title: Measuring E-mail header injections on the world wide web

abstract: E-mail header injection vulnerability is a class of vulnerability that can occur in web applications that use user input to construct e-mail messages. E-mail header injection vulnerabilities exist in the built-in e-mail functionality of the popular languages PHP, Java, Python, and Ruby. With the proper injection string, this vulnerability can be exploited to allow an attacker to inject additional headers, modify existing headers, and alter the content of the e-mail. While E-mail header injection vulnerabilities are known to the community, and some commercial vulnerability scanners claim to discover E-mail header injection vulnerabilities, they have never been studied by the academic community. This paper presents a scalable mechanism to automatically detect E-mail header injection vulnerabilities and uses this mechanism to quantify the prevalence of E-mail header injection vulnerabilities on the web. From crawling 23,553,796 URLs, we found 994 vulnerable URLs across 414 domains. 135 of these domains are in the Alexa top 1 million, and five of them are in the top 20,000. 137 of the vulnerable domains are using anti-spoofing mechanisms such as DKIM, SPF, or DMARC, and E-mail header injection renders this protection useless. This work shows that E-mail header injection vulnerabilities are widespread and deserve future research attention.


title: Modeling and Control of a Power-Split Hybrid Vehicle

abstract: Toyota hybrid system (THS) is used in the current best selling hybrid vehicle on the market-the Toyota Prius. This hybrid system contains a power-split planetary gear system which combines the benefits of series and parallel hybrid vehicles. In this paper, we developed a dynamic model of the THS powertrain and then apply it for model-based control development. Two control algorithms are introduced: one based on the stochastic dynamic programming method, and the other based on the equivalent consumption minimization strategy. Both approaches determine the engine power based on the overall vehicle efficiency and apply the electrical machines to optimize the engine operation. The performance of these two algorithms is assessed by comparing against the dynamic programming results, which are non-causal but provide theoretical benchmarks for other implementable control algorithms.


title: Comparative study on power conversion methods for wireless battery charging platform

abstract: In this paper, four different power conversion methods (voltage control, duty-cycle control, frequency control and phase-shift control) are compared for wireless power transfer applications by considering the energy transfer efficiency, electromagnetic interference, stability, and implementation complexity. The phase-shift control is found to be the optimal scheme with good efficiency and low electromagnetic interference. Its constant frequency feature is also within the framework of the new international wireless charging standard called ‘Qi’. A high system efficiency of 72% for 5W wireless charging applications has been achieved practically.


title: Malware classification using static analysis based features

abstract: Anti-virus vendors receive hundreds of thousands of malware to be analysed each day. Some are new malware while others are variations or evolutions of existing malware. Because analyzing each malware sample by hand is impossible, automated techniques to analyse and categorize incoming samples are needed. In this work, we explore various machine learning features extracted from malware samples through static analysis for classification of malware binaries into already known malware families. We present a new feature based on control statement shingling that has a comparable accuracy to ordinary opcode n-gram based features while requiring smaller dimensions. This, in turn, results in a shorter training time.


title: Real-time texture-mapped vector glyphs

abstract: We present a vector graphics representation suitable for real-time rendering on GPUs. Our representation can be used in place of a texture map, and renders precise antialiased edges at any magnification. A combination of texture data and procedural computation is used to evaluate an exact signed distance to a contour and its gradient. An optimized uniform grid accelerator is created using Voronoi analysis and redundancy elimination, so only the distances to a small constant number of features need be computed at every access. Contours and sharp features can be exactly reconstructed using a constant amount of computation per pixel. Our representation supports inexpensive high-quality anisotropic antialiasing as well as special effects such as outlining (with both rounded and sharp miters) and embossing.We have applied our representation to the important application of glyph rendering. Variations in glyph complexity are handled by storing different glyphs at different grid resolutions. Large blocks of glyphs can be rendered efficiently with a single indirection through an index texture.


title: Effective information technology (IT) governance mechanisms: An IT outsourcing perspective

abstract: Effective IT governance will ensure alignment between IT and business goals. Organizations with ineffective IT governance will suffer due to poor performance of IT resources such as inaccurate information quality, inefficient operating costs, runaway IT project and even the demise of its IT department. This study seeks to examine empirically the individual IT governance mechanisms that influence the overall effectiveness of IT governance. Furthermore, this study examines the relationship of effective IT governance, the extent of IT outsourcing decisions within the organizations, and the level of IT Intensity in the organizations. We used structural equation modeling analysis to examine 110 responses from members of ISACA (Information Systems and Audit Control Association) Australia in which their organizations have outsourced their IT functions. Results suggest significant positive relationships between the overall level of effective IT governance and the following mechanisms: the involvement of senior management in IT, the existence of ethic or culture of compliance in IT, and corporate communication systems.


title: Cannabis and anxiety: a critical review of the evidence

abstract: Background Anxiety reactions and panic attacks are the acute symptoms most frequently associated with cannabis use. Understanding the relationship between cannabis and anxiety may clarify the mechanism of action of cannabis and the pathophysiology of anxiety. Aims of the present study were to review the nature of the relationship between cannabis use and anxiety, as well as the possible clinical, diagnostic and causal implications. Method Systematic review of the Medline, PsycLIT and EMBASE literature. Results Frequent cannabis users consistently have a high prevalence of anxiety disorders and patients with anxiety disorders have relatively high rates of cannabis use. However, it is unclear if cannabis use increases the risk of developing long-lasting anxiety disorders. Many hypotheses have been proposed in an attempt to explain these relationships, including neurobiological, environmental and social influences. Conclusions The precise relationship between cannabis use and anxiety has yet to be established. Research is needed to fully clarify the mechanisms of such the association. Copyright # 2009 John Wiley & Sons, Ltd.


Nitrate ion effects on uranium chemistry in the tributylphosphate-dodecane system


title: A radiographic and histologic study of fracture healing in osteopetrotic rats.

abstract: Repair of tibial fractures in osteopetrotic rats was delayed in comparison to that of normal littermates, due to reduced remodeling. Reduced bone resorption, known to be the cause of the disease in this mutation, is expressed in both skeletal development and fracture repair. The possible implications for human juvenile osteopetrosis are discussed.


title: Explanation-based generalization: A unifying view

abstract: The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.


title: Real-Time Dense Geometry from a Handheld Camera

abstract: We present a novel variational approach to estimate dense depth maps from multiple images in real-time. By using robust penalizers for both data term and regularizer, our method preserves discontinuities in the depth map. We demonstrate that the integration of multiple images substantially increases the robustness of estimated depth maps to noise in the input images. The integration of our method into recently published algorithms for camera tracking allows dense geometry reconstruction in real-time using a single handheld camera. We demonstrate the performance of our algorithm with real-world data.


title: Recognition of Alzheimer's disease and Mild Cognitive Impairment with multimodal image-derived biomarkers and Multiple Kernel Learning

abstract: Computer-Aided Diagnosis (CAD) of Alzheimer's disease (AD) has drawn the attention of computer vision research community over the last few years. Several attempts have been made to adapt pattern recognition approaches to specific neuroimaging data such as Structural MRI (sMRI) for early AD diagnosis. One strategy is to boost the discrimination power of such approaches by integrating complementary imaging modalities in a single learning framework. Diffusion Tensor Imaging (DTI) is a new and promising modality giving complementary information to the anatomical MRI. However, including relevant DTI information from such modality is a challenging problem. In this paper, we propose to extract local image-derived biomarkers from DTI and sMRI to construct multimodal AD signatures. To assess the relevance of such modalities as well as to optimize the classifier, we integrate complementary information using a Multiple Kernel Learning (MKL) framework for AD subjects recognition. To evaluate our method, we perform experiments on a subset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Both T1-weighted MRI and Mean Diffusivity (MD) maps from the DTI modality of 45 AD patients, 52 Normal Control (NC) and 58 Mild Cognitive Impairment (MCI) subjects have been used. The obtained results indicate that our multimodal approach yields significant improvement in accuracy over using each single modality independently. The classification accuracies obtained by the proposed method are 90.2%, 79.42% and 76.63% for respectively AD vs. NC, MCI vs. NC and AD vs. MCI binary classification problems. For the MCI classification problem, the proposed fusion framework leads to an average increase about at least 9% for the accuracy, 5% for the specificity and 15% for the sensitivity.


title: An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges

abstract: Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly focused on single-media retrieval. However, the requirements of users are highly flexible, such as retrieving the relevant audio clips with one query of image. So challenges stemming from the “media gap,” which means that representations of different media types are inconsistent, have attracted increasing attention. Cross-media retrieval is designed for the scenarios where the queries and retrieval results are of different media types. As a relatively new research topic, its concepts, methodologies, and benchmarks are still not clear in the literature. To address these issues, we review more than 100 references, give an overview including the concepts, methodologies, major challenges, and open issues, as well as build up the benchmarks, including data sets and experimental results. Researchers can directly adopt the benchmarks to promptly evaluate their proposed methods. This will help them to focus on algorithm design, rather than the time-consuming compared methods and results. It is noted that we have constructed a new data set XMedia , which is the first publicly available data set with up to five media types (text, image, video, audio, and 3-D model). We believe this overview will attract more researchers to focus on cross-media retrieval and be helpful to them.


title: Gaussian neural networks for glass bottles inspection: a learning procedure.

abstract: In glass bottle inspection, the defects detection is of first importance. For online system detection, high speed and robust detection of faults are highly required. Neural networks have recently, and successfully, been applied to fault detection in many manufacturing processes. In this study, a Gaussian neural network, an extension of the RBF network, trained through a competitive algorithm, has been chosen for fault detection. Four parameters extracted from images of the bottle are used as inputs of the network. The number of Gaussian units is adjusted by an informational criterion. Experimental results show that the performance of this network are better than classical parametric and non parametric classifiers.


Political and Economic Causes of Civil Wars in African Countries Based on Econometric Findings


Nalmefene for reduced-risk drinking: it is not only fancy term for harm reduction


title: THE EVOLUTION OF ACCOUNTING INFORMATION SYSTEMS

abstract: Technological evolution becomes more and more a daily reality for businesses and individuals who use information systems as for supporting their operational activities. This article focuses on the way technological evolution changes the accounting practices, starting from the analysis of the traditional model and trying to determine future trends and arising challenges to face. From data input to consolidation and reporting, accountants’ function and operations are dissected in order to identify to what extent the development of new concepts, such as cloud computing, cloud accounting, real-time accounting or mobile accounting may affect the financial-accounting process, as well as the challenges that arise from the changing environment.


title: Wearable Vibrotactile Haptic Device for Stiffness Discrimination during Virtual Interactions

abstract: In this paper, we discuss the development of cost effective, wireless, and wearable vibrotactile haptic device for stiffness perception during an interaction with virtual objects. Our experimental setup consists of haptic device with five vibrotactile actuators, virtual ::: reality environment tailored in Unity 3D integrating the Oculus Rift Head Mounted Display (HMD) and the Leap Motion controller. The virtual environment is able to capture touch inputs from users. Interaction forces are then rendered at 500 Hz and fed back to the wearable setup stimulating fingertips with ERM vibrotactile actuators. Amplitude and frequency of vibrations are modulated proportionally to the interaction force to simulate the stiffness of a virtual object. A quantitative and qualitative study is done to compare the discrimination of stiffness on virtual linear spring in three sensory modalities: visual only feedback, tactile only feedback, and their combination. A common psychophysics method called the Two Alternative Forced Choice (2AFC) approach is used for quantitative analysis using Just Noticeable Difference (JND) and Weber Fractions (WF). According to the psychometric experiment result, average Weber fraction values of 0.39 for visual only feedback was improved to 0.25 by adding the tactile feedback.


title: Learning to Detect Human-Object Interactions

abstract: We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.


title: Parallel Long Short-Term Memory for multi-stream classification

abstract: Recently, machine learning methods have provided a broad spectrum of original and efficient algorithms based on Deep Neural Networks (DNN) to automatically predict an outcome with respect to a sequence of inputs. Recurrent hidden cells allow these DNN-based models to manage long-term dependencies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Nevertheless, these RNNs process a single input stream in one (LSTM) or two (Bidirectional LSTM) directions. But most of the information available nowadays is from multistreams or multimedia documents, and require RNNs to process these information synchronously during the training. This paper presents an original LSTM-based architecture, named Parallel LSTM (PLSTM), that carries out multiple parallel synchronized input sequences in order to predict a common output. The proposed PLSTM method could be used for parallel sequence classification purposes. The PLSTM approach is evaluated on an automatic telecast genre sequences classification task and compared with different state-of-the-art architectures. Results show that the proposed PLSTM method outperforms the baseline n-gram models as well as the state-of-the-art LSTM approach.


A SIR Model for Spread of Dengue Fever Disease (Simulation for South


title: Hate Source: White Supremacist Hate Groups and Hate Crime

abstract: The relationship between hate group activity and hate crime is theoretically ambiguous. Hate groups may incite criminal behavior in support of their beliefs. On the other hand, hate groups may reduce hate crime by serving as a forum for members to verbally vent their frustrations or as protection from future biased violence. I find that the presence of an active white supremacist hate group chapter is associated with an 18.7 percent higher hate crime rate. White supremacist groups are not associated with the level of anti-white hate crimes committed by non-whites, nor do they form in expectation of future hate crimes by non-whites.


title: A Robust Multi-camera 3D Ellipse Fitting for Contactless Measurements

abstract: Ellipses are a widely used cue in many 2D and 3D object recognition pipelines. In fact, they exhibit a number of useful properties. First, they are naturally occurring in many man-made objects. Second, the projective invariance of the class of ellipses makes them detectable even without any knowledge of the acquisition parameters. Finally, they can be represented by a compact set of parameters that can be easily adopted within optimization tasks. While a large body of work exists in the literature about the localization of ellipses as 2D entities in images, less effort has been put in the direct localization of ellipses in 3D, exploiting images coming from a known camera network. In this paper we propose a novel technique for fitting elliptical shapes in 3D space, by performing an initial 2D guess on each image followed by a multi-camera optimization refining a 3D ellipse simultaneously on all the calibrated views. The proposed method is validated both with synthetic data and by measuring real objects captured by a specially crafted imaging head. Finally, to evaluate the feasibility of the approach within real-time industrial scenarios, we tested the performance of a GPU-based implementation of the algorithm.


title: Comparison of Various Rhinoplasty Techniques and Long-Term Results

abstract: Background ::: Various techniques are used in rhinoplasty. These techniques can be classified under transcolumellar approaches and endonasal procedures. Open rhinoplasty without transcolumellar incision (ORWTI) procedure can be described as a combination of these two techniques.


A Survey of Machine Learning Techniques for Sentiment Classification


title: Performance Comparison of the RPL and LOADng Routing Protocols in a Home Automation Scenario

abstract: RPL, the routing protocol proposed by IETF for IPv6/6LoWPAN Low Power and Lossy Networks has significant complexity. Another protocol called LOADng, a lightweight variant of AODV, emerges as an alternative solution. In this paper, we compare the performance of the two protocols in a Home Automation scenario with heterogenous traffic patterns including a mix of multipoint-to-point and point-to-multipoint routes in realistic dense non-uniform network topologies. We use Contiki OS and Cooja simulator to evaluate the behavior of the ContikiRPL implementation and a basic non-optimized implementation of LOADng. Unlike previous studies, our results show that RPL provides shorter delays, less control overhead, and requires less memory than LOADng. Nevertheless, enhancing LOADng with more efficient flooding and a better route storage algorithm may improve its performance.


title: Ontology based context modeling and reasoning using OWL

abstract: Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources.


title: Motion capture using joint skeleton tracking and surface estimation

abstract: This paper proposes a method for capturing the performance of a human or an animal from a multi-view video sequence. Given an articulated template model and silhouettes from a multi-view image sequence, our approach recovers not only the movement of the skeleton, but also the possibly non-rigid temporal deformation of the 3D surface. While large scale deformations or fast movements are captured by the skeleton pose and approximate surface skinning, true small scale deformations or non-rigid garment motion are captured by fitting the surface to the silhouette. We further propose a novel optimization scheme for skeleton-based pose estimation that exploits the skeleton's tree structure to split the optimization problem into a local one and a lower dimensional global one. We show on various sequences that our approach can capture the 3D motion of animals and humans accurately even in the case of rapid movements and wide apparel like skirts.


title: Fast and Provably Good Seedings for k-Means

abstract: Seeding - the task of finding initial cluster centers - is critical in obtaining high-quality clusterings for k-Means. However, k-means++ seeding, the state of the art algorithm, does not scale well to massive datasets as it is inherently sequential and requires k full passes through the data. It was recently shown that Markov chain Monte Carlo sampling can be used to efficiently approximate the seeding step of k-means++. However, this result requires assumptions on the data generating distribution. We propose a simple yet fast seeding algorithm that produces *provably* good clusterings even *without assumptions* on the data. Our analysis shows that the algorithm allows for a favourable trade-off between solution quality and computational cost, speeding up k-means++ seeding by up to several orders of magnitude. We validate our theoretical results in extensive experiments on a variety of real-world data sets.


title: Reinforced backpropagation improves test performance of deep networks: a toy-model study.

abstract: Standard error backpropagation is used in almost all modern deep network training. However, it typically suffers from proliferation of saddle points in high-dimensional parameter space. Therefore, it is highly desirable to design an efficient algorithm to escape from these saddle points and reach a good parameter region of better generalization capabilities, especially based on rough insights about the landscape of the error surface. Here, we propose a simple extension of the backpropagation, namely reinforced backpropagation, which simply adds previous first-order gradients in a stochastic manner with a probability that increases with learning time. Extensive numerical simulations on a toy deep learning model verify its excellent performance. The reinforced backpropagation can significantly improve test performance of the deep network training, especially when the data are scarce. The performance is even better than that of state-of-the-art stochastic optimization algorithm called Adam, with an extra advantage of less computer memory required.


Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study


title: Dermoscopy in female androgenic alopecia: Method standardization and diagnostic criteria

abstract: Objective: Establishing the trichoscopy criteria of female androgenic alopecia (FAGA). Design: Trichoscopy images were retrospectively evaluated. Setting: Dermatologic hospital-based clinic and private practice offices. Patients and methods: One hundred and thirty-one females (59 with androgenic alopecia, 33 with chronic telogen effluvium (CTE), 39 healthy controls). The diagnosis was based on clinical examination and confirmed by histopatology. Main Outcome Measure: Trichoscopy results obtained in frontal, occipital and both temporal areas of the scalp under a 20-fold and 70-fold magnification, including average hair thickness, number of 'yellow dots' and vellus hairs, number of hairs in one pilosebaceous unit and percentage of follicular ostia with perifollicullar hyperpigmentation. Results: Average hair thickness in frontal area versus occiput was, respectively, 0.061 ± 0.008 mm versus 0.058 ± 0.007 mm in healthy controls, 0.047 ± 0.007 mm versus 0.052 ± 0.008 mm in androgenic alopecia and 0.056 ± 0.007 mm versus 0.053 ± 0.009 mm in CTE. Mean percentage of thin hairs (< 0.03 mm) in androgenic alopecia was 20.9 ± 12% and was significantly higher than in healthy controls (6.15 ± 4.6%, P < 0.001) or in CTE (10.4 ± 3.9%, P < 0.001). The number of yellow dots, pilosebaceous units with only one hair and with perifollicular hyperpigmentation was significantly increased in androgenic alopecia. Classification and Regression Tree Analysis was performed to establish diagnostic criteria for FAGA. Conclusion: FAGA may be differentiated from CTE based on trichoscopy criteria. Major criteria are ratio of (1) more than four yellow dots in four images (70-fold magnification) in the frontal area, (2) lower average hair thickness in the frontal area compared to the occiput and (3) more than 10% of thin hairs (below 0.03 mm) in the frontal area. Minor criteria encompass increased frontal to occipital ratio of (1) single-hair pilosebaceous units, (2) vellus hairs and (3) perifollicular discoloration. Fulfillment of two major criteria or one major and two minor criteria allows to diagnose FAGA based on trichoscopy with a 98% specificity.


title: Serious Video Games for Health: How Behavioral Science Guided the Development of a Serious Video Game

abstract: Serious video games for health are designed to entertain players while attempting to modify some aspect of their health behavior. Behavior is a complex process influenced by multiple factors, often making it difficult to change. Behavioral science provides insight into factors that influence specific actions that can be used to guide key game design decisions. This article reports how behavioral science guided the design of a serious video game to prevent Type 2 diabetes and obesity among youth, two health problems increasing in prevalence. It demonstrates how video game designers and behavioral scientists can combine their unique talents to create a highly focused serious video game that entertains while promoting behavior change.


title: Crowdsourcing the Verification of Fake News and Alternative Facts

abstract: Fake news and alternative facts have dominated the news cycle of late. In this paper, we present a prototype system that uses social argumentation to verify the validity of proposed alternative facts and help in the detection of fake news. We utilize fundamental argumentation ideas in a graph-theoretic framework that also incorporates semantic web and linked data principles. The argumentation structure is crowdsourced and mediated by expert moderators in a virtual community.


title: MFC: A multi-scale fully convolutional approach for visual instance retrieval

abstract: Previous work has shown that feature maps of deep convolutional neural networks (CNNs) can be interpreted as feature representation of an image. Image features aggregated from these feature maps have achieved steady progress in terms of performances on visual instance retrieval tasks in recent years. The key to the success of such methods is feature representation. In this paper, we study how to represent an image using discriminative features. We demonstrate first that image size is an important factor which affects the performance of instance retrieval but has not been thoroughly discussed in previous work. Based on experimental evaluations, we propose a multi-scale fully convolutional (MFC) approach to encode the image efficiently and effectively. The proposed method is simple to implement, which does not employ sophisticated post-processing techniques such as query expansion, yet shows promising results on four public datasets.


title: Research and application of web development based on ASP.NET 2.0+Ajax

abstract: Ajax is a new concept of web application development proposed in 2005. It is the acronym of Asynchronous JavaScript and XML. Once Ajax appeared, it is rapidly applied to the fields of Web development. Ajax application is different from the traditional Web development model, using asynchronous interaction. The client unnecessarily waits while the server processes the data submitted. So the use of Ajax can create Web user interface which is direct, highly available, richer, more dynamic and closer to a local desktop application. This article introduces the main technology and superiority of Ajax firstly, and then practices Web development using ASP.NET 2.0+Ajax. In this paper, Ajax is applied to the Website pass, which enables user to have better registration experience and enhances the user's enthusiasm. The registration functions are enhanced greatly as well. The experiments show that the Ajax Web application development model is superior to the traditional Web application development model significantly.


title: An IoT Middleware Framework for Industrial Applications

abstract: Starting from the RFID and the wireless sensor networks, the Internet of connected things has attracted the attention of major IT companies and later, of the industrial environment that recognized the concept as one of their key axes for future growth and development. The implementation of IoT in the industrial environment raises some significant issues related to the diversity of fieldbuses, the large number of devices and their configuration. The requirements related to reliability, security and real-time are very important. This paper proposes an industrial IoT and communications at the edge framework which has some outstanding features related to: the easy integration of fieldbuses and devices used in industrial environments with automatic configuration features, integration of multiple middleware technologies (CORBA, OPC and DDS), the uncoupling of the industrial activity from the publishing data on the Internet, security at different levels of the framework. Another important feature of the proposed framework is that it is based on mature standards and on open source or public implementations of these standards. The framework is modular, allowing the easy integration of new fieldbus protocols, middleware technologies and new objects in the client application. This paper is focused mainly on CORBA and DDS approaches.


title: Total Variation Regularized RPCA for Irregularly Moving Object Detection Under Dynamic Background

abstract: Moving object detection is one of the most fundamental tasks in computer vision. Many classic and contemporary algorithms work well under the assumption that backgrounds are stationary and movements are continuous, but degrade sharply when they are used in a real detection system, mainly due to: 1) the dynamic background (e.g., swaying trees, water ripples and fountains in real scenarios, as well as raindrops and snowflakes in bad weather) and 2) the irregular object movement (like lingering objects). This paper presents a unified framework for addressing the difficulties mentioned above, especially the one caused by irregular object movement. This framework separates dynamic background from moving objects using the spatial continuity of foreground, and detects lingering objects using the temporal continuity of foreground. The proposed framework assumes that the dynamic background is sparser than the moving foreground that has smooth boundary and trajectory. We regard the observed video as being made up of the sum of a low-rank static background, a sparse and smooth foreground, and a sparser dynamic background. To deal with this decomposition, i.e., a constrained minimization problem, the augmented Lagrangian multiplier method is employed with the help of the alternating direction minimizing strategy. Extensive experiments on both simulated and real data demonstrate that our method significantly outperforms the state-of-the-art approaches, especially for the cases with dynamic backgrounds and discontinuous movements.


title: Relational learning via latent social dimensions

abstract: Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. However, connections in social media are often multi-dimensional. An actor can connect to another actor for different reasons, e.g., alumni, colleagues, living in the same city, sharing similar interests, etc. Collective inference normally does not differentiate these connections. In this work, we propose to extract latent social dimensions based on network information, and then utilize them as features for discriminative learning. These social dimensions describe diverse affiliations of actors hidden in the network, and the discriminative learning can automatically determine which affiliations are better aligned with the class labels. Such a scheme is preferred when multiple diverse relations are associated with the same network. We conduct extensive experiments on social media data (one from a real-world blog site and the other from a popular content sharing site). Our model outperforms representative relational learning methods based on collective inference, especially when few labeled data are available. The sensitivity of this model and its connection to existing methods are also examined.


title: The traveling salesman problem: An overview of exact and approximate algorithms

abstract: In this paper, some of the main known algorithms for the traveling salesman problem are surveyed. The paper is organized as follows: 1) definition; 2) applications; 3) complexity analysis; 4) exact algorithms; 5) heuristic algorithms; 6) conclusion.


Low Cost Augmented Reality Framework for Construction Applications


title: Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation

abstract: Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users’ online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users’ activity data. In this paper, we proposed PrivRank, a customizable and continuous privacy-preserving social media data publishing framework protecting users against inference attacks while enabling personalized ranking-based recommendations. Its key idea is to continuously obfuscate user activity data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which bounds the ranking loss incurred from the data obfuscation process in order to preserve the utility of the data for enabling recommendations. An empirical evaluation on both synthetic and real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized ranking-based recommendation. Compared to state-of-the-art approaches, PrivRank achieves both a better privacy protection and a higher utility in all the ranking-based recommendation use cases we tested.


title: A Finite Difference Domain Decomposition Method Using Local Corrections for the Solution of Poisson’s Equation

abstract: Parallel algorithms have become increasingly important in the field of scientific computing since the fastest computers currently all have some form of parallel architecture. Numerical solutions of elliptic partial differential equations are especially difficult to parallelize because the solution at each point in the domain requires global information. Many methods are available to facilitate efficient propagation of global information, but most are inherently iterative and still require a large amount of inter-processor communication. Parallel machines are generally able to perform computation on each processor orders of magnitude more quickly than communication among processors. As a result, the performance of algorithms with large communication requirements is often limited by communication rather than computational speed. ::: We present here a domain decomposition method for computing finite difference solutions to the Poisson equation with infinite domain boundary conditions. Our method is a finite difference analogue of Anderson's Method of Local Corrections. The solution is computed in three steps. First, fine grid solutions are computed in parallel using infinite domain boundary conditions on each subdomain. Second, information is transferred globally through a coarse grid representation of the charge, and a global coarse grid solution is found. Third, a fine grid solution is computed on each subdomain using boundary conditions set with the global coarse solution, corrected locally with fine grid information from nearby subdomains. ::: There are three important features of our algorithm. First, our method requires only a single iteration between the local fine grid solutions and the global coarse representation. Second, the computed solution is second-order accurate, independent of the number of subdomains. Third, the accuracy of the method is only weakly dependent on the coarse grid spacing. As a result of these features, we are able to compute accurate solutions in parallel with minimal communication overhead. ::: We present results from implementations of our algorithm in two different parallel language environments, tested on a network of workstations. These results verify the overall accuracy, confirm the small communication costs, and demonstrate the parallel scalability of the method.


title: SQL Support over MongoDB using Metadata

abstract: New requirements are arising in environments where we have higher volumes of data with high operation rates, agile development and cloud computing. In recent years, a growing number of companies have adopted various types of non- relational database, commonly referred to as NoSQL database. This reflects the growing interactivity of applications which are becoming more networked and social, driving more requests to the database where high-performance NoSQL database such as MongoDB becomes favorable. This paper attempts to use NoSQL database to replace the relational database. It mainly focuses on one of the boosting technology of NoSQL database i.e. MongoDB, and makes a comparison with MySQL and thus justifies why MongoDB is preferred over MySQL. Lastly, a method is proposed to integrate these two types of database by adding a middleware (Metadata) between application layer and database layer.


title: Sport Activity for Health!! The Effects of Karate Participants’ Involvement, Perceived Value, and Leisure Benefits on Recommendation Intention

abstract: This study intends to discuss the effects of participants’ involvement, perceived value, and leisure benefits on recommendation intention in the sport of karate. The questionnaires were collected online by karate clubs on Facebook and included 369 valid participants. The research findings show that karate participants from different places of residence do not display significant differences in involvement, perceived value, leisure benefits, and recommendation intention. Furthermore, “attraction” in the involvement category reveals the highest mean, “paid spirit and energy being worthy” in perceived value appears as the highest mean, and “physiological benefits” in leisure benefits shows the highest mean. The Pearson correlation analysis result presents significant strong positive correlations between involvement, perceived value, leisure benefits, and recommendation intention. Finally, multiple regression analysis reveals that leisure benefits, except “physiological benefits”, show notably positive effects on recommendation intention. According to the research results, suggestions are proposed for the reference of karate teaching business managers, participants, and future research.


title: Using a Natural Language Understanding System to Generate Semantic Web Content

abstract: We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over fifteen years, uses a specia lly constructed NLP-oriented ontology and an ontologicalsemantic lexicon to translate English text into a custom ontology-motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an e xperimental web service that monitors RSS news sources, processes the summaries of the news stories and publishes a structured representation of the meaning of the text in the news story.


title: NetCDF: an interface for scientific data access

abstract: The network common data form (NetCDF), a data abstraction for storing and retrieving multidimensional data, is described. NetCDF is distributed as a software library that provides a concrete implementation of that abstraction. The implementation provides a machine-independent format for representing scientific data. Together, the abstraction, library, and data format support the creation, access, and sharing of scientific information. NetCDF is useful for supporting objects that contain dissimilar kinds of data in a heterogeneous network environment and for writing application software that does not depend on application-specific formats. Independence from particular machine representations is achieved by using a nonproprietary standard for external data representation. The discussion covers NetCDF data abstraction and interface; dimensions, variables, and attributes; direct access and hyperslab access, the NetCDF library; the data format; ncdump and ncgen utilities; experience, usability, and performance; limitations of NetCDF; and future plans. >


title: Fast Branch Convolutional Neural Network for Traffic Sign Recognition

abstract: In this paper, we propose a novel framework for speeding up the test-time of traffic sign recognition, which is named Branch Convolution Neural Network. It is the first time to introduce a branch-output mechanism into a deep Convolution Neural Network. Our model has an accuracy as high as a deep convolution neural network model, while it performs faster at the same condition during test stage. It is a significantly accelerated framework for designing a real-time deep neural network system. We present a detail process to change a regular pre-trained Convolution Neural Network into a Branch Convolution Neural Network: train several simple branch classifiers, bias classifiers and optimize branches. Experiment applied on GTSRB shows that large number of traffic signs are unnecessary to go through all layers in a deep model and they can be separated out in a relative shallow neural network. This framework speeds up the recognition progress, while keeping the accuracy within an extremely minor drop.


title: Acupuncture Stimulation of Taichong (Liv3) and Hegu (LI4) Modulates the Default Mode Network Activity in Alzheimer’s Disease

abstract: Objectives:The acupuncture has been used in the therapy of Alzheimer disease (AD), however, its neural underpins are still unclear. The aim of this study is to examine the acupuncture effect on the default mode network (DMN) in AD by using resting state functional magnetic resonance imaging (RS-fMRI).Methods:Twenty-eight subjects (14 AD and 14 normal controls (NC)) participated in this study. RS-fMRI data were acquired before and after acupuncture, while during the acupuncture, the procession of acupuncture stimulation on the acupoints of Tai chong (Liv3) and Hegu (LI4) lasted for 3 minutes.Results:Region of interest analysis showed that the impaired DMN connectivity in AD (identified by comparing the pre-acupuncture RS-fMRI of AD and NC), specifically the left cingulate gyrus (CG) and right inferior parietal lobule (IPL), were significantly changed for the better. The whole-brain exploratory analysis further demonstrated these results and found some new regions respond to the acupuncture effect on AD, wi...


title: IBM High-Five: Highlights From Intelligent Video Engine

abstract: We introduce a novel multi-modal system for auto-curating golf highlights that fuses information from players' reactions (celebration actions), spectators (crowd cheering), and commentator (tone of the voice and word analysis) to determine the most interesting moments of a game. The start of a highlight is determined with additional metadata (player's name and the hole number), allowing personalized content summarization and retrieval. Our system was demonstrated at Masters 2017, a major golf tournament, generating real-time highlights from four live video streams over four days.


title: Software-Defined Network Function Virtualization: A Survey

abstract: Diverse proprietary network appliances increase both the capital and operational expense of service providers, meanwhile causing problems of network ossification. Network function virtualization (NFV) is proposed to address these issues by implementing network functions as pure software on commodity and general hardware. NFV allows flexible provisioning, deployment, and centralized management of virtual network functions. Integrated with SDN, the software-defined NFV architecture further offers agile traffic steering and joint optimization of network functions and resources. This architecture benefits a wide range of applications (e.g., service chaining) and is becoming the dominant form of NFV. In this survey, we present a thorough investigation of the development of NFV under the software-defined NFV architecture, with an emphasis on service chaining as its application. We first introduce the software-defined NFV architecture as the state of the art of NFV and present relationships between NFV and SDN. Then, we provide a historic view of the involvement from middlebox to NFV. Finally, we introduce significant challenges and relevant solutions of NFV, and discuss its future research directions by different application domains.


title: Shelter from the Raging Wind: Religious Needs of Victims of Intimate Partner Violence and Faith Leaders' Responses

abstract: This paper integrates the findings from three studies on religious methods to cope with or prevent intimate partner violence (IPV). These analyses examined religious coping methods from multiple perspectives. One study surveyed Christian women's experiencing of coping with IPV, another presented findings from interviews with Christian women IPV victims, and a third investigated faith leaders' beliefs about the occurrence of IPV and the methods they utilize to support victims in their congregations. Findings describe mechanisms that may underlie how intrinsic religion and negative religious coping influence distress. Results highlight responses of leaders that may cause unintentional harm to IPV victims. Suggestions for religious leaders are provided on how to best approach this issue.


title: Analysis of keyword spotting performance across IARPA babel languages

abstract: With the completion of the IARPA Babel program, it is possible to systematically analyze the performance of speech recognition systems across a wide variety of languages. We select 16 languages from the dataset and compare performance using a deep neural network-based acoustic model. The focus is on keyword spotting using the actual term-weighted value (ATWV) metric. We demonstrate that ATWV is keyword dependent, and that this must be accounted for in any cross-language analysis. Further, we show that while performance across languages does not track with any particular feature of the language, it is correlated with inter-annotator agreement.


title: Unacceptable Results with an Accepted Soft Tissue Filler: Polyacrylamide Hydrogel

abstract: Background ::: Polyacrylamide hydrogel, considered a safe and biocompatible soft tissue filler, is widely used in cosmetic procedures. Its use for facial contouring and breast augmentation in Iran has increased dramatically in recent years. Most patients and many doctors are unaware of possible and reported adverse effects related to its administration.


title: Evaluating Cloud Computing Techniques for Smart Power Grid Design Using Parallel Scripting

abstract: Applications used to evaluate next-generation electrical power grids(``smart grids'') are anticipated to be compute and data-intensive. In this work, we parallelize and improve performance of one such application which was run sequentially prior to the use of our cloud-based configuration. We examine multiple cloud computing offerings, both commercial and academic, to evaluate their potential for improving the turnaround time for application results. Since the target application does not fit well into existing computational paradigms for the cloud, we employ parallel scripting tool, as a first step toward a broader program of adapting portable, scalable computational tools for use as enablers of the future smart grids. We use multiple clouds as a way to reassure potential users that the risk of cloud-vendor lock-in can be managed. This paper discusses our methods and results. Our experience sheds light on some of the issues facing computational scientists and engineers tasked with adapting new paradigms and infrastructures for existing engineering design problems.


title: Implications of location and touch for on-body projected interfaces

abstract: Very recently, there has been a perfect storm of technical advances that has culminated in the emergence of a new interaction modality: on-body interfaces. Such systems enable the wearer to use their body as an input and output platform with interactive graphics. Projects such as PALMbit and Skinput sought to answer the initial and fundamental question: whether or not on-body interfaces were technologically possible. Although considerable technical work remains, we believe it is important to begin shifting the question away from how and what, and towards where, and ultimately why. These are the class of questions that inform the design of next generation systems. To better understand and explore this expansive space, we employed a mixed-methods research process involving more than two thousand individuals. This started with high-resolution, but low-detail crowdsourced data. We then combined this with rich, expert interviews, exploring aspects ranging from aesthetics to kinesthetics. The results of this complimentary, structured exploration, point the way towards more comfortable, efficacious, and enjoyable on-body user experiences.


title: SGN: Sequential Grouping Networks for Instance Segmentation

abstract: In this paper, we propose Sequential Grouping Networks (SGN) to tackle the problem of object instance segmentation. SGNs employ a sequence of neural networks, each solving a sub-grouping problem of increasing semantic complexity in order to gradually compose objects out of pixels. In particular, the first network aims to group pixels along each image row and column by predicting horizontal and vertical object breakpoints. These breakpoints are then used to create line segments. By exploiting two-directional information, the second network groups horizontal and vertical lines into connected components. Finally, the third network groups the connected components into object instances. Our experiments show that our SGN significantly outperforms state-of-the-art approaches in both, the Cityscapes dataset as well as PASCAL VOC.


title: A model of visual adaptation for realistic image synthesis

abstract: In this paper we develop a computational model of visual adaptation for realistic image synthesis based on psychophysical experiments. The model captures the changes in threshold visibility, color appearance, visual acuity, and sensitivity over time that are caused by the visual system’s adaptation mechanisms. We use the model to display the results of global illumination simulations illuminated at intensities ranging from daylight down to starlight. The resulting images better capture the visual characteristics of scenes viewed over a wide range of illumination levels. Because the model is based on psychophysical data it can be used to predict the visibility and appearance of scene features. This allows the model to be used as the basis of perceptually-based error metrics for limiting the precision of global illumination computations. CR


A Framework for Computational Thinking Based on a Systematic Research Review


title: Curiosity, Creativity, and Surprise as Analytic Tools: Grounded Theory Method

abstract: Grounded Theory Method offers a rigorous way to explore a domain, with an emphasis on discovering new insights, testing those insights, and building partial understandings into a broader theory of the domain. It begins with observations of a phenomenon for which no theory yet exists. Through layered coding of these observations and continual reexamination of the data, a theory emerges.


title: Status Quo Bias in Decision Making

abstract: Most real decisions, unlike those of economics texts, have a status quo alternative—that is, doing nothing or maintaining one's current or previous decision. A series of decision-making experiments shows that individuals disproportionately stick with the status quo. Data on the selections of health plans and retirement programs by faculty members reveal that the status quo bias is substantial in important real decisions. Economics, psychology, and decision theory provide possible explanations for this bias. Applications are discussed ranging from marketing techniques, to industrial organization, to the advance of science.


title: Controlling Perceptual Factors in Neural Style Transfer

abstract: Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer.


title: Exploring the nonlinear effects of patent H index, patent citations, and essential technological strength on corporate performance by using artificial neural network

abstract: This study utilizes the artificial neural network to explore the nonlinear relationships between patent performance and the corporate performance of the pharmaceutical companies. Patent performance measured from patent H index, patent citations, and essential technological strength (ETS). The result shows that patent H index, patent citations, and ETS has the nonlinear effect on the corporate performance of the pharmaceutical companies.


title: Mobile application testing matrix and challenges

abstract: The adoption of smart phones and the usages of mobile applications are increasing rapidly. Consequently, within limited time-range, mobile Internet usages have managed to take over the desktop usages particularly since the first smart phone-touched application released by iPhone in 2007. This paper is proposed to provide solution and answer the most demandable questions related to mobile application automated and manual testing limitations. Moreover, Mobile application testing requires agility and physically testing. Agile testing is to detect bugs through automated tools, whereas the compatibility testing is more to ensure that the apps operates on mobile OS (Operation Systems) as well as on the different real devices. Moreover, we have managed to answer automated or manual questions through two mobile application case studies MES (Mobile Exam System) and MLM (Mobile Lab Mate) by creating test scripts for both case studies and our experiment results have been discussed and evaluated on whether to adopt test on real devices or on emulators? In addition to this, we have introduced new mobile application testing matrix for the testers and some enterprises to obtain knowledge from


title: An analysis of social anxiety in anorexic, bulimic, social phobic, and control women

abstract: Responses to the Social Phobia and Anxiety Inventory (SPAI) were examined in 23 women with anorexia nervosa, 54 women with bulimia nervosa, 50 female college undergraduates, and 43 social phobic women. Results indicated that women with anorexia nervosa and bulimia nervosa scored comparably high to social phobic women on measures of social anxiety and that these fears were not limited to fears of eating or drinking in public. This study suggests that fears of negative evaluation in women with eating disorders may generalize beyond the fears of scrutiny of body shape and size to more traditional social situations.


title: Predicting Multiplayer Online Battle Arena (MOBA) Game Outcome Based on Hero Draft Data

abstract: DotA 2 is a popular multi-player online battle area (MOBA) game. A critical part of the game play involves choosing from a pool of more than one hundred heroes to form two five-players team. However, as different heroes have their unique attributes and skill sets, selecting a strong combination of heroes (i.e., hero drafting) is an challenging task for new players which requires extensive knowledge and experience. Previous studies have shown that using hero draft data alone can achieve as high as 69.8% of accuracy in predicting game outcomes. However, many aspects in hero draft remains to be further investigated. In this study, we aimed to achieve higher accuracy by adding game length as an input feature. In addition, we used multi-layer feedforward neural networks to predict the game outcome with GPU enabled. However, the results showed that adding game length does not improve the performance significantly nor did neural networks outperform logistic regression significantly.


title: A C/X/Ku-band dual polarized Cassegrain antenna system

abstract: There is an increasing demand for multiple frequency band capability in satellite Earth station antenna systems. This paper describes an 18.3 m shaped Cassegrain antenna with a single aperture, tri-band, six-port receive-only, feed system. The frequencies of operation are 3.4-4.2 GHz, 7.25-7.75 GHz, and 10.9-12.75 GHz. The C- and X-band feed ports offer dual circular polarization, and the Ku-band ports offer dual linear polarization with an internal polarization vector angle adjustment mechanism. Frequency and polarization multiplexing is performed by a novel OMJ structure.


title: A Comprehensive Survey of Deep Learning for Image Captioning

abstract: Generating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey paper, we aim to present a comprehensive review of existing deep learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning.


title: Class-Weighted Convolutional Features for Visual Instance Search

abstract: Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet.


title: Mimic: visual analytics of online micro-interactions

abstract: We present Mimic, an input capture and visual analytics system that records online user behavior to facilitate the discovery of micro-interactions that may affect problem understanding and decision making. As aggregate statistics and visualizations can mask important behaviors, Mimic can help interaction designers to improve the usability of their designs by going beyond aggregates to examine many individual user sessions in detail. To test Mimic, we replicate a recent crowd-sourcing experiment to better understand why participants consistently perform poorly in answering a canonical conditional probability question called the Mammography Problem. To analyze the micro-interactions, the Mimic web application is used to play back user sessions collected through remote logging of client-side events. We use Mimic to demonstrate the value of using advanced visual interfaces to interactively study interaction data. In the Mammography Problem, issues like user confusion, low confidence, and divided-attention were found based on participants changing their answers, doing repeated scrolling, and overestimating a base rate. Mimic shows how helpful detailed observational data can be and how important the careful design of micro-interactions is in helping users to successfully understand a problem, find a solution, and achieve their goals.


title: Physical Elements Display Elements Input Output Interaction Metaphor

abstract: Most interactive computer graphics appear on a screen separate from the real world and the user's surroundings. However this does not always have to be the case. In augmented reality (AR) interfaces, three-dimensional virtual images appear superimposed over real objects. AR applications typically use head-mounted or handheld displays to make computer graphics appear in the user's environment.


title: Compliant motion control for continuum robots with intrinsic actuation sensing

abstract: Novel minimally invasive surgical paradigms accessing deep surgical sites present a new challenge of safe instrument insertion and navigation. This paper addresses this challenge by presenting a new framework for compliant motion control of multi-backbone continuum robots subject to whole-arm contacts. This control framework does not rely on knowledge of contact locations along the length of a continuum robot. Instead, the forces at joint level are applied as controller inputs to generate compliant motion. The paper first presents a new mapping of the external wrenches to a generalized force in the configuration space of a single-stage multi-backbone continuum robot. A closed-form analytic expression for the passive stiffness of a multi-backbone continuum robot segment is also presented. A controller, robust to uncertainties of the system model, is proposed to provide compliant motion of the continuum robot segment by using the generalized force and stiffness definitions. Stability, convergence, and controller properties are shown through experimental validation. The presented framework defines a method for providing compliant motion to continuum robots without explicit knowledge of the environment. We believe this work enables new control algorithms for rapidly deployable surgical robots and supports novel surgical paradigms by increasing safety during unstructured interaction with flexible anatomy.


title: A Multigenerational View of Inequality

abstract: The study of intergenerational mobility and most population research are governed by a two-generation (parent-to-offspring) view of intergenerational influence, to the neglect of the effects of grandparents and other ancestors and nonresident contemporary kin. While appropriate for some populations in some periods, this perspective may omit important sources of intergenerational continuity of family-based social inequality. Social institutions, which transcend individual lives, help support multigenerational influence, particularly at the extreme top and bottom of the social hierarchy, but to some extent in the middle as well. Multigenerational influence also works through demographic processes because families influence subsequent generations through differential fertility and survival, migration, and marriage patterns, as well as through direct transmission of socioeconomic rewards, statuses, and positions. Future research should attend more closely to multigenerational effects; to the tandem nature of demographic and socioeconomic reproduction; and to data, measures, and models that transcend coresident nuclear families.


title: Set of X-band distributed absorptive limiter GaAs MMICs

abstract: A set of X-band absorptive limiter GaAs MMICs has been designed and realised using both the PPH25x foundry process from UMS and the PP50-10 process from WIN semiconductors. The innovative limiter concepts have been extensively characterised by both pulsed and CW measurements. Both passive and active topologies have been implemented. The passive limiter design has a typical small-signal insertion loss of 1.5 dB at 10 GHz, and it can withstand (absorb) up to 4 Watts (36 dBm) of CW power without degradation or damage, while keeping the output power level below 100 mW (20 dBm). The active limiter handles up to 10 Watts of CW input power, at the cost of higher small-signal insertion loss. For all designs the input reflection remains low for any input power level. The used GaAs surface ranges from 2 to 3 mm2 .


title: Fabrication of Nb3Al Superconducting Wires Through Rapid Heating/Quenching in Open Air

abstract: Nb ::: 3 ::: Al wire requires a simplified fabrication process as well as improved critical current density. We found that the reel-to-reel Rapid Heating/Quenching (RHQ) treatment could be safely operated in open air without an expensive dynamic vacuum system or a large metal vacuum chamber. It should provide a significant cost reduction. The electrode distance between the Cu pulley and the liquid Ga bath surface is set for 125 mm. The precursor wire is moving at speeds of 0.3, 0.6, or 1.0 m/s. The wire surface material (Nb) reacted with oxygen and made a homogeneous Ga-Nb oxide layer of several microns in thickness. Its surface morphology was very smooth, and it acted as a lubricant for the metal drawing dies of which would be considered a similar effect as an anode oxidation drawing technique. The Nb-Al supersaturated bcc solid solution alloy could be synthesized by RHQ in open air, and its synthesis condition might be shifted to slightly lower input heating energy. The heating energy in open air is probably adding an additional heating energy from the surface oxidation reaction.


title: TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

abstract: In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation. We introduce TimbreTron, a method for musical timbre transfer which applies "image" domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer. We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance. Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.


title: A 4266 Mb/s/pin LPDDR4 Interface With An Asynchronous Feedback CTLE and An Adaptive 3-Step Eye Detection Algorithm for Memory Controller

abstract: A 4266Mb/s/pin LPDDR4 interface with an asynchronous feedback continuous-time linear equalizer (AF-CTLE) and an adaptive 3-step eye detection algorithm for memory controller is presented. The AF-CTLE removes the glitch of DQS without training by applying an offset larger than the noise, and improves read margin by operating as a decision feedback equalizer in DQ path. The adaptive 3-step eye detection algorithm reduces power consumption and black-out time in initialization sequence and retraining in comparison to the 2-D full scanning. A prototype chip was implemented in a 65-nm CMOS process with fine-pitch ball grid array package and tested with commodity LPDDR4 memory. The write margin was 0.36 UI and 148 mV; and the read margin was enhanced from 0.30 UI and 76 mV without AF-CTLE to 0.47 UI and 80 mV to with AF-CTLE. The power efficiency during burst write and read were 5.68 pJ/bit and 1.83 pJ/bit, respectively.


title: Simplification and Repair of Polygonal Models Using Volumetric Techniques

abstract: Two important tools for manipulating polygonal models are simplification and repair and we present voxel-based methods for performing both of these tasks. We describe a method for converting polygonal models to a volumetric representation in a way that handles models with holes, double walls, and intersecting parts. This allows us to perform polygon model repair simply by converting a model to and from the volumetric domain. We also describe a new topology-altering simplification method that is based on 3D morphological operators. Visually unimportant features such as tubes and holes may be eliminated from a model by the open and close morphological operators. Our simplification approach accepts polygonal models as input, scan converts these to create a volumetric description, performs topology modification, and then converts the results back to polygons. We then apply a topology-preserving polygon simplification technique to produce a final model. Our simplification method produces results that are everywhere manifold.


title: Foundations of computational neuroscience

abstract: Most computational neuroscientists assume that nervous systems compute and process information. We discuss foundational issues such as what we mean by ‘computation’ and ‘information processing’ in nervous systems; whether computation and information processing are matters of objective fact or of conventional, observer-dependent description; and how computational descriptions and explanations are related to other levels of analysis and organization.


title: External memory algorithms and data structures: dealing with massive data

abstract: Data sets in large applications are often too massive to fit completely inside the computers internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck. In this article we survey the state of the art in the design and analysis of external memory (or EM) algorithms and data structures, where the goal is to exploit locality in order to reduce the I/O costs. We consider a variety of EM paradigms for solving batched and online problems efficiently in external memory. For the batched problem of sorting and related problems such as permuting and fast Fourier transform, the key paradigms include distribution and merging. The paradigm of disk striping offers an elegant way to use multiple disks in parallel. For sorting, however, disk striping can be nonoptimal with respect to I/O, so to gain further improvements we discuss distribution and merging techniques for using the disks independently. We also consider useful techniques for batched EM problems involving matrices (such as matrix multiplication and transposition), geometric data (such as finding intersections and constructing convex hulls), and graphs (such as list ranking, connected components, topological sorting, and shortest paths). In the online domain, canonical EM applications include dictionary lookup and range searching. The two important classes of indexed data structures are based upon extendible hashing and B-trees. The paradigms of filtering and bootstrapping provide a convenient means in online data structures to make effective use of the data accessed from disk. We also reexamine some of the above EM problems in slightly different settings, such as when the data items are moving, when the data items are variable-length (e.g., text strings), or when the allocated amount of internal memory can change dynamically. Programming tools and environments are available for simplifying the EM programming task. During the course of the survey, we report on some experiments in the domain of spatial databases using the TPIE system (transparent parallel I/O programming environment). The newly developed EM algorithms and data structures that incorporate the paradigms we discuss are significantly faster than methods currently used in practice.


title: A framework for constructing features and models for intrusion detection systems

abstract: Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, and extensible. Given these requirements and the complexities of today's network environments, we need a more systematic and automated IDS development process rather that the pure knowledge encoding and engineering approaches. This article describes a novel framework, MADAM ID, for Mining Audit Data for Automated Models for Instrusion Detection. This framework uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records taht are processed according to the feature definitions to generate intrusion detection rules. Results from the 1998 DARPA Intrusion Detection Evaluation showed that our ID model was one of the best performing of all the participating systems. We also briefly discuss our experience in converting the detection models produced by off-line data mining programs to real-time modules of existing IDSs.


title: Use of SIMD Vector Operations to Accelerate Application Code Performance on Low-Powered ARM and Intel Platforms

abstract: Augmenting a processor with special hardware that is able to apply a Single Instruction to Multiple Data(SIMD) at the same time is a cost effective way of improving processor performance. It also offers a means of improving the ratio of processor performance to power usage due to reduced and more effective data movement and intrinsically lower instruction counts. This paper considers and compares the NEON SIMD instruction set used on the ARM Cortex-A series of RISC processors with the SSE2 SIMD instruction set found on Intel platforms within the context of the Open Computer Vision (OpenCV) library. The performance obtained using compiler auto-vectorization is compared with that achieved using hand-tuning across a range of five different benchmarks and ten different hardware platforms. On the ARM platforms the hand-tuned NEON benchmarks were between 1.05× and 13.88× faster than the auto-vectorized code, while for the Intel platforms the hand-tuned SSE benchmarks were between 1.34× and 5.54× faster.


title: Unsupervised Learning of Discriminative Attributes and Visual Representations

abstract: Attributes offer useful mid-level features to interpret visual data. While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neural Networks (CNNs) to output a set of discriminative, binary attributes often with semantic meanings. Specifically, we first train a CNN coupled with unsupervised discriminative clustering, and then use the cluster membership as a soft supervision to discover shared attributes from the clusters while maximizing their separability. The learned attributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. The visual representations learned in this way are also transferrable to other tasks such as object detection. We show other convincing results on the related tasks of image retrieval and classification, and contour detection.


title: Multi-scale Features for Approximate Alignment of Point-based Surfaces

abstract: We introduce a novel method for approximate alignment of point-based surfaces. Our approach is based on detecting a set of salient feature points using a scale-space representation. For each feature point we compute a signature vector that is approximately invariant under rigid transformations. We use the extracted signed feature set in order to obtain approximate alignment of two surfaces. We apply our method for the automatic alignment of multiple scans using both scan-to-scan and scan-to-model matching capabilities.


title: Modeling Brain Function: The World of Attractor Neural Networks

abstract: From the Publisher: ::: Substantial progress in understanding memory, the learning process, and self-organization by studying the properties of models of neural networks have resulted in discoveries of important parallels between the properties of statistical, nonlinear cooperative systems in physics and neural networks.


title: Secure Communication via Shared Knowledge and a Salted Hash in Ad-Hoc Environments

abstract: Decentralized, message-based communication networks commonly require mechanisms for message confidentiality and integrity. While these needs are traditionally provided through methods of channel encryption and signing, such mechanisms are often difficult or impossible to implement in the ad-hoc, decentralized environments seen in sensor networks, collaborative intrusion detection systems, or other similar peer-to-peer networks. Using the concepts from one-way hashing and language-derived relevance theory, we propose five novel contributions relevant to ad-hoc communications and security: one-way cryptographic hashing as a mechanism for securely communicating in an environment where preexisting shared knowledge exists, hashed shared knowledge messages as a basis for secure formation of self-selecting subgroups and trust building, adding salt to the shared knowledge hashes to remove the static nature of common messages and defend against precomputed table attacks, integration of variable complexity hash functions to dynamically adjust hash complexity relative to message complexity, and a message integrity element based on the secrecy of the original shared knowledge within a hashed message. Although our proposed mechanisms are likely implemented without difficulty from a network and encryption standpoint, they do require significant integration and awareness within the applications relying on them. The method also assumes a static value from a large existing set of shared knowledge, which does not always exist.


title: Taking Informed Action on Student Activity in MOOCs

abstract: This paper presents a novel approach to understand specific student behavior in MOOCs. Instructors currently perceive participants only as one homogeneous group. In order to improve learning outcomes, they encourage students to get active in the discussion forum and remind them of approaching deadlines. While these actions are most likely helpful, their actual impact is often not measured. Additionally, it is uncertain whether such generic approaches sometimes cause the opposite effect, as some participants are bothered with irrelevant information. On the basis of fine granular events emitted by our learning platform, we derive metrics and enable teachers to employ clustering, in order to divide the vast field of participants into meaningful subgroups to be addressed individually.


title: A Support Vector Machine approach for reliable detection of atrial fibrillation events

abstract: There is a need for accurate and reliable detectors of asymptomatic atrial fibrillation (AF). Several ECG-based algorithms have been described in the literature, but no open comparison of features on out of-sample data has been published. Therefore, ten R-peak related features were selected from detectors available in literature and their classification performances were assessed both univariately and when combined using a Support Vector Machine (SVM). The MIT-BIH AFDB was used as the training set, and the MIT-BIH Normal Sinus Rhythm Database (NSRDB) and the MIT-BIH Arrhythmia Database were used for out-of-sample test performance assessment. During the training phase, the optimal number of beats for accurate detection was determined using cross validation. The SVMs hyper-parameters were optimized with a grid search. On the training set the SVM had a Sensitivity (Se) of 99.07% and a Positive Predictive Value (PPV) of 98.27%. During independent testing on the MIT-BIH NSRDB the SVM had a Sp=99.72% which was superior to any single feature or previous detector. The SVM also provided a Sp=99.70% on series 100 of the MIT-BIH Arrhythmia Database and a Sensitivity of 100% on series 200 of the same datase. A good Specificity (82.00%) and Accuracy (85.45%) were also obtained. Results are superior to any previously reported, for both training and testing and robust across multiple databases.


title: Robustifying eye center localization by head pose cues

abstract: Head pose and eye location estimation are two closely related issues which refer to similar application areas. In recent years, these problems have been studied individually in numerous works in the literature. Previous research shows that cylindrical head models and isophote based schemes provide satisfactory precision in head pose and eye location estimation, respectively. However, the eye locator is not adequate to accurately locate eye in the presence of extreme head poses. Therefore, head pose cues may be suited to enhance the accuracy of eye localization in the presence of severe head poses. In this paper, a hybrid scheme is proposed in which the transformation matrix obtained from the head pose is used to normalize the eye regions and, in turn the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to (1) enhance the accuracy of eye location estimations in low resolution videos, (2) to extend the operating range of the eye locator and (3) to improve the accuracy and re-initialization capabilities of the pose tracker. From the experimental results it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Further, it considerably extends its operating range by more than 15°, by overcoming the problems introduced by extreme head poses. Finally, the accuracy of the head pose tracker is improved by 12% to 24%.


A 3 . 5 GHz Low Noise , High Gain Narrow Band Differential Low Noise Amplifier Design for WiMAX Applications


title: Automated Classification of Steel Samples : An investigation using Convolutional Neural Networks

abstract: Automated image recognition software has earlier been used for various analyses in the steel making industry. In this study, the possibility to apply such software to classify Scanning Electron Mic ...


title: Quasi-octave bandwidth phase matched K/Ka antenna feed subsystem for dual RHCP/LHCP polarization

abstract: The architecture concept and fabrication of a quasi-octave bandwidth waveguide feed network for circular polarized antennas is discussed here. The main advantages of this extremely compact topology rely on three key achievements: the instantaneous bandwidth capability, the axial ratio improvement and the significant size reduction which is in many cases determinant in antenna feed subsystems. Experimental measurements exhibit very good agreement with the predicted results given by conventional mode matching techniques. The overall structure provides an axial ratio as low as 0.7 dB along with a return loss better than 25 dB, at every waveguide port, over the whole bandwidth (60%), which represents the today's state-of-the-art. Moreover, yield analysis and simple scaling properties assure the use up to Q band.


Data mining approach for subscription-fraud detection in telecommunication sector


title: How did the discussion go: Discourse act classification in social media conversations

abstract: We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook.


title: Hybrid Partitioned SRAM-Based Ternary Content Addressable Memory

abstract: Although content addressable memory (CAM) provides fast search operation; however, CAM has disadvantages like low bit density and high cost per bit. This paper presents a novel memory architecture called hybrid partitioned static random access memory-based ternary content addressable memory (HP SRAM-based TCAM), which emulates TCAM functionality with conventional SRAM, thereby eliminating the inherited disadvantages of conventional TCAMs. HP SRAM-based TCAM logically dissects conventional TCAM table in a hybrid way (column-wise and row-wise) into TCAM sub-tables, which are then processed to be mapped to their corresponding SRAM memory units. Search operation in HP SRAM-based TCAM involves two SRAM accesses followed by a logical ANDing operation. To validate and justify our approach, 512 × 36 HP SRAM-based TCAM has been implemented in Xilinx Virtex-5 field programmable gate array (FPGA) and designed using 65-nm CMOS technology. Implementation in FPGA is advantageous and a beauty of our proposed TCAM because classical TCAMs cannot be implemented in FPGA. After a thorough analysis, we have concluded that energy/bit/search of the proposed TCAM is 85.72 fJ.


title: Engineering animations in user interfaces

abstract: Graphical User Interfaces used to be static, graphically representing one software state after the other. However, animated transitions between these static states are an integral part in modern user interfaces and processes for both their design and implementation remain a challenge for designers and developers. This paper proposes a Petri net model-based approach to support the design, implementation and validation of animated user interfaces by providing a complete and unambiguous description of the entire user interface including animations. A process for designing interactive systems focusing on animations is presented, along with a framework for the definition and implementation of animation in user interfaces. The framework proposes a two levels approach for defining a high-level view of an animation (focusing on animated objects, their properties to be animated and on the composition of animations) and a low-level one dealing with detailed aspects of animations such as timing and optimization. A case study (in the domain of interactive Television) elaborating the application of the presented process and framework exemplifies the contribution.


title: Privacy wizards for social networking sites

abstract: Privacy is an enormous problem in online social networking sites. While sites such as Facebook allow users fine-grained control over who can see their profiles, it is difficult for average users to specify this kind of detailed policy. In this paper, we propose a template for the design of a social networking privacy wizard. The intuition for the design comes from the observation that real users conceive their privacy preferences (which friends should be able to see which information) based on an implicit set of rules. Thus, with a limited amount of user input, it is usually possible to build a machine learning model that concisely describes a particular user's preferences, and then use this model to configure the user's privacy settings automatically. As an instance of this general framework, we have built a wizard based on an active learning paradigm called uncertainty sampling. The wizard iteratively asks the user to assign privacy "labels" to selected ("informative") friends, and it uses this input to construct a classifier, which can in turn be used to automatically assign privileges to the rest of the user's (unlabeled) friends. To evaluate our approach, we collected detailed privacy preference data from 45 real Facebook users. Our study revealed two important things. First, real users tend to conceive their privacy preferences in terms of communities, which can easily be extracted from a social network graph using existing techniques. Second, our active learning wizard, using communities as features, is able to recommend high-accuracy privacy settings using less user input than existing policy-specification tools.


title: The interspeech 2011 speaker state challenge

abstract: While the first open comparative challenges in the field of paralinguistics targeted more ‘conventional’ phenomena such as emotion, age, and gender, there still exists a multiplicity of not yet covered, but highly relevant speaker states and traits. The INTERSPEECH 2011 Speaker State Challenge thus addresses two new sub-challenges to overcome the usually low compatibility of results: In the Intoxication Sub-Challenge, alcoholisation of speakers has to be determined in two classes; in the Sleepiness Sub-Challenge, another two-class classification task has to be solved. This paper introduces the conditions, the Challenge corpora “Alcohol Language Corpus” and “Sleepy Language Corpus”, and a standard feature set that may be used. Further, baseline results are given. Index Terms: Speaker State Challenge, Intoxication, Sleepiness


title: Variable Importance Assessment in Regression: Linear Regression versus Random Forest

abstract: Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods for decomposing R2 are among the state-of-the-art methods, although the mechanism behind their behavior is not (yet) completely understood. Random forests—a machine-learning tool for classification and regression proposed a few years ago—have an inherent procedure of producing variable importances. This article compares the two approaches (linear model on the one hand and two versions of random forests on the other hand) and finds both striking similarities and differences, some of which can be explained whereas others remain a challenge. The investigation improves understanding of the nature of variable importance in random forests. This article has supplementary material online.


title: Regression modelling strategies for improved prognostic prediction.

abstract: Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.