., when a query is presented and a decision or prediction must be made). When a query is received, these methods generally answer the query by retrieving and analyzing a small subset of the training data--namely, data in the immediate neighborhood of the query point. In short, memory-based methods are "lazy" (they wait until the query) and "local" (they use only a local neighborhood). The purpose of this workshop was to review the state-of-the-art in memory-based methods and to understand their relationship to "eager" and "global" learning algorithms such as batch backpropagation.


title: Efficacy of a 3 month training program on the jump-landing technique in jump-landing sports. Design of a cluster randomized controlled trial

abstract: BackgroundWith the relatively high rate of injuries to the lower extremity due to jump-landing movement patterns and the accompanied high costs, there is need for determining potential preventive programs. A program on the intervention of jump-landing technique is possibly an important preventative measure since it appeared to reduce the incidence of lower extremity injuries. In real life situations, amateur sports lack the infrastructure and funds to have a sports physician or therapist permanently supervising such a program. Therefore the current prevention program is designed so that it could be implemented by coaches alone.ObjectiveThe objective of this randomized controlled trial is to evaluate the effect of a coach supervised intervention program targeting jump-landing technique on the incidence of lower extremity injuries.MethodsOf the 110 Flemish teams of the elite division, 24 teams are included and equally randomized to two study groups. An equal selection of female and male teams with allocation to intervention and control group is obtained. The program is a modification of other prevention programs previously proven to be effective. All exercises in the current program are adjusted so that a more progressive development in the exercise is presented. Both the control and intervention group continue with their normal training routine, while the intervention group carries out the program on jump-landing technique. The full intervention program has a duration of three months and is performed 2 times a week during warm-up (5-10 min). Injuries are registered during the entire season.DiscussionThe results of this study can give valuable information on the effect of a coach supervised intervention program on jump-landing technique and injury occurrence. Results will become available in 2011.Trial registrationTrial registration number: NTR2560


title: ISABELA-QA: Query-driven analytics with ISABELA-compressed extreme-scale scientific data

abstract: Efficient analytics of scientific data from extreme-scale simulations is quickly becoming a top-notch priority. The increasing simulation output data sizes demand for a paradigm shift in how analytics is conducted. In this paper, we argue that query-driven analytics over compressed — rather than original, full-size — data is a promising strategy in order to meet storage-and-I/O-bound application challenges. As a proof-of-principle, we propose a parallel query processing engine, called ISABELA-QA that is designed and optimized for knowledge priors driven analytical processing of spatio-temporal, multivariate scientific data that is initially compressed, in situ, by our ISABELA technology. With ISABELA-QA, the total data storage requirement is less than 23%–30% of the original data, which is upto eight-fold less than what the existing state-of-the-art data management technologies that require storing both the original data and the index could offer. Since ISABELA-QA operates on the metadata generated by our compression technology, its underlying indexing technology for efficient query processing is light-weight; it requires less than 3% of the original data, unlike existing database indexing approaches that require 30%–300% of the original data. Moreover, ISABELA-QA is specifically optimized to retrieve the actual values rather than spatial regions for the variables that satisfy user-specified range queries — a functionality that is critical for high-accuracy data analytics. To the best of our knowledge, this is the first techology that enables query-driven analytics over the compressed spatio-temporal floating-point double-or single-precision data, while offering a light-weight memory and disk storage footprint solution with parallel, scalable, multi-node, multi-core, GPU-based query processing.


title: A Low Radar Cross Section and Low Profile Antenna Co-Designed With Absorbent Frequency Selective Radome

abstract: A low radar cross section (RCS) and low profile antenna co-designed with absorbent frequency selective radome (AFSR) is investigated. A pair of circular slot resonators is embedded on surface of the AFSR to realize a transmission window in the vertical polarization, while the wide absorption band is still maintained in the horizontal polarization. When a patch antenna is etched within the AFSR, where the metal grounds of the patch antenna and AFSR are co-used, the co-designed antenna with low RCS and low profile is thus realized. For demonstration, an AFSR is designed with its transmission window has a minimal insertion loss of 0.45 dB at 8.9 GHz, and two separate absorption bands (a lower absorption band from 4.8 to 7.5 GHz and an upper absorption band from 10 to 13 GHz) in the vertical polarization, a wide absorption band (from 4.5 to 12.5 GHz) in the horizontal polarization. A patch antenna etched within the AFSR is optimized to operate at 8.9 GHz, then it is simulated and fabricated. The measured results demonstrate that the proposed antenna not only has good radiation patterns, but also obtains significant RCS reduction.


title: Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

abstract: Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.


title: Impostor Networks for Fast Fine-Grained Recognition

abstract: In this work we introduce impostor networks, an architecture that allows to perform fine-grained recognition with high accuracy and using a light-weight convolutional network, making it particularly suitable for fine-grained applications on low-power and non-GPU enabled platforms. Impostor networks compensate for the lightness of its `backend' network by combining it with a lightweight non-parametric classifier. The combination of a convolutional network and such non-parametric classifier is trained in an end-to-end fashion. Similarly to convolutional neural networks, impostor networks can fit large-scale training datasets very well, while also being able to generalize to new data points. At the same time, the bulk of computations within impostor networks happen through nearest neighbor search in high-dimensions. Such search can be performed efficiently on a variety of architectures including standard CPUs, where deep convolutional networks are inefficient. In a series of experiments with three fine-grained datasets, we show that impostor networks are able to boost the classification accuracy of a moderate-sized convolutional network considerably at a very small computational cost.


title: HGR-Net: A Two-stage Convolutional Neural Network for Hand Gesture Segmentation and Recognition.

abstract: Robust recognition of hand gesture in real-world applications is still a challenging task due to the many aspects such as cluttered backgrounds and uncontrolled environment factors. In most existing methods hand segmentation is a primary step for hand gesture recognition, because it reduces redundant information from the image background, before passing them to the recognition stages. Therefore, in this paper we propose a two-stage deep convolutional neural network (CNN) architecture called HGR-Net, where the first stage performs accurate pixel-level semantic segmentation into hand region and the second stage identifies hand gesture style. The segmentation stage architecture is based on the combination of fully convolutional deep residual neural network and atrous spatial pyramid pooling. Although the segmentation sub-network is trained without using depth information, it is robust enough against challenging situations such as changes in the lightning and complex backgrounds. In the recognition stage a two-stream CNN is used to obtain the best classification score. We also apply an effective data augmentation technique for maximizing the generalization capability of HGR-Net. Extensive experiments on public hand gesture datasets show that our deep architecture achieves prominent performance in segmentation and recognition for static hand gestures.