title: Analysis of Lightweight Encryption Scheme for Fog-to-Things Communication

abstract: The growing concerns in cybersecurity is preventing unknowns which evolve from time to time. Internet of Things (IoT) is one of the emerging fields that have been applied for smart cities and industries. The promises of IoTs could be confronted with the growth in the number and sophistication of cyberattacks. The extension of digital world into physical environment adds new attack surfaces on the existing security threats of traditional Internet. The major challenge brought about by physical connectivity of IoTs is to implement distributed security mechanisms for resource constrain of IoT devices. As an emerging architecture supporting IoT applications, fog computing can be considered to solve the resource and distribution issues in securing fog-to-things communication. Security functions and services, such as cryptography, could be offloaded to fog nodes to reduce computational and storage burdens on IoT devices. The distribution of fog nodes can also solve the scalability of cloud by reducing central processing and communications. On the other hand, lightweight cryptographic functions, such as elliptic curve cryptography, have been proved to be suitable for embedded systems. In this paper, we have analyzed security challenges in terms of cybersecurity principles and proposed a novel encryption scheme for fog-to-things communication.


title: A roadmap on QoE metrics and models

abstract: 5G communications are expected to adopt a user-centric service provisioning approach, transforming the current Quality of Service (QoS) networking paradigm to a Quality of Experience (QoE) platform. Towards this direction, the design of QoE quantification methods is a vast necessity, prior to the design of QoE-driven service/network management techniques and the proposal of QoE-aware customer experience management and charging policies. Contributing to this effort, in this paper we provide a comprehensive overview and inventory of the currently available QoE estimation approaches and models. More specifically, we discuss the relation between QoS and QoE, the major QoE influence factors, the QoE evaluation end estimation process, and the dominant objective QoE estimation models.


title: Generating Adversarial Examples with Adversarial Networks

abstract: Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.


title: Efficient Hand Pose Estimation from a Single Depth Image

abstract: We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location, Candidate generation step produces a set of 3D pose candidate from the Hough voting space with the help of the rotational invariant depth features, Verification step delivers the final 3D hand pose as the solution to an optimization problem. We analyze the depth noises, and suggest tips to minimize their negative impacts on the overall performance. Our approach is able to work with Kinect-type noisy depth images, and reliably produces pose estimations of general motions efficiently (12 frames per second). Extensive experiments are conducted to qualitatively and quantitatively evaluate the performance with respect to the state-of-the-art methods that have access to additional RGB images. Our approach is shown to deliver on par or even better results.


title: Single-Port Reconfigurable Magneto-Electric Dipole Antenna With Quad-Polarization Diversity

abstract: We propose a magneto-electric (ME) dipole antenna which is able to provide four states of polarization control, namely, the linear polarization (LP) in $x$ -direction, LP in $y$ -direction, left-hand circular polarization (CP), and right-hand CP. Under each of the two LP states, the reconfigurable ME dipole can be further tuned to work in two adjacent frequency ranges (i.e., a relatively higher band and a lower band). The uniqueness of this design arises from the fact that all the polarization and frequency agilities can be realized with a single port and without complicate feeding network, thus eliminating the insertion loss and considerably increased design complexity associated with a reconfigurable feeding network. Other advantages that make the proposed design attractive comparing with conventional reconfigurable microstrip antennas include the comparatively stable and high gain, nearly the same gain for both linear and CPs, symmetrical radiation pattern, and low back radiation level at the operating frequencies.


A Complete Logical Calculus for Record Structures Representing Linguistic Information


title: Embedding Factorization Models for Jointly Recommending Items and User Generated Lists

abstract: Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, user generated lists contain rich signal about item co-occurrence, as items within a list are usually gathered based on a specific theme. On the other hand, a user's preference over a list also indicate her preference over items within the list. We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list interactions are properly utilized, and the relationship between a list and its contained items is discovered, the performance of user-item and user-list recommendations can be mutually reinforced. Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. Specifically, we employ factorization model to capture users' preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists. The gap between the two types of models is bridged by sharing items' latent factors. Remarkably, our proposed framework is capable of solving the new-item cold-start problem, where items have never been consumed by users but exist in user generated lists. Overall performance comparisons and micro-level analyses demonstrate the promising performance of our proposed approaches.


L EARNING HOW TO EXPLAIN NEURAL NETWORKS : P ATTERN N ET AND P ATTERN A TTRIBUTION


title: A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion

abstract: In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively.


title: Deep Semantic Segmentation Neural Networks of Railway Scene

abstract: It is crucial that the situation of railway scene needs to be periodically monitored to ensure railway safety. Usually, the camera is placed in the driver's cab on a moving vehicle to generate a large number of high-resolution images of railway scene. The traditional image processing inspection methods can only inspect railway scene of the local and simple background. They are feasible sometimes, but not universal and efficient. In this paper, a novel and practical approach is proposed to visual railway scene detection with Deep Semantic Segmentation Convolutional Neural Networks(DSSCNN). It is helpful for precisely locating and detailedly describe important parts of the railway scene in the entire image instead of separately detecting like traditional methods. In addition, three particular blocks named Conv Block, Dilated Conv Block, and Sum Block in DSSCNN are designed to extract more context information from railway images and enhance the segmentation performance. It shows that DSSCNN trained end-to-end and encoder-decoder network structure for semantic segmentation of railway scene are more superior than UNet and Fully Convolutional Networks(FCN) on the same datasets. At the same time, it is also verified that it is feasible and efficient to describe, visual and locate the key parts of the railway scene by DSSCNN.


title: Design and Implementation of New AGV System Based on Two-layer Controller Structure

abstract: Automated guided vehicle (AGV) is playing an major role in industrial applications to move materials around a manufacturing facility or warehouse. As task complexity increases, however, the traditional design of AGV can't meet user's demands any longer. Therefore, a new AGV control system is introduced in this paper, which is designed by the two-layer controller system structure. This structure includes the lower computer and the upper computer, which is the main controller with autonomous navigation functions. To exchange control messages, there is also a special communication protocol between the two controllers. In addition, different algorithms and circuits are designed according to the differences of each task and the characters of two controllers. The implementation approaches are presented in both hardware and algorithms as well. Finally, a series of experiments has been done, and the results show that the AGV from this new two-layer controller system design has perfect performance in motion, localization, path planning and other aspects.


title: A Hierarchical Extreme Learning Machine Algorithm for Advertisement Click-Through Rate Prediction

abstract: Click-through rate (CTR) prediction plays a predominant role in the online advertisements. CTR prediction is a problem of binary classification with imbalanced data. Many existing approaches for imbalance learning only focus on over-sampling and under-sampling, but these methods definitely ignore some vital information of the original data. In this paper, we first propose a weighted output extreme learning machine (WO-ELM) to learn the imbalanced data. A hierarchical extreme learning machine (H-C-ELM) is proposed based on the proposed WO-ELM and the weighted extreme learning machine (W-ELM). The H-C-ELM has two levels in its structure. In the first level, the WO-ELM and the W-ELM are trained on different combined fields of the CTR (each field has some attributes). The two extreme learning machines (ELMs) output their predicted scores of the corresponding combined fields of the CTR. The WO-ELM and the W-ELM have different predicted results on the same combined fields because of the difference of the two ELMs. Therefore, in the second level, another ELM is applied based on the outputs of the two ELMs in the first level and the actual outputs in order to improve the prediction accuracy. The experimental results demonstrate that the proposed H-C-ELM method has better performance for the binary classification with imbalanced data than the other related algorithms on CTR prediction, such as the WO-ELM, the W-ELM, and the stacked autoencoder-logistic regression.


title: Preventive interventions among children exposed to trauma of armed conflict: a literature review

abstract: Increasing research is available on the preconditions for child mental health and optimal development in traumatic conditions, whereas less is known how to translate the findings into effective interventions to help traumatized children. This literature review analyses the effectiveness of psychosocial preventive interventions and treatments and their theoretical bases among children traumatized in the context of armed conflicts (war, military violence, terrorism and refugee). The first aim is to evaluate the effectiveness of preventive interventions in preventing emotional distress and impairment and promoting optimal emotional-cognitive and social development. The second task is to analyze the nature of the underlying mechanisms for the success of preventive interventions, and the theoretical premises of the choice of intervention techniques, procedures and tools. We found 16 relevant published studies, but an examination of them revealed that only four of them had experimental designs strong enough that they could be included in the meta-analysis. While the subjective reports of the researchers suggested that systematic preventive interventions were effective in decreasing PTSD and depressive symptoms among children traumatized due to armed conflict, the more objective results of the meta-analysis and the weaknesses in designs uncovered during the meta-analysis undermine such a conclusion. Additionally, a majority of the reported preventive interventions focused only on children's biased cognitive processes and negative emotions, while only a few aimed at influencing multiple domains of child development and improving developmental functioning on emotional, social and psychophysiological levels. It is concluded that substantial additional work needs to be done in developing effective preventive interventions and treatments for children traumatized by exposure to war and violence. Language: en


title: Spectral regularization algorithms for learning large incomplete matrices

abstract: We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm SOFT-IMPUTE iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity of order linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices; for example SOFT-IMPUTE takes a few hours to compute low-rank approximations of a 106 X 106 incomplete matrix with 107 observed entries, and fits a rank-95 approximation to the full Netflix training set in 3.3 hours. Our methods achieve good training and test errors and exhibit superior timings when compared to other competitive state-of-the-art techniques.


A tutorial on case-based reasoning


title: Localization using automotive laser scanners and local pattern matching

abstract: Autonomous driving requires vehicle positioning with accuracies of a few decimeters. Typical low-cost GNSS sensors, as they are commonly used for navigation systems, are limited to an accuracy of several meters. Also, they are restricted in reliability because of outages and multipath effects. To improve accuracy and reliability, 3D features can be used, such as pole-like objects and planes, measured by a laser scanner. These features have to be matched to the reference data, given by a landmark map. If we use a nearest neighbor approach to match the data, we will likely get wrong matches, especially at positions with a low initial accuracy. To reduce the number of wrong matches, we use feature patterns. These patterns describe the spatial relationship of a specific number of features and are determined for every possible feature combination, separated in reference and online features. Given these patterns, the correspondences of the measured features can be determined by finding the corresponding patterns in the reference data. We acquired reference data by a high precision Mobile Mapping System. In an area of 2.8 km 2 we automatically extracted 1390 pole-like objects and 2006 building facades. A (second) vehicle equipped with an automotive laser scanner was used to generate features with lower accuracy and reliability. In every scan of the laser scanner we extracted landmarks (poles and planes) online. We then used our proposed feature matching to find correspondences. In this paper, we show the performance of the approach for different parameter settings and compare it to the nearest neighbor matching commonly used. Our experimental results show that, by using feature patterns, the rate of false matches can be reduced from about 80 % down to 20 %, compared to a nearest neighbor approach.


title: Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

abstract: In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.


title: Multisensory anatomical pathways

abstract: In order to interact with the multisensory world that surrounds us, we must integrate various sources of sensory information (vision, hearing, touch...). A fundamental question is thus how the brain integrates the separate elements of an object defined by several sensory components to form a unified percept. The superior colliculus was the main model for studying multisensory integration. At the cortical level, until recently, multisensory integration appeared to be a characteristic attributed to high-level association regions. First, we describe recently observed direct cortico-cortical connections between different sensory cortical areas in the non-human primate and discuss the potential role of these connections. Then, we show that the projections between different sensory and motor cortical areas and the thalamus enabled us to highlight the existence of thalamic nuclei that, by their connections, may represent an alternative pathway for information transfer between different sensory and/or motor cortical areas. The thalamus is in position to allow a faster transfer and even an integration of information across modalities. Finally, we discuss the role of these non-specific connections regarding behavioral evidence in the monkey and recent electrophysiological evidence in the primary cortical sensory areas.


title: Neurogenesis and affective disorders

abstract: The neurogenesis hypothesis of depression was originally formed upon the demonstration that stress impacts levels of adult neurogenesis in the hippocampus. Since then much work has established that newborn neurons in the dentate gyrus are required for mediating some of the beneficial effects of antidepressant treatment. Recent studies combining behavioral, molecular and electrophysiological approaches have attempted to make sense of the role young neurons play in modulating mood by demonstrating a potential role in regulating the circuitry in the brain that underlies depression. Here we discuss the work that led to the neurogenesis hypothesis of depression, and the subsequent studies that have sought to test this hypothesis. We also discuss different animal models of depression that have been used to test the role of neurogenesis in mediating the antidepressant response.


title: Critical Thinking about Critical Periods: Perspectives on a Critical Period for Language Acquisition

abstract: This invention relates to a reflex camera device having a single lens for the photography of right and left images from an object space transmitted as first and second light ray bundles from two positions separated by an interocular distance onto a single frame of a single film strip; a first exterior means for distinguishing from each other the first and second light ray bundles; a single frame aperture; a second means near the image plane to separate the first and second light ray bundles from each position respectively into right and left adjacent images onto the single frame; means to control convergence, focus and interocular distance; and a reflex viewer containing a third distinguishing means near the image plane to enable the simultaneous viewing and photographing of right and left adjacent images; and optionally an indicium marking means for said film strip at the side of the frame line.


Understanding Citizen ’ s Continuance Intention to Use e-Government Website : a Composite View of Technology Acceptance Model and Computer Self-Efficacy


title: Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach

abstract: Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists – and probably the most crucial one – is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.


title: Weisfeiler-Lehman Graph Kernels

abstract: In this article, we propose a family of efficient kernels for large graphs with discrete node labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler-Lehman test of isomorphism on graphs. It maps the original graph to a sequence of graphs, whose node attributes capture topological and label information. A family of kernels can be defined based on this Weisfeiler-Lehman sequence of graphs, including a highly efficient kernel comparing subtree-like patterns. Its runtime scales only linearly in the number of edges of the graphs and the length of the Weisfeiler-Lehman graph sequence. In our experimental evaluation, our kernels outperform state-of-the-art graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime. Our kernels open the door to large-scale applications of graph kernels in various disciplines such as computational biology and social network analysis.


title: An empirical study on evaluation metrics of generative adversarial networks

abstract: Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address the problem of how to evaluate the evaluation metrics. We start with a few necessary conditions for metrics to produce meaningful scores, such as distinguishing real from generated samples, identifying mode dropping and mode collapsing, and detecting overfitting. With a series of carefully designed experiments, we comprehensively investigate existing sample-based metrics and identify their strengths and limitations in practical settings. Based on these results, we observe that kernel Maximum Mean Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to satisfy most of the desirable properties, provided that the distances between samples are computed in a suitable feature space. Our experiments also unveil interesting properties about the behavior of several popular GAN models, such as whether they are memorizing training samples, and how far they are from learning the target distribution.


title: IoT based smart emergency response system for fire hazards

abstract: The Internet of Things pertains to connecting currently unconnected things and people. It is the new era in transforming the existed systems to amend the cost effective quality of services for the society. To support Smart city vision, Urban IoT design plans exploit added value services for citizens as well as administration of the city with the most advanced communication technologies. To make emergency response real time, IoT enhances the way first responders and provides emergency managers with the necessary up-to-date information and communication to make use those assets. IoT mitigates many of the challenges to emergency response including present problems like a weak communication network and information lag. In this paper it is proposed that an emergency response system for fire hazards is designed by using IoT standardized structure. To implement this proposed scheme a low-cost Espressif wi-fi module ESP-32, Flame detection sensor, Smoke detection sensor (MQ-5), Flammable gas detection sensor and one GPS module are used. The sensors detects the hazard and alerts the local emergency rescue organizations like fire departments and police by sending the hazard location to the cloud-service through which all are connected. The overall network utilizes a light weighted data oriented publish-subscribe message protocol MQTT services for fast and reliable communication. Thus, an intelligent integrated system is designed with the help of IoT.


title: An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment

abstract: In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system’s process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system.


title: Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics

abstract: Visual saliency has been an increasingly active research area in the last ten years with dozens of saliency models recently published. Nowadays, one of the big challenges in the field is to find a way to fairly evaluate all of these models. In this paper, on human eye fixations, we compare the ranking of 12 state-of-the art saliency models using 12 similarity metrics. The comparison is done on Jian Li's database containing several hundreds of natural images. Based on Kendall concordance coefficient, it is shown that some of the metrics are strongly correlated leading to a redundancy in the performance metrics reported in the available benchmarks. On the other hand, other metrics provide a more diverse picture of models' overall performance. As a recommendation, three similarity metrics should be used to obtain a complete point of view of saliency model performance.


title: Discovering Support and Affiliated Features from Very High Dimensions

abstract: In this paper, a novel learning paradigm is presented to automatically identify groups of informative and correlated features from very high dimensions. Specifically, we explicitly incorporate correlation measures as constraints and then propose an efficient embedded feature selection method using recently developed cutting plane strategy. The benefits of the proposed algorithm are two-folds. First, it can identify the optimal discriminative and uncorrelated feature subset to the output labels, denoted here as Support Features, which brings about significant improvements in prediction performance over other state of the art feature selection methods considered in the paper. Second, during the learning process, the underlying group structures of correlated features associated with each support feature, denoted as Affiliated Features, can also be discovered without any additional cost. These affiliated features serve to improve the interpretations on the learning tasks. Extensive empirical studies on both synthetic and very high dimensional real-world datasets verify the validity and efficiency of the proposed method.


title: A novel RGB-D SLAM algorithm based on points and plane-patches

abstract: In this work, we present a novel RGB-D SLAM algorithm. The novelty of the proposed algorithm lies in the use of both feature points and plane patches for pose estimation. A plane patch is defined as a small-sized patch constructed by using a feature point with small curvature. The feature points with small curvature are called plane points. The remaining feature points are classified as either smooth points which represent smooth surfaces or structural points which represent edges or corners of some objects. Two criteria (coplanarity and overlap) are used to match the plane patches from two frames. The proposed algorithm is able to weight various types of feature points in order to improve the robustness of SLAM algorithms in favouring plane patches but not the isolated feature points, and maintain the real-time performance of SLAM algorithms in favouring small plane patches, but not the large size of planes. We evaluate the proposed algorithm on multiple benchmark datasets. A large scale experiment is also presented to show the robustness of our algorithm.


Design, Modeling and Simulation of UPQC System with PV Array


title: Real-time tracking on adaptive critic design with uniformly ultimately bounded condition

abstract: In this paper, we proposed a new nonlinear tracking controller based on heuristic dynamic programming (HDP) with the tracking filter. Specifically, we integrate a goal network into the regular HDP design and provide the critic network with detailed internal reward signal to help the value function approximation. The architecture is explicitly explained with the tracking filter, goal network, critic network and action network, respectively. We provide the stability analysis of our proposed controller with Lyapunov approach. It is shown that the filtered tracking errors and the weights estimation errors in neural networks are all uniformly ultimately bounded (UUB) under certain conditions. Finally, we compare our proposed approach with regular HDP approach in virtual reality (VR)/Simulink environment to justify the improved control performance.


IEEE Signal Processing Magazine, Special Issue on Signal Processing on Platforms with Multiple Cores, Nov. 2009 TRENDS IN MULTI-CORE DSP PLATFORMS


title: Optique: Ontology-Based Data Access Platform

abstract: Ontology-Based Data Access (OBDA) is an approach to query relational data via a unified semantic access point powered by an ontology that is ‘connected’ to the underlying databases via mappings. OPTIQUE is an end-to-end OBDA platform. It offers support for semi-automatic bootstrapping of ontologies and mappings from relational databases thus facilitating system deployment, an intuitive interface to pose queries over a deployed system, and a query processing and optimisation module that allows to efficiently answer user queries. In this demonstration attendees will be able to experience OPTIQUE with data from the oil and gas industry and data from the music domain.


Declaratively Capturing Local Label Correlations with Multi-Label Trees


title: Low-Power and Fast Full Adder by Exploring New XOR and XNOR Gates

abstract: In this paper, novel circuits for XOR/XNOR and simultaneous XOR–XNOR functions are proposed. The proposed circuits are highly optimized in terms of the power consumption and delay, which are due to low output capacitance and low short-circuit power dissipation. We also propose six new hybrid 1-bit full-adder (FA) circuits based on the novel full-swing XOR–XNOR or XOR/XNOR gates. Each of the proposed circuits has its own merits in terms of speed, power consumption, power-delay product (PDP), driving ability, and so on. To investigate the performance of the proposed designs, extensive HSPICE and Cadence Virtuoso simulations are performed. The simulation results, based on the 65-nm CMOS process technology model, indicate that the proposed designs have superior speed and power against other FA designs. A new transistor sizing method is presented to optimize the PDP of the circuits. In the proposed method, the numerical computation particle swarm optimization algorithm is used to achieve the desired value for optimum PDP with fewer iterations. The proposed circuits are investigated in terms of variations of the supply and threshold voltages, output capacitance, input noise immunity, and the size of transistors.


title: Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation

abstract: Deep neural networks have largely failed to effectively utilize synthetic data when applied to real images due to the covariate shift problem. In this paper, we show that by applying a straightforward modification to an existing photorealistic style transfer algorithm, we achieve state-of-the-art synthetic-to-real domain adaptation results. We conduct extensive experimental validations on four synthetic-to-real tasks for semantic segmentation and object detection, and show that our approach exceeds the performance of any current state-of-the-art GAN-based image translation approach as measured by segmentation and object detection metrics. Furthermore we offer a distance based analysis of our method which shows a dramatic reduction in Frechet Inception distance between the source and target domains, offering a quantitative metric that demonstrates the effectiveness of our algorithm in bridging the synthetic-to-real gap.


title: Structured Sparsity Models for Reverberant Speech Separation

abstract: We tackle the speech separation problem through modeling the acoustics of the reverberant chambers. Our approach exploits structured sparsity models to perform speech recovery and room acoustic modeling from recordings of concurrent unknown sources. The speakers are assumed to lie on a two-dimensional plane and the multipath channel is characterized using the image model. We propose an algorithm for room geometry estimation relying on localization of the early images of the speakers by sparse approximation of the spatial spectrum of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings of spatially stationary sources demonstrate the effectiveness of the proposed approach for speech separation and recognition.


title: On Maximizing Sensor Network Lifetime by Energy Balancing

abstract: Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.


title: A framework to explore results in multiple media information aggregated search

abstract: On web information exists in the form of text, audio, image, and video objects often referred to multiple media objects. Vertical web search provides the search of multiple media information usually via keyword-based queries. The search results in different media formats usually presented in separate panels/tabs; integration is mostly non-blended. Therefore, results exploration via vertical web search engines require the selection of a source and scrolling of a linear ranked list of results. Relationships in the results presented in separate panels/tabs are mostly not considered. Search aggregations unify results from several vertical web sources via blended integration, but exploration still requires scrolling of a linear ranked list. Multimedia search frameworks provide the exploration of results in different media formats but more focused towards the retrieval issues. We proposed a multiple media information search framework to address issues, particularly in aggregated search. Our search framework provides a mechanism to explore results via non-linear ways. The search framework realized by suggesting a framework architecture design and instantiating a search tool. The effectiveness of blended integration and browsing is measured via precision and click through rate respectively. Search task support in results exploration mechanism measured via task-based evaluation. We also validated the conformance of various search/exploration attributes discussed in the state-of-the-art in our frameworks.


title: An ultra-lightweight design for imperceptible plastic electronics

abstract: Electronic sensor foils only 2 μm thick are extremely light, 27-fold lighter than office paper, durable and flexible and conform to curvilinear surfaces for many innovative applications.


title: The feasibility of pharmacological volume control of sickle cells is dependent on the quantization of the transport pathways. A model study.

abstract: Normal erythrocytes are under physiological conditions characterized by low cation and high anion conductance. However, in the case of sickle cell anemia the erythrocytes contain a modified haemoglobin, HbS, which under low oxygen tension gives rise to sickling. This condition is preceded by an increase in cation conductance, especially due to the Ca2+-activated K+-channels, leading to net-efflux of KCI and thereby decreased cellular volume, which is part of the pathological condition.A possible symptomatic treatment could be application of conductance blockers, targeting the Ca2+-activated K+-channel or the anion conductance in order to minimize the passive transport of ions and solvent. It has been argued, that due to the high anion conductance, solute loss depended at moderately increased cation conductances on the cation only. Consequently the Ca2+-activated K+-conductance should be the target for attempts to modify solute loss.It is shown that: knowledge of mean conductances (time averages) for pathways showing fluctuations are insufficient to predict the quantitative effect of conductance inhibitors, since inhibition is strongly dependent on the kinetics of the mechanisms mediating the translocation and a block of the high conductance anion pathway can be as effective as inhibition of the Ca+-activated K+-conductance with regard to net salt loss.


title: Scene Text Recognition in Mobile Applications by Character Descriptor and Structure Configuration

abstract: Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods.


title: Lactulose: production, purification and potential applications.

abstract: Abstract Lactulose a “bifidus factor” is composed of galactose and fructose, which can be produced by the isomerization of lactose. It is a prebiotic carbohydrate which stimulates the growth of health-promoting bacteria in the gastrointestinal tract, such as bifidobacteria and lactobacilli and at the same time inhibits growth of pathogenic bacteria such as Salmonella. It can also be used for the treatment of constipation, hepatic encephalopathy, tumour prevention, and to maintain blood glucose and insulin level. This review provides comprehensive information on the different techniques used for the production of lactulose, purification and analysis. Besides this mechanism of action and its potential applications in food and pharmaceutical industries have also been discussed.


title: OCA: Opinion corpus for Arabic

abstract: Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research. © 2011 Wiley Periodicals, Inc.


title: CAN WORK BE FUN ? IMPROVING TASK MOTIVATION AND HELP -SEEKING THROUGH GAME MECHANICS Research-in-Progress

abstract: Gamification has become an increasingly important design paradigm for information systems, impacting both research and practice. Effective gamification requires designers to tap into intrinsic motivations by framing tasks in ways that evoke positive attitudes through challenge and friendly competition. These positive attitudes can lead to greater engagement and encourage help-seeking behavior, a key goal of developing motivated learners. The objective of this paper is to understand the conditions and types of mechanisms that that encourage participants to become engaged in a task and seek help to improve. Based on the literature on the game element hierarchy and goal orientation, we propose a theoretical model to explain how different types of learners will respond to different game mechanisms. We outline the design an experiment to test our hypotheses.


title: An improved firefly algorithm for solving dynamic multidimensional knapsack problems

abstract: There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively new and hot research area and has attracted notable attention of the researchers in the past decade. Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to evaluate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.


title: Concept and Attention-Based CNN for Question Retrieval in Multi-View Learning

abstract: Question retrieval, which aims to find similar versions of a given question, is playing a pivotal role in various question answering (QA) systems. This task is quite challenging, mainly in regard to five aspects: synonymy, polysemy, word order, question length, and data sparsity. In this article, we propose a unified framework to simultaneously handle these five problems. We use the word combined with corresponding concept information to handle the synonymy problem and the polysemous problem. Concept embedding and word embedding are learned at the same time from both the context-dependent and context-independent views. To handle the word-order problem, we propose a high-level feature-embedded convolutional semantic model to learn question embedding by inputting concept embedding and word embedding. Due to the fact that the lengths of some questions are long, we propose a value-based convolutional attentional method to enhance the proposed high-level feature-embedded convolutional semantic model in learning the key parts of the question and the answer. The proposed high-level feature-embedded convolutional semantic model nicely represents the hierarchical structures of word information and concept information in sentences with their layer-by-layer convolution and pooling. Finally, to resolve data sparsity, we propose using the multi-view learning method to train the attention-based convolutional semantic model on question–answer pairs. To the best of our knowledge, we are the first to propose simultaneously handling the above five problems in question retrieval using one framework. Experiments on three real question-answering datasets show that the proposed framework significantly outperforms the state-of-the-art solutions.


title: Combined Star-Delta Winding Analysis

abstract: The combined star-delta winding has been attracting the attention of industry because of its potential to improve the efficiency of electrical machines. In order to show the efficiency benefits of this type of winding, many authors have used methods to determine the improvements in winding factors, published in different papers, which have not been validated yet. Some of these methods contradict each other. Furthermore, as a demerit of this winding, a unidirectional rotation has been reported. In this paper, a set of new formulas to calculate the winding factors for combined star-delta windings is proposed. In order to check the validity of the proposed formulas, a 2-D FEM and experimental tests on a salient pole synchronous machine are presented, which allows different combined star-delta winding connections. In contrast to other publications, the measurements show that combined windings can be applied for both directions of rotation without any problems.


Improved PEDOT:PSS/c-Si hybrid solar cell using inverted structure and effective passivation


title: Transcranial Electrical Stimulation: Transcranial Direct Current Stimulation (tDCS), Transcranial Alternating Current Stimulation (tACS), Transcranial Pulsed Current Stimulation (tPCS), and Transcranial Random Noise Stimulation (tRNS)

abstract: Abstract Neurological disorders are not only the direct result of an initial insult, but also represent the consequences of dynamic, plastic changes in distributed neural networks as the entire nervous system attempts to adapt. In such cases, cerebral plastic changes after brain insult may lead to additional neuropathic functional disability (maladaptive response). In this context, transcranial electrical stimulation (tES) aims to enhance adaptive patterns of activity and suppress maladaptive patterns of activity, restoring the equilibrium in imbalanced neural networks in the patient’s brain. Similarly, tES has the aim of enhancing intact human abilities. Although there are various methods of non-invasive delivery of electrical current to the brain, the most prominent of currently used methods include transcranial direct current stimulation (tDCS); transcranial alternating current stimulation (tACS); transcranial pulsed current stimulation (tPCS), which includes cranial electrotherapy stimulation (CES); and transcranial random noise stimulation (tRNS). In this chapter, we will focus on these four non-invasive transcranial electrical stimulation (tES) approaches: tDCS, tPCS, tACS, and tRNS. We will also provide a comprehensive overview of the underlying principles, and present examples illustrating the therapeutic potential of non-invasive electrical brain stimulation for relevant brain dysfunctional conditions and for cognitive enhancement.


title: A Comparative Evolutionary Models for Solving Sudoku

abstract: Evolutionary algorithms have become robust tool in data processing and modeling of dynamic, complex and non-linear processes due to their flexible mathematical structure to yield optimal results even with imprecise, ambiguity and noise at its input. The study investigates evolutionary algorithms for solving Sudoku task. Various hybrids are presented here as veritable algorithm for computing dynamic and discrete states in multipoint search in CSPs optimization with application areas to include image and video analysis, communication and network design/reconstruction, control, OS resource allocation and scheduling, multiprocessor load balancing, parallel processing, medicine, finance, security and military, fault diagnosis/recovery, cloud and clustering computing to mention a few. Solution space representation and fitness functions (as common to all algorithms) were discussed. For support and confidence model adopted 1=0.2 and 2=0.8 respectively yields better convergence rates – as other suggested value combinations led to either a slower or non-convergence. CGA found an optimal solution in 32 seconds after 188 iterations in 25runs; while GSAGA found its optimal solution in 18seconds after 402 iterations with a fitness progression achieved in 25runs and consequently, GASA found an optimal solution 2.112seconds after 391 iterations with fitness progression after 25runs respectively.


title: Deeptransport: prediction and simulation of human mobility and transportation mode at a citywide level

abstract: Traffic congestion causes huge economic loss worldwide in every year due to wasted fuel, excessive air pollution, lost time, and reduced productivity. Understanding how humans move and select the transportation mode throughout a large-scale transportation network is vital for urban congestion prediction and transportation scheduling. In this study, we collect big and heterogeneous data (e.g., GPS records and transportation network data), and we build an intelligent system, namely DeepTransport, for simulating and predicting human mobility and transportation mode at a citywide level. The key component of DeepTransport is based on the deep learning architecture that that aims to understand human mobility and transportation patterns from big and heterogeneous data. Based on the learning model, given any time period, specific location of the city or people's observed movements, our system can automatically simulate or predict the persons' future movements and their transportation mode in the large-scale transportation network. Experimental results and validations demonstrate the efficiency and superior performance of our system, and suggest that human transportation mode may be predicted and simulated more easily than previously thought.


title: Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents

abstract: The goal of sentence and document modeling is to accurately represent the meaning of sentences and documents for various Natural Language Processing tasks. In this work, we present Dependency Sensitive Convolutional Neural Networks (DSCNN) as a general-purpose classification system for both sentences and documents. DSCNN hierarchically builds textual representations by processing pretrained word embeddings via Long Short-Term Memory networks and subsequently extracting features with convolution operators. Compared with existing recursive neural models with tree structures, DSCNN does not rely on parsers and expensive phrase labeling, and thus is not restricted to sentence-level tasks. Moreover, unlike other CNN-based models that analyze sentences locally by sliding windows, our system captures both the dependency information within each sentence and relationships across sentences in the same document. Experiment results demonstrate that our approach is achieving state-of-the-art performance on several tasks, including sentiment analysis, question type classification, and subjectivity classification.


title: Enterprise resource planning systems, strategic enterprise management systems and management accounting: A Danish study

abstract: Purpose – The purpose of this paper is to contribute to the body of knowledge about to what extent integrated information systems, such as ERP and SEM systems, affect the ability to solve different management accounting tasks.Design/methodology/approach – The relationship between IIS and management accounting practices was investigated quantitatively. A total of 349 responses were collected using a survey, and the data were analysed using linear regression models.Findings – Analyses indicate that ERP systems support the data collection and the organisational breadth of management accounting better than SEM systems. SEM systems, on the other hand, seem to be better at supporting reporting and analysis. In addition, modern management accounting techniques involving the use of non‐financial data are better supported by an SEM system. This indicates that different management accounting tasks are supported by different parts of the IIS.Research limitations/implications – The study applies the methods of quanti...


title: Greater petrosal nerve schwannomas—analysis of four cases and review of the literature

abstract: Schwannomas arising from the greater petrosal nerve (GPN) are exceedingly rare: only 10 such cases have been reported in the English literature. We report on four cases of GPN schwannomas and discuss the surgical approach for their removal. Four patients with GPN schwannomas underwent surgery at Keio University Hospital. We present the pre- and postoperative clinical findings and describe the structures around the GPN schwannomas as observed during the surgery. Histological sections were performed around the GPN using Masson’s trichrome stain to elucidate the membrane structures. Three patients presented with xerophthalmia, and one with facial palsy, hearing disturbance, and generalized convulsions. Contrast-enhanced magnetic resonance images revealed tumors in the temporal lobe. Bone-window computed tomography showed erosion of the anterior petrous apex. During the operation, the temporal lobe was retracted epidurally. The tumors were visible inside the interdural space and covered with the epineurium. In three cases, the tumors were completely removed, and in one case, the tumor was subtotally removed with intraoperative facial monitoring. In the histological sections, we confirmed that the GPN ran within the interdural space. Approaching epi- and interdurally is suitable for the removal of GPN schwannomas because the GPN is located within the interdural space. The advantage of this approach is that the landmark points can be identified. Moreover, the possibility of injuring the temporal lobe is low because the temporal lobe is not exposed in this approach.


title: Implementation of high speed radix-10 parallel multiplier using Verilog

abstract: The emerging computational complexities arises the need of fast multiplication unit. The importance of multiplication in various applications necessitates improvement in its design so as to obtain the multiplication result efficiently. Multiplication operation can be improved by reducing the number of partial products to be added and by enhancing the adder unit for obtaining sum. The number of partial products can be reduced by using higher radix multiplication. For better speed applications a radix-10 multiplier is proposed which uses recoded multiplier digits as in conventional parallel multiplier design. The multiplier digits are encoded using Signed Digit (SD) radix-10 method which converts the digit set to {-5 to 5} from {0 to 9} and also generate a sign bit. This recoding leads to minimized calculations as only five multiples are required to be calculated and the negative multiples are obtained using 2's complement approach. The negative multiples are used when sign bit is high. A different approach is availed in the multiples generation of the multiplicand digit and during accumulation of partial product obtained during multiplication procedure. A modified BCD adder is used which eliminates the post correction while calculating the sum of two BCD digits. The modified architecture eliminates the extra recoding logic thereby reducing the area of overall architecture. This paper delivers the design and implementation of 16-Bit multiplication unit. The design entry is done in Verilog Hardware Description Language (HDL) and simulated using ISIM Simulator. It is synthesized and implemented using Xilinx ISE 12.2. Synthesis results have shown that 20.3% reduction in 4-Input LUTs and 20.4% reduction in the number of slices is observed in the modified methodology. Further 11.5% reduction of maximum combinational path delay is also observed in the modified architecture, thereby leading to high speed multiplication for VLSI applications.


title: Chan-Vese Segmentation

abstract: While many segmentation methods rely heavily in some way on edge detection, the “Active Contours Without Edges” method by Chan and Vese [7, 9] ignores edges completely. Instead, the method optimally fits a two-phase piecewise constant model to the given image. The segmentation boundary is represented implicitly with a level set function, which allows the segmentation to handle topological changes more easily than explicit snake methods. This article describes the level set formulation of the Chan-Vese model and its numerical solution using a semi-implicit gradient descent. We also discuss the Chan–Sandberg–Vese method [8], a straightforward extension of Chan–Vese for vector-valued images.


title: A generative computational model for human hide and seek behavior

abstract: Hiding and seeking is a cognitive ability frequently demonstrated by humans in both real life and video games. We use machine learning to automatically construct the first computational model of hide/seek behavior in adult humans in a video game like setting. The model is then run generatively in a novel environment and its behavior is found indistinguishable from actual human behavior by a panel of human judges. In doing so the artificial intelligence agent using the model appears to have passed a version of the Turing test for hiding and seeking.


title: Eco-visualization: combining art and technology to reduce energy consumption

abstract: Can creative visualizations of real time energy consumption patterns trigger more ecologically responsible behavior? Media art that displays the real time usage of key resources such as electricity offers new strategies to conserve energy in the home and workplace. This paper details the development of a public art project created for the National Center for Supercomputing Applications that measures electricity usage in real time for the purpose of education and curtailment of power usage. A version of this piece will be on view in the exhibition, Speculative Data and the Creative Imaginary, a component of the 2007 Creativity and Cognition conference.


title: Towards Language-Universal End-to-End Speech Recognition

abstract: Building speech recognizers in multiple languages typically involves replicating a monolingual training recipe for each language, or utilizing a multi-task learning approach where models for different languages have separate output labels but share some internal parameters. In this work, we exploit recent progress in end-to-end speech recognition to create a single multilingual speech recognition system capable of recognizing any of the languages seen in training. To do so, we propose the use of a universal character set that is shared among all languages. We also create a language-specific gating mechanism within the network that can modulate the network's internal representations in a language-specific way. We evaluate our proposed approach on the Microsoft Cortana task across three languages and show that our system outperforms both the individual monolingual systems and systems built with a multi-task learning approach. We also show that this model can be used to initialize a monolingual speech recognizer, and can be used to create a bilingual model for use in code-switching scenarios.


title: A bootstrapping approach for identifying stakeholders in public-comment corpora

abstract: A stakeholder is an individual, group, organization, or community that has an interest or stake in a consensus-building process. The goal of stakeholder identification is identifying stakeholder mentions in natural language text. We present novel work in using a bootstrapping approach for the identification of stakeholders in public comment corpora. We refine the definition of a stakeholder by categorizing stakeholders into 2 distinct stakeholder types and experiment with automatically identifying one of these two types: instances where the author identifies him/herself as a member of a particular group. An existing bootstrapping information extraction algorithm is combined individually with 3 distinct extraction pattern templates. Results show that this stakeholder group can be learned in a minimally supervised bootstrapping framework using 2 of the 3 extraction pattern templates. An experimental analysis explores the challenges in applying the third extraction pattern template to this problem. Results on all 3 extraction pattern templates provide insight on the unique and novel challenge of identifying stakeholders.


title: Hybrid Control of the Berkeley Lower Extremity Exoskeleton (BLEEX)

abstract: The Berkeley Lower Extremity Exoskeleton is the first functional energetically autonomous load carrying human exoskeleton and was demonstrated at U.C. Berkeley, walking at the average speed of 0.9 m/s (2 mph) while carrying a 34 kg (75 lb) payload. The original published controller, called the BLEEX Sensitivity Amplification Controller, was based on positive feedback and was designed to increase the closed loop system sensitivity to its wearer's forces and torques without any direct measurement from the wearer. This controller was successful at allowing natural and unobstructed load support for the pilot. This article presents an improved control scheme we call "hybrid" BLEEX control that adds robustness to changing BLEEX backpack payload. The walking gait cycle is divided into stance control and swing control phases. Position control is used for the BLEEX stance leg (including the torso and backpack) and a sensitivity amplification controller is used for the swing leg. The controller is also designed to smoothly transition between these two schemes as the pilot walks. With hybrid control, the controller does not require a good model of the BLEEX torso and payload, which is difficult to obtain and subject to change as payload is added and removed. As a tradeoff, the position control used in this method requires the human to wear seven inclinometers to measure human limb and torso angles. These additional sensors require careful design to securely fasten them to the human and increase the time to don and doff BLEEX.


title: STATL: An Attack Language for State-based Intrusion Detection

abstract: STATL is an extensible state/transition-based attack description language designed to support intrusion detection. The language allows one to describe computer penetrations as sequences of actions that an attacker performs to compromise a computer system. A STATL description of an attack scenario can be used by an intrusion detection system to analyze a stream of events and detect possible ongoing intrusions. Since intrusion detection is performed in different domains (i.e., the network or the hosts) and in different operating environments (e.g., Linux, Solaris, or Windows NT), it is useful to have an extensible language that can be easily tailored to different target environments. STATL defines domain-independent features of attack scenarios and provides constructs for extending the language to describe attacks in particular domains and environments. The STATL language has been successfully used in describing both network-based and host-based attacks, and it has been tailored to very different environments, e.g., Sun Microsystems' Solaris and Microsoft's Windows NT. An implementation of the runtime support for the STATL language has been developed and a toolset of intrusion detection systems based on STATL has been implemented. The toolset was used in a recent intrusion detection evaluation effort, delivering very favorable results. This paper presents the details of the STATL syntax and its semantics. Real examples from both the host and network-based extensions of the language are also presented.


title: Personalized explanation in machine learning

abstract: Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personalized explanation by defining and structuring the problem based on prior work on machine learning explanation, personalization (in machine learning) and concepts and techniques from other domains such as privacy and knowledge elicitation. We perform a categorization of explainee information used in the process of personalization as well as describing means to collect this information. We also identify three key explanation properties that are amendable to personalization: complexity, decision information and presentation. We also enhance existing work on explanation by introducing additional desiderata and measures to quantify the quality of personalized explanations.


title: Text Recognition from Silent Lip Movement Video

abstract: Lip reading, the ability to recognize text information from the movement of a speaker’s mouth, is a difficult and challenging task. Recently, the end-to-end model that maps a variable-length sequence of video frames to text performs poorly in real life situation where people unintentionally move the lips instead of speaking. The goal of this work is to improve the performance of lip reading task in real life. The model proposed in this article consists of two networks that are visual to audio feature network and audio feature to text network. Our experiments showed that the model proposed in this article can achieve 92.76% accuracy in lip reading task on the dataset that the unintentional lips movement was added.


MT Quality Estimation for E-Commerce Data


title: Let me grab this: a comparison of EMS and vibration for haptic feedback in free-hand interaction

abstract: Free-hand interaction with large displays is getting more common, for example in public settings and exertion games. Adding haptic feedback offers the potential for more realistic and immersive experiences. While vibrotactile feedback is well known, electrical muscle stimulation (EMS) has not yet been explored in free-hand interaction with large displays. EMS offers a wide range of different strengths and qualities of haptic feedback. In this paper we first systematically investigate the design space for haptic feedback. Second, we experimentally explore differences between strengths of EMS and vibrotactile feedback. Third, based on the results, we evaluate EMS and vibrotactile feedback with regard to different virtual objects (soft, hard) and interaction with different gestures (touch, grasp, punch) in front of a large display. The results provide a basis for the design of haptic feedback that is appropriate for the given type of interaction and the material.


title: Creating audio keywords for event detection in soccer video

abstract: This paper presents a novel framework called audio keywords to assist event detection in soccer video. Audio keyword is a middle-level representation that can bridge the gap between low-level features and high-level semantics. Audio keywords are created from low-level audio features by using support vector machine learning. The created audio keywords can be used to detect semantic events in soccer video by applying a heuristic mapping. Experiments of audio keywords creation and event detection based on audio keywords have illustrated promising results. According to the experimental results, we believe that audio keyword is an effective representation that is able to achieve more intuitionistic result for event detection in sports video compared with the method of event detection directly based on low-level features.


title: Active capacitor multiplier in Miller-compensated circuits

abstract: A technique is presented whereby the compensating capacitor of an internally compensated linear regulator, Miller-compensated two-stage amplifier, is effectively multiplied. Increasing the capacitance with a current-mode multiplier allows the circuit to occupy less silicon area and to more effectively drive capacitive loads. Reducing physical area requirements while producing the same or perhaps better performance is especially useful in complex systems where most, if not all, functions are integrated onto a single integrated circuit. Die area in such systems is a luxury. The increasing demand for mobile battery-operated devices is a driving force toward higher integration. The enhanced Miller-compensation technique developed in this paper helps enable higher integration while being readily applicable to any process technology, be it CMOS, bipolar, or BiCMOS. Furthermore, the technique applies, in general, to amplifier circuits in feedback configuration. Experimentally, the integrated linear regulator (fabricated in a 1-/spl mu/m BiCMOS process technology) proved to be stable for a wide variety of loading conditions: load currents of up to 200 mA, equivalent series resistance of up to 3 /spl Omega/, and load capacitors ranging from 1.5 nF to 20 /spl mu/E The total quiescent current flowing through the regulator was less than 30 /spl mu/A during zero load-current conditions.


title: Mechanical load on the upper extremity during wheelchair activities.

abstract: OBJECTIVE ::: To determine the net moments on the glenohumeral joint and elbow joint during wheelchair activities. ::: ::: ::: DESIGN ::: Kinematics and external forces were measured during wheelchair activities of daily living (level propulsion, riding on a slope, weight-relief lifting, reaching, negotiating a curb) and processed in an inverse dynamics biomechanic model. ::: ::: ::: SETTING ::: Biomechanics laboratory. ::: ::: ::: PARTICIPANTS ::: Five able-bodied subjects, 8 subjects with paraplegia, and 4 subjects with tetraplegia. ::: ::: ::: INTERVENTIONS ::: Not applicable. ::: ::: ::: MAIN OUTCOME MEASURE ::: Net moments on the glenohumeral joint and elbow joint. ::: ::: ::: RESULTS ::: Peak shoulder and elbow moments were significantly higher for negotiating a curb and weight-relief lifting than for reaching, level propulsion, and riding on a slope. Overall, the elbow extension moments were significantly lower for subjects with tetraplegia than for those with paraplegia. ::: ::: ::: CONCLUSIONS ::: The net moments during weight-relief lifting and negotiating a curb were high when compared with wheelchair propulsion tasks. Taking the effect of frequency and duration into account, these loads might imply a considerable risk for joint damage in the long term.


title: TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation

abstract: We address the challenge of personalized recommendation of high quality content producers in social media. While some candidates are easily identifiable (say, by being "favorited" many times), there is a long-tail of potential candidates for whom we have little evidence. Through careful modeling of contextual factors like the geo-spatial, topical, and social preferences of users, we propose a tensor-based personalized expert recommendation framework that integrates these factors for revealing latent connections between homogeneous entities (e.g., users and users) and between heterogeneous entities (e.g., users and experts). Through extensive experiments over geo-tagged Twitter data, we find that the proposed framework can improve the quality of recommendation by over 30% in both precision and recall compared to the state-of-the-art.


title: RHSBoost: Improving classification performance in imbalance data

abstract: Imbalance data are defined as a dataset whose proportion of classes is severely skewed. Classification performance of existing models tends to deteriorate due to class distribution imbalance. In addition, over-representation by majority classes prevents a classifier from paying attention to minority classes, which are generally more interesting. An effective ensemble classification method called RHSBoost has been proposed to address the imbalance classification problem. This classification rule uses random undersampling and ROSE sampling under a boosting scheme. According to the experimental results, RHSBoost appears to be an attractive classification model for imbalance data.


title: Discovering Gender-Specific Knowledge from Finnish Basic Education using PISA Scale Indices

abstract: The Programme for International Student Assessment, PISA, is a worldwide study to assess knowledge and skills of 15year-old students. Results of the latest PISA survey conducted in 2012 were published in December 2013. According to the results, Finland is one of the few countries where girls performed better in mathematics than boys. The purpose of this work is to refine the analysis of this observation by using education data mining techniques. More precisely, as part of standard PISA preprocessing phase certain scale indices are constructed based on information gathered from the background questionnaire of each participating student. The indices describe, e.g., students’ engagement, drive and self-beliefs, especially related to mathematics, the main assessment area in PISA 2012. However, around 30% of the scale indices are missing so that a nonstructured sparsity pattern must be dealt with. We handle this using a special, robust clustering technique, which is then applied to Finnish subset of PISA data. Already direct interpretation of the created clusters reveals interesting patterns. Clusterwise analysis through relationship mining refines the confidence on our final conclusion that attitudes towards mathematics which are often gender-specific are the most important factors to explain the performance in mathematics.


title: Pajek -- analysis and visualization of large networks

abstract: Pajek (spider, in Slovene) is a program package, for Windows (32 bit), for analysis and visualization of large networks (having thousands of vertices). It is freely available, for noncommercial use, at its home page: http://vlado.fmf.uni-lj.si/pub/networks/pajek/


title: Systematic review of sedentary behaviour and health indicators in school-aged children and youth

abstract: Accumulating evidence suggests that, independent of physical activity levels, sedentary behaviours are associated with increased risk of cardio-metabolic disease, all-cause mortality, and a variety of physiological and psychological problems. Therefore, the purpose of this systematic review is to determine the relationship between sedentary behaviour and health indicators in school-aged children and youth aged 5-17 years. Online databases (MEDLINE, EMBASE and PsycINFO), personal libraries and government documents were searched for relevant studies examining time spent engaging in sedentary behaviours and six specific health indicators (body composition, fitness, metabolic syndrome and cardiovascular disease, self-esteem, pro-social behaviour and academic achievement). 232 studies including 983,840 participants met inclusion criteria and were included in the review. Television (TV) watching was the most common measure of sedentary behaviour and body composition was the most common outcome measure. Qualitative analysis of all studies revealed a dose-response relation between increased sedentary behaviour and unfavourable health outcomes. Watching TV for more than 2 hours per day was associated with unfavourable body composition, decreased fitness, lowered scores for self-esteem and pro-social behaviour and decreased academic achievement. Meta-analysis was completed for randomized controlled studies that aimed to reduce sedentary time and reported change in body mass index (BMI) as their primary outcome. In this regard, a meta-analysis revealed an overall significant effect of -0.81 (95% CI of -1.44 to -0.17, p = 0.01) indicating an overall decrease in mean BMI associated with the interventions. There is a large body of evidence from all study designs which suggests that decreasing any type of sedentary time is associated with lower health risk in youth aged 5-17 years. In particular, the evidence suggests that daily TV viewing in excess of 2 hours is associated with reduced physical and psychosocial health, and that lowering sedentary time leads to reductions in BMI.


title: Liquid Biopsy in Liquid Tumors

abstract: The availability of a minimally invasive patient simple, capable of providing tumor information, represents a valuable clinical tool. The liquid biopsy has the potential to achieve this need. Circulating cell free DNA (ccfDNA), other circulating nucleic acids such as microRNA and circulating tumor cells (CTCs), can be obtained from a peripheral blood sample. Liquid biopsy has been particularly studied in solid tumors, specially of the epitelial origin, such as a pancreatic carcinoma and advanced breast cancer. It has been considerably less applied to the study of non-solid tumors. It represents an important source for diagnosis, prognosis and predictive information. Also it is suitable to evaluate response to therapy and drugs pharmacokinetics. It provides a unique opportunity to evaluate the disease evolution in serial blood samples collection, otherwise difficult to obtain. Liquid biopsy can be rehearsed using different circulating biological fluids such as whole blood, serum, plasma and lymph, as well as, non-circulating fluids such as urine, feces, saliva, bile and accumulated pathological fluids such as ascites. This review summarizes the current status of circulating material analysis in non-solid tunors. It is specially focused on Hodgkin Lymphoma and among Non-Hodgkin Lymphoma, it refers particularly to Diffuse Large B cell Lymphoma, the most common aggressive Non-Hodgkin Lymphoma derived from germinal center B-cells in adults. It further discusses the benefit of liquid biopsy in oncohemtaological diseases and potential clinical applications.


title: Real-time American Sign Language recognition from video using hidden Markov models

abstract: Hidden Markov models (HMMs) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. We describe a real-time HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers.


title: www.elsevier.com/locate/ijhcs Understanding e-learning continuance intention: An extension of the Technology Acceptance Model

abstract: Based on the expectancy disconfirmation theory, this study proposes a decomposed technology acceptance model in the context of an e-learning service. In the proposed model, the perceived performance component is decomposed into perceived quality and perceived usability. A sample of 172 respondents took part in this study. The results suggest that users' continuance intention is determined by satisfaction, which in turn is jointly determined by perceived usefulness, information quality, confirmation, service quality, system quality, perceived ease of use and cognitive absorption.


title: Circularly polarized single-fed wide-slot antenna for UHF RFID reader

abstract: This paper presents a design of the universal reader antenna applied for the radio frequency identification (RFID) system with the operating frequency from 840 MHz to 960 MHz. The antenna structure consists of a rectangular loop and a rectangular feeding patch which are the important elements to produce the circular polarization. The total size of the reader antenna is 120 mm ×120 mm fabricated on the FR4 substrate with thickness of 3.18 mm and with the dielectric constant of 4.3. From the measured results, it is obvious that the proposed reader antenna radiates a bidirectional pattern with the circular polarization, covering the operating frequency from 750 MHz to 1150 MHz with axial ratio bandwidth of 42%. The axial ratio and gain at the boresight direction are 1.03 dB and 3.62 dBic, considering at 900 MHz which the center frequency band of universal RFID standard.


title: Integration of scheduling and control-Theory or practice?

abstract: Abstract Today's industries are more than ever forced to lower their operational costs, especially during the ongoing economic recession where the whole existence of many businesses is endangered. This has created a need of increased information sharing between various decision-support systems. The target is to have full transparency and ensure that the overall result of individual optimization tasks is “globally optimal”, i.e., that the decisions taken on different levels do not negate each other. This leads inevitably to more complex optimization systems as the models tend to cover larger problem entities in order to merge previously separated optimization targets or include earlier not considered aspects in order to avoid getting stuck into unfavorable local optima. Another complexity arises from the need of communication between the tasks—earlier isolated problems become networked, which means that the definition of optimization goals cannot be derived from local facts alone. This paper discusses some issues emerging from the integration of production scheduling and control. Some existing standards to support the integration challenge are presented, followed by an overview of recent research results. An interesting question is to what extent such an integration activity is the task of a scientist or a practitioner? The paper concludes by highlighting important open questions and raises some remaining challenges for the future.


title: Development of 24 GHz rectennas for Fixed Wireless Access

abstract: We need electricity to use wireless information. If we reduce amount of batteries or electrical wires with a wireless power transmission technology via microwave (MPT), it is a green communication system. We Kyoto University propose a Fixed Wireless Access (FWA) system with the MPT with NTT, Japan. In this paper, we show mainly development results of 24GHz rectennas, rectifying antenna, for FWA. We developed some types of the rectennas. Finally we achieve 65% of RF-DC conversion efficiency with output filter of harmonic balance.


title: Rapid Skill Capture in a First-Person Shooter

abstract: Various aspects of computer game design, including adaptive elements of game levels, characteristics of “bot” behavior, and player matching in multiplayer games, would ideally be sensitive to a player’s skill level. Yet, while game difficulty and player learning have been explored in the context of games, there has been little work analyzing skill per se, and how this is related to the interaction of a player with the controls of the game—the player’s input. To this end, we present a data set of 476 game logs from over 40 players of a first-person shooter game (Red Eclipse) as a basis of a case study. We then extract features from the keyboard and mouse input and provide an analysis in relation to skill. Finally, we show that a player’s skill can be predicted using less than a minute of their keyboard presses. We suggest that the techniques used here are useful for adapting games to match players’ skill levels rapidly, arguably more rapidly than solutions based on performance averaging such as TrueSkill.


lationships between nurses ’ empathy , self-compassion and mensions of professional quality of life : A cross-sectional udy


title: Fast and High Quality Highlight Removal From a Single Image

abstract: Specular reflection exists widely in photography and causes the recorded color deviating from its true value, so fast and high quality highlight removal from a single nature image is of great importance. In spite of the progress in the past decades in highlight removal, achieving wide applicability to the large diversity of nature scenes is quite challenging. To handle this problem, we propose an analytic solution to highlight removal based on an L2 chromaticity definition and corresponding dichromatic model. Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively. In the former illumination orthogonal subspace, which is specular-free, we can conduct robust clustering with an explicit criterion to determine the cluster number adaptively. In the latter illumination parallel subspace, a property called pure diffuse pixels distribution rule (PDDR) helps map each specular-influenced pixel to its diffuse component. In terms of efficiency, the proposed approach involves few complex calculation, and thus can remove highlight from high resolution images fast. Experiments show that this method is of superior performance in various challenging cases.


title: Hybrid Control of a Vision-Guided Robot Arm by EOG, EMG, EEG Biosignals and Head Movement Acquired via a Consumer-Grade Wearable Device

abstract: Simultaneous acquisition of electrooculogram, jaw electromyogram, electroencephalogram, and head movement via consumer-grade wearable devices has become possible. Such devices offer new opportunities to deploy practical biosignal-based interfaces for assistive robots; however, they also pose challenges related to the available signals and their characteristics. In this proof-of-concept study, we demonstrate the possibility of successful control of a 5 + 1 degrees-of-freedom robot arm based on a consumer wireless headband in the form of four control modes predicated on distinct signal combinations. We propose a control approach hybrid at two levels, which seeks a compromise between robot controllability and maintaining the user goal rather than being process-focused. First, robot arm steering combines discrete and proportional aspects. Second, after the robot has been steered toward the approximate target direction, a sparse approach is followed and the user only needs to issue a single command, after which steering adjustment and grasping are performed automatically under stereoscopic vision guidance. We present in detail the associated algorithms, whose implementation is publicly available. Within this framework, we also demonstrate the control of arm posture and grasping force based, respectively, on object visual features and user input. We regard the interface proposed herein as a viable blueprint for future work on controlling wheelchair-mounted and meal-assisting robot arms.


title: The active NPC converter for medium-voltage applications

abstract: The three-level neutral point clamped voltage source converter (NPC VSC) is widely used in high-power, medium-voltage applications. To overcome its major drawback-the unequal loss distribution among the semiconductors-the use of active NPC switches (ANPC) was proposed previously. In this paper, a simple feedforward loss-control scheme for the ANPC converter is presented. The principle of operation of the ANPC VSC is verified experimentally for the first time. Electrical measurements on a low-voltage test bench prove the proper function of the ANPC converter. Infrared junction temperature measurements confirm the improved loss distribution. Finally, the authors demonstrate the superior performance of the ANPC converter in various medium-voltage applications.


title: HAL: Hybrid Assistive Limb Based on Cybernics

abstract: We aim to develop the Hybrid Assistive Lims (HAL) in order to enhance and upgrade the human capabilities based on the frontier science Cybernics. Cybernics is a new domain of interdisciplinary research centered on cybernetics, mechatronics, and informatics, and integrates neuroscience, robotics, systems engineering, information technology, “kansei” engineering, ergonomics, physiology, social science, law, ethics, management, economics etc. Robot Suit HAL is a cyborg type robot that can expand, augment and support physical capability. The robot suit HAL has two types of control systems such as “Cybernic Voluntary Control System” and “Cybernic Autonomous Control System”. The application fields of HAL are medical welfare, heavy work support and entertainment etc. In this paper, the outline of HAL and some of the important algorithms and recent challenges are described.


UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields


title: Grid connected residential photovoltaic energy systems with Plug-In Hybrid electric Vehicles(PHEV) as energy storage

abstract: This paper proposes an approach for the control and optimization of power flow by integrating Plug-In Hybrid Vehicles (PHEV) to an existing residential photovoltaic system. Optimization of Power flow reduces the dependency for electric power from the grid and control involves determining the source from which residential load will be catered. The system built to achieve the goal is a combination of the existing residential photovoltaic system, PHEV, Power Conditioning Unit (PCU) and a controller. The PCU involves two DC-DC Boost Converters and an inverter. This paper emphasizes on developing the controller logic and its implementation in order to accommodate the flexibility and benefits of the proposed integrated system.


title: Improved Detection of Human Respiration Using Data Fusion Basedon a Multistatic UWB Radar

abstract: This paper investigated the feasibility for improved detection of human respiration using data fusion based on a multistatic ultra-wideband (UWB) radar. UWB-radar-based respiration detection is an emerging technology that has great promise in practice. It can be applied to remotely sense the presence of a human target for through-wall surveillance, post-earthquake search and rescue, etc. In these applications, a human target’s position and posture are not known a priori. Uncertainty of the two factors results in a body orientation issue of UWB radar, namely the human target’s thorax is not always facing the radar. Thus, the radial component of the thorax motion due to respiration decreases and the respiratory motion response contained in UWB radar echoes is too weak to be detected. To cope with the issue, this paper used multisensory information provided by the multistatic UWB radar, which took the form of impulse radios and comprised one transmitting and four separated receiving antennas. An adaptive Kalman filtering algorithm was then designed to fuse the UWB echo data from all the receiving channels to detect the respiratory-motion response contained in those data. In the experiment, a volunteer’s respiration was correctly detected when he curled upon a camp bed behind a brick wall. Under the same scenario, the volunteer’s respiration was detected based on the radar’s single transmitting-receiving channels without data fusion using conventional algorithm, such as adaptive line enhancer and single-channel Kalman filtering. Moreover, performance of the data fusion algorithm was experimentally investigated with different channel combinations and antenna deployments. The experimental results show that the body orientation issue for human respiration detection via UWB radar can be dealt well with the multistatic UWB radar and the Kalman-filter-based data fusion, which can be applied to improve performance of UWB radar in real applications.