title: Face Spoof Detection With Image Distortion Analysis

abstract: Automatic face recognition is now widely used in applications ranging from deduplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA). Four different features (specular reflection, blurriness, chromatic moment, and color diversity) are extracted to form the IDA feature vector. An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e.g., printed photo and replayed video), is used to distinguish between genuine (live) and spoof faces. The proposed approach is extended to multiframe face spoof detection in videos using a voting-based scheme. We also collect a face spoof database, MSU mobile face spoofing database (MSU MFSD), using two mobile devices (Google Nexus 5 and MacBook Air) with three types of spoof attacks (printed photo, replayed video with iPhone 5S, and replayed video with iPad Air). Experimental results on two public-domain face spoof databases (Idiap REPLAY-ATTACK and CASIA FASD), and the MSU MFSD database show that the proposed approach outperforms the state-of-the-art methods in spoof detection. Our results also highlight the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.


Support vector machines for multi-class pattern recognition


title: Time series clustering by a robust autoregressive metric with application to air pollution

abstract: Abstract In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propose a robust clustering model for classifying time series. In particular, by adopting a fuzzy partitioning around medoids approach, the suggested clustering model is able to define the so-called medoid time series, which is a representative time series of each cluster, and the membership degrees of each time series to the different clusters. The robustness of the proposed clustering model is guaranteed by the adoption of a suitable robust metric for time series, i.e. the so-called exponential distance measure. In this way, the clustering model is able to tolerate the presence of outlier time series in the clustering process. In particular, it is capable of neutralizing and smoothing the disruptive effect of outlier time series, preserving the original clustering structure of the dataset, by assigning to outlier time series approximately the same membership degrees across clusters. To illustrate the usefulness and effectiveness of the suggested time series clustering model, a simulation study and an application to air pollution time series are carried out. Comparison with some existing clustering procedures suggested in the literature shows several advantages of the proposed model.


title: A mobile vision system for robust multi-person tracking

abstract: We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.


title: Semi-global Stereo Matching with Surface Orientation Priors

abstract: Semi-Global Matching (SGM) is a widely-used efficient stereo matching technique. It works well for textured scenes, but fails on untextured slanted surfaces due to its fronto-parallel smoothness assumption. To remedy this problem, we propose a simple extension, termed SGM-P, to utilize precomputed surface orientation priors. Such priors favor different surface slants in different 2D image regions or 3D scene regions and can be derived in various ways. In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes. We also explore surface normal priors derived from Manhattan-world assumptions, and we analyze the potential performance gains using oracle priors derived from ground-truth data. SGM-P only adds a minor computational overhead to SGM and is an attractive alternative to more complex methods employing higher-order smoothness terms.


title: Compact wide-band multimode antennas for MIMO and diversity

abstract: This paper presents broadband multimode antennas for multiple-input multiple-output (MIMO) and diversity applications. The antenna system is not based on spatial diversity, as usual MIMO systems, but on a combination of pattern and polarization diversity. Different modes of self-complementary, thus extremely broadband, spiral and sinuous antennas are used to decorrelate the signals. It is shown that only one antenna is necessary to receive three uncorrelated signals, thus the space required to place the MIMO antenna is very small. Simulation results and measurements of a typical indoor scenario are given.


title: Multimodal Energy Harvesting System: Piezoelectric and Electromagnetic

abstract: In this study, we report a multimodal energy harvesting device that combines electromagnetic and piezoelectric energy harvesting mechanism. The device consists of piezoelectric crystals bonded to a cantilever beam. The tip of the cantilever beam has an attached permanent magnet which, oscillates within a stationary coil fixed to the top of the package. The permanent magnet serves two purpose (i) acts as a tip mass for the cantilever beam and lowers the resonance frequency, and (ii) acts as a core which oscillates between the inductive coils resulting in electric current generation through Faraday's effect. Thus, this design combines the energy harvesting from two different mechanisms, piezoelectric and electromagnetic, on the same platform. The prototype system was optimized using the finite element software, ANSYS, to find the resonance frequency and stress distribution. The power generated from the fabricated prototype was found to be 0.25 W using the electromagnetic mechanism and 0.25 mW using the piezoelectric mechanism at 35 g acceleration and 20 Hz frequency.


title: Holographic and Light-Field Imaging as Future 3-D Displays

abstract: Light-field imaging and holographic imaging are currently the two mostly investigated 3-D imaging technologies because of their potentials to create the viewing environment conforming to a natural viewing condition. The basic optical geometries for image display in these imaging are not different from that of integral photography. The images in the two type of imaging are a set of different view images. These images are arranged as a 2-D point image array, and each point image is expanded with a certain angle to form a viewing zone. The differences between the two types of imaging are the number of point images in the array and the physical entities forming the images. Holographic imaging has many more point images than light-field imaging, and each image in the array consists of coherent right rays from different positions of an object. In light-field imaging, an array of pixels represents a direction view of the object. Despite these differences, they share the same goal of providing a continuous parallax to viewers and require display panels of almost the same characteristics. It is expected that in the future these two imaging techniques will be integrated into the same flat panel along with the plane image.


title: DEPTH CAMERA TECHNOLOGY COMPARISON AND PERFORMANCE EVALUATION

abstract: How good are cheap depth cameras, namely the Microsoft Kinect, compared to state of the art Time-of- Flight depth cameras? In this paper several depth cameras of different types were put to the test on a variety of tasks in order to judge their respective performance and to find out their weaknesses. We will concentrate on the common area of applications for which both types are specified, i.e. near field indoor scenes. The characteristics and limitations of the different technologies as well as the concrete hardware implementations are discussed and evaluated with a set of experimental setups. Especially, the noise level and the axial and angular resolutions are compared. Additionally, refined formulas to generate depth values based on the raw measurements of the Kinect are presented.


title: Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

abstract: In this study, a fuzzy autoregressive (fuzzy-AR) model is proposed to describe the traffic characteristics of high-speed networks. The fuzzy-AR model approximates a nonlinear time-variant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzy-AR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzy-AR modeling is treated by a clustering algorithm developed from actual traffic data in high-speed networks. Based on the adaptive AR-prediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for high-speed networks. Finally, using the actual Ethernet-LAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for high-speed network traffic control.


title: Removal Attacks on Logic Locking and Camouflaging Techniques

abstract: With the adoption of a globalized and distributed IC design flow, IP piracy, reverse engineering, and counterfeiting threats are becoming more prevalent. Logic obfuscation techniques including logic locking and IC camouflaging have been developed to address these emergent challenges. A major challenge for logic locking and camouflaging techniques is to resist Boolean satisfiability (SAT) based attacks that can circumvent state-of-the-art solutions within minutes. Over the past year, multiple SAT attack resilient solutions such as Anti-SAT and AND-tree insertion (ATI) have been presented. In this paper, we perform a security analysis of these countermeasures and show that they leave structural traces behind in their attempts to thwart the SAT attack. We present three attacks, namely “signal probability skew” (SPS) attack, “AppSAT guided removal (AGR) attack, and “sensitization guided SAT” (SGS) attack”, that can break Anti-SAT and ATI, within minutes.


title: Learning to Generate Time-Lapse Videos Using Multi-stage Dynamic Generative Adversarial Networks

abstract: Taking a photo outside, can we predict the immediate future, e.g., how would the cloud move in the sky? We address this problem by presenting a generative adversarial network (GAN) based two-stage approach to generating realistic time-lapse videos of high resolution. Given the first frame, our model learns to generate long-term future frames. The first stage generates videos of realistic contents for each frame. The second stage refines the generated video from the first stage by enforcing it to be closer to real videos with regard to motion dynamics. To further encourage vivid motion in the final generated video, Gram matrix is employed to model the motion more precisely. We build a large scale time-lapse dataset, and test our approach on this new dataset. Using our model, we are able to generate realistic videos of up to $128\times 128$ resolution for 32 frames. Quantitative and qualitative experiment results have demonstrated the superiority of our model over the state-of-the-art models.


title: A Rare Presentation of Imperforate Hymen: A Case Report

abstract: Introduction. Acute urinary retention in a child is rare. Haematocolpos can cause a mechanical obstruction, resulting in acute urinary retention. Case Report. A 12-year-old girl presented to the surgical department with a one-day history of acute urinary retention and suprapubic tenderness. She had not started menses but had described period-like pains every month for the past six months. On examination, she had a palpable bladder with over 500 mls of residual urine and a bluish-grey bulge posterior to her urethral meatus. An US scan showed a large mass posterior to her bladder resembling a haematocolpos, and this was confirmed with an MRI scan. She was catheterised and eventually underwent a hymenectomy using a cruciate incision. She made a good recovery postoperatively. Conclusion. In the case of a peripubertal female presenting with acute urinary retention, haematocolpos should be considered as a diagnosis.


Peekquence : Visual Analytics for Event Sequence Data


title: One-Shot Learning in Discriminative Neural Networks

abstract: We consider the task of one-shot learning of visual categories. In this paper we explore a Bayesian procedure for updating a pretrained convnet to classify a novel image category for which data is limited. We decompose this convnet into a fixed feature extractor and softmax classifier. We assume that the target weights for the new task come from the same distribution as the pretrained softmax weights, which we model as a multivariate Gaussian. By using this as a prior for the new weights, we demonstrate competitive performance with state-of-the-art methods whilst also being consistent with 'normal' methods for training deep networks on large data.


title: Oriented assembly of polyhedral plasmonic nanoparticle clusters.

abstract: Shaped colloids can be used as nanoscale building blocks for the construction of composite, functional materials that are completely assembled from the bottom up. Assemblies of noble metal nanostructures have unique optical properties that depend on key structural features requiring precise control of both position and connectivity spanning nanometer to micrometer length scales. Identifying and optimizing structures that strongly couple to light is important for understanding the behavior of surface plasmons in small nanoparticle clusters, and can result in highly sensitive chemical and biochemical sensors using surface-enhanced Raman spectroscopy (SERS). We use experiment and simulation to examine the local surface plasmon resonances of different arrangements of Ag polyhedral clusters. High-resolution transmission electron microscopy shows that monodisperse, atomically smooth Ag polyhedra can self-assemble into uniform interparticle gaps that result in reproducible SERS enhancement factors from assembly to assembly. We introduce a large-scale, gravity-driven assembly method that can generate arbitrary nanoparticle clusters based on the size and shape of a patterned template. These templates enable the systematic examination of different cluster arrangements and provide a means of constructing scalable and reliable SERS sensors.


title: Time flies when you're having fun: cognitive absorption and beliefs about information technology usage 1

abstract: Extant explanations of why users behave in particular ways toward information technologies have tended to focus predominantly on instrumental beliefs as drivers of individual usage intentions. Prior work in individual psychology, however, suggests that holistic experiences with technology as captured in constructs such as enjoyment and flow are potentially important explanatory variables in technology acceptance theories. In this paper, we describe a multi-dimensional construct labeled cognitive absorption and defined as a state of deep involvement with software. Cognitive absorption, theorized as being exhibited through the five dimensions of ::: ::: temporal dissociation, focused immersion, heightened enjoyment, control, and curiosity, is posited to be a proximal antecedent of two important beliefs about technology use: perceived usefulness and perceived ease of use. In addition, we propose that the individual traits of playfulness and personal innovativeness are important determinants of cognitive absorption. Based on the conceptual definition of this construct, operational measures for each dimension are developed. Using the World Wide Web as the target technology, scale validation indicates that the operational measures have acceptable psychometric properties and confirmatory factor analysis supports the proposed multi-dimensional structure. Structural equation analysis provides evidence for the theorized nomological net of cognitive absorption. Theoretical and practical implications are offered


title: Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering

abstract: Ambient-assisted living (AAL) is promising to become a supplement of the current care models, providing enhanced living experience to people within context-aware homes and smart environments. Activity recognition based on sensory data in AAL systems is an important task because 1) it can be used for estimation of levels of physical activity, 2) it can lead to detecting changes of daily patterns that may indicate an emerging medical condition, or 3) it can be used for detection of accidents and emergencies. To be accepted, AAL systems must be affordable while providing reliable performance. These two factors hugely depend on optimizing the number of utilized sensors and extracting robust features from them. This paper proposes a generic feature engineering method for selecting robust features from a variety of sensors, which can be used for generating reliable classification models. From the originally recorded time series and some newly generated time series [i.e., magnitudes, first derivatives, delta series, and fast Fourier transformation (FFT)-based series], a variety of time and frequency domain features are extracted. Then, using two-phase feature selection, the number of generated features is greatly reduced. Finally, different classification models are trained and evaluated on an independent test set. The proposed method was evaluated on five publicly available data sets, and on all of them, it yielded better accuracy than when using hand-tailored features. The benefits of the proposed systematic feature engineering method are quickly discovering good feature sets for any given task than manually finding ones suitable for a particular task, selecting a small feature set that outperforms manually determined features in both execution time and accuracy, and identification of relevant sensor types and body locations automatically. Ultimately, the proposed method could reduce the cost of AAL systems by facilitating execution of algorithms on devices with limited resources and by using as few sensors as possible.


DETECTING PACKED EXECUTABLES BASED ON RAW BINARY DATA


title: Evaluation of Resource-based App Repackaging Detection in Android

abstract: Android app repackaging threatens the health of application markets, as repackaged apps, besides stealing revenue for honest developers, are also a source of malware distribution. Techniques that rely on visual similarity of Android apps recently emerged as a way to tackle the repackaging detection problem, as code-based detection techniques often fail in terms of efficiency, and effectiveness when obfuscation is applied [19, 21]. Among such techniques, the resource-based repackaging detection approach that compares sets of files included in apks has arguably the best performance [10, 17, 20]. Yet, this approach has not been previously validated on a dataset of repackaged apps.


title: Exploring patterns in dictionary definitions for synonym extraction

abstract: Automatic determination of synonyms and/or semantically related words has various applications in Natural Language Processing. Two mainstream paradigms to date, lexicon-based and distributional approaches, both exhibit pros and cons with regard to coverage, complexity, and quality. In this paper, we propose three novel methods—two rule-based methods and one machine learning approach—to identify synonyms from definition texts in a machine-readable dictionary. Extracted synonyms are evaluated in two extrinsic experiments and one intrinsic experiment. Evaluation results show that our pattern-based approach achieves best performance in one of the experiments and satisfactory results in the other, comparable to corpus-based state-of-the-art results.


title: A Unified Tensor-based Active Appearance Face Model

abstract: Appearance variations result in many difficulties in face image analysis. To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces. In contrast with the classical Tensor-based AAM (T-AAM), the proposed UT-AAM has four advantages: First, for each type of face information, namely shape and texture, we construct a tensor model capturing all relevant appearance variations. This unified tensor model contrasts with the variation-specific models of T-AAM. Second, a strategy for dealing with self-occluded faces is proposed to obtain consistent shape and texture representations of faces across large pose variations. Third, our UT-AAM is capable of constructing the model from an incomplete training dataset, using tensor completion methods. Last, we use an effective cascaded-regression-based method for UT-AAM fitting. With these improvements, the utility of UT-AAM in practice is considerably enhanced in comparison with the classical T-AAM. As an example, we demonstrate the improvements in training facial landmark detectors through the use of UT-AAM to synthesise a large number of virtual samples. Experimental results obtained using the Multi-PIE and 300-W face datasets demonstrate the merits of the proposed approach.


title: Different Classifiers Find Different Defects Although With Different Level of Consistency

abstract: BACKGROUND -- During the last 10 years hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. OBJECTIVE -- We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. METHOD -- We perform a sensitivity analysis to compare the performance of Random Forest, Naive Bayes, RPart and SVM classifiers when predicting defects in 12 NASA data sets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty is compared against different classifiers. RESULTS -- Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. CONCLUSIONS -- Our results confirm that a unique sub-set of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Classifier ensembles with decision making strategies not based on majority voting are likely to perform best.


title: Unsupervised Steganalysis Based on Artificial Training Sets

abstract: In this paper, an unsupervised steganalysis method that combines artificial training sets and supervised classification is proposed. We provide a formal framework for unsupervised classification of stego and cover images in the typical situation of targeted steganalysis (i.e., for a known algorithm and approximate embedding bit rate). We also present a complete set of experiments using (1) eight different image databases, (2) image features based on Rich Models, and (3) three different embedding algorithms: Least Significant Bit (LSB) matching, Highly undetectable steganography (HUGO) and Wavelet Obtained Weights (WOW). We show that the experimental results outperform previous methods based on Rich Models in the majority of the tested cases. At the same time, the proposed approach bypasses the problem of Cover Source Mismatch - when the embedding algorithm and bit rate are known - since it removes the need of a training database when we have a large enough testing set. Furthermore, we provide a generic proof of the proposed framework in the machine learning context. Hence, the results of this paper could be extended to other classification problems similar to steganalysis.


title: The Impact of the Big Five Personality Traits on the Acceptance of Social Networking Website

abstract: Social networking websites are a relatively new and wildly popular form of technology. While millions of users have adopted this technology, it is not currently known whether these users have any commonalities or whether these users represent a certain personality type. This study investigates the impact of the Big Five personality variables on the acceptance of social networking technology. Because the Technology Acceptance Model and the Unified Theory of the Acceptance and Use of Technology assume the technology in question is used for productivity gains, a new model framework will be used, specifically created for the acceptance of hedonic information systems.


title: NDC: Analyzing the impact of 3D-stacked memory+logic devices on MapReduce workloads

abstract: While Processing-in-Memory has been investigated for decades, it has not been embraced commercially. A number of emerging technologies have renewed interest in this topic. In particular, the emergence of 3D stacking and the imminent release of Micron's Hybrid Memory Cube device have made it more practical to move computation near memory. However, the literature is missing a detailed analysis of a killer application that can leverage a Near Data Computing (NDC) architecture. This paper focuses on in-memory MapReduce workloads that are commercially important and are especially suitable for NDC because of their embarrassing parallelism and largely localized memory accesses. The NDC architecture incorporates several simple processing cores on a separate, non-memory die in a 3D-stacked memory package; these cores can perform Map operations with efficient memory access and without hitting the bandwidth wall. This paper describes and evaluates a number of key elements necessary in realizing efficient NDC operation: (i) low-EPI cores, (ii) long daisy chains of memory devices, (iii) the dynamic activation of cores and SerDes links. Compared to a baseline that is heavily optimized for MapReduce execution, the NDC design yields up to 15X reduction in execution time and 18X reduction in system energy.


title: On Ladder Logic Bombs in Industrial Control Systems

abstract: In industrial control systems, devices such as Programmable Logic Controllers (PLCs) are commonly used to directly interact with sensors and actuators, and perform local automatic control. PLCs run software on two different layers: (a) firmware (i.e. the OS) and (b) control logic (processing sensor readings to determine control actions).


title: Toward human-like motion planning in urban environments

abstract: Abstract— Prior autonomous navigation systems focused on the demonstration of the technological feasibility. But as the technology evolves, improving user experience through learning expert’s or individual’s driving pattern emerges as a promising research direction. As a first step toward this goal, we investigate methods to learn from human demonstrations in urban scenarios without any environmental disturbances (traffic-free). We propose a path model that generates a reference path with smooth and peak-value-reduced curvature, and a parameterized speed model to be fitted by human driving data. Model parameters are then learned through regression methods, and certain statistical human driving patterns are revealed. The learned model is then evaluated by comparing the generated plan with the collected data by the same human driver.


title: Design Optimization and Implementation for RF Energy Harvesting Circuits

abstract: A new design for an energy harvesting device is proposed in this paper, which enables scavenging energy from radiofrequency (RF) electromagnetic waves. Compared to common alternative energy sources like solar and wind, RF harvesting has the least energy density. The existing state-of-the-art solutions are effective only over narrow frequency ranges, are limited in efficiency response, and require higher levels of input power. This paper has a twofold contribution. First, we propose a dual-stage energy harvesting circuit composed of a seven-stage and ten-stage design, the former being more receptive in the low input power regions, while the latter is more suitable for higher power range. Each stage here is a modified voltage multiplier, arranged in series and our design provides guidelines on component choice and precise selection of the crossover operational point for these two stages between the high (20 dBm) and low power (-20 dBm) extremities. Second, we fabricate our design on a printed circuit board to demonstrate how such a circuit can run a commercial Mica2 sensor mote, with accompanying simulations on both ideal and non-ideal conditions for identifying the upper bound on achievable efficiency. With a simple yet optimal dual-stage design, experiments and characterization plots reveal approximately 100% improvement over other existing designs in the power range of -20 to 7 dBm.


title: Comparison of scapular posterior tilting exercise alone and scapular posterior tilting exercise after pectoralis minor stretching on scapular alignment and scapular upward rotators activity in subjects with short pectoralis minor.

abstract: OBJECTIVES ::: To compare scapular posterior tilting exercise alone and scapular posterior tilting exercise after pectoralis minor (PM) stretching on the PM index (PMI), scapular anterior tilting index, scapular upward rotation angle, and scapular upward rotators' activity in subjects with a short PM. ::: ::: ::: PARTICIPANTS ::: Fifteen subjects with a short PM participated in this study. ::: ::: ::: MAIN OUTCOME MEASURES ::: The PMI, scapular anterior tilting index, and scapular upward rotation angle were measured after scapular posterior tilting exercise alone and scapular posterior tilting exercise after PM stretches. Scapular upward rotators' activities were collected during scapular posterior tilting exercise alone and scapular posterior tilting exercise after PM stretches. ::: ::: ::: RESULTS ::: The PMI and scapular upward rotation angle, as well as the activity of the upper trapezius, lower trapezius, and serratus anterior muscles, were significantly greater for scapular posterior tilting exercise after PM stretching and the scapular anterior tilting index was significantly lower for scapular posterior tilting exercise after PM stretching than the scapular posterior tilting exercise alone. ::: ::: ::: CONCLUSIONS ::: Scapular posterior tilting exercise after PM stretching in subjects with a short PM could be an effective method of modifying scapular alignment and scapular upward rotator activity.


title: An overview of traffic sign detection and classification methods

abstract: Over the last few years, different traffic sign recognition systems were proposed. The present paper introduces an overview of some recent and efficient methods in the traffic sign detection and classification. Indeed, the main goal of detection methods is localizing regions of interest containing traffic sign, and we divide detection methods into three main categories: color-based (classified according to the color space), shape-based, and learning-based methods (including deep learning). In addition, we also divide classification methods into two categories: learning methods based on hand-crafted features (HOG, LBP, SIFT, SURF, BRISK) and deep learning methods. For easy reference, the different detection and classification methods are summarized in tables along with the different datasets. Furthermore, future research directions and recommendations are given in order to boost TSR’s performance.


title: Energy-efficient target coverage in wireless sensor networks

abstract: A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing. In this paper we propose un efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [M. Cardei et al], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches.


Rapid Prototyping of Form-driven Dialogue Systems Using an Open-source Framework


title: Major Issues in Software Engineering Project Management

abstract: Software engineering project management (SEPM) has been the focus of much recent attention because of the enormous penalties incurred during software development and maintenance resulting from poor management. To date there has been no comprehensive study performed to determine the most significant problems of SEPM, their relative importance, or the research directions necessary to solve them. We conducted a major survey of individuals from all areas of the computer field to determine the general consensus on SEPM problems. Twenty hypothesized problems were submitted to several hundred individuals for their opinions. The 294 respondents validated most of these propositions. None of the propositions was rejected by the respondents as unimportant. A number of research directions were indicated by the respondents which, if followed, the respondents believed would lead to solutions for these problems.


title: Investigation of STI diodes as electrostatic discharge (ESD) protection devices in deep submicron (DSM) CMOS process

abstract: The goal of this paper is to investigate design parameters of the CMOS STI diodes, intended to be used as ESD protection devices, and evaluate their performance for use in the deep submicron CMOS process. The 2-D simulations of multiple diode structures and geometries have been performed using SEQUOIA Device Designer, and the results allow to accurately predict the failure point and to optimize the different diode structures for high-speed RF applications. The proposed methodology can be used in practice to aid the design of ESD protection in deep submicron CMOS.


Definition and interpretation in cross-cultural organizational culture research : Some pointers from the GLOBE research program


Investor Protection and Corporate Governance


title: Low-Power Approaches to High-Speed Current-Steering Digital-to-Analog Converters in 0.18-$\mu$m CMOS

abstract: -This paper will discuss a number of circuit approaches which lower the power consumed by a current steering digital-to-analog converter while maintaining both DC and AC performance levels. An example design provides 14-bit resolution and 200 MSPS conversion rate in a one-poly four-metal (1P4M) 0.18-μm CMOS process. The inclusion of optional 3.3-V compatible devices allows operation over a supply range from 1.7 to 3.6 V. A power dissipation/conversion rate figure of merit of as low as 0.17 mW/MSPS was achieved for 1.8-V operation and as low as 0.28 mW/MSPS at 3.3 V. A measured single-tone SFDR of 70 dB is achieved at a 50-MHz output frequency, with a two-tone IMD of -75 dBc at 71 MHz output.


title: A comparison of query-by-example methods for spoken term detection

abstract: Abstract : In this paper we examine an alternative interface for phonetic search, namely query-by-example, that avoids OOV issues associated with both standard word-based and phonetic search methods. We develop three methods that compare query lattices derived from example audio against a standard ngrambased phonetic index and we analyze factors affecting the performance of these systems. We show that the best systems under this paradigm are able to achieve 77% precision when retrieving utterances from conversational telephone speech and returning 10 results from a single query (performance that is better than a similar dictionary-based approach) suggesting significant utility for applications requiring high precision. We also show that these systems can be further improved using relevance feedback: By incorporating four additional queries the precision of the best system can be improved by 13.7% relative. Our systems perform well despite high phone recognition error rates (> 40%) and make use of no pronunciation or letter-to-sound resources.


title: Working Memory Deficits in Adults With Attention-Deficit/Hyperactivity Disorder (ADHD): An Examination of Central Executive and Storage/Rehearsal Processes

abstract: The current study was the first to use a regression approach to examine the unique contributions of central executive (CE) and storage/rehearsal processes to working memory (WM) deficits in adults with ADHD. Thirty-seven adults (ADHD 21, HC 16) completed phonological (PH) and visuospatial (VS) working memory tasks. While both groups performed significantly better during the PH task relative to the VS task, adults with ADHD exhibited significant deficits across both working memory modalities. Further, the ADHD group recalled disproportionately fewer PH and VS stimuli as set-size demands increased. Overall, the CE and PH storage/rehearsal processes of adults with ADHD were both significantly impaired relative to those of the healthy control adults; however, the magnitude of the CE effect size was much smaller compared to previous studies of children with the disorder. Collectively, results provide support for a lifelong trajectory of WM deficits in ADHD.


title: REACT: Context-Sensitive Recommendations for Data Analysis

abstract: Data analysis may be a difficult task, especially for non-expert users, as it requires deep understanding of the investigated domain and the particular context. In this demo we present REACT, a system that hooks to the analysis UI and provides the users with personalized recommendations of analysis actions. By matching the current user session to previous sessions of analysts working with the same or other data sets, REACT is able to identify the potentially best next analysis actions in the given user context. Unlike previous work that mainly focused on individual components of the analysis work, REACT provides a holistic approach that captures a wider range of analysis action types by utilizing novel notions of similarity in terms of the individual actions, the analyzed data and the entire analysis workflow. We demonstrate the functionality of REACT, as well as its effectiveness through a digital forensics scenario where users are challenged to detect cyber attacks in real life data achieved from honeypot servers.


title: Effect of Al3+ and H+ on the elastic properties of stishovite

abstract: X-ray diffraction, Brillouin, and Raman scattering measurements were performed on Al 3+ and H + -bearing stishovite at ambient conditions. Samples with different Al 3+ and H + contents were used to examine the effects of these minor constituents on the density, acoustic velocities, single-crystal and aggregate elastic moduli. The X-ray diffraction and compositional data suggest that the incorporation mechanism of Al 3+ into stishovite involves the formation of oxygen vacancies, in addition to the incorporation H + in the structure. Our data show an overall linear decrease of the acoustic velocities, single crystal ( c ij ) and aggregate ( K 0 , G 0 ) elastic moduli as a function of Al 3+ concentrations. For the sample of stishovite containing 6.07(5) wt% Al 2 O 3 and 0.24(2) wt% H 2 O we obtained: zero-pressure adiabatic bulk modulus, K S0 = 290(3) GPa, and the shear modulus, G S0 = 207(2) GPa, with a calculated density of ρ = 4.16(1) g/cm 3 based on X-ray diffraction. Stishovite containing 4.37(12) wt% Al 2 O 3 and 0.29(3) wt% H 2 O possesses higher aggregate moduli: K S0 = 296(3) GPa and G S0 = 213(2) GPa, with a calculated density of ρ = 4.21(1) g/cm 3 based on X-ray diffraction. We conclude that the formation of oxygen vacancies has a stronger effect on the density and thus elastic properties of stishovite than does the incorporation of hydrogen.


title: Coaching and guidance with patient decision aids: A review of theoretical and empirical evidence

abstract: Coaching and guidance are structured approaches that can be used within or alongside patient decision aids (PtDAs) to facilitate the process of decision making. Coaching is provided by an individual, and guidance is embedded within the decision support materials. The purpose of this paper is to: a) present updated definitions of the concepts “coaching” and “guidance”; b) present an updated summary of current theoretical and empirical insights into the roles played by coaching/guidance in the context of PtDAs; and c) highlight emerging issues and research opportunities in this aspect of PtDA design. We identified literature published since 2003 on shared decision making theoretical frameworks inclusive of coaching or guidance. We also conducted a sub-analysis of randomized controlled trials included in the 2011 Cochrane Collaboration Review of PtDAs with search results updated to December 2010. The sub-analysis was conducted on the characteristics of coaching and/or guidance included in any trial of PtDAs and trials that allowed the impact of coaching and/or guidance with PtDA to be compared to another intervention or usual care. Theoretical evidence continues to justify the use of coaching and/or guidance to better support patients in the process of thinking about a decision and in communicating their values/preferences with others. In 98 randomized controlled trials of PtDAs, 11 trials (11.2%) included coaching and 63 trials (64.3%) provided guidance. Compared to usual care, coaching provided alongside a PtDA improved knowledge and decreased mean costs. The impact on some other outcomes (e.g., participation in decision making, satisfaction, option chosen) was more variable, with some trials showing positive effects and other trials reporting no differences. For values-choice agreement, decisional conflict, adherence, and anxiety there were no differences between groups. None of these outcomes were worse when patients were exposed to decision coaching alongside a PtDA. No trials evaluated the effect of guidance provided within PtDAs. Theoretical evidence continues to justify the use of coaching and/or guidance to better support patients to participate in decision making. However, there are few randomized controlled trials that have compared the effectiveness of coaching used alongside PtDAs to PtDAs without coaching, and no trials have compared the PtDAs with guidance to those without guidance.


title: Triple Generative Adversarial Nets

abstract: Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and the discriminator (i.e. the classifier) may not be optimal at the same time; and (2) the generator cannot control the semantics of the generated samples. The problems essentially arise from the two-player formulation, where a single discriminator shares incompatible roles of identifying fake samples and predicting labels and it only estimates the data without considering the labels. To address the problems, we present triple generative adversarial net (Triple-GAN), which consists of three players---a generator, a discriminator and a classifier. The generator and the classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. We design compatible utilities to ensure that the distributions characterized by the classifier and the generator both converge to the data distribution. Our results on various datasets demonstrate that Triple-GAN as a unified model can simultaneously (1) achieve the state-of-the-art classification results among deep generative models, and (2) disentangle the classes and styles of the input and transfer smoothly in the data space via interpolation in the latent space class-conditionally.


title: Methodological guidelines for reusing general ontologies

abstract: Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have.


title: A survey of MRI-based medical image analysis for brain tumor studies

abstract: MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.


title: Chemical-induced disease relation extraction via convolutional neural network

abstract: This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. ::: ::: ::: Database URL ::: http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/.


title: Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints

abstract: We investigate two new optimization problems — minimizing a submodular function subject to a submodular lower bound constraint (submodular cover) and maximizing a submodular function subject to a submodular upper bound constraint (submodular knapsack). We are motivated by a number of real-world applications in machine learning including sensor placement and data subset selection, which require maximizing a certain submodular function (like coverage or diversity) while simultaneously minimizing another (like cooperative cost). These problems are often posed as minimizing the difference between submodular functions [9, 25] which is in the worst case inapproximable. We show, however, that by phrasing these problems as constrained optimization, which is more natural for many applications, we achieve a number of bounded approximation guarantees. We also show that both these problems are closely related and an approximation algorithm solving one can be used to obtain an approximation guarantee for the other. We provide hardness results for both problems thus showing that our approximation factors are tight up to log-factors. Finally, we empirically demonstrate the performance and good scalability properties of our algorithms.


title: A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

abstract: A neuromorphic device based on the stable electrochemical fine-tuning of the conductivity of an organic ionic/electronic conductor is realized. These devices show high linearity, low noise and extremely low switching voltage.


title: A double oracle algorithm for zero-sum security games on graphs

abstract: In response to the Mumbai attacks of 2008, the Mumbai police have started to schedule a limited number of inspection checkpoints on the road network throughout the city. Algorithms for similar security-related scheduling problems have been proposed in recent literature, but security scheduling in networked domains when targets have varying importance remains an open problem at large. In this paper, we cast the network security problem as an attacker-defender zero-sum game. The strategy spaces for both players are exponentially large, so this requires the development of novel, scalable techniques. ::: ::: We first show that existing algorithms for approximate solutions can be arbitrarily bad in general settings. We present Rugged (Randomization in Urban Graphs by Generating strategies for Enemy and Defender), the first scalable optimal solution technique for such network security games. Our technique is based on a double oracle approach and thus does not require the enumeration of the entire strategy space for either of the players. It scales up to realistic problem sizes, as is shown by our evaluation of maps of southern Mumbai obtained from GIS data.


title: View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data

abstract: Skeleton-based human action recognition has recently attracted increasing attention due to the popularity of 3D skeleton data. One main challenge lies in the large view variations in captured human actions. We propose a novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action. Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end. Extensive experiment analyses show that the proposed view adaptive RNN model strives to (1) transform the skeletons of various views to much more consistent viewpoints and (2) maintain the continuity of the action rather than transforming every frame to the same position with the same body orientation. Our model achieves significant improvement over the state-of-the-art approaches on three benchmark datasets.


title: Ridge-based vessel segmentation in color images of the retina

abstract: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.


title: Giant Condyloma Acuminatum of Vulva in an HIV-Infected Woman

abstract: First described in 1925, giant condyloma acuminatum also known as Buschke-Lowenstein tumor (BLT) is a benign, slow-growing, locally destructive cauliflower-like lesion usually in the genital region. The disease is usually locally aggressive and destructive with a potential for malignant transformation. The causative organism is human papilloma virus. The most common risk factor is immunosuppression with HIV; however, any other cause of immunodeficiency can be a predisposing factor. We present a case of 33-year-old female patient, a known HIV patient on antiretroviral therapy for ten months. She presented with seven-month history of an abnormal growth in the genitalia that was progressive accompanied with foul smelling yellowish discharge and friable. Surgical excision was performed successfully. Pap smear of the excised tissue was negative. Despite being a rare condition, giant condyloma acuminatum is relatively common in HIV-infected patients.


title: Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval

abstract: Several recent works have shown that part-based image representation provides state-of-the-art performance for fine-grained categorization. Moreover, it has also been shown that image global representation generated by aggregating deep convolutional features provides excellent performance for image retrieval. In this paper we propose a novel aggregation method, which utilizes the information of retrieval object parts. The proposed part-based weighting aggregation (PWA) method utilizes the normalized feature maps as part detectors to weight and aggregate the convolutional features. The part detectors which are selected by the unsupervised method highlight the discriminative parts of objects and effectively suppress the noise of background. We experiment on five public standard datasets for image retrieval. Our unsupervised PWA method outperforms the state-of-the-art approaches based on pre-trained networks and achieves comparable accuracy with the fine-tuned methods. It is worth noting that our unsupervised method is very suitable and effective for the situation where the annotated training dataset is difficult to collect.


title: Parallel Mining of Association Rules

abstract: We consider the problem of mining association rules on a shared nothing multiprocessor. We present three algorithms that explore a spectrum of trade-offs between computation, communication, memory usage, synchronization, and the use of problem specific information. The best algorithm exhibits near perfect scaleup behavior, yet requires only minimal overhead compared to the current best serial algorithm.


title: Non-determinism in Byzantine Fault-Tolerant Replication

abstract: Service replication distributes an application over many processes for tolerating faults, attacks, and misbehavior among a subset of the processes. The established state-machine replication paradigm inherently requires the application to be deterministic. This paper distinguishes three models for dealing with non-determinism in replicated services, where some processes are subject to faults and arbitrary behavior (so-called Byzantine faults): first, a modular approach that does not require any changes to the potentially non-deterministic application (and neither access to its internal data); second, a master-slave approach, in which ties are broken by a leader and the other processes validate the choices of the leader; and finally, a treatment of applications that use cryptography and secret keys. Cryptographic operations and secrets must be treated specially because they require strong randomness to satisfy their goals. ::: The paper also introduces two new protocols. The first uses the modular approach for filtering out non-de\-ter\-min\-istic operations in an application. It ensures that all correct processes produce the same outputs and that their internal states do not diverge. The second protocol implements cryptographically secure randomness generation with a verifiable random function and is appropriate for certain security models. All protocols are described in a generic way and do not assume a particular implementation of the underlying consensus primitive.


title: rev.ng: a unified binary analysis framework to recover CFGs and function boundaries

abstract: Static binary analysis is a key tool to assess the security of third-party binaries and legacy programs. Most forms of binary analysis rely on the availability of two key pieces of information: the program's control-flow graph and function boundaries. However, current tools struggle to provide accurate and precise results, in particular when dealing with hand-written assembly functions and non-trivial control-flow transfer instructions, such as tail calls. In addition, most of the existing solutions are ad-hoc, rely on hand-coded heuristics, and are tied to a specific architecture. In this paper we highlight the challenges faced by an architecture agnostic static binary analysis framework to provide accurate information about a program's CFG and function boundaries without employing debugging information or symbols. We propose a set of analyses to address predicate instructions, noreturn functions, tail calls, and context-dependent CFG. rev.ng, our binary analysis framework based on QEMU and LLVM, handles all the 17 architectures supported by QEMU and produces a compilable LLVM IR. We implement our described analyses on top of LLVM IR. In an extensive evaluation, we test our tool on binaries compiled for MIPS, ARM, and x86-64 using GCC and clang and compare them to the industry's state of the art tool, IDA Pro, and two well-known academic tools, BAP/ByteWeight and angr. In all cases, the quality of the CFG and function boundaries produced by rev.ng is comparable to or improves over the alternatives.


title: Benchmarking approach for designing a mapreduce performance model

abstract: In MapReduce environments, many of the programs are reused for processing a regularly incoming new data. A typical user question is how to estimate the completion time of these programs as a function of a new dataset and the cluster resources. In this work1 , we offer a novel performance evaluation framework for answering this question. We observe that the execution of each map (reduce) tasks consists of specific, well-defined data processing phases. Only map and reduce functions are custom and their executions are user-defined for different MapReduce jobs. The executions of the remaining phases are generic and depend on the amount of data processed by the phase and the performance of underlying Hadoop cluster. First, we design a set of parameterizable microbenchmarks to measure generic phases and to derive a platform performance model of a given Hadoop cluster. Then using the job past executions, we summarize job's properties and performance of its custom map/reduce functions in a compact job profile. Finally, by combining the knowledge of the job profile and the derived platform performance model, we offer a MapReduce performance model that estimates the program completion time for processing a new dataset. The evaluation study justifies our approach and the proposed framework: we are able to accurately predict performance of the diverse set of twelve MapReduce applications. The predicted completion times for most experiments are within 10% of the measured ones (with a worst case resulting in 17% of error) on our 66-node Hadoop cluster.


– A New Dimension of PKI Hardware Accelerator


title: Secure and Privacy-Preserving Physical-Layer-Assisted Scheme for EV Dynamic Charging System

abstract: Dynamic charging system will enable moving electrical vehicles (EVs) to charge their batteries through magnetic induction by charging pads (CPs) placed on a portion of the roadbed. To realize such a system, the EVs need to communicate with the various parts of the system that include a bank, a charging service provider (CSP), road side units (RSUs), and CPs. In this paper, we propose a secure and privacy-preserving physical-layer-assisted scheme for dynamic charging systems to secure payment and authentication and also preserve the drivers’ location privacy. We develop an efficient hierarchical authentication scheme that considers the scalability nature of the system. Efficient cryptosystems are used to authenticate the EVs to the bank, CSP, and RSUs, but our evaluations indicate that the contact time of fast moving EVs and the CPs is too short to exchange multiple messages and execute time consuming operations. Therefore, we develop an efficient physical-layer-based authorization scheme that utilizes autocorrelation demodulation and hypothesis testing to enable the CPs to identify and charge the authorized EVs. Through extensive analysis, simulation, and practical experiments, we demonstrate that the proposed scheme is secure against the considered attacks and can achieve fast authentication and high authorization rate. Moreover, the proposed scheme can achieve full anonymity where no entity or even colluding entities can know the drivers’ locations.


title: Plant host habitat and root exudates shape soil bacterial community structure

abstract: The rhizosphere is active and dynamic in which newly generated carbon, derived from root exudates, and ancient carbon, in soil organic matter (SOM), are available for microbial growth. Stable isotope probing (SIP) was used to determine bacterial communities assimilating each carbon source in the rhizosphere of four plant species. Wheat, maize, rape and barrel clover (Medicago truncatula) were grown separately in the same soil under (13)CO(2) (99% of atom (13)C) and DNA extracted from rhizosphere soil was fractionated by isopycnic centrifugation. Bacteria-assimilating root exudates were characterized by denaturing gradient gel electrophoresis (DGGE) analysis of (13)C-DNA and root DNA, whereas those assimilating SOM were identified from (12)C-DNA. Plant species root exudates significantly shaped rhizosphere bacterial community structure. Bacteria related to Sphingobacteriales and Myxococcus assimilated root exudates in colonizing roots of all four plants, whwereas bacteria related to Sphingomonadales utilized both carbon sources, and were identified in light, heavy and root compartment DNA. Sphingomonadales were specific to monocotyledons, whereas bacteria related to Enterobacter and Rhizobiales colonized all compartments of all four plants, used both fresh and ancient carbon and were considered as generalists. There was also evidence for an indirect important impact of root exudates, through stimulation of SOM assimilation by a diverse bacterial community.


title: Context-Aware Self-Attention Networks

abstract: Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which have proven useful for modeling dependencies among neural representations in various natural language tasks. In this work, we focus on improving self-attention networks through capturing the richness of context. To maintain the simplicity and flexibility of the self-attention networks, we propose to contextualize the transformations of the query and key layers, which are used to calculates the relevance between elements. Specifically, we leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources. Experimental results on WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness and universality of the proposed methods. Furthermore, we conducted extensive analyses to quantity how the context vectors participate in the self-attention model.


title: The practical and principled problems with educational neuroscience.

abstract: The core claim of educational neuroscience is that neuroscience can improve teaching in the classroom. Many strong claims are made about the successes and the promise of this new discipline. By contrast, I show that there are no current examples of neuroscience motivating new and effective teaching methods, and argue that neuroscience is unlikely to improve teaching in the future. The reasons are twofold. First, in practice, it is easier to characterize the cognitive capacities of children on the basis of behavioral measures than on the basis of brain measures. As a consequence, neuroscience rarely offers insights into instruction above and beyond psychology. Second, in principle, the theoretical motivations underpinning educational neuroscience are misguided, and this makes it difficult to design or assess new teaching methods on the basis of neuroscience. Regarding the design of instruction, it is widely assumed that remedial instruction should target the underlying deficits associated with learning disorders, and neuroscience is used to characterize the deficit. However, the most effective forms of instruction may often rely on developing compensatory (nonimpaired) skills. Neuroscience cannot determine whether instruction should target impaired or nonimpaired skills. More importantly, regarding the assessment of instruction, the only relevant issue is whether the child learns, as reflected in behavior. Evidence that the brain changed in response to instruction is irrelevant. At the same time, an important goal for neuroscience is to characterize how the brain changes in response to learning, and this includes learning in the classroom. Neuroscientists cannot help educators, but educators can help neuroscientists. (PsycINFO Database Record


title: The Concurrency Challenge

abstract: The evolutionary path of microprocessor design includes both multicore and many-core architectures. Harnessing the most computing throughput from these architectures requires concurrent or parallel execution of instructions. The authors describe the challenges facing the industry as parallel-computing platforms become even more widely available.


title: Control design for PMM-based starter generator system for More Electric Aircraft

abstract: The paper deals with the control design of aircraft starter-generator system based on permanent magnet machine fed by active front end rectifier. The analysis of the starter-generator system can be split into 2 parts; starter and generator mode. Their respective control structures are proposed along with mathematical equations that represent the power system. Direct current field weakening method is selected for this high speed application while DC current control is considered during the generator mode. The nature of dynamic limiter employed along with the field weakening controller creates 2 states of operation. Linearized open loop plant models for both starter and generator modes are derived. The plants are verified with equivalent linear and non-linear Simulink models. Appropriate controllers are designed based on the closed loop analysis.


title: A rare cause of recalcitrant coccydynia: benign dermoid cyst masquerading as coccygeal pain

abstract: Purpose ::: Coccydynia is a common entity in orthopedic practice, and various etiologies have been described for it. However, benign dermoid cyst causing coccydynia has not yet been reported.


Rectifier Nonlinearities Improve Neural Network Acoustic Models


title: The Role of Means Efficacy When Predicting Creative Performance

abstract: According to the Internal-External Efficacy model, self-efficacy is an insufficient explanation for self-regulated behavior because it ignores the influence of external resources. Applying this theory of motivation to the prediction of creative performance, the extent to which means efficacy or the belief in the utility of external resources predicted independent assessments of creativity was tested. In Study 1, student webmasters’ (N =124) means efficacy predicted subject matter expert's ratings of creativity of student organization homepages. In Study 2, employees’ (N = 128) means efficacy predicted individual supervisory ratings of creative performance. In addition, means efficacy enhanced the positive relationship between creative self-efficacy and supervisors’ ratings of creative performance. Implications for enhancing creativity in the workplace are discussed.


title: What Motivates People to Purchase Digital Items on Virtual Community Websites? The Desire for Online Self-Presentation

abstract: The sale of digital items, such as avatars and decorative objects, is becoming an important source of revenue for virtual community (VC) websites. However, some websites are unable to leverage this source of revenue, and there is a corresponding lack of understanding about what motivates people to purchase digital items in VCs. To explain the phenomenon, we develop a model based on the theory of self-presentation. The model proposes that the desire for online self-presentation is a key driver for such purchases. We also hypothesize that the social influence factors of online self-presentation norms and VC involvement as well as personal control in the form of online presentation self-efficacy are antecedents of the desire for online self-presentation. The model was validated by using survey data collected from Cyworld (N=217) and Habbo (N=197), two online social network communities that have been pioneers in the sale of digital items. This work contributes to our understanding of the purchase of digital items by extending the theory of self-presentation and adds to the broader line of research on online identity. It also lends insights into how VC providers can tap this source of revenue.


Machine Learning for Sentiment Analysis on the Experience Project


title: An Ensemble Model with Ranking for Social Dialogue

abstract: Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.


title: An STS-Based Map of the Human Genome

abstract: A physical map has been constructed of the human genome containing 15,086 sequence-tagged sites (STSs), with an average spacing of 199 kilobases. The project involved assembly of a radiation hybrid map of the human genome containing 6193 loci and incorporated a genetic linkage map of the human genome containing 5264 loci. This information was combined with the results of STS-content screening of 10,850 loci against a yeast artificial chromosome library to produce an integrated map, anchored by the radiation hybrid and genetic maps. The map provides radiation hybrid coverage of 99 percent and physical coverage of 94 percent of the human genome. The map also represents an early step in an international project to generate a transcript map of the human genome, with more than 3235 expressed sequences localized. The STSs in the map provide a scaffold for initiating large-scale sequencing of the human genome.


title: Using Computational Thinking Patterns to Scaffold Program Design in Introductory Programming Course

abstract: Visual programming languages (VPLs), such as Scratch and StarLogo TNG, can make computer science education more accessible to everyone. Current researches in the study of using VPLs for educational purposes primarily focus on understanding motivational benefits and computational thinking gains. All these educational VPLs claim to scaffold students learning computational thinking concepts. Although the evaluations show that students may exhibit more enthusiasm, it is not clear what computational thinking concepts are actually learned by students. In this paper, we attempt to develop a visual programs recognition tool for student-created StarLogo TNG simulations which representing the computational thinking concepts implemented by the students. Through collecting student's created projects over time, this visual programs recognition tool can possibly indicate the patterns of computational thinking in science simulations created by StarLogo TNG.


6 The Implicit Association Test at Age 7: A Methodological and Conceptual Review


Adaptive Impedance Matching for Magnetically Coupled Resonators


title: Real-Time High Resolution 3D Data on the HoloLens

abstract: The recent appearance of augmented reality headsets, such as the Microsoft HoloLens, is a marked move from traditional 2D screen to 3D hologram-like interfaces. Striving to be completely portable, these devices unfortunately suffer multiple limitations, such as the lack of real-time, high quality depth data, which severely restricts their use as research tools. To mitigate this restriction, we provide a simple method to augment a HoloLens headset with much higher resolution depth data. To do so, we calibrate an external depth sensor connected to a computer stick that communicates with the HoloLens headset in real-time. To show how this system could be useful to the research community, we present an implementation of small object detection on HoloLens device.


title: Documents and professional practice: “bad” organisational reasons for “good” clinical records

abstract: Despite the widespread introduction of information technology into primary health care within the United Kingdom, medical practitioners continue to use the more traditional paper medical record often alongside the computerised system. The resilience of the paper document is not simply a consequence of an impoverished design, but rather a product of the socially organised practices and reasoning which surround the use of the record within day to day consultative work. The practices that underpin the use of the medical records may have a range of important implications, not only for the general design of systems to support collaborative work, but also for our conceptions of ‘writers’, ‘readers’, ‘objects’ and ‘records’ utilised in those designs.


title: Robust facial expression recognition using local binary patterns

abstract: A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, whilst still retaining facial information efficiently. Template matching with weighted Chi square statistic and support vector machine are adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade Database illustrate that the LBP features are effective and efficient for facial expression discrimination. Additionally, experiments on face images with different resolutions show that the LBP features are robust to low-resolution images, which is critical in real-world applications where only low-resolution video input is available.


title: MedBlock: Efficient and Secure Medical Data Sharing Via Blockchain

abstract: With the development of electronic information technology, electronic medical records (EMRs) have been a common way to store the patients’ data in hospitals. They are stored in different hospitals’ databases, even for the same patient. Therefore, it is difficult to construct a summarized EMR for one patient from multiple hospital databases due to the security and privacy concerns. Meanwhile, current EMRs systems lack a standard data management and sharing policy, making it difficult for pharmaceutical scientists to develop precise medicines based on data obtained under different policies. To solve the above problems, we proposed a blockchain-based information management system, MedBlock, to handle patients’ information. In this scheme, the distributed ledger of MedBlock allows the efficient EMRs access and EMRs retrieval. The improved consensus mechanism achieves consensus of EMRs without large energy consumption and network congestion. In addition, MedBlock also exhibits high information security combining the customized access control protocols and symmetric cryptography. MedBlock can play an important role in the sensitive medical information sharing.


title: Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders

abstract: Asking good questions in large-scale, open-domain conversational systems is quite significant yet rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of {\it interrogatives}, {\it topic words}, and {\it ordinary words}. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (\textit{soft typed decoder} and \textit{hard typed decoder}) in which a type distribution over the three types is estimated and used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.


title: Robust Tumor Localization with Pyramid Grad-CAM

abstract: A meningioma is a type of brain tumor that requires tumor volume size follow ups in order to reach appropriate clinical decisions. A fully automated tool for meningioma detection is necessary for reliable and consistent tumor surveillance. There have been various studies concerning automated lesion detection. Studies on the application of convolutional neural network (CNN)-based methods, which have achieved a state-of-the-art level of performance in various computer vision tasks, have been carried out. However, the applicable diseases are limited, owing to a lack of strongly annotated data being present in medical image analysis. In order to resolve the above issue we propose pyramid gradient-based class activation mapping (PG-CAM) which is a novel method for tumor localization that can be trained in weakly supervised manner. PG-CAM uses a densely connected encoder-decoder-based feature pyramid network (DC-FPN) as a backbone structure, and extracts a multi-scale Grad-CAM that captures hierarchical features of a tumor. We tested our model using meningioma brain magnetic resonance (MR) data collected from the collaborating hospital. In our experiments, PG-CAM outperformed Grad-CAM by delivering a 23 percent higher localization accuracy for the validation set.


title: Fingerprint Model Based on Fingerprint Image Topology and Ridge Count Values

abstract: In this paper the authors propose a mathematical model of fingerprint images based on the vectors of ridge count values. The model takes into account the instability of the values of the ridge count in the region of the lines curvature and possible mutations of minutiae. This increases the stability of fingerprint template and reliability of identification. The vectors of ridge count are formed using topological descriptors, which are constructed for the neighborhood of all fingerprint minutiae. In the proposed model, a template of a fingerprint image keeps the list of minutiae, the list of topological vectors and the list of the ridge count vectors. The value of the ridge count can be represented as a fractional number.


title: Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss

abstract: We present a novel unsupervised learning framework for single view depth estimation using monocular videos. It is well known in 3D vision that enlarging the baseline can increase the depth estimation accuracy, and jointly optimizing a set of camera poses and landmarks is essential. In previous monocular unsupervised learning frameworks, only part of the photometric and geometric constraints within a sequence are used as supervisory signals. This may result in a short baseline and overfitting. Besides, previous works generally estimate a low resolution depth from a low resolution impute image. The low resolution depth is then interpolated to recover the original resolution. This strategy may generate large errors on object boundaries, as the depth of background and foreground are mixed to yield the high resolution depth. In this paper, we introduce a bundle adjustment framework and a super-resolution network to solve the above two problems. In bundle adjustment, depths and poses of an image sequence are jointly optimized, which increases the baseline by establishing the relationship between farther frames. The super resolution network learns to estimate a high resolution depth from a low resolution image. Additionally, we introduce the clip loss to deal with moving objects and occlusion. Experimental results on the KITTI dataset show that the proposed algorithm outperforms the state-of-the-art unsupervised methods using monocular sequences, and achieves comparable or even better result compared to unsupervised methods using stereo sequences.


large.pdf


title: Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

abstract: Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when generalizing MAPF methods to real-world scenarios and four research directions that address them. We emphasize the importance of addressing these issues as opposed to developing faster methods for the standard formulation of the MAPF problem.


A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding


title: Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge

abstract: We introduce \emph{LL-RNNs} (Log-Linear RNNs), an extension of Recurrent Neural Networks that replaces the softmax output layer by a log-linear output layer, of which the softmax is a special case. This conceptually simple move has two main advantages. First, it allows the learner to combat training data sparsity by allowing it to model words (or more generally, output symbols) as complex combinations of attributes without requiring that each combination is directly observed in the training data (as the softmax does). Second, it permits the inclusion of flexible prior knowledge in the form of \emph{a priori} specified modular features, where the neural network component learns to dynamically control the weights of a log-linear distribution exploiting these features. We provide some motivating illustrations, and argue that the log-linear and the neural-network components contribute complementary strengths to the LL-RNN: the LL aspect allows the model to incorporate rich prior knowledge, while the NN aspect, according to the "representation learning" paradigm, allows the model to discover novel combination of characteristics.


title: Combinatorial Optimization: Algorithms and Complexity

abstract: This clearly written , mathematically rigorous text includes a novel algorithmic exposition of the simplex method and also discusses the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; approximation algorithms, local search heuristics for NPcomplete problems, more. All chapters are supplemented by thoughtprovoking problems. A useful work for graduate-level students with backgrounds in computer science, operations research, and electrical engineering. Mathematicians wishing a self-contained introduction need look no further.—American Mathematical Monthly. 1982 ed.


title: Object Classification and Detection in High Dimensional Feature Space

abstract: Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object positions and the appearance variations of object classes. This thesis improves upon several classical machine learning algorithms, enabling large computational gains in high dimensional feature space. A common trend in machine learning and computer vision research is to go large scale. In particular, the advent of huge datasets mined from the Internet, and the combination of multiple feature sources have considerably broadened the applications of computer vision. Tasks which were thought impossible a few years ago, such as human action recognition or pose estimation, automatic outdoor navigation, etc., now seem within reach. This dissertation is divided into two parts. The first one deals with the efficient training of a classifier or detector based on a large number of feature extractors, outside the control of the learning algorithm, and therefore of unknown suitability to the task at hand. More precisely, this part presents two kinds of strategies to accelerate the training of Boosting algorithms in such a context: (a) a method to better deal with the increasingly common case where features come from multiple sources (e.g. color, shape, texture, etc., in the case of images) and therefore can be partitioned into meaningful subsets; (b) new algorithms which balance at every Boosting iteration the number of weak learners and the number of training examples to look at in order to maximize the expected loss reduction. Experiments in image classification and object recognition on four standard computer vision datasets show that the adaptive techniques we propose outperform both basic sampling and state-of-the-art bandit methods. The second part deals with linear object detectors, currently the most popular class of detection systems, encompassing template matching, deformable part models, poselets, convolutional neural networks (which internally use linear filters), etc. The main bottleneck of many of those systems is the computational cost of the convolutions between the multiple rescalings of the image to process and the linear filters. We make use of properties of the Fourier transform and clever implementation strategies to obtain a speedup factor proportional to the filter size, both while training and at test time. We also introduce a few modifications to the original Deformable Part Model (DPM) of Felzenszwalb et al. improving its detection accuracy. The gains in performance are demonstrated on the well-known Pascal VOC benchmark, where an increase by one order of magnitude in the speed of said convolutions, and an average improvement of 15% in the accuracy of the detector are established.


title: Optimal PID controller for coupled-tank liquid-level control system using bat algorithm

abstract: Nature inspired algorithms are among the most potent and robust algorithms for optimization for solving the real life problems. This paper introduces the Bat algorithm for the optimization based design of the PID controllers. The optimized PID controller has been implemented for liquid level control system which finds a wide application in petrochemical, food processing, and water treatment industries. The quality of control directly affects the quality of products and safety of the process. The simulations indicate the eminence of BA over classical methods of PID tuning.


title: Duke Activity Status Index for Cardiovascular Diseases: Validation of the Portuguese Translation

abstract: Background: The Duke Activity Status Index (DASI) assesses the functional capacity of patients with cardiovascular disease (CVD), but there is no Portuguese version validated for CVD. Objectives: To translate and adapt cross-culturally the DASI for the Portuguese-Brazil language, and to verify its psychometric properties in the assessment of functional capacity of patients with CVD. Methods: The DASI was translated into Portuguese, then checked by back-translation into English and evaluated by an expert committee. The pre-test version was first evaluated in 30 subjects. The psychometric properties and correlation with exercise testing was performed in a second group of 67 subjects. An exploratory factor analyses was performed in all 97 subjects to verify the construct validity of the DASI. Results: The intraclass correlation coefficient for test-retest reliability was 0.87 and for the inter-rater reliability was 0.84. Cronbach's α for internal consistency was 0.93. The concurrent validity was verified by significant positive correlations of DASI scores with the VO2max (r = 0.51, p < 0.001). The factor analysis yielded two factors, which explained 54% of the total variance, with factor 1 accounting for 40% of the variance. Application of the DASI required between one and three and a half minutes per patient. Conclusions: The Brazilian version of the DASI appears to be a valid, reliable, fast and easy to administer tool to assess functional capacity among patients with CVD.


title: kR^X: Comprehensive Kernel Protection against Just-In-Time Code Reuse

abstract: The abundance of memory corruption and disclosure vulnerabilities in kernel code necessitates the deployment of hardening techniques to prevent privilege escalation attacks. As more strict memory isolation mechanisms between the kernel and user space, like Intel's SMEP, become commonplace, attackers increasingly rely on code reuse techniques to exploit kernel vulnerabilities. Contrary to similar attacks in more restrictive settings, such as web browsers, in kernel exploitation, non-privileged local adversaries have great flexibility in abusing memory disclosure vulnerabilities to dynamically discover, or infer, the location of certain code snippets and construct code-reuse payloads. Recent studies have shown that the coupling of code diversification with the enforcement of a "read XOR execute" (R^X) memory safety policy is an effective defense against the exploitation of userland software, but so far this approach has not been applied for the protection of the kernel itself. In this paper, we fill this gap by presenting kR^X: a kernel hardening scheme based on execute-only memory and code diversification. We study a previously unexplored point in the design space, where a hypervisor or a super-privileged component is not required. Implemented mostly as a set of GCC plugins, kR^X is readily applicable to the x86-64 Linux kernel and can benefit from hardware support (e.g., MPX on modern Intel CPUs) to optimize performance. In full protection mode, kR^X incurs a low runtime overhead of 4.04%, which drops to 2.32% when MPX is available.


title: Parallel Banding Algorithm to compute exact distance transform with the GPU

abstract: We propose a Parallel Banding Algorithm (PBA) on the GPU to compute the exact Euclidean Distance Transform (EDT) for a binary image in 2D and higher dimensions. Partitioning the image into small bands to process and then merging them concurrently, PBA computes the exact EDT with optimal linear total work, high level of parallelism and a good memory access pattern. This work is the first attempt to exploit the enormous power of the GPU in computing the exact EDT, while prior works are only on approximation. Compared to these other algorithms in our experiments, our exact algorithm is still a few times faster in 2D and 3D for most input sizes. We illustrate the use of our algorithm in applications such as computing the Euclidean skeleton using the integer medial axis transform, performing morphological operations of 3D volumetric data, and constructing 2D weighted centroidal Voronoi diagrams.


Autosomal trisomy syndromes: a detailed study of 27 cases of Edwards' syndrome and 27 cases of Patau's syndrome.


title: An OFDM System Concept for Joint Radar and Communications Operations

abstract: In this paper the possibility of designing an OFDM system for simultaneous radar and communications operations is discussed. A novel approach to OFDM radar processing is introduced that overcomes the typical drawbacks of correlation based processing. A suitable OFDM system parameterization for operation at 24 GHz is derived that fulfills the requirements for both applications. The operability of the proposed system concept is verified with MatLab simulations. Keywords-OFDM; Radar, Communications


title: Online Video Game Therapy for Mental Health Concerns: A Review

abstract: Background: There has been research on the use of offline video games for therapeutic purposes but online video game therapy is still fairly under-researched. Online therapeutic interventions have only recently included a gaming component. Hence, this review represents a timely first step toward taking advantage of these recent technological and cultural innovations, particularly for the treatment of special-needs groups such as the young, the elderly and people with various conditions such as ADHD, anxiety and autism spectrum disorders. Material: A review integrating research findings on two technological advances was conducted: the home computer boom of the 1980s, which triggered a flood of research on therapeutic video games for the treatment of various mental health conditions; and the rise of the internet in the 1990s, which caused computers to be seen as conduits for therapeutic interaction rather than replacements for the therapist. Discussion: We discuss how video games and the internet can now be combined in therapeutic interventions, as attested by a consideration of pioneering studies. Conclusion: Future research into online video game therapy for mental health concerns might focus on two broad types of game: simple society games, which are accessible and enjoyable to players of all ages, and online worlds, which offer a unique opportunity for narrative content and immersive remote interaction with therapists and fellow patients. Both genres might be used for assessment and training purposes, and provide an unlimited platform for social interaction. The mental health community can benefit from more collaborative efforts between therapists and engineers, making such innovations a reality.