title: A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification

abstract: Pedestrian re-identification is a difficult problem due to the large variations in a person's appearance caused by different poses and viewpoints, illumination changes, and occlusions. Spatial alignment is commonly used to address these issues by treating the appearance of different body parts independently. However, a body part can also appear differently during different phases of an action. In this paper we consider the temporal alignment problem, in addition to the spatial one, and propose a new approach that takes the video of a walking person as input and builds a spatio-temporal appearance representation for pedestrian re-identification. Particularly, given a video sequence we exploit the periodicity exhibited by a walking person to generate a spatio-temporal body-action model, which consists of a series of body-action units corresponding to certain action primitives of certain body parts. Fisher vectors are learned and extracted from individual body-action units and concatenated into the final representation of the walking person. Unlike previous spatio-temporal features that only take into account local dynamic appearance information, our representation aligns the spatio-temporal appearance of a pedestrian globally. Extensive experiments on public datasets show the effectiveness of our approach compared with the state of the art.


title: The Organic Presentation Tool

abstract: Conventional presentation software suits a very specific style of presenting, but as previous research has shown, other ways of navigation and visualization are often preferred. We present an alternative approach that combines features such as planar information structures, focus-plus-context visualizations and multi-touch interaction. Our main goal is to enable presenters to create stories, but also to make sure that they can easily divert from and return to these stories „on-the-fly‟, while giving the presentation. We explore how this type of presentations can be supported by a tool that makes a combination of creative storytelling and interaction with the audience (influencing the flow of the presentation) possible.


title: Cryptagram: photo privacy for online social media

abstract: While Online Social Networks (OSNs) enable users to share photos easily, they also expose users to several privacy threats from both the OSNs and external entities. The current privacy controls on OSNs are far from adequate, resulting in inappropriate flows of information when users fail to understand their privacy settings or OSNs fail to implement policies correctly. OSNs may further complicate privacy expectations when they reserve the right to analyze uploaded photos using automated face identification techniques. In this paper, we propose the design, implementation and evaluation of Cryptagram, a system designed to enhance online photo privacy. Cryptagram enables users to convert photos into encrypted images, which the users upload to OSNs. Users directly manage access control to those photos via shared keys that are independent of OSNs or other third parties. OSNs apply standard image transformations (JPEG compression) to all uploaded images so Cryptagram provides an image encoding and encryption mechanism that is tolerant to these transformations. Cryptagram guarantees that the recipient with the right credentials can completely retrieve the original image from the transformed version of the uploaded encrypted image while the OSN cannot infer the original image. Cryptagram's browser extension integrates seamlessly with preexisting OSNs, including Facebook and Google+, and currently has over 400 active users.


title: Analysis and Implementation of Transformerless LCL Resonant Power Supply for Ozone Generation

abstract: This paper describes the analysis and design of an LCL resonant power supply for ozone generation. The main advantage of the proposed topology is the absence of high-voltage transformer; the high voltage gain is achievable by means of double-resonance phenomena. Furthermore, the bandwidth is wider than the ordinary LC and its phase difference is constant over specific frequency range; as a result, an open-loop operation can be implemented. The complete analysis and design procedure of the power supply is presented. The design procedure is verified by implementing the power supply to drive a dielectric barrier discharge prototype ozone chamber. The hardware results are found to be in close agreement with simulation and thus justify the validity of the design procedures. The proposed circuit is suitable for portable ozone power supply fed by low-voltage source such as battery or photovoltaic module.


title: Adaptive Image Denoising by Mixture Adaptation

abstract: We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the expectation-maximization (EM) adaptation, takes a generic prior learned from a generic external database and adapts it to the noisy image to generate a specific prior. Different from existing methods that combine internal and external statistics in ad hoc ways, the proposed algorithm is rigorously derived from a Bayesian hyper-prior perspective. There are two contributions of this paper. First, we provide full derivation of the EM adaptation algorithm and demonstrate methods to improve the computational complexity. Second, in the absence of the latent clean image, we show how EM adaptation can be modified based on pre-filtering. The experimental results show that the proposed adaptation algorithm yields consistently better denoising results than the one without adaptation and is superior to several state-of-the-art algorithms.


title: Training Deep Neural Networks in Generations: A More Tolerant Teacher Educates Better Students

abstract: We focus on the problem of training a deep neural network in generations. The flowchart is that, in order to optimize the target network (student), another network (teacher) with the same architecture is first trained, and used to provide part of supervision signals in the next stage. While this strategy leads to a higher accuracy, many aspects (e.g., why teacher-student optimization helps) still need further explorations.This paper studies this problem from a perspective of controlling the strictness in training the teacher network. Existing approaches mostly used a hard distribution (e.g., one-hot vectors) in training, leading to a strict teacher which itself has a high accuracy, but we argue that the teacher needs to be more tolerant, although this often implies a lower accuracy. The implementation is very easy, with merely an extra loss term added to the teacher network, facilitating a few secondary classes to emerge and complement to the primary class. Consequently, the teacher provides a milder supervision signal (a less peaked distribution), and makes it possible for the student to learn from inter-class similarity and potentially lower the risk of over-fitting. Experiments are performed on standard image classification tasks (CIFAR100 and ILSVRC2012). Although the teacher network behaves less powerful, the students show a persistent ability growth and eventually achieve higher classification accuracies than other competitors. Model ensemble and transfer feature extraction also verify the effectiveness of our approach.


title: Detecting credit card fraud by genetic algorithm and scatter search

abstract: In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong classification of each transaction do not have the same effect in that if a card is in the hand of fraudsters its whole available limit is used up. Thus, the misclassification cost should be taken as the available limit of the card. This is what we aim at minimizing in this study. As for the solution method, we suggest a novel combination of the two well known meta-heuristic approaches, namely the genetic algorithms and the scatter search. The method is applied to real data and very successful results are obtained compared to current practice.


title: Denoising Autoencoders for Overgeneralization in Neural Networks

abstract: Despite recent developments that allowed neural networks to achieve impressive performance on a variety of applications, these models are intrinsically affected by the problem of overgeneralization, due to their partitioning of the full input space into the fixed set of target classes used during training. Thus it is possible for novel inputs belonging to categories unknown during training or even completely unrecognizable to humans to fool the system into classifying them as one of the known classes, even with a high degree of confidence. This problem can lead to security problems in critical applications, and is closely linked to open set recognition and 1-class recognition. This paper presents a novel way to compute a confidence score using the reconstruction error of denoising autoencoders and shows how it can correctly identify the regions of the input space close to the training distribution. The proposed solution is tested on benchmarks of ‘fooling’, open set recognition and 1-class recognition constructed from the MNIST and Fashion-MNIST datasets.


title: Uplink NOMA with Multi-Antenna

abstract: In this paper, we propose a uplink (UL) non-orthogonal multiple access (NOMA) systems equipped with multiple antennas to enhance the sum capacity of UL. In the proposed UL NOMA system, a base station (BS) with N antennas can support 2N users by sharing the space resource. However, sharing the space resource leads to interference between users. Therefore, to mitigate the interference and improve the sum capacity, we additionally propose a set selection algorithm and power control scheme. The set selection algorithm, which selects 2N users based on orthogonality and gain-difference between their channels, reduces the interference between users. Furthermore, the power control scheme can maximize the sum capacity with ensuring minimum target rate. Numerical results show that the proposed system improves the sum capacity over that of the conventional orthogonal multiple access (OMA) system.


title: Unsupervised Cross-Domain Word Representation Learning

abstract: Meaning of a word varies from one domain to another. Despite this important domain dependence in word semantics, existing word representation learning methods are bound to a single domain. Given a pair of \emph{source}-\emph{target} domains, we propose an unsupervised method for learning domain-specific word representations that accurately capture the domain-specific aspects of word semantics. First, we select a subset of frequent words that occur in both domains as \emph{pivots}. Next, we optimize an objective function that enforces two constraints: (a) for both source and target domain documents, pivots that appear in a document must accurately predict the co-occurring non-pivots, and (b) word representations learnt for pivots must be similar in the two domains. Moreover, we propose a method to perform domain adaptation using the learnt word representations. Our proposed method significantly outperforms competitive baselines including the state-of-the-art domain-insensitive word representations, and reports best sentiment classification accuracies for all domain-pairs in a benchmark dataset.


title: Dermoscopic Findings in Laugier-Hunziker Syndrome

abstract: Background Laugier-Hunziker syndrome (LHS) is a rare, acquired mucocutaneous hyperpigmentation often associated with longitudinal melanonychia. The clinical behavior of mucocutaneous pigmented lesions ranges from benign to highly malignant. Therefore, in most cases, the clinical diagnosis should be confirmed by further diagnostic methods. Dermoscopy is a noninvasive technique that has been used to make more accurate diagnoses of pigmented skin lesions. Nevertheless, to our knowledge, the dermoscopic features of the pigmented lesions in LHS have not been described previously. Herein, we report a case of LHS together with its dermoscopic features. Observations The clinical examination revealed macular hyperpigmentation on the oral and genital mucosa, conjunctiva, and palmoplantar region together with longitudinal melanonychia. Dermoscopic examination of mucosal lesions on the patient's lips and vulva revealed a parallel pattern. Longitudinal homogeneous pigmentation was observed on the toenails. The pigmented macules on the palms and the sole showed a parallel furrow pattern. A skin biopsy sample taken from the labial lesion was compatible with a diagnosis of mucosal melanosis. Conclusions By means of this case report, the dermoscopic features of the pigmented lesions in LHS are described for the first time, which facilitates diagnosis with a noninvasive technique. Future reports highlighting the dermoscopic features of this syndrome may simplify the diagnosis of LHS, which is thought to be underdiagnosed.


AuDoscore: Automatic Grading of Java or Scala Homework


title: Augmented Skeleton Space Transfer for Depth-Based Hand Pose Estimation

abstract: Crucial to the success of training a depth-based 3D hand pose estimator (HPE) is the availability of comprehensive datasets covering diverse camera perspectives, shapes, and pose variations. However, collecting such annotated datasets is challenging. We propose to complete existing databases by generating new database entries. The key idea is to synthesize data in the skeleton space (instead of doing so in the depth-map space) which enables an easy and intuitive way of manipulating data entries. Since the skeleton entries generated in this way do not have the corresponding depth map entries, we exploit them by training a separate hand pose generator (HPG) which synthesizes the depth map from the skeleton entries. By training the HPG and HPE in a single unified optimization framework enforcing that 1) the HPE agrees with the paired depth and skeleton entries; and 2) the HPG-HPE combination satisfies the cyclic consistency (both the input and the output of HPG-HPE are skeletons) observed via the newly generated unpaired skeletons, our algorithm constructs a HPE which is robust to variations that go beyond the coverage of the existing database. Our training algorithm adopts the generative adversarial networks (GAN) training process. As a by-product, we obtain a hand pose discriminator (HPD) that is capable of picking out realistic hand poses. Our algorithm exploits this capability to refine the initial skeleton estimates in testing, further improving the accuracy. We test our algorithm on four challenging benchmark datasets (ICVL, MSRA, NYU and Big Hand 2.2M datasets) and demonstrate that our approach outperforms or is on par with state-of-the-art methods quantitatively and qualitatively.


title: New rocker-bogie and terramechanics-based wheel/soil interaction models for planetary rovers

abstract: Mars rovers have to this point been almost completely reliant on the solar panel/rechargeable battery combination as a source of power. Curiosity, currently en route, relies on radio isotope decay as its source of electrical power. Given the limited amount of space available for solar panels and that the wattage available from radioisotope decay is limited; power is clearly a critical resource for any rover. The goal of this work is to estimate the energy cost of traversing terrains of various properties. Knowledge of energy costs for terrain traversal will allow for more efficient path planning enabling rovers to have longer periods of activity. Our system accepts grid-based terrain elevation data in the form of a Digital Elevation Model (DEM) along with rover and soil parameters, and uses a newly developed model of the most common rover suspension design (rocker-bogie) along with a terramechanics-based wheel-soil interaction model to build a map of the estimated torque required by each wheel to move the rover to each adjacent terrain square. Future work will involve real world testing and verification of our model.


title: , 1 D bar code reading on camera phones

abstract: The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.


title: Classes of Bivariate Orthogonal Polynomials

abstract: We introduce a class of orthogonal polynomials in two variables which generalizes the disc polynomials and the 2-D Hermite polynomials. We identify certain interesting members of this class including a one variable generalization of the 2-D Hermite polynomials and a two variable extension of the Zernike or disc polynomials. We also give q-analogues of all these extensions. In each case in addition to generating functions and three term recursions we provide raising and lowering operators and show that the polynomials are eigenfunctions of second-order partial differential or q-difference operators.


title: Techniques of EMG signal analysis: detection, processing, classification and applications (Correction)

abstract: This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, “Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.” should read “Costanza E, Inverso SA, Allen R. ‘Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller’ in Proc CHI2005, April 2005, Portland, OR, USA.”


title: Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

abstract: We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art on several metrics.


title: ExprGAN: Facial Expression Editing with Controllable Expression Intensity

abstract: Facial expression editing is a challenging task as it needs a high-level semantic understanding of the input face image. In conventional methods, either paired training data is required or the synthetic face resolution is low. Moreover, only the categories of facial expression can be changed. To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity. An expression controller module is specially designed to learn an expressive and compact expression code in addition to the encoder-decoder network. This novel architecture enables the expression intensity to be continuously adjusted from low to high. We further show that our ExprGAN can be applied for other tasks, such as expression transfer, image retrieval, and data augmentation for training improved face expression recognition models. To tackle the small size of the training database, an effective incremental learning scheme is proposed. Quantitative and qualitative evaluations on the widely used Oulu-CASIA dataset demonstrate the effectiveness of ExprGAN.


title: Internet of underground things: Sensing and communications on the field for precision agriculture

abstract: The projected increases in World population and need for food have recently motivated adoption of information technology solutions in crop fields within precision agriculture approaches. Internet of underground things (IOUT), which consists of sensors and communication devices, partly or completely buried underground for real-time soil sensing and monitoring, emerge from this need. This new paradigm facilitates seamless integration of underground sensors, machinery, and irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. In this paper, state-of-the-art communication architectures are reviewed, and underlying sensing technology and communication mechanisms for IOUT are presented. Recent advances in the theory and applications of wireless underground communication are also reported. Major challenges in IOUT design and implementation are identified.


title: Deep CORAL: Correlation Alignment for Deep Domain Adaptation

abstract: Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, requiring unsupervised adaptation. CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation. Here, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL). Experiments on standard benchmark datasets show state-of-the-art performance.


title: Model-based Adversarial Imitation Learning

abstract: Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle $D$ that discriminates between the expert's data distribution and that of the generative model $G$. The generative model is trained to capture the expert's distribution by maximizing the probability of $D$ misclassifying the data it generates. Overall, the system is \emph{differentiable} end-to-end and is trained using basic backpropagation. This type of learning was successfully applied to the problem of policy imitation in a model-free setup. However, a model-free approach does not allow the system to be differentiable, which requires the use of high-variance gradient estimations. In this paper we introduce the Model based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach for the problem of adversarial imitation learning. We show how to use a forward model to make the system fully differentiable, which enables us to train policies using the (stochastic) gradient of $D$. Moreover, our approach requires relatively few environment interactions, and fewer hyper-parameters to tune. We test our method on the MuJoCo physics simulator and report initial results that surpass the current state-of-the-art.


title: Employees' Information Security Awareness and Behavior: A Literature Review

abstract: Today's organizations are highly dependent on information management and processes. Information security is one of the top issues for researchers and practitioners. In literature, there is consent that employees are the weakest link in IS security. A variety of researchers discuss explanations for employees' security related awareness and behavior. This paper presents a theory-based literature review of the extant approaches used within employees' information security awareness and behavior research over the past decade. In total, 113 publications were identified and analyzed. The information security research community covers 54 different theories. Focusing on the four main behavioral theories, a state-of-the-art overview of employees' security awareness and behavior research over the past decade is given. From there, gaps in existing research are uncovered and implications and recommendations for future research are discussed. The literature review might also be useful for practitioners that need information about behavioral factors that are critical to the success of a organization's security awareness.


title: Ethics for Big Data and Analytics

abstract: This article investigates some issues associated with big data and analytics (big data) and ethics, examining how big data ethics are different than computer ethics and other more general ethical frameworks. In so doing, the author briefly investigates some of the previous research in "computer ethics," reviews some codes of ethics, analyzes the application of two frameworks on ethics to big data, and provides a definition of big data ethics. Ultimately, the author argues that in order to get sufficient specificity, a code of ethics for big data is necessary.


title: BayesSDT : Software for Bayesian inference with signal detection theory

abstract: This article describes and demonstrates the BayesSDT MATLAB-based software package for performing Bayesian analysis with equal-variance Gaussian signal detection theory (SDT). The software uses WinBUGS to draw samples from the posterior distribution of six SDT parameters: discriminability, hit rate, false alarm rate, criterion, and two alternative measures of bias. The software either provides a simple MATLAB graphical user interface or allows a more general MATLAB function call to produce graphs of the posterior distribution for each parameter of interest for each data set, as well as to return the full set of posterior samples.


title: www.elsevier.com/locate/patcog Hiding data in images by simple LSB substitution

abstract: In this paper, a data hiding scheme by simple LSB substitution is proposed. By applying an optimal pixel adjustment process to the stego-image obtained by the simple LSB substitution method, the image quality of the stego-image can be greatly improved with low extra computational complexity. The worst case mean-square-error between the stego-image and the cover-image is derived. Experimental results show that the stego-image is visually indistinguishable from the original cover-image. The obtained results also show a significant improvement with respect to a previous work.


title: Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

abstract: We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field size, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field size. We analyze the effective receptive field in several architecture designs, and the effect of sub-sampling, skip connections, dropout and nonlinear activations on it. This leads to suggestions for ways to address its tendency to be too small.


title: Selecting Films for Sex Research: Gender Differences in Erotic Film Preference

abstract: The aim of this study was to explore gender differences in sexual responsiveness to erotic films that had been selected for their differential appeal for men and women. A secondary objective was to identify variables that influence sexual arousal and explore whether these variables differ for men and women. Fifteen men (M age = 26 yrs) and 17 women (M age = 24 yrs) were presented with 20 film clips depicting heterosexual interactions, half of which were female- and the other half male-selected, and were asked to rate the clips on a number of dimensions. Overall, men found the film clips more sexually arousing than did the women. Gender differences in arousal were negligible for female-selected clips but substantial for male-selected clips. Furthermore, men and women experienced higher levels of sexual arousal to clips selected for individuals of their own gender. Cluster regression analyses, explaining 77% of the variance for male and 65% for female participants, revealed that men's sexual arousal was dependent upon the attractiveness of the female actor, feeling interested, and both “imagining oneself as a participant” and “watching as an observer.” For women, with all variables entered, only “imagining oneself as a participant” contributed to sexual arousal ratings. The findings suggest that how films are selected in sex research is an important variable in predicting levels of sexual arousal reported by men and women.


title: Bioethanol production from Gracilaria verrucosa, a red alga, in a biorefinery approach.

abstract: Abstract In this study, Gracilaria verrucosa , red seaweed has been used for production of agar and bioethanol. The algae harvested at various time durations resulted in extraction of ∼27–33% agar. The leftover pulp was found to contain ∼62–68% holocellulose, which on enzymatic hydrolysis yielded 0.87 g sugars/g cellulose. The enzymatic hydrolysate on fermentation with Saccharomyces cerevisiae produced ethanol with an ethanol yield of 0.43 g/g sugars. The mass balance evaluation of the complete process demonstrates that developing biorefinery approach for exploiting Gracilaria verrucosa , a red alga, could be commercially viable.


VALUE BASED INTELLIGENT REQUIREMENT PRIORITIZATION ( VIRP ) : EXPERT DRIVEN FUZZY LOGIC BASED PRIORITIZATION TECHNIQUE


title: Practical guide to controlled experiments on the web: listen to your customers not to the hippo

abstract: The web provides an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called randomized experiments (single factor or factorial designs), A/B tests (and their generalizations), split tests, Control/Treatment tests, and parallel flights. Controlled experiments embody the best scientific design for establishing a causal relationship between changes and their influence on user-observable behavior. We provide a practical guide to conducting online experiments, where end-users can help guide the development of features. Our experience indicates that significant learning and return-on-investment (ROI) are seen when development teams listen to their customers, not to the Highest Paid Person's Opinion (HiPPO). We provide several examples of controlled experiments with surprising results. We review the important ingredients of running controlled experiments, and discuss their limitations (both technical and organizational). We focus on several areas that are critical to experimentation, including statistical power, sample size, and techniques for variance reduction. We describe common architectures for experimentation systems and analyze their advantages and disadvantages. We evaluate randomization and hashing techniques, which we show are not as simple in practice as is often assumed. Controlled experiments typically generate large amounts of data, which can be analyzed using data mining techniques to gain deeper understanding of the factors influencing the outcome of interest, leading to new hypotheses and creating a virtuous cycle of improvements. Organizations that embrace controlled experiments with clear evaluation criteria can evolve their systems with automated optimizations and real-time analyses. Based on our extensive practical experience with multiple systems and organizations, we share key lessons that will help practitioners in running trustworthy controlled experiments.


title: Improving Offline Handwritten Chinese Character Recognition by Iterative Refinement

abstract: We present an iterative refinement module that can be applied to the output feature maps of any existing convolutional neural networks in order to further improve classification accuracy. The proposed module, implemented by an attention-based recurrent neural network, can iteratively use its previous predictions to update attention and thereafter refine current predictions. In this way, the model is able to focus on a sub-region of input images to distinguish visually similar characters (see Figure 1 for an example). We evaluate its effectiveness on handwritten Chinese character recognition (HCCR) task and observe significant performance gain. HCCR task is challenging due to large number of classes and small differences between certain characters. To overcome these difficulties, we further propose a novel convolutional architecture that utilizes both low-level visual cues and high-level structural information. Together with the proposed iterative refinement module, our approach achieves an accuracy of 97.37%, outperforming previous methods that use raw images as input on ICDAR-2013 dataset [1].


title: Applying Social Media Intelligence for Predicting and Identifying On-line Radicalization and Civil Unrest Oriented Threats

abstract: Research shows that various social media platforms on Internet such as Twitter, Tumblr (micro-blogging websites), Facebook (a popular social networking website), YouTube (largest video sharing and hosting website), Blogs and discussion forums are being misused by extremist groups for spreading their beliefs and ideologies, promoting radicalization, recruiting members and creating online virtual communities sharing a common agenda. Popular microblogging websites such as Twitter are being used as a real-time platform for information sharing and communication during planning and mobilization if civil unrest related events. Applying social media intelligence for predicting and identifying online radicalization and civil unrest oriented threats is an area that has attracted several researchers' attention over past 10 years. There are several algorithms, techniques and tools that have been proposed in existing literature to counter and combat cyber-extremism and predicting protest related events in much advance. In this paper, we conduct a literature review of all these existing techniques and do a comprehensive analysis to understand state-of-the-art, trends and research gaps. We present a one class classification approach to collect scholarly articles targeting the topics and subtopics of our research scope. We perform characterization, classification and an in-depth meta analysis meta-anlaysis of about 100 conference and journal papers to gain a better understanding of existing literature.


title: How green is multipath TCP for mobile devices?

abstract: Multipath TCP is a new transport protocol that enables systems to exploit available paths through multiple network interfaces. MPTCP is particularly useful for mobile devices, which frequently have multiple wireless interfaces. However, these devices have limited power capacity and thus judicious use of these interfaces is required. In this work, we develop a model for MPTCP energy consumption derived from experimental measurements using MPTCP on a mobile device with both cellular and WiFi interfaces. Using our MPTCP energy model, we identify the operating region where MPTCP can be more power efficient than either standard TCP or MPTCP. Based on our findings, we also design and implement an improved energy-efficient MPTCP that reduces power consumption by up to 8% in our experiments, while preserving the availability and robustness benefits of MPTCP.


title: An acid-pasting approach towards perylenetetracarboxylic diimide based lithium/sodium ion battery cathodes with high rate performances

abstract: Abstract An acid-pasting approach is developed in this work to fabricate the composite of perylenetetracarboxylic diimide (PTCDI) and carbon black in concentrated sulfuric acid. Different from traditional mechanical mixing, the solution-based fabrication strategy enables the sufficient blending of PTCDI as active component and carbon black as conductive agent in the PTCDI/CB composite. Serving as the cathode material, the PTCDI/CB composite shows excellent electrochemical performances in both lithium ion battery (137 mAh g−1 at 100 mA g−1 and 110 mAh g−1 at 2000 mA g−1) and sodium ion battery (134 mAh g−1 at 100 mA g−1 and 99 mAh g−1 at 1000 mA g−1), which are comparable to the state-of-art organic/inorganic cathode materials. Considering the simplicity of the processing procedures and the low cost of the starting materials, the acid-pasting approach is a highly attractive method for the solution-based manufacturing of organic secondary battery electrodes.


title: Temporal and spatial occlusion of advanced visual information constrains movement (re)organization in one-handed catching behaviors

abstract: Abstract Dynamic interceptive actions are performed under severe spatial and temporal constraints. Here, behavioral processes underpinning anticipation in one-handed catching were examined using novel technology to implement a spatial and temporal occlusion design. Video footage of an actor throwing a ball was manipulated to create four temporal and five spatial occlusion conditions. Data from twelve participants' hand kinematics and gaze behaviors were recorded while attempting to catch a projected ball synchronized with the video footage. Catching performance decreased with earlier occlusion of the footage. Movement onset of the catching hand and initiation of visual ball tracking emerged earlier when footage of the thrower was occluded at a later time point in the throwing action. Spatial occlusion did not affect catching success, although movement onset emerged later when increased visual information of the actor was occluded. Later movement onset was countered by greater maximum velocity of the catching hand. Final stages of action (e.g., grasping action of the hand) remained unchanged across both spatial and temporal conditions suggesting that later phases of the action were organized using ball flight information. Findings highlighted the importance of maintaining information-movement coupling during performance of interceptive actions, since movement behaviors were continuously (re)organized using kinematic information from a thrower's actions and ball flight information.


title: Cyber security risk assessment for SCADA and DCS networks

abstract: The growing dependence of critical infrastructures and industrial automation on interconnected physical and cyber-based control systems has resulted in a growing and previously unforeseen cyber security threat to supervisory control and data acquisition (SCADA) and distributed control systems (DCSs). It is critical that engineers and managers understand these issues and know how to locate the information they need. This paper provides a broad overview of cyber security and risk assessment for SCADA and DCS, introduces the main industry organizations and government groups working in this area, and gives a comprehensive review of the literature to date. Major concepts related to the risk assessment methods are introduced with references cited for more detail. Included are risk assessment methods such as HHM, IIM, and RFRM which have been applied successfully to SCADA systems with many interdependencies and have highlighted the need for quantifiable metrics. Presented in broad terms is probability risk analysis (PRA) which includes methods such as FTA, ETA, and FEMA. The paper concludes with a general discussion of two recent methods (one based on compromise graphs and one on augmented vulnerability trees) that quantitatively determine the probability of an attack, the impact of the attack, and the reduction in risk associated with a particular countermeasure.


title: Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations

abstract: We investigate whether wording, stylistic choices, and online behavior can be used to predict the age category of blog authors. Our hypothesis is that significant changes in writing style distinguish pre-social media bloggers from post-social media bloggers. Through experimentation with a range of years, we found that the birth dates of students in college at the time when social media such as AIM, SMS text messaging, MySpace and Facebook first became popular, enable accurate age prediction. We also show that internet writing characteristics are important features for age prediction, but that lexical content is also needed to produce significantly more accurate results. Our best results allow for 81.57% accuracy.


title: A non-photorealistic rendering framework with temporal coherence for augmented reality

abstract: Many augmented reality (AR) applications require a seamless blending of real and virtual content as key to increased immersion and improved user experiences. Photorealistic and non-photorealistic rendering (NPR) are two ways to achieve this goal. Compared with photorealistic rendering, NPR stylizes both the real and virtual content and makes them indistinguishable. Maintaining temporal coherence is a key challenge in NPR. We propose a NPR framework with support for temporal coherence by leveraging model-space information. Our systems targets painterly rendering styles of NPR. There are three major steps in this rendering framework for creating coherent results: tensor field creation, brush anchor placement, and brush stroke reshaping. To achieve temporal coherence for the final rendered results, we propose a new projection-based surface sampling algorithm which generates anchor points on model surfaces. The 2D projections of these samples are uniformly distributed in image space for optimal brush stroke placement. We also propose a general method for averaging various properties of brush stroke textures, such as their skeletons and colors, to further improve the temporal coherence. We apply these methods to both static and animated models to create a painterly rendering style for AR. Compared with existing image space algorithms our method renders AR with NPR effects with a high degree of coherence.


Modulo Multiplier by using Radix-8 Modified Booth Algorithm


Experimental Investigations of Laminar Separation Bubble for a Flow Past an Airfoil


title: Decision strategies and visual-field asymmetries in same-different judgments of word meaning

abstract: The accuracy with which observers judged whether two words belonged to the same semantic category was determined from a detection-theoretic analysis ofsame-different judgments. In Experiment 1, one word was presented centrally and the other word in either the left visual field (LVF) or the right visual field (RVF); in Experiment 2, both words were presented to either the LVF or the RVF. In order to obtain receiver-operating characteristics (ROCs) of performance, observers were asked to rate their confidence that the two words belonged to the same semantic category. Two models of the decision strategy were fitted to the obtained characteristics: a differencing model, in which the decision variable was the difference between the two observations; and an optimal model, in which each observation was judged in relation to a criterion. In both experiments, the optimal model provided a better fit than the differencing model to the obtained characteristics. Maximum-likelihood estimates of both the criterion-free parameter,d′, and the area under the operating characteristic,p(A), were greater for words presented in the RVF than for those presented in the LVF.


title: Roads Detection from Satellite Images Based on Active Contour Model and Distance Transform

abstract: Automatic man-made objects detection from aerial and satellite images be a very important research field to understand the changes in our environment and gives an important source of information to be used in many fields. This study describes and evaluates an automated method for roads detection using a region-based active contour model and distance transform. The first step is pre-processing of images to reduce noses and increase the contrast between contours and to improve the quality of the initial image by using the median filtering, the second step deals with using an active contour with a region-based signed pressure force (SPF) function that is adapted with the given images and in the last step centerlines of road are extracted with Euclidian distance transformation. The proposed method is tested on several images with high resolution and experiments results show that can detect both urban and suburban roads.


title: Complexities in Internet peering: Understanding the “Black” in the “Black Art”

abstract: Peering in the Internet interdomain network has long been considered a “black art”, understood in-depth only by a select few peering experts while the majority of the network operator community only scratches the surface employing conventional rules-of-thumb to form peering links through ad hoc personal interactions. Why is peering considered a black art? What are the main sources of complexity in identifying potential peers, negotiating a stable peering relationship, and utility optimization through peering? How do contemporary operational practices approach these problems? In this work we address these questions for Tier-2 Network Service Providers. We identify and explore three major sources of complexity in peering: (a) inability to predict traffic flows prior to link formation (b) inability to predict economic utility owing to a complex transit and peering pricing structure (c) computational infeasibility of identifying the optimal set of peers because of the network structure. We show that framing optimal peer selection as a formal optimization problem and solving it is rendered infeasible by the nature of these problems. Our results for traffic complexity show that 15% NSPs lose some fraction of customer traffic after peering. Additionally, our results for economic complexity show that 15% NSPs lose utility after peering, approximately, 50% NSPs end up with higher cumulative costs with peering than transit only, and only 10% NSPs get paid-peering customers.


title: Beyond Social Graphs: User Interactions in Online Social Networks and their Implications

abstract: Social networks are popular platforms for interaction, communication, and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, Web browsing, and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone. This leads to the question: “Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially enhanced applications?” In this article, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of “interaction graphs” to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the “small-world” properties present in their social graph counterparts. This means that these graphs have fewer “supernodes” with extremely high degree, and overall graph diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate several well-known social-based applications that rely on graph properties to infuse new functionality into Internet applications, including Reliable Email (RE), SybilGuard, and the weighted cascade influence maximization algorithm. The results reveal new insights into each of these systems, and confirm our hypothesis that to obtain realistic and accurate results, ongoing research on social network applications studies of social applications should use real indicators of user interactions in lieu of social graphs.


title: Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

abstract: We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach. Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial. To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation. We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice. Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods (see videos at this https URL).


title: Bootstrapping Ontologies for Web Services

abstract: Ontologies have become the de-facto modeling tool of choice, employed in many applications and prominently in the semantic web. Nevertheless, ontology construction remains a daunting task. Ontological bootstrapping, which aims at automatically generating concepts and their relations in a given domain, is a promising technique for ontology construction. Bootstrapping an ontology based on a set of predefined textual sources, such as web services, must address the problem of multiple, largely unrelated concepts. In this paper, we propose an ontology bootstrapping process for web services. We exploit the advantage that web services usually consist of both WSDL and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation. Our proposed ontology bootstrapping process integrates the results of both methods and applies a third method to validate the concepts using the service free text descriptor, thereby offering a more accurate definition of ontologies. We extensively validated our bootstrapping method using a large repository of real-world web services and verified the results against existing ontologies. The experimental results indicate high precision. Furthermore, the recall versus precision comparison of the results when each method is separately implemented presents the advantage of our integrated bootstrapping approach.


title: Modular control-flow integrity

abstract: Control-Flow Integrity (CFI) is a software-hardening technique. It inlines checks into a program so that its execution always follows a predetermined Control-Flow Graph (CFG). As a result, CFI is effective at preventing control-flow hijacking attacks. However, past fine-grained CFI implementations do not support separate compilation, which hinders its adoption. We present Modular Control-Flow Integrity (MCFI), a new CFI technique that supports separate compilation. MCFI allows modules to be independently instrumented and linked statically or dynamically. The combined module enforces a CFG that is a combination of the individual modules' CFGs. One challenge in supporting dynamic linking in multithreaded code is how to ensure a safe transition from the old CFG to the new CFG when libraries are dynamically linked. The key technique we use is to have the CFG represented in a runtime data structure and have reads and updates of the data structure wrapped in transactions to ensure thread safety. Our evaluation on SPECCPU2006 benchmarks shows that MCFI supports separate compilation, incurs low overhead of around 5%, and enhances security.


title: Marrying safety with privacy: A holistic solution for location privacy in VANETs

abstract: Existing privacy measures often conflict with the requirements of future vehicular networks. First and foremost, in attempting to achieve local privacy, they interfere with the primary purpose of vehicular networks: to improve road safety. Other solutions undermine accountability or introduce too high overhead to be of practical use. For this reason, proper privacy protection is disregarded in many field experiments, proposals, and standardization documents. In this paper, we take a structured approach to deriving a holistic solution for location privacy protection in Vehicular Ad-Hoc Networks (VANETs): Under reasonable assumptions about an adversary's capability, local privacy is neither required nor can it be achieved without compromising traffic safety. Our approach is therefore based on synchronized time-slotted pseudonym pools and the local announcing of pseudonym changes. By this, we overcome the privacy-safety problem while at the same time increasing privacy for all users. Our system is fully compatible with the requirements of vehicular networks and current standardization efforts.


title: Assessing Relative Sentence Complexity using an Incremental CCG Parser

abstract: Given a pair of sentences, we present computational models to assess if one sentence is simpler to read than the other. While existing models explored the usage of phrase structure features using a non-incremental parser, experimental evidence suggests that the human language processor works incrementally. We empirically evaluate if syntactic features from an incremental CCG parser are more useful than features from a non-incremental phrase structure parser. Our evaluation on Simple and Standard Wikipedia sentence pairs suggests that incremental CCG features are indeed more useful than phrase structure features achieving 0.44 points gain in performance. Incremental CCG parser also gives significant improvements in speed (12 times faster) in comparison to the phrase structure parser. Furthermore, with the addition of psycholinguistic features, we achieve the strongest result to date reported on this task. Our code and data can be downloaded from https://github. com/bharatambati/sent-compl.


title: CRISPR-Cas9: a new and promising player in gene therapy

abstract: First introduced into mammalian organisms in 2013, the RNA-guided genome editing tool CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease 9) offers several advantages over conventional ones, such as simple-to-design, easy-to-use and multiplexing (capable of editing multiple genes simultaneously). Consequently, it has become a cost-effective and convenient tool for various genome editing purposes including gene therapy studies. In cell lines or animal models, CRISPR-Cas9 can be applied for therapeutic purposes in several ways. It can correct the causal mutations in monogenic disorders and thus rescue the disease phenotypes, which currently represents the most translatable field in CRISPR-Cas9-mediated gene therapy. CRISPR-Cas9 can also engineer pathogen genome such as HIV for therapeutic purposes, or induce protective or therapeutic mutations in host tissues. Moreover, CRISPR-Cas9 has shown potentials in cancer gene therapy such as deactivating oncogenic virus and inducing oncosuppressor expressions. Herein, we review the research on CRISPR-mediated gene therapy, discuss its advantages, limitations and possible solutions, and propose directions for future research, with an emphasis on the opportunities and challenges of CRISPR-Cas9 in cancer gene therapy.


title: Behavior-based steering control for four wheel independent steering vehicle

abstract: This paper proposed a behavior-based steering controller for four wheel independent steering vehicle. The proposed controller acts as virtual linkages among each wheel to minimize wheel slip resulted by the misalignment of the orientations of wheels effectively. As there is no trajectory planning needed in the control algorithm, the proposed controller is especially suitable for non-autonomous vehicle which the driving path cannot be pre-determined. Numerical simulations to examine the performance of the proposed controller are implemented. Results show the effectiveness and robustness of the proposed behavior-based controller.


title: Android Malware Clustering through Malicious Payload Mining

abstract: Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party libraries in Android application development and the widespread use of repackaging in malware development. We design and implement an Android malware clustering system through iterative mining of malicious payload and checking whether malware samples share the same version of malicious payload. Our system utilizes a hierarchical clustering technique and an efficient bit-vector format to represent Android apps. Experimental results demonstrate that our clustering approach achieves precision of 0.90 and recall of 0.75 for Android Genome malware dataset, and average precision of 0.98 and recall of 0.96 with respect to manually verified ground-truth.


title: Depression and memory impairment: A meta-analysis of the association, its pattern, and specificity.

abstract: The existing evidence paints an unclear picture of whether an association exists between depression and memory impairment. The purpose of this investigation was to determine whether depression is associated with memory impairment, whether moderator variables determine the extent of this association, and whether any obtained association is unique to depression. Meta-analytic techniques were used to synthesize data from 99 studies on recall and 48 studies on recognition in clinically depressed and nondepressed samples. Associations between memory impairment and other psychiatric disorders (e.g., schizophrenia, dementia) were also examined. A significant, stable association between depression and memory impairment was revealed. Further analyses indicated, however, that it is likely that depression is linked to particular aspects of memory, the linkage is found in particular subsets of depressed individuals, and memory impairment is not unique to depression.


title: Cartesian admittance control with on-line gravity and friction observer compensation for elastic joint robots

abstract: In this paper, a Cartesian admittance controller with on-line gravity and friction observer compensation based on passivity theory for elastic joint robots is proposed. For this study, a compliance and position controller for joint and Cartesian level has been described with dual loop construction: an outer-loop admittance control, and an inner-loop motor side position with torque feedback control. In terms of torque feedback, a physical interpretation is addressed and analyzed. Regarding as admittance controller of the elastic joint, two type (stiffness and damping) of the systematic control model can be represented. In addition to an on-line gravity compensation based-on output-side encodes has been proposed. Moreover, in order to achieve high trajectory tracking performance, a friction observer based compensation is applied also. Furthermore, aimed at the control performance of the stiffness, damping, and admittance control algorithm are assessed by a simulation, respectively. Finally, the simulation studies reveal a better effect regarding as the position tracking and torque output in contrast to without friction passivity algorithm and proportional-derivative (PD) control. In addition, the response of proposed compliance control method is effective for external force.


title: A 7–21 GHz Dual-Polarized Planar Ultrawideband Modular Antenna (PUMA) Array

abstract: The design, fabrication, and measurement of a 16 × 16 dual-polarized planar ultrawideband modular antenna (PUMA) array operating over 7-21 GHz (3:1 bandwidth) are presented. The array is comprised of tightly coupled dipoles printed on a grounded dielectric substrate and are excited by an unbalanced feeding scheme that eliminates external wideband baluns and feed organizers. The array can be assembled modularly, where each low-profile, fully planar, low-cost tile is fabricated using standard multilayer microwave PCB techniques. A unique solderless, modular interconnect mates the array to a dilation fixture that facilitates measurements using standard surface-mount assembly (SMA) connectors and terminations. After presenting the most critical design trends, simulation results of the final array in infinite, infinite × finite, and finite × finite models are compared with measurements. This prototype array exhibits a measured active VSWR <; 2.1 and close to ideal gain at broadside, and VSWR <; 2.8 with low cross-polarization out to θ = 45° in all planes, showing close agreement with simulations.


title: Text-enhanced representation learning for knowledge graph

abstract: Learning the representations of a knowledge graph has attracted significant research interest in the field of intelligent Web. By regarding each relation as one translation from head entity to tail entity, translation-based methods including TransE, TransH and TransR are simple, effective and achieving the state-of-the-art performance. However, they still suffer the following issues: (i) low performance when modeling 1-to-N, N-to-1 and N-to-N relations. (ii) limited performance due to the structure sparseness of the knowledge graph. In this paper, we propose a novel knowledge graph representation learning method by taking advantage of the rich context information in a text corpus. The rich textual context information is incorporated to expand the semantic structure of the knowledge graph and each relation is enabled to own different representations for different head and tail entities to better handle 1-to-N, N-to-1 and N-to-N relations. Experiments on multiple benchmark datasets show that our proposed method successfully addresses the above issues and significantly outperforms the state-of-the-art methods.


title: Application-specific quantum for multi-core platform scheduler

abstract: Scheduling has a significant influence on application performance. Deciding on a quantum length can be very tricky, especially when concurrent applications have various characteristics. This is actually the case in virtualized cloud computing environments where virtual machines from different users are colocated on the same physical machine. We claim that in a multi-core virtualized platform, different quantum lengths should be associated with different application types. We apply this principle in a new scheduler called AQL_Sched. We identified 5 main application types and experimentally found the best quantum length for each of them. Dynamically, AQL_Sched associates an application type with each virtual CPU (vCPU) and schedules vCPUs according to their type on physical CPU (pCPU) pools with the best quantum length. Therefore, each vCPU is scheduled on a pCPU with the best quantum length. We implemented a prototype of AQL_Sched in Xen and we evaluated it with various reference benchmarks (SPECweb2009, SPECmail2009, SPEC CPU2006, and PARSEC). The evaluation results show that AQL_Sched outperforms Xen's credit scheduler. For instance, up to 20%, 10% and 15% of performance improvements have been obtained with SPECweb2009, SPEC CPU2006 and PARSEC, respectively.


title: Effects of voice-adaptation and social dialogue on perceptions of a robotic learning companion

abstract: With a growing number of applications involving social human-robot interactions, there is an increasingly important role for socially responsive speech interfaces that can effectively engage the user. For example, learning companions provide both task-related feedback and motivational support for students with the goal of improving learning. As a learning companion's ability to be socially responsive increases, so do learning outcomes. This paper presents a socially responsive speech interface for an embodied, robotic learning companion. We explore two methods of social responsiveness. The first method introduces social responses into the dialogue, while the second method augments these responses with voice-adaptation based on acoustic-prosodic entrainment. We evaluate the effect of a social, voice-adaptive robotic learning companion on social variables such as social presence and rapport, and we compare this to a companion with only social dialogue and one with neither social dialogue nor voice-adaptions. We contrast the effects against those of individual factors, such as gender. We find (1) that social presence is significantly higher with a social voice-adaptive speech interface than with purely social dialogue, and (2) that females feel significantly more rapport and are significantly more persistent in interactions with a robotic learning companion than males.


title: Semantic topic multimodal hashing for cross-media retrieval

abstract: Multimodal hashing is essential to cross-media similarity search for its low storage cost and fast query speed. Most existing multimodal hashing methods embedded heterogeneous data into a common low-dimensional Hamming space, and then rounded the continuous embeddings to obtain the binary codes. Yet they usually neglect the inherent discrete nature of hashing for relaxing the discrete constraints, which will cause degraded retrieval performance especially for long codes. For this purpose, a novel Semantic Topic Multimodal Hashing (STMH) is developed by considering latent semantic information in coding procedure. It first discovers clustering patterns of texts and robust factorizes the matrix of images to obtain multiple semantic topics of texts and concepts of images. Then the learned multimodal semantic features are transformed into a common subspace by their correlations. Finally, each bit of unified hash code can be generated directly by figuring out whether a topic or concept is contained in a text or an image. Therefore, the obtained model by STMH is more suitable for hashing scheme as it directly learns discrete hash codes in the coding process. Experimental results demonstrate that the proposed method outperforms several state-of-the-art methods.


title: Go Meta! A Case for Generative Programming and DSLs in Performance Critical Systems

abstract: Most performance critical software is developed using very low-level techniques. We argue that this needs to change, and that generative programming is an effective avenue to enable the use of high-level languages and programming techniques in many such circumstances.


title: QoE Doctor: Diagnosing Mobile App QoE with Automated UI Control and Cross-layer Analysis

abstract: Smartphones have become increasingly prevalent and important in our daily lives. To meet users' expectations about the Quality of Experience (QoE) of mobile applications (apps), it is essential to obtain a comprehensive understanding of app QoE and identify the critical factors that affect it. However, effectively and systematically studying the QoE of popular mobile apps such as Facebook and YouTube still remains a challenging task, largely due to a lack of a controlled and reproducible measurement methodology, and limited insight into the complex multi-layer dynamics of the system and network stacks. In this paper, we propose QoE Doctor, a tool that supports accurate, systematic, and repeatable measurements and analysis of mobile app QoE. QoE Doctor uses UI automation techniques to replay QoE-related user behavior, and measures the user-perceived latency directly from UI changes. To better understand and analyze QoE problems involving complex multi-layer interactions, QoE Doctor supports analysis across the application, transport, network, and cellular radio link layers to help identify the root causes. We implement QoE Doctor on Android, and systematically quantify various factors that impact app QoE, including the cellular radio link layer technology, carrier rate-limiting mechanisms, app design choices and user-side configuration options.


title: Trading structure for randomness in wireless opportunistic routing

abstract: Opportunistic routing is a recent technique that achieves high throughput in the face of lossy wireless links. The current opportunistic routing protocol, ExOR, ties the MAC with routing, imposing a strict schedule on routers' access to the medium. Although the scheduler delivers opportunistic gains, it misses some of the inherent features of the 802.11 MAC. For example, it prevents spatial reuse and thus may underutilize the wireless medium. It also eliminates the layering abstraction, making the protocol less amenable to extensions to alternate traffic types such as multicast. ::: This paper presents MORE, a MAC-independent opportunistic routing protocol. MORE randomly mixes packets before forwarding them. This randomness ensures that routers that hear the same transmission do not forward the same packets. Thus, MORE needs no special scheduler to coordinate routers and can run directly on top of 802.11. Experimental results from a 20-node wireless testbed show that MORE's median unicast throughput is 22% higher than ExOR, and the gains rise to 45% over ExOR when there is a chance of spatial reuse. For multicast, MORE's gains increase with the number of destinations, and are 35-200% greater than ExOR.


title: A new view of automatic relevance determination

abstract: Automatic relevance determination (ARD) and the closely-related sparse Bayesian learning (SBL) framework are effective tools for pruning large numbers of irrelevant features leading to a sparse explanatory subset. However, popular update rules used for ARD are either difficult to extend to more general problems of interest or are characterized by non-ideal convergence properties. Moreover, it remains unclear exactly how ARD relates to more traditional MAP estimation-based methods for learning sparse representations (e.g., the Lasso). This paper furnishes an alternative means of expressing the ARD cost function using auxiliary functions that naturally addresses both of these issues. First, the proposed reformulation of ARD can naturally be optimized by solving a series of re-weighted l1 problems. The result is an efficient, extensible algorithm that can be implemented using standard convex programming toolboxes and is guaranteed to converge to a local minimum (or saddle point). Secondly, the analysis reveals that ARD is exactly equivalent to performing standard MAP estimation in weight space using a particular feature- and noise-dependent, non-factorial weight prior. We then demonstrate that this implicit prior maintains several desirable advantages over conventional priors with respect to feature selection. Overall these results suggest alternative cost functions and update procedures for selecting features and promoting sparse solutions in a variety of general situations. In particular, the methodology readily extends to handle problems such as non-negative sparse coding and covariance component estimation.


title: Additional DOFs and sensors for bio-inspired locomotion: Towards active spine, ankle joints, and feet for a quadruped robot

abstract: In this paper, we present the design of biologically inspired structural components which should effectively improve the locomotion and mobility characteristics when applied to a robotic system. The aim is to increase the overall performance of a complex walking robot by the purposeful use of intelligent structures. In order to achieve this goal, an improved perception of the environment and the robot's own condition is needed. The structures contain a variety of functions which not only can extend the already existing locomotion behaviors of robots, but also permit further relevant applications like the contemporaneous use as supporting structure and sensor system. A precise perception of the environment is realizable with a high number of various sensors which results in large amount of data. To obtain the necessary information, a hierarchical sensor concept is used. Rigid or connecting elements are extended to single subsystems, including locally preprocessed and evaluated sensor information. The description in this paper includes the single subsystems and the planned prototype which will be used to demonstrate the possibilities of these subsystems by acting collaboratively.


title: A Practical Bayesian Framework for Backpropagation Networks

abstract: A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible (1) objective comparisons between solutions using alternative network architectures, (2) objective stopping rules for network pruning or growing procedures, (3) objective choice of magnitude and type of weight decay terms or additive regularizers (for penalizing large weights, etc.), (4) a measure of the effective number of well-determined parameters in a model, (5) quantified estimates of the error bars on network parameters and on network output, and (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian "evidence" automatically embodies "Occam's razor," penalizing overflexible and overcomplex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalization ability and the Bayesian evidence is obtained.


title: Current concepts in mild cognitive impairment.

abstract: The field of aging and dementia is focusing on the characterization of the earliest stages of cognitive impairment. Recent research has identified a transitional state between the cognitive changes of normal aging and Alzheimer's disease (AD), known as mild cognitive impairment (MCI). Mild cognitive impairment refers to the clinical condition between normal aging and AD in which persons experience memory loss to a greater extent than one would expect for age, yet they do not meet currently accepted criteria for clinically probable AD. When these persons are observed longitudinally, they progress to clinically probable AD at a considerably accelerated rate compared with healthy age-matched individuals. Consequently, this condition has been recognized as suitable for possible therapeutic intervention, and several multicenter international treatment trials are under way. Because this is a topic of intense interest, a group of experts on aging and MCI from around the world in the fields of neurology, psychiatry, geriatrics, neuropsychology, neuroimaging, neuropathology, clinical trials, and ethics was convened to summarize the current state of the field of MCI. Participants reviewed the world scientific literature on aging and MCI and summarized the various topics with respect to available evidence on MCI. Diagnostic criteria and clinical outcomes of these subjects are available in the literature. Mild cognitive impairment is believed to be a high-risk condition for the development of clinically probable AD. Heterogeneity in the use of the term was recognized, and subclassifications were suggested. While no treatments are recommended for MCI currently, clinical trials regarding potential therapies are under way. Recommendations concerning ethical issues in the diagnosis and the management of subjects with MCI were made.


title: Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset

abstract: The integration of brain monitoring into the man-machine interface holds great promise for real-time assessment of operator status and intelligent allocation of tasks between machines and humans. This article presents an integrated hardware and software solution for acquisition and real-time analysis of the electroencephalogram (EEG) to monitor indexes of alertness, cognition, and memory. Three experimental paradigms were evaluated in a total of 45 participants to identify EEG indexes associated with changes in cognitive workload: the Warship Commander Task (WCT), a simulated navy command and control environment that allowed workload levels to be systematically manipulated; a cognitive task with three levels of difficulty and consistent sensory inputs and motor outputs; and a multisession image learning and recognition memory test. Across tasks and participants, specific changes in the EEG were identified that were reliably associated with levels of cognitive workload. The EEG indexes were also shown to c...


title: Predicting the Risk of Heart Attacks usingNeural Network and Decision Tree

abstract: The healthcare environment is more and more data enriched, but the amount of knowledge getting from those data is very less, because lack of data analysis tools. We need to get the hidden relationships from the data. In the healthcare system to predict the heart attack perfectly, there are some techniques which are already in use. There is some lack of accuracy in the available techniques like Naive Bayes. Here, this paper proposes the system which uses neural network and Decision tree (ID3) to predict the heart attacks. Here the dataset with 6 attributes is used to diagnose the heart attacks. The dataset used is acath heart attack dataset provided by UCI machine learning repository. The results of the prediction give more accurate output than the other techniques.


title: Designing eco-feedback systems for everyday life

abstract: Eco-feedback systems currently frame householders as micro-resource managers, who weigh up the costs and benefits of their consumption, and make autonomous, rational and efficient decisions. Reporting on findings from a qualitative study of three Australian energy and water eco-feedback programs utilising an in-home display (IHD) system, this paper challenges this view. The research finds that householders consume energy and water to carry out everyday practices, such as showering, laundering and cooling, which are mediated by social, cultural, technical and institutional dynamics. The paper proposes an alternative design paradigm for eco-feedback systems premised on the realities of everyday life and identifies several design directions that emerge from this new starting point.


title: Enclothed Cognition and Controlled Attention during Insight Problem-Solving

abstract: Individual differences in working memory capacity (WMC) increase the ability and tendency to devote greater attentional control to a task—improving performance on a wide range of skills. In addition, recent research on enclothed cognition demonstrates that the situational influence of wearing a white lab coat increases controlled attention, due to the symbolic meaning and physical experience of wearing the coat. We examined whether these positive influences on attentional control lead to negative performance outcomes on insight problem-solving, a task thought to rely on associative processes that operate largely outside of explicit attentional control. Participants completed matchstick arithmetic problems while either wearing a white lab coat or in a no-coat control condition. Higher WMC was associated with lower insight problem-solving accuracy in the no-coat condition. In the coat condition, the insight problem-solving accuracy of lower WMC individuals dropped to the level of those higher in WMC. These results indicate that wearing a white lab coat led individuals to increase attentional control towards problem solving, hindering even lower WMC individuals from engaging in more diffuse, associative problem-solving processes, at which they otherwise excel. Trait and state factors known to increase controlled attention and improve performance on more attention-demanding tasks interact to hinder insight problem-solving. Correspondence: Marci S. DeCaro, Department of Psychological and Brain Sciences, 2301 S. Third Street, Louisville KY 40292. Email: marci.decaro@louisville.edu


title: Virtual reality: a proposal for pelvic floor muscle training

abstract: INTRODUCTION AND HYPOTHESIS ::: This video's proposal was to present one of the pelvic floor muscle (PFM) training programs, used in our research, that we designed as a virtual reality intervention protocol and investigated its effects on PFM contractility. ::: ::: ::: METHODS ::: Two clinical, controlled and prospective studies were conducted, one with 19 nulliparous women without urinary symptoms, who were evaluated by both electromyography and digital palpation (DP) and another with 27 postmenopausal women with mixed urinary symptoms (assessed by both ICIQ UI-SF and ICIQ-OAB), evaluated by vaginal dynamometry and DP, with a total of 46 women in both studies. This protocol was designed so that the participant would play a video game, seated on a pressure base platform, while commanding it through her pelvic movements. Using a virtual reality game, five activities were performed during 30 min, twice a week, with a total of 10 sessions. ::: ::: ::: RESULTS ::: A significant increase in PFM strength was found in both the nulliparous (p = 0.0001) and the postmenopausal (p = 0.0001) groups of women, as ascertained by DP. A significant increase in postmenopausal women's muscle strength and endurance assessed by dynamometry (p = 0.05) and a concomitant decrease in their urinary symptoms, were observed. ::: ::: ::: CONCLUSION ::: This virtual reality program promoted an increase in PFM contractility and a decrease in postmenopausal urinary symptoms.


title: Deep Learning Features at Scale for Visual Place Recognition

abstract: The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area.


title: Artificial Neural Networks in Medical Diagnosis

abstract: Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; data is the disease symptoms. The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: infected and non-infected. Classification is an important tool in medical diagnosis decision support. Feed-forward back propagation neural network is used as a classifier to distinguish between infected or non-infected person in both cases. The results of applying the artificial neural networks methodology to acute nephritis diagnosis based upon selected symptoms show abilities of the network to learn the patterns corresponding to symptoms of the person. In this study, the data were obtained from UCI machine learning repository in order to diagnosed diseases. The data is separated into inputs and targets. The targets for the neural network will be identified with 1's as infected and will be identified with 0's as non-infected. In the diagnosis of acute nephritis disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 99 percent while in the diagnosis of heart disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 95 percent.


title: MP-Shield: A Framework for Phishing Detection in Mobile Devices

abstract: Today, there is an exponential growth of e-services requiring the exchange of personal and sensible data over the Internet. Phishing techniques are emerging as the easiest solution to break the weakest link of the security chain: the end user. Social engineering attacks are deployed by financial/cyber criminals at a very low cost to induce naive Internet users to reveal user credentials such as bank account and credit card numbers. This problem needs to be addressed in the mobile field as well, due to the large diffusion of mobile devices such as smartphones, tablet, etc. In this paper we propose a novel framework for phishing detection in Android mobile devices which, on the one hand exploits well-known techniques already implemented by popular web browsers plug-in, such as public blacklist search, and, on the other hand, implements a machine learning detection engine which ensure zero-hour protection from new phishing campaigns.


title: Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible

abstract: We consider deep multi-layered generative models such as Boltzmann machines or Hopfield nets in which computation (which implements inference) is both recurrent and stochastic, but where the recurrence is not to model sequential structure, only to perform computation. We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values. It means that after the feedforward initialization, the recurrent network is very close to a fixed point of the network dynamics, where the energy gradient is 0. The main condition is that consecutive layers form a good auto-encoder, or more generally that different groups of inputs into the unit (in particular, bottom-up inputs on one hand, top-down inputs on the other hand) are consistent with each other, producing the same contribution into the total weighted sum of inputs. In biological terms, this would correspond to having each dendritic branch correctly predicting the aggregate input from all the dendritic branches, i.e., the soma potential. This is consistent with the prediction that the synaptic weights into dendritic branches such as those of the apical and basal dendrites of pyramidal cells are trained to minimize the prediction error made by the dendritic branch when the target is the somatic activity. Whereas previous work has shown how to achieve fast negative phase inference (when the model is unclamped) in a predictive recurrent model, this contribution helps to achieve fast positive phase inference (when the target output is clamped) in such recurrent neural models.


title: E-FallD: A fall detection system using android-based smartphone

abstract: With the rapid growth of the population of the elderly in the world, many countries in the world have been in the aging society. Falls are a major public health concern and one of the greatest obstacles to independent living for elder adults in terms of social services. In this paper, we present a fall detection system derived from some motion sensor via an android-based smart phone in which we utilize adaptive threshold algorithm for fall detection. Furthermore, we implement this system called E-FallD on the HTC G8 mobile phone. As demonstrated by the experimental results, E-FallD has a good detection performance and it is an efficient and smart application. In the mean time, using adaptive threshold algorithm could increase sensitivity dramatically. In a word ,our system provides a realizable, effective and portal solution to fall detection for users.


title: Accelerating Apache Hive with MPI for Data Warehouse Systems

abstract: Data warehouse systems, like Apache Hive, have been widely used in the distributed computing field. However, current generation data warehouse systems have not fully embraced High Performance Computing (HPC) technologies even though the trend of converging Big Data and HPC is emerging. For example, in traditional HPC field, Message Passing Interface (MPI) libraries have been optimized for HPC applications during last decades to deliver ultra-high data movement performance. Recent studies, like DataMPI, are extending MPI for Big Data applications to bridge these two fields. This trend motivates us to explore whether MPI can benefit data warehouse systems, such as Apache Hive. In this paper, we propose a novel design to accelerate Apache Hive by utilizing DataMPI. We further optimize the DataMPI engine by introducing enhanced non-blocking communication and parallelism mechanisms for typical Hive workloads based on their communication characteristics. Our design can fully and transparently support Hive workloads like Intel HiBench and TPC-H with high productivity. Performance evaluation with Intel HiBench shows that with the help of light-weight DataMPI library design, efficient job start up and data movement mechanisms, Hive on DataMPI performs 30% faster than Hive on Hadoop averagely. And the experiments on TPC-H with ORCFile show that the performance of Hive on DataMPI can improve 32% averagely and 53% at most more than that of Hive on Hadoop. To the best of our knowledge, Hive on DataMPI is the first attempt to propose a general design for fully supporting and accelerating data warehouse systems with MPI.


title: Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization

abstract: Recent vision and learning studies show that learning compact hash codes can facilitate massive data processing with significantly reduced storage and computation. Particularly, learning deep hash functions has greatly improved the retrieval performance, typically under the semantic supervision. In contrast, current unsupervised deep hashing algorithms can hardly achieve satisfactory performance due to either the relaxed optimization or absence of similarity-sensitive objective. In this work, we propose a simple yet effective unsupervised hashing framework, named Similarity-Adaptive Deep Hashing (SADH), which alternatingly proceeds over three training modules: deep hash model training, similarity graph updating and binary code optimization. The key difference from the widely-used two-step hashing method is that the output representations of the learned deep model help update the similarity graph matrix, which is then used to improve the subsequent code optimization. In addition, for producing high-quality binary codes, we devise an effective discrete optimization algorithm which can directly handle the binary constraints with a general hashing loss. Extensive experiments validate the efficacy of SADH, which consistently outperforms the state-of-the-arts by large gaps.


title: Hierarchical Attention and Context Modeling for Group Activity Recognition

abstract: Group activity recognition in videos is a challenging task, with two major issues, i.e. attending to those persons and their body parts that contribute significantly to the activity, and modeling contextual person structures in the group. Most previous approaches fail to provide a practical solution to jointly address both issues, however. In this paper, we propose to simultaneously deal with both issues via a hierarchical attention and context modeling framework based on Long Short-Term Memory (LSTM) networks. For the former, we propose ‘Hierarchical Attention Networks' applied at the part/person level, capable of attending distinctively to different persons and their body parts. For the latter, we build ‘Hierarchical Context Networks' that take the attentively pooled person-level features as input and recurrently model intra/inter-group contextual structures. The attentive and contextual representations are concatenated and fed into another LSTM to generate high-level discriminative temporal representations for group activity recognition. Extensive experiments on two widely-used group activity datasets demonstrate the effectiveness and superiority of the proposed framework.


title: Some affective consequences of social comparison and reflection processes: the pain and pleasure of being close.

abstract: A self-evaluation maintenance (SEM) model of social behavior was described. According to the comparison process, when another outperforms the self on a task high in relevance to the self, the closer the other the greater the threat to self-evaluation. According to the reflection process, when another outperforms the self on a task low in relevance to the self, the closer the other the greater the promise of augmentation to self-evaluation. Affect was assumed to reflect threats and promises to self-evaluation. In three studies, subjects were given feedback about own performance and the performance of a close (friend) and distant (stranger) other on tasks that were either low in self-relevance (Study 2) or that varied in self-relevance (Studies 1 and 3). In Study 1 (N = 31), subjects' performance on simple and complex tasks after each feedback trial served as a measure of arousal. Being outperformed by a close other resulted in greater arousal than being outperformed by a distant other. In Study 2 (N = 30), evaluative ratings of words unrelated to task performance served as an indirect measure of affect. Results indicated that when relevance is low, more positive affect is associated with a friend's outperforming the self than either a friend's performing at a level equal to the self or being outperformed by a stranger. In Study 3 (N = 31), subjects received feedback while their facial expressions were monitored. Pleasantness of expression was an interactive function of relevance of task, relative performance, and closeness of comparison other. The results of all three studies were interpreted as being generally consistent with the SEM model.


title: How Can SMEs Benefit from Big Data? Challenges and a Path Forward

abstract: Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. ::: ::: The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.


title: Recognizing Text Entailment via Syntactic Tree Matching

abstract: paper, we present our approach for Chinese Binary-class (BC) subtask of Recognizing Inference in Text (RITE) task in NTCIR-9 (9). Our system is to judge whether a given sentence can entail another or not. Firstly each sentence is parsed to a syntactic tree in which nodes represent words or phrase, and links represent syntactic relationships between nodes. Then, the entailment between two sentences is recognized by syntactic tree matching. In addition, to compute the similarity between two words or phrases, the external resources (Tongyici Cilin, HowNet 1 , and Hudong Wiki 2 ) are employed. The evaluation results show that our system can reach the accuracy of 87.1% in recognizing pairs with entailment relationship.