title: FPGA accelerated parallel sparse matrix factorization for circuit simulations

abstract: Sparse matrix factorization is a critical step for the circuit simulation problem, since it is time consuming and computed repeatedly in the flow of circuit simulation. To accelerate the factorization of sparse matrices, a parallel CPU+FPGA based architecture is proposed in this paper. While the preprocessing of the matrix is implemented on CPU, the parallelism of numeric factorization is explored by processing several columns of the sparse matrix simultaneously on a set of processing elements (PE) in FPGA. To cater for the requirements of circuit simulation, we also modified the Gilbert/Peierls (G/P) algorithm and considered the scalability of our architecture. Experimental results on circuit matrices from the University of Florida Sparse Matrix Collection show that our architecture achieves speedup of 0.5x-5.36x compared with the CPU KLU results.


title: Cognitive-behavioural therapy v. social activity therapy for people with psychosis and a history of violence: randomised controlled trial

abstract: Background ::: ::: Aggression and violence are serious problems in schizophrenia. Cognitive–behavioural therapy (CBT) has been shown to be an effective treatment for psychosis although there have been no studies to date evaluating the impact of CBT for people with psychosis and a history of violence. ::: ::: Aims ::: ::: To investigate the effectiveness of CBT on violence, anger, psychosis and risk outcomes with people who had a diagnosis of schizophrenia and a history of violence. ::: ::: Method ::: ::: This was a single-blind randomised controlled trial of CBT v . social activity therapy (SAT) with a primary outcome of violence and secondary outcomes of anger, symptoms, functioning and risk. Outcomes were evaluated by masked assessors at 6 and 12 months (trial registration: NRR NO50087441). ::: ::: Results ::: ::: Significant benefits were shown for CBT compared with control over the intervention and follow-up period on violence, delusions and risk management. ::: ::: Conclusions ::: ::: Cognitive–behavioural therapy targeted at psychosis and anger may be an effective treatment for reducing the occurrence of violence and further investigation of its benefits is warranted.


The Relevance of Data Warehousing and Data Mining in the Field of Evidence-based Medicine to Support Healthcare Decision Making


title: ICCLP: An Inexact Chance-Constrained Linear Programming Model for Land-Use Management of Lake Areas in Urban Fringes

abstract: Lake areas in urban fringes are under increasing urbanization pressure. Consequently, the conflict between rapid urban development and the maintenance of water bodies in such areas urgently needs to be addressed. An inexact chance-constrained linear programming (ICCLP) model for optimal land-use management of lake areas in urban fringes was developed. The ICCLP model was based on land-use suitability assessment and land evaluation. The maximum net economic benefit (NEB) was selected as the objective of land-use allocation. The total environmental capacity (TEC) of water systems and the public financial investment (PFI) at different probability levels were considered key constraints. Other constraints included in the model were land-use suitability, governmental requirements on the ratios of various land-use types, and technical constraints. A case study implementing the system was performed for the lake area of Hanyang at the urban fringe of Wuhan, central China, based on our previous study on land-use suitability assessment. The Hanyang lake area is under significant urbanization pressure. A 15-year optimal model for land-use allocation is proposed during 2006 to 2020 to better protect the water system and to gain the maximum benefits of development. Sixteen constraints were set for the optimal model. The model results indicated that NEB was between $1.48 × 109 and $8.76 × 109 or between $3.98 × 109 and $16.7 × 109, depending on the different urban-expansion patterns and land demands. The changes in total developed area and the land-use structure were analyzed under different probabilities (q i ) of TEC. Changes in q i resulted in different urban expansion patterns and demands on land, which were the direct result of the constraints imposed by TEC and PFI. The ICCLP model might help local authorities better understand and address complex land-use systems and develop optimal land-use management strategies that better balance urban expansion and grassland conservation.


title: Joint Named Entity Recognition and Disambiguation

abstract: Extracting named entities in text and linking extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link extracted names to a knowledge base. NER and linking models are usually trained separately, and the mutual dependency between the two tasks is ignored. We propose JERL, Joint Entity Recognition and Linking, to jointly model NER and linking tasks and capture the mutual dependency between them. It allows the information from each task to improve the performance of the other. To the best of our knowledge, JERL is the first model to jointly optimize NER and linking tasks together completely. In experiments on the CoNLL’03/AIDA data set, JERL outperforms state-of-art NER and linking systems, and we find improvements of 0.4% absolute F1 for NER on CoNLL’03, and 0.36% absolute precision@1 for linking on AIDA.


title: Space Robotics: An Overview of Challenges, Applications and Technologies

abstract: While space exploration may be considered anything but dull, it certainly is very dangerous. Expanding our knowledge on the solar system to look for clues to such fundamental questions as the origins of life, or a sustained human presence on anything other than earth may well be worth the risk. The involved costs for mitigating the risk of human space flight are prohibitive. Robotic missions, like the hugely successful Mars Exploration Rovers, have shown that robotics as a sub-field of Artificial Intelligence can perform scientific exploration activities without human presence, and will play an even more prominent role in future mission scenarios. Worldwide technology research efforts are continuously expanding the capabilities of mobile robotic systems. This article provides an overview of the special conditions and examples of technological solutions for the development of space robots, as well as different fields of application.


title: A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

abstract: We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.


title: Biometric Template Protection: Bridging the performance gap between theory and practice

abstract: Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users? biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric ?template protection.


title: Group Anomaly Detection using Deep Generative Models

abstract: Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media, and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real-world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies.


title: Analyzing content-based publish/subscribe systems

abstract: Publish/subscribe communication is increasingly often the basis for distributed, event-based, and loosely-coupled applications. With this communication paradigm, application components communicate indirectly by publishing notifications and by subscribing to those notifications of interest. To precisely specify the subscribers' interests, content-based publish/subscribe systems provide expressive filter models that, on the one hand, ease implementing versatile and entangled interaction patterns. On the other hand, however, resulting notification flows evolve dynamically and implicitly making an analysis the more challenging and complex. In this paper, we focus on modeling the interrelationship between the notifications being advertised, produced, and requested by publishers and subscribers, respectively. The presented analytical models, in their general form, support various data and filter models to compute matching and forwarding probabilities, which provide the basis to derive message rates as well as more advanced performance metrics. To demonstrate their applicability, we instantiate the general models with a specific data and filter model based on a non-uniform, multi-dimensional distribution of published and subscribed notifications. Our results show the impact of the distributions' skewness and, thereby, prove the necessity to precisely model the content interrelationships in order to make reliable performance predictions.


title: Theory and Design of Class-J Power Amplifiers With Dynamic Load Modulation

abstract: A theory for class-J microwave amplifier operation as a function of drive level and fundamental load impedance is derived. Calculations show that, under appropriate operating conditions, it is sufficient to modulate the transistor load reactance to enable high-efficiency operation (>;70%) over a large output power dynamic range (>;10 dB) with high transistor power utilization. Such dynamic load modulation (DLM) networks are an ideal application of continuously tunable varactor technologies. Multiharmonic load-pull measurements are performed on a GaN HEMT and experimentally verify the theory of operation. A demonstrator amplifier using an SiC varactor technology is then designed and characterized by static measurements. The amplifier has a peak power of 38 dBm at 2.08 GHz and maintains efficiencies above 45% over 8 dB of power dynamic range. An analysis of the load network losses is performed to show the potential of the class-J DLM transmitter concept.


title: HydraNets: Specialized Dynamic Architectures for Efficient Inference

abstract: There is growing interest in improving the design of deep network architectures to be both accurate and low cost. This paper explores semantic specialization as a mechanism for improving the computational efficiency (accuracy-per-unit-cost) of inference in the context of image classification. Specifically, we propose a network architecture template called HydraNet, which enables state-of-the-art architectures for image classification to be transformed into dynamic architectures which exploit conditional execution for efficient inference. HydraNets are wide networks containing distinct components specialized to compute features for visually similar classes, but they retain efficiency by dynamically selecting only a small number of components to evaluate for any one input image. This design is made possible by a soft gating mechanism that encourages component specialization during training and accurately performs component selection during inference. We evaluate the HydraNet approach on both the CIFAR-100 and ImageNet classification tasks. On CIFAR, applying the HydraNet template to the ResNet and DenseNet family of models reduces inference cost by 2-4A— while retaining the accuracy of the baseline architectures. On ImageNet, applying the HydraNet template improves accuracy up to 2.5% when compared to an efficient baseline architecture with similar inference cost.


title: Real-time 3D segmentation of cluttered scenes for robot grasping

abstract: We present a real-time algorithm that segments unstructured and highly cluttered scenes. The algorithm robustly separates objects of unknown shape in congested scenes of stacked and partially occluded objects. The model-free approach finds smooth surface patches, using a depth image from a Kinect camera, which are subsequently combined to form highly probable object hypotheses. The real-time capabilities and the quality of the algorithm are evaluated on a benchmark database. Advantages compared to existing approaches as well as weaknesses are discussed. We also report on an autonomous grasping experiment with the Shadow Robot Hand which employs the estimated shape and pose of objects given by our algorithm in a task in which it cleans a table.


title: Using Echo State Networks for Cryptography

abstract: Echo state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate sequences of data. We make use of this property to realize a novel neural cryptography scheme. The key idea is to assume that Alice and Bob share a copy of an echo state network. If Alice trains her copy to memorize a message, she can communicate the trained part of the network to Bob who plugs it into his copy to regenerate the message. Considering a byte-level representation of in- and output, the technique applies to arbitrary types of data (texts, images, audio files, etc.) and practical experiments reveal it to satisfy the fundamental cryptographic properties of diffusion and confusion.


title: Febrl -: an open source data cleaning, deduplication and record linkage system with a graphical user interface

abstract: Matching records that refer to the same entity across data-bases is becoming an increasingly important part of many data mining projects, as often data from multiple sources needs to be matched in order to enrich data or improve its quality. Significant advances in record linkage techniques have been made in recent years. However, many new techniques are either implemented in research proof-of-concept systems only, or they are hidden within expensive 'black box' commercial software. This makes it difficult for both researchers and practitioners to experiment with new record linkage techniques, and to compare existing techniques with new ones. The Febrl (Freely Extensible Biomedical Record Linkage) system aims to fill this gap. It contains many recently developed techniques for data cleaning, deduplication and record linkage, and encapsulates them into a graphical user interface (GUI). Febrl thus allows even inexperienced users to learn and experiment with both traditional and new record linkage techniques. Because Febrl is written in Python and its source code is available, it is fairly easy to integrate new record linkage techniques into it. Therefore, Febrl can be seen as a tool that allows researchers to compare various existing record linkage techniques with their own ones, enabling the record linkage research community to conduct their work more efficiently. Additionally, Febrl is suitable as a training tool for new record linkage users, and it can also be used for practical linkage projects with data sets that contain up to several hundred thousand records.


title: A new improvement to the Powell and Chau linear phase IIR filters

abstract: An improvement to the realization of the linear-phase IIR filters is described. It is based on the rearrangement of the numerator polynomials of the IIR filter functions that are used in the real-time realizations proposed in literature. The new realization has better total harmonic distortion when a sine input is used, and it has smaller phase and group delay errors due to finite section length.


title: Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

abstract: Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.


title: Polynomial texture maps

abstract: In this paper we present a new form of texture mapping that produces increased photorealism. Coefficients of a biquadratic polynomial are stored per texel, and used to reconstruct the surface color under varying lighting conditions. Like bump mapping, this allows the perception of surface deformations. However, our method is image based, and photographs of a surface under varying lighting conditions can be used to construct these maps. Unlike bump maps, these Polynomial Texture Maps (PTMs) also capture variations due to surface self-shadowing and interreflections, which enhance realism. Surface colors can be efficiently reconstructed from polynomial coefficients and light directions with minimal fixed-point hardware. We have also found PTMs useful for producing a number of other effects such as anisotropic and Fresnel shading models and variable depth of focus. Lastly, we present several reflectance function transformations that act as contrast enhancement operators. We have found these particularly useful in the study of ancient archeological clay and stone writings.


Let Me Tell You a Story - On How to Build Process Models.


title: How Software Project Risk Affects Project Performance: An Investigation of the Dimensions of Risk and an Exploratory Model

abstract: To reduce the high failure rate of software projects, managers need better tools to assess and manage software project risk. In order to create such tools, however, information systems researchers must first develop a better understanding of the dimensions of software project risk and how they can affect project performance. Progress in this area has been hindered by: (1) a lack of validated instruments for measuring software project risk that tap into the dimensions of risk that are seen as important by software project managers, and (2) a lack of theory to explain the linkages between various dimensions of software project risk and project performance. In this study, six dimensions of software project risk were identified and reliable and valid measures were developed for each. Guided by sociotechnical systems theory, an exploratory model was developed and tested. The results show that social subsystem risk influences technical subsystem risk, which, in turn, influences the level of project management risk, and ultimately, project performance. The implications of these findings for research and practice are discussed.


title: Marked Temporal Dynamics Modeling based on Recurrent Neural Network

abstract: We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynamics, i.e., when the next event will take place and what its mark will be. Existing methods either predict only the mark or the time of the next event, or predict both of them, yet separately. Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them. To tackle this problem, in this paper, we propose to model marked temporal dynamics by using a mark-specific intensity function to explicitly capture the dependency between the mark and the time of the next event. Experiments on two datasets demonstrate that the proposed method outperforms the state-of-the-art methods at predicting marked temporal dynamics.


title: Algebraic Transformations of Objective Functions

abstract: Abstract Many neural networks can be derived as optimization dynamics for suitable objective functions. We show that such networks can be designed by repeated transformations of one objective into another with the same fixpoints. We exhibit a collection of algebraic transformations which reduce network cost and increase the set of objective functions that are neurally implementable. The transformations include simplification of products of expressions, functions of one or two expressions, and sparse matrix products (all of which may be interpreted as Legendre transformations); also the minimum and maximum of a set of expressions. These transformations introduce new interneurons which force the network to seek a saddle point rather than a minimum. Other transformations allow control of the network dynamics, by reconciling the Lagrangian formalism with the need for fixpoints. We apply the transformations to simplify a number of structured neural networks, beginning with the standard reduction of the winner-take-all network from ∂(N2) connections to ∂(N). Also susceptible are inexact graph-matching, random dot matching, convolutions and coordinate transformations, and sorting. Simulations show that fixpoint-preserving transformations may be applied repeatedly and elaborately, and the example networks still robustly converge.


title: Reusing ontologies on the Semantic Web : A feasibility study

abstract: Technologies for the efficient and effective reuse of ontological knowledge are one of the key success factors for the Semantic Web. Putting aside matters of cost or quality, being reusable is an intrinsic property of ontologies, originally conceived of as a means to enable and enhance the interoperability between computing applications. This article gives an account, based on empirical evidence and real-world findings, of the methodologies, methods and tools currently used to perform ontology-reuse processes. We study the most prominent case studies on ontology reuse, published in the knowledge-/ontology-engineering literature from the early nineties. This overview is complemented by two self-conducted case studies in the areas of eHealth and eRecruitment in which we developed Semantic Web ontologies for different scopes and purposes by resorting to existing ontological knowledge on the Web. Based on the analysis of the case studies, we are able to identify a series of research and development challenges which should be addressed to ensure reuse becomes a feasible alternative to other ontology-engineering strategies such as development from scratch. In particular, we emphasize the need for a context- and task-sensitive treatment of ontologies, both from an engineering and a usage perspective, and identify the typical phases of reuse processes which could profit considerably from such an approach. Further on, we argue for the need for ontology-reuse methodologies which optimally exploit human and computational intelligence to effectively operationalize reuse processes.


title: From natural language business requirements to executable models via SBVR

abstract: This paper presents a methodology for transforming business rules (BR) written in natural language (NL) such as English into a set of executable models as Unified Modeling Language (UML), Structured Query language (SQL), etc. As the direct automatic transformation of NL specifications to executable models is very difficult due to the inherent ambiguities of NL, this methodology aims at using the Semantics of Business Vocabulary and Business Rules (SBVR) as an intermediate model front-ended by Micro-Systemic Linguistic Analysis (MSLA) because of their mathematical underpinnings. SBVR is a Semantic Metamodel (SMM) introduced by the Object Management Group (OMG) for specifying semantic models of business using NL. SBVR is not only easy to process by machine since it is grounded in formal logic, but it is also easy to understand both by software developers and other stakeholders. Given that SBVR is fully integrated in OMG's Model Driven Architecture (MDA) and behaves as a Computational Independent Model (CIM), our approach advocates model transformation which is the key constituent of the MDA standard.


title: Real-time stereo matching for depth estimation using GPU

abstract: Real-time technology of stereo matching to generate depth map from stereo images is one of the important computer vision challenges in recent years. The depth map are used in various fields such as the robot binocular vision, 3D recognition system, 3D model reconstruction and so on. Because the initial depth estimation method cannot generate good results, most of the methods will accompany a refinement process afterwards. The aim of this paper is to improve the quality of initial depth map estimation without any depth refinement in real time via GPU. This paper proposes a real-time effective stereo matching using adaptive shape window on compute unified device architecture (CUDA). The proposed method estimate depth map by cost aggregation over shape-adaptive regions and generate the accurate initial depth map among the real-time methods by evaluating in Middlebury stereo benchmark.


title: DETECTING ERRORS AND IMPUTING MISSING DATA FOR SINGLE LOOP SURVEILLANCE SYSTEMS

abstract: Single loop detectors provide the most abundant source of traffic data in California, but loop data samples are often missing or invalid. We describe a method that detects bad data samples and imputes missing or bad samples to form a complete grid of clean data in real time. The diagnostics algorithm and the imputation algorithm that implement this method are operational on 14,871 loops in six Districts of the California Department of Transportation. The diagnostics algorithm detects bad (malfunctioning) single loop detectors from their volume and occupancy measurements. Its novelty is its use of time series of many samples, rather than basing decisions on single samples, as in previous approaches. The imputation algorithm models the relationship between neighboring loops as linear, and uses linear regression to estimate the value of missing or bad samples. This gives a better estimate than previous methods because it uses historical data to learn how pairs of neighboring loops behave. Detection of bad loops and imputation of loop data are important because they allow algorithms that use loop data to perform analysis without requiring them to compensate for missing or incorrect data samples


title: Wireless Sensor Networks for Environmental Monitoring: The SensorScope Experience

abstract: While wireless sensor networks have been extensively studied in the past few years, most results are of theoretical nature and were obtained outside of a practical context. This can be problematic for real applications, especially in the area of environmental monitoring where many factors, such as harsh weather conditions, can greatly influence the performance of such a network, while reliable delivery and high-quality measurements are required. SensorScope is an interdisciplinary project, elaborated by environmental and networking researchers, that aims at narrowing the gap between theory and practice. Several successful real-world deployments have already been undertaken in rugged environments. In this paper, we analyze the particular requirements of environmental monitoring and how these requirements have been met in the SensorScope project. We also present an application example of a deployment, undertaken in a harsh mountain environment.


title: Space shift keying modulation for MIMO channels

abstract: In this paper, we present space shift keying (SSK) as a new modulation scheme, which is based on spatial modulation (SM) concepts. Fading is exploited for multiple-input multiple-output(MIMO) channels to provide better performance over conventional amplitude/phase modulation (APM) techniques. In SSK, it is the antenna index used during transmission that relays information, rather than the transmitted symbols themselves. This absence of symbol information eliminates the transceiver elements necessary for APM transmission and detection (such as coherent detectors). As well, the simplicity involved in modulation reduces the detection complexity compared to that of SM, while achieving almost identical performance gains. Throughout the paper, we illustrate SSK's strength by studying its interaction with the fading channel. We obtain tight upper bounds on bit error probability, and discuss SSK's performance under some non-ideal channel conditions (estimation error and spatial correlation). Analytical and simulation results show performance gains over APM systems (3 dB at a bit error rate of 10-5), making SSK an interesting candidate for future wireless applications. We then extend SSK concepts to incorporate channel coding, where in particular, we consider a bit interleaved coded modulation (BICM) system using iterative decoding for both convolutional and turbo codes. Capacity results are derived, and improvements over APM are illustrated (up to 1 bits/s/Hz), with performance gains of up to 5 dB.


title: Clutchable series-elastic actuator: Implications for prosthetic knee design

abstract: Currently, the mobility of above-knee amputees is limited by the lack of available prostheses that can efficiently replicate biologically accurate movements. In this study, a powered knee prosthesis was designed utilizing a novel mechanism, known as a clutchable series-elastic actuator (CSEA).The CSEA includes a low-power clutch in parallel with an electric motor within a traditional series-elastic actuator. The stiffness of the series elasticity was tuned to match the elastically conservative region of the knee's torque-angle relationship during the stance phase of locomotion. During this region, the clutch was used to efficiently store energy in the series elasticity. The fully autonomous knee prosthesis design utilized a brushless electric motor, ballscrew transmission and cable drive, as well as commercial electrical components. The knee was lighter than the eighth percentile and shorter than the first percentile male shank segment. The CSEA Knee was tested in a unilateral above-knee amputee walking at 1.3 m/s. During walking, the CSEA Knee provided biomechanically accurate torque-angle behavior, agreeing within 17% of the net work and 27% of the stance flexion angle produced by the biological knee. In addition, the process of locomotion reduced the net electrical energy consumption of the CSEA Knee. The knee's motor generated 1.8 J/stride, and the net energy consumption was 3.6 J/stride, an order of magnitude less energy than previously published powered knee prostheses.


title: 5G-5 Dual-Annular-Ring CMUT Array for ForwardLooking IVUS Imaging

abstract: We investigate a dual-annular-ring CMUT array configuration for forward-looking intravascular ultrasound (FL-IVUS) imaging. The array consists of separate, concentric transmit and receive ring arrays built on the same silicon substrate. This configuration has the potential for independent optimization of each array and uses the silicon area more effectively without any particular drawback. We designed and fabricated a 1 mm diameter test array which consists of 24 transmit and 32 receive elements. We investigated synthetic phased array beamforming with a non-redundant subset of transmit-receive element pairs of the dual-annular-ring array. For imaging experiments, we designed and constructed a programmable FPGA-based data acquisition and phased array beamforming system. Pulse-echo measurements along with imaging simulations suggest that dual-ring-annular array should provide performance suitable for real-time FL-IVUS applications


title: Deep Coupled Metric Learning for Cross-Modal Matching

abstract: In this paper, we propose a new deep coupled metric learning (DCML) method for cross-modal matching, which aims to match samples captured from two different modalities (e.g., texts versus images, visible versus near infrared images). Unlike existing cross-modal matching methods which learn a linear common space to reduce the modality gap, our DCML designs two feedforward neural networks which learn two sets of hierarchical nonlinear transformations (one set for each modality) to nonlinearly map samples from different modalities into a shared latent feature subspace, under which the intraclass variation is minimized and the interclass variation is maximized, and the difference of each data pair captured from two modalities of the same class is minimized, respectively. Experimental results on four different cross-modal matching datasets validate the efficacy of the proposed approach.


title: Risk analysis of Thalassemia using knowledge representation model: Diagnostic Bayesian Networks

abstract: Bayesian Networks (BNs) is one of the most effective theoretical models applied to make medical diagnostic decisions. In particular, it has been applied to Thalassemia, which is one of the most common genetic disorders in the world. The main problems of diagnosing this disease are the complex processes for diagnosing the several types of Thalassemia which occur in Thailand. Moreover, diagnostic methods are slow and rely on expert knowledge and experience as well as expensive equipment. The advantage of BNs is that they are used to represent the diagnostic domain in the form of graphical statistical models. The propose of this paper is to construct a Diagnostic Bayesian Networks for risk analysis of Thalassemia using polychromatic model for screening each type of Thalassemia, including related variables. The model will be used to elicit and calculate the probabilities of each type of Thalassemia in future research.


title: Reflection Removal Using Low-Rank Matrix Completion

abstract: The images taken through glass often capture a target transmitted scene as well as undesired reflected scenes. In this paper, we propose a low-rank matrix completion algorithm to remove reflection artifacts automatically from multiple glass images taken at slightly different camera locations. We assume that the transmitted scenes are more dominant than the reflected scenes in typical glass images. We first warp the multiple glass images to a reference image, where the gradients are consistent in the transmission images while the gradients are varying across the reflection images. Based on this observation, we compute a gradient reliability such that the pixels belonging to the salient edges of the transmission image are assigned high reliability. Then we suppress the gradients of the reflection images and recover the gradients of the transmission images only, by solving a low-rank matrix completion problem in gradient domain. We reconstruct an original transmission image using the resulting optimal gradient map. Experimental results show that the proposed algorithm removes the reflection artifacts from glass images faithfully and outperforms the existing algorithms on typical glass images.


title: GPR phase-based techniques for profiling rough surfaces and detecting small, low-contrast landmines under flat ground

abstract: We present a new technique whereby phase variation signatures are used to profile two-dimensional (2-D) rough surfaces and to discern shallowly buried, small, low-contrast landmines under a flat ground. The method has been tested using data measured over a composite surface containing two rough dielectric surface patches, and over a flat ground under which small, low-contrast antipersonnel landmines are shallowly buried. The results show that the phase-based technique is capable of profiling rough surfaces and of detecting small, low-contrast landmines with different internal structures buried underneath a flat ground.


title: Phishing URL Detection Using URL Ranking

abstract: The openness of the Web exposes opportunities for criminals to upload malicious content. In fact, despite extensive research, email based spam filtering techniques are unable to protect other web services. Therefore, a counter measure must be taken that generalizes across web services to protect the user from phishing host URLs. This paper describes an approach that classifies URLs automatically based on their lexical and host-based features. Clustering is performed on the entire dataset and a cluster ID (or label) is derived for each URL, which in turn is used as a predictive feature by the classification system. Online URL reputation services are used in order to categorize URLs and the categories returned are used as a supplemental source of information that would enable the system to rank URLs. The classifier achieves 93-98% accuracy by detecting a large number of phishing hosts, while maintaining a modest false positive rate. URL clustering, URL classification, and URL categorization mechanisms work in conjunction to give URLs a rank.


title: Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands

abstract: Recently Dutch government, as well as many other governments around the world, has digitized a major portion of its public services. With this development electronic services finally arrive at the transaction level. The risks of electronic services on the transactional level are more profound than at the informational level. The public needs to trust the integrity and 'information management capacities' of the government or other involved organizations, as well as trust the infrastructure and those managing the infrastructure. In this process, the individual citizen will have to decide to adopt the new electronic government services by weighing its benefits and risks. In this paper, we present a study which aims to identify the role of risk perception and trust in the intention to adopt government e-services. In January 2003, a sample of 238 persons completed a questionnaire. The questionnaire tapped people's intention to adopt e-government electronic services. Based on previous research and theories on technology acceptance, the questionnaire measured perceived usefulness of e-services, risk perception, worry, perceived behavioural control, subjective norm, trust and experience with e-services. Structural equation modelling was used to further analyze the data (Amos) and to design a theoretical model predicting the individual's intention to adopt e-services. This analysis showed that the perceived usefulness of electronic services in general is the main determinant of the intention to use e-government services. Risk perception, personal experience, perceived behavioural control and subjective norm were found to significantly predict the perceived usefulness of electronic services in general, while trust in e-government was the main determinant of the perceived usefulness of e-government services.


title: Security awareness of computer users: A phishing threat avoidance perspective

abstract: Phishing is an online identity theft, which aims to steal confidential information such as username, password and online banking details from its victims. To prevent this, anti-phishing education needs to be considered. Therefore, the research reported in this paper examines whether conceptual knowledge or procedural knowledge has a positive effect on computer users' self-efficacy to thwart phishing threats. In order to accomplish this, a theoretical model based on Liang and Xue's (2010) Technology Threat Avoidance Theory (TTAT) has been proposed and evaluated. Data was collected from 161 regular computer users to elicit their feedback through an online questionnaire. The study findings revealed that the interaction effect of conceptual and procedural knowledge positively impacts on computer users' self-efficacy, which enhances their phishing threat avoidance behaviour. It can therefore be argued that well-designed end-user security education contributes to thwart phishing threats.


title: Low-Voltage Super class AB CMOS OTA cells with very high slew rate and power efficiency

abstract: A simple technique to achieve low-voltage power-efficient class AB operational transconductance amplifiers (OTAs) is presented. It is based on the combination of class AB differential input stages and local common-mode feedback (LCMFB) which provides additional dynamic current boosting, increased gain-bandwidth product (GBW), and near-optimal current efficiency. LCMFB is applied to various class AB differential input stages, leading to different class AB OTA topologies. Three OTA realizations based on this technique have been fabricated in a 0.5-/spl mu/m CMOS technology. For an 80-pF load they show enhancement factors of slew rate and GBW of up to 280 and 3.6, respectively, compared to a conventional class A OTA with the same 10-/spl mu/A quiescent currents and /spl plusmn/1-V supply voltages. In addition, the overhead in terms of common-mode input range, output swing, silicon area, noise, and static power consumption, is minimal.


title: A Novel Single-Inductor Dual-Input Dual-Output DC–DC Converter With PWM Control for Solar Energy Harvesting System

abstract: A novel single-inductor dual-input dual-output dc-dc converter with pulse width modulation control is proposed for a solar energy harvesting system. The first input of the converter is from photovoltaic (PV) cells and the second input is a rechargeable battery. Apart from the conventional role of providing a regulated output voltage to power the loading circuits, the converter also clamps the PV cells' voltage to the maximum power point value to maximize efficiency. When the PV cells harvest more power than the load, the surplus energy is used to charge the rechargeable battery. When the PV cells cannot harvest sufficient power, the converter schedules the PV cells and the battery to power the load together. A test chip was fabricated using a 0.35-μm CMOS process and measured to verify the operation of the proposed dc-dc converter and to demonstrate the power transfer efficiency of the solar power management system.


title: High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

abstract: Fast computation of singular value decomposition (SVD) is of great interest in various machine learning tasks. Recently, SVD methods based on randomized linear algebra have shown significant speedup in this regime. This paper attempts to further accelerate the computation by harnessing a modern computing architecture, namely graphics processing unit (GPU), with the goal of processing large-scale data that may not fit in the GPU memory. It leads to a new block randomized algorithm that fully utilizes the power of GPUs and efficiently processes large-scale data in an out-of- core fashion. Our experiment shows that the proposed block randomized SVD (BRSVD) method outperforms existing randomized SVD methods in terms of speed with retaining the same accuracy. We also show its application to convex robust principal component analysis, which shows significant speedup in computer vision applications.


title: Credit Card Fraud Detection Using Neural Networks

abstract: Today, people are more used to online shopping and payment due to which frauds related to online payment is on the rise. The main modes of online payment are credits cards, debit cards, net banking. The fraud related to credit cards is increasing nowadays because people prefer using them as an easy mode of payment. In this paper, we have implemented a mechanism to detect credit card fraud using neural networks using Neuroph IDE. This paper presents the various parameters that were considered while training and testing the neural network. Also, we have presented the results and conclusions of our experiment.


title: Alternative Seating for Young Children with Autism Spectrum Disorder: Effects on Classroom Behavior

abstract: A single subject, withdrawal design was used to investigate the effects of therapy balls as seating on engagement and in-seat behavior of young children with Autism Spectrum Disorder (ASD). In addition, social validity was assessed to evaluate teachers' opinions regarding the intervention. During baseline and withdrawal (A phases) participants used their typical classroom seating device (chair, bench or carpet square). During the intervention (B phases) participants sat on therapy balls. Results indicated substantial improvements in engagement and in-seat behavior when participants were seated on therapy balls. Social validity findings indicated that the teachers' preferred the therapy balls. This study suggests therapy balls as classroom seating may facilitate engagement and in-seat behavior and create opportunities to provide effective instruction.


title: A virtual shared metadata storage for HDFS

abstract: Hadoop is a popular open-source framework that allows distributed analysis of large datasets using the MapReduce programming model. A distributed file system HDFS is implemented to provide high-throughput access to datasets. HDFS can achieve high performance metadata service but has two disadvantages. First, when the metadata server stores metadata on persistent devices, it is restricted to read and write operations of local disks. Second, it also lacks effective methods for metadata synchronization and replication, which is critical for metadata availability and reliability. In this research, we introduce a novel Virtual Shared Storage Pool (VSSP) concept and design for storing and sharing metadata in HDFS. The VSSP is a virtual storage device which is built on existing servers and transparent to upper layers. Two strategies, a journal synchronization based on the 2PC protocol and a fine-grained image replication, are introduced in the VSSP according to different metadata access features. The VSSP not only reduces the overhead on metadata modification operations, but also improves the I/O performance for namespace storage. Experimental results show that the VSSP improved the average performance by 40.51% and 23.46% when writing logs compared with the BookKeeper and Hadoop QJM. The average image read and write throughput was nearly 5 times and 2.4 times better than NFS and the original approach. These results confirm that the proposed VSSP solution significantly improves the metadata access performance, scalability, and reliability for HDFS.


title: Cognitive skill acquisition

abstract: ▪ Abstract Cognitive skill acquisition is acquiring the ability to solve problems in intellectual tasks, where success is determined more by subjects' knowledge than by their physical prowess. This review considers research conducted in the past ten years on cognitive skill acquisition. It covers the initial stages of acquiring a single principle or rule, the initial stages of acquiring a collection of interacting pieces of knowledge, and the final stages of acquiring a skill, wherein practice causes increases in speed and accuracy.


title: Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

abstract: The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the “gas of near circles” active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts.


title: A tale of two clouds: Computing on data encrypted under multiple keys

abstract: Cloud computing provides a convenient platform for big data computation such as machine learning and data mining. However, privacy conscious users often encrypt their data with their own keys before uploading them to the cloud. Existing techniques for computation on encrypted data are either in the single key setting or far from practical. In this paper, we show how two non-colluding servers can leverage proxy re-encryption to jointly compute arithmetic functions over the ciphertexts of multiple users without learning the inputs, intermediate or final results. Moreover, the computation is non-interactive to users and only requires minimal server-to-server interactions. Experimental results demonstrate that our schemes significantly improve the efficiency of outsourced computation when compared to the existing approach.


title: Designing an educational game: case study of 'Europe 2045'

abstract: This paper presents a theoretical framework, which has been adopted in designing an on-line multi-player strategy game Europe 2045. Europe 2045 is an educational tool for high school social science courses, aimed at familiarizing students with political, economic, and social issues in contemporary Europe. Apart from learning facts, players develop a range of key skills: discussion ability, negotiation, teamwork, and group decision-making. The presented theoretical framework is based on a critical analysis of crucial issues, which seem to determine the success or failure of development and implementation of an educational game in the formal school environment. It demonstrates key approaches the authors of Europe 2045 have adopted in order to overcome already known problems related to game-based learning. On a general level this paper discusses issues related to formal fact learning in educational systems and the possible role of educational games in enhancing these systems.


title: Deployment of Cloud Computing into VANET to Create Ad Hoc Cloud Network Architecture

abstract: Vehicular Ad Hoc Network (VANET) has been drawing interest among the researchers for the past couple of years. Though ad hoc network or mobile ad hoc network is very common in military environment, the real world practice of ad hoc network is still very low. On the other hand, cloud computing is supposed to be the next big thing because of its scalability, PaaS, IaaS, SaaS and other important characteristics. In this paper we have tried to propose a model of ad hoc cloud network architecture. We have specially focused on vehicular ad hoc network architecture or VANET which will enable us to create a "cloud on the run" model. The major parts of this proposed model are wireless devices mounted on vehicles which will act as a mobile multihop network and a public or private cloud created by the vehicles called vehicular cloud.


title: A Riemannian Network for SPD Matrix Learning

abstract: Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds. In this paper we build a Riemannian network architecture to open up a new direction of SPD matrix non-linear learning in a deep model. In particular, we devise bilinear mapping layers to transform input SPD matrices to more desirable SPD matrices, exploit eigenvalue rectification layers to apply a non-linear activation function to the new SPD matrices, and design an eigenvalue logarithm layer to perform Riemannian computing on the resulting SPD matrices for regular output layers. For training the proposed deep network, we exploit a new backpropagation with a variant of stochastic gradient descent on Stiefel manifolds to update the structured connection weights and the involved SPD matrix data. We show through experiments that the proposed SPD matrix network can be simply trained and outperform existing SPD matrix learning and state-of-the-art methods in three typical visual classification tasks.


title: A rational analysis of rule-based concept learning

abstract: This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts—a more natural setting in several ways—and again finds that the model explains human performance.


title: Position-based impedance control of an industrial hydraulic manipulator

abstract: This paper addresses the problem of impedance control in hydraulic robots. Whereas most impedance and hybrid force/position control formulations have focussed on electrically-driven robots with controllable actuator torques, torque control of hydraulic actuators is a difficult task. A position-based impedance controller is proposed and demonstrated on an existing industrial hydraulic robot (a Unimate MKII-2000). A nonlinear proportional-integral (NPI) controller is first developed to meet the accurate positioning requirements of this impedance control formulation. The NPI controller is shown to make the manipulator match a range of second-order target impedances. Various experiments in free space and in environmental contact, including a simple impedance modulation experiment, demonstrate the feasibility and the promise of the technique.


title: A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH)

abstract: Wireless sensor networks with thousands of tiny sensor nodes are expected to find wide applicability and increasing deployment in coming years, as they enable reliable monitoring and analysis of the environment. In this paper we propose a modification to a well-known protocol for sensor networks called Low Energy Adaptive Clustering Hierarchy (LEACH). This last is designed for sensor networks where end- user wants to remotely monitor the environment. In such situation, the data from the individual nodes must be sent to a central base station, often located far from the sensor network, through which the end-user can access the data. In this context our contribution is represented by building a two-level hierarchy to realize a protocol that saves better the energy consumption. Our TL-LEACH uses random rotation of local cluster base stations (primary cluster-heads and secondary cluster-heads). In this way we build, where it is possible, a two-level hierarchy. This permits to better distribute the energy load among the sensors in the network especially when the density of network is higher. TL- LEACH uses localized coordination to enable scalability and robustness. We evaluated the performances of our protocol with NS-2 and we observed that our protocol outperforms the LEACH in terms of energy consumption and lifetime of the network.


title: Robust Iris Segmentation under Unconstrained Settings

abstract: The rising challenges in the field of iris recognition, concerning the development of accurate recognition algorithms using images acquired under an unconstrained set of conditions, is leading to the a renewed interest in the area. Although several works already report excellent recognition rates, these values are obtained by acquiring images in very controlled environments. The use of such systems in daily security activities, such as airport security and bank account management, is therefore hindered by the inherent unconstrained nature under which images are to be acquired. The proposed work focused on mutual context information from iris centre and iris limbic contour to perform robust and accurate iris segmentation in noisy images. A random subset of the UBIRIS.v2 database was tested with a promising E classification rate of 0.0109.


title: How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics

abstract: How brain structures and neuronal circuits mechanistically underpin symbolic meaning has recently been elucidated by neuroimaging, neuropsychological, and neurocomputational research. Modality-specific ‘embodied' mechanisms anchored in sensorimotor systems appear to be relevant, as are ‘disembodied' mechanisms in multimodal areas. In this paper, four semantic mechanisms are proposed and spelt out at the level of neuronal circuits: referential semantics, which establishes links between symbols and the objects and actions they are used to speak about; combinatorial semantics, which enables the learning of symbolic meaning from context; emotional-affective semantics, which establishes links between signs and internal states of the body; and abstraction mechanisms for generalizing over a range of instances of semantic meaning. Referential, combinatorial, emotional-affective, and abstract semantics are complementary mechanisms, each necessary for processing meaning in mind and brain.


title: Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers

abstract: With the integration of data and information obtained from a variety of chemical and electrical tests on transformer insulating oil, it is possible to evaluate the health condition of the insulation system of an in-service power transformer. This paper develops an intelligent algorithm for automatically processing the data collected from oil tests and determining a health index for the transformer insulation system. This intelligent algorithm adopts a fuzzy support vector machine (FSVM) approach, which constructs a statistical model using a training database based on the historic data collected from 181 in-service power transformers. The procedure of constructing the training database, the formulation and implementation of FSVM and the data preprocessing methods for dealing with a class imbalanced training database is presented in this paper. Numerical experiments are also conducted to evaluate the performance of the algorithms developed in the paper.


Language Oriented Programming: The Next Programming Paradigm


title: Towards a neural basis of auditory sentence processing

abstract: Abstract Functional dissociations within the neural basis of auditory sentence processing are difficult to specify because phonological, syntactic and semantic information are all involved when sentences are perceived. In this review I argue that sentence processing is supported by a temporo–frontal network. Within this network, temporal regions subserve aspects of identification and frontal regions the building of syntactic and semantic relations. Temporal analyses of brain activation within this network support syntax-first models because they reveal that building of syntactic structure precedes semantic processes and that these interact only during a later stage.


title: Single-chip CMUT-on-CMOS front-end system for real-time volumetric IVUS and ICE imaging

abstract: Intravascular ultrasound (IVUS) and intracardiac echography (ICE) catheters with real-time volumetric ultrasound imaging capability can provide unique benefits to many interventional procedures used in the diagnosis and treatment of coronary and structural heart diseases. Integration of capacitive micromachined ultrasonic transducer (CMUT) arrays with front-end electronics in single-chip configuration allows for implementation of such catheter probes with reduced interconnect complexity, miniaturization, and high mechanical flexibility. We implemented a single-chip forward-looking (FL) ultrasound imaging system by fabricating a 1.4-mm-diameter dual-ring CMUT array using CMUT-on-CMOS technology on a front-end IC implemented in 0.35-μm CMOS process. The dual-ring array has 56 transmit elements and 48 receive elements on two separate concentric annular rings. The IC incorporates a 25-V pulser for each transmitter and a low-noise capacitive transimpedance amplifier (TIA) for each receiver, along with digital control and smart power management. The final shape of the silicon chip is a 1.5-mm-diameter donut with a 430-μm center hole for a guide wire. The overall front-end system requires only 13 external connections and provides 4 parallel RF outputs while consuming an average power of 20 mW. We measured RF A-scans from the integrated single- chip array which show full functionality at 20.1 MHz with 43% fractional bandwidth. We also tested and demonstrated the image quality of the system on a wire phantom and an ex vivo chicken heart sample. The measured axial and lateral point resolutions are 92 μm and 251 μm, respectively. We successfully acquired volumetric imaging data from the ex vivo chicken heart at 60 frames per second without any signal averaging. These demonstrative results indicate that single-chip CMUT-on-CMOS systems have the potential to produce realtime volumetric images with image quality and speed suitable for catheter-based clinical applications.


title: Tensorial Recurrent Neural Networks for Longitudinal Data Analysis

abstract: Traditional Recurrent Neural Networks assume vectorized data as inputs. However many data from modern science and technology come in certain structures such as tensorial time series data. To apply the recurrent neural networks for this type of data, a vectorisation process is necessary, while such a vectorisation leads to the loss of the precise information of the spatial or longitudinal dimensions. In addition, such a vectorized data is not an optimum solution for learning the representation of the longitudinal data. In this paper, we propose a new variant of tensorial neural networks which directly take tensorial time series data as inputs. We call this new variant as Tensorial Recurrent Neural Network (TRNN). The proposed TRNN is based on tensor Tucker decomposition.


title: Combining Naive Bayes and n-Gram Language Models for Text Classification

abstract: We augment the naive Bayes model with an n-gram language model to address two shortcomings of naive Bayes text classifiers. The chain augmented naive Bayes classifiers we propose have two advantages over standard naive Bayes classifiers. First, a chain augmented naive Bayes model relaxes some of the independence assumptions of naive Bayes--allowing a local Markov chain dependence in the observed variables--while still permitting efficient inference and learning. Second, smoothing techniques from statistical language modeling can be used to recover better estimates than the Laplace smoothing techniques usually used in naive Bayes classification. Our experimental results on three real world data sets show that we achieve substantial improvements over standard naive Bayes classification, while also achieving state of the art performance that competes with the best known methods in these cases.


1 TOWARDS OPTIMAL DESIGN OF SHIP HULL SHAPES


title: Dynamic Selection of Virtual Machines for Application Servers in Cloud Environments

abstract: Autoscaling is a hallmark of cloud computing as it allows flexible just-in-time allocation and release of computational resources in response to dynamic and often unpredictable workloads. This is especially important for web applications, whose workload is time dependent and prone to flash crowds. Most of them follow the 3-tier architectural pattern, and are divided into presentation, application/domain and data layers. In this work, we focus on the application layer. Reactive autoscaling policies of the type “Instantiate a new Virtual Machine (VM) when the average server CPU utilisation reaches X%” have been used successfully since the dawn of cloud computing. But which VM type is the most suitable for the specific application at the moment remains an open question. In this work, we propose an approach for dynamic VM type selection. It uses a combination of online machine learning techniques, works in real time and adapts to changes in the users’ workload patterns, application changes as well as middleware upgrades and reconfigurations. We have developed a prototype, which we tested with the CloudStone benchmark deployed on AWS EC2. Results show that our method quickly adapts to workload changes and reduces the total cost compared to the industry standard approach.


title: Non-orthogonal Multiple Access (NOMA) with Successive Interference Cancellation for Future Radio Access

abstract: SUMMARY This paper presents our investigation of non-orthogonal multiple access (NOMA) as a novel and promising power-domain user multiplexing scheme for future radio access. Based on information theory, we can expect that NOMA with a successive interference canceller (SIC) applied to the receiver side will offer a better tradeoff between system efficiency and user fairness than orthogonal multiple access (OMA), which is widely used in 3.9 and 4G mobile communication systems. This improvement becomes especially significant when the channel conditions among the non-orthogonally multiplexed users are significantly different. Thus, NOMA can be expected to efficiently exploit the near-far effect experienced in cellular environments. In this paper, we describe the basic principle of NOMA in both the downlink and uplink and then present our proposed NOMA scheme for the scenario where the base station is equipped with multiple antennas. Simulation results show the potential system-level


title: Visually-guided irrigation in patients with early knee osteoarthritis: a multicenter randomized, controlled trial

abstract: Summary Objective: To determine if visually-guided arthroscopic irrigation is an effective therapeutic intervention in patients with early knee osteoarthritis. Design: Ninety patients with knee osteoarthritis were randomized in a double-blind fashion to receive either arthroscopic irrigation with 3000 ml of saline (treatment group) or the minimal amount of irrigation (250 ml) required to perform arthroscopy (placebo group). The primary outcome variable was aggregate WOMAC score. Results: The study did not demonstrate an effect of irrigation on arthritis severity as measured by aggregate WOMAC scores, the primary outcome variable; the mean change in aggregate WOMAC score at 12 months was 15.5 (95% CI 7.7, 23.4) for the full irrigation group compared to 8.9 (95% CI 4.9, 13.0) for the minimal irrigation group (P=0.10). Full irrigation did have a statistically significant effect on patients’ self-reported pain as measured by the WOMAC pain subscale and by a visual analog scale (VAS) (the secondary outcome variables). Mean change in WOMAC pain scores decreased by 4.2 (95% CI �0.9, 9.4) for the full irrigation group compared with a mean decrease of 2.3 (95% CI �0.1, 4.7) in the minimal irrigation group (P=0.04). Mean VAS pain scores decreased by 1.47 (95% CI �1.2, 4.1) in the full irrigation group compared to a mean decrease of 0.12 (95% CI 0.0, 0.3) in the minimal irrigation group (P=0.02). A hypothesis-generating post-hoc analysis of the effect of positively birefrigent intraarticular crystals showed that patients with and without intraarticular crystals had statistically significant improvements in pain assessments and aggregate WOMAC scores at 12 months; patients with crystals had statistically greater improvements in pain.


AFFECT IN TEXT AND SPEECH


title: Modulating peroxisome proliferator–activated receptors for therapeutic benefit? Biology, clinical experience, and future prospects

abstract: Clinical trials of cardiovascular disease (CVD) prevention in patients with type 2 diabetes mellitus primarily have been directed at the modification of a single major risk factor; however, in trials that enroll patients with and without diabetes, the absolute risk in CVD events remains higher in patients with diabetes. Efforts to reduce the macrovascular and microvascular residual risk have been directed toward a multifactorial CVD risk-factor modification; nonetheless, long-term complications remain high. Dual-peroxisome proliferator–activated receptor (PPAR) α/γ agonists may offer opportunities to lower macrovascular and microvascular complications of type 2 diabetes mellitus beyond the reductions achieved with conventional risk-factor modification. The information presented elucidates the differentiation of compound-specific vs class-effect properties of PPARs as the basis for future development of a new candidate molecule. Prior experience with thiazolidinediones, an approved class of PPARγ agonists, and glitazars, investigational class of dual-PPARα/γ agonists, also provides important lessons about the risks and benefits of targeting a nuclear receptor while revealing some of the future challenges for regulatory approval.


Classification of Abnormalities in Brain MRI Images Using PCA and SVM


Piaget’s Constructivism, Papert’s Constructionism: What’s the difference?


title: Squibs: What Is a Paraphrase?

abstract: Paraphrases are sentences or phrases that convey the same meaning using different wording. Although the logical definition of paraphrases requires strict semantic equivalence, linguistics accepts a broader, approximate, equivalence—thereby allowing far more examples of “quasi-paraphrase.” But approximate equivalence is hard to define. Thus, the phenomenon of paraphrases, as understood in linguistics, is difficult to characterize. In this article, we list a set of 25 operations that generate quasi-paraphrases. We then empirically validate the scope and accuracy of this list by manually analyzing random samples of two publicly available paraphrase corpora. We provide the distribution of naturally occurring quasi-paraphrases in English text.


title: SAR complex image data compression based on quadtree and zerotree Coding in Discrete Wavelet Transform Domain: A Comparative Study

abstract: Abstract A SAR complex image data compression algorithm based on quadtree coding (QC) in discrete wavelet transform (DWT) domain (QC-DWT) is proposed. We show that QC-DWT achieves the best performance for SAR complex image compression. Besides this, in this work, we observed a novel phenomenon that QC-DWT outperforms the zerotree based wavelet coding algorithms, e.g., Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC) and Set Partitioning in Hierarchical Trees algorithm (SPIHT) for SAR complex image data, and there exists deficiency of CCSDS-IDC for SAR complex image data compression. This is because the DWT coefficients of SAR complex image data always have intrascale clustering characteristic and no interscale attenuation characteristic, which is different from that of SAR amplitude images and other optical images.


title: An open virtual testbed for industrial control system security research

abstract: Industrial control system security has been a topic of scrutiny and research for several years, and many security issues are well known. However, research efforts are impeded by a lack of an open virtual industrial control system testbed for security research. This paper describes a virtual testbed framework using Python to create discrete testbed components including virtual devices and process simulators. The virtual testbed is designed such that the testbeds are inter-operable with real industrial control system devices and such that the virtual testbeds can provide comparable industrial control system network behavior to a laboratory testbed. Two virtual testbeds modeled upon actual laboratory testbeds have been developed and have been shown to be inter-operable with real industrial control system equipment and vulnerable to attacks in the same manner as a real system. Additionally, these testbeds have been quantitatively shown to produce traffic close to laboratory systems.


title: Passphone: Outsourcing Phone-Based Web Authentication While Protecting User Privacy

abstract: This work introduces Passphone, a new smartphone-based authentication scheme that outsources user verification to a trusted third party without sacrificing privacy: neither can the trusted third party learn the relation between users and service providers, nor can service providers learn those of their users to others. When employed as a second factor in conjunction with, for instance, passwords as a first factor, our scheme maximizes the deployability of two-factor authentication for service providers while maintaining user privacy. We conduct a twofold formal analysis of our scheme, the first regarding its general security, and the second regarding anonymity and unlinkability of its users. Moreover, we provide an automatic analysis using AVISPA, a comparative evaluation to existing schemes under Bonneau et al.’s framework, and an evaluation of a prototypical implementation.


title: Waveform Design for MIMO Radars

abstract: We consider the problem of waveform design for multiple input/multiple output (MIMO) radars, where the transmit waveforms are adjusted based on target and clutter statistics. A model for the radar returns which incorporates the transmit waveforms is developed. The target detection problem is formulated for that model. Optimal and suboptimal algorithms are derived for designing the transmit waveforms under different assumptions regarding the statistical information available to the detector. The performance of these algorithms is illustrated by computer simulation.


title: Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention

abstract: Improvement of the quality and efficiency of healthcare in medicine, both at home and in hospital, is becoming more and more important for patients and society at large. As many technologies (micro technologies, telecommunication, low-power design, new textiles, and flexible sensors) are now available, new user-friendly devices can be developed to enhance the comfort and security of the patient. As clothes and textiles are in direct contact with about 90% of the skin surface, smart sensors and smart clothes with noninvasive sensors are an attractive solution for home-based and ambulatory health monitoring. Moreover, wearable devices or smart homes with exosensors are also potential solutions. All these systems can provide a safe and comfortable environment for home healthcare, illness prevention, and citizen medicine.


title: Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches

abstract: Face Aging has raised considerable attentions and interest from the computer vision community in recent years. Numerous approaches ranging from purely image processing techniques to deep learning structures have been proposed in literature. In this paper, we aim to give a review of recent developments of modern deep learning based approaches, i.e. Deep Generative Models, for Face Aging task. Their structures, formulation, learning algorithms as well as synthesized results are also provided with systematic discussions. Moreover, the aging databases used in most methods to learn the aging process are also reviewed.


title: Neural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems

abstract: User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Cross-model evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.


title: Humanoid robosoccer goal detection using hough transform

abstract: Goal posts detection is a critical robot soccer ability which is needed to be accurate, robust and efficient. A goal detection method using Hough transform to get the detailed goal features is presented in this paper. In the beginning, the image preprocessing and Hough transform implementation are described in detail. A new modification on the θ parameter range in Hough transform is explained and applied to speed up the detection process. Line processing algorithm is used to classify the line detected, and then the goal feature extraction method, including the line intersection calculation, is done. Finally, the goal distance from the robot body is estimated using triangle similarity. The experiment is performed on our university humanoid robot with the goal dimension of 225 cm in width and 110 cm in height, in yellow color. The result shows that the goal detection method, including the modification in Hough transform, is able to extract the goal features seen by the robot correctly, with the lowest speed of 5 frames per second. Additionally, the goal distance estimation is accomplished with maximum error of 20 centimeters.


title: Context-Aware collaborative filtering system: predicting the user's preference in the ubiquitous computing environment

abstract: In this paper we present a context-aware collaborative filtering system that predicts a user's preference in different context situations based on past experiences. We extend collaborative filtering techniques so that what other like-minded users have done in similar context can be used to predict a user's preference towards an activity in the current context. Such a system can help predict the user's behavior in different situations without the user actively defining it. For example, it could recommend activities customized for Bob for the given weather, location, and traveling companion(s), based on what other people like Bob have done in similar context.


title: Correction of Short Nose Deformity Using a Septal Extension Graft Combined with a Derotation Graft

abstract: In patients having a short nose with a short septal length and/or severe columellar retraction, a septal extension graft is a good solution, as it allows the dome to move caudally and pushes down the columellar base. Fixing the medial crura of the alar cartilages to a septal extension graft leads to an uncomfortably rigid nasal tip and columella, and results in unnatural facial animation. Further, because of the relatively small and weak septal cartilage in the East Asian population, undercorrection of a short nose is not uncommon. To overcome these shortcomings, we have used the septal extension graft combined with a derotation graft. Among 113 patients who underwent the combined procedure, 82 patients had a short nose deformity alone; the remaining 31 patients had a short nose with columellar retraction. Thirty-two patients complained of nasal tip stiffness caused by a septal extension graft from previous operations. In addition to the septal extension graft, a derotation graft was used for bridging the gap between the alar cartilages and the septal extension graft for tip lengthening. Satisfactory results were obtained in 102 (90%) patients. Eleven (10%) patients required revision surgery. This combination method is a good surgical option for patients who have a short nose with small septal cartilages and do not have sufficient cartilage for tip lengthening by using a septal extension graft alone. It can also overcome the postoperative nasal tip rigidity of a septal extension graft.


title: Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

abstract: In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i.e., increased awareness of events of small probability and high consequences. Accordingly, the objective of this paper is to present efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs), where risk is represented via a chance constraint or a constraint on the conditional value-at-risk (CVaR) of the cumulative cost. We collectively refer to such problems as percentile risk-constrained MDPs. ::: Specifically, we first derive a formula for computing the gradient of the Lagrangian function for percentile risk-constrained MDPs. Then, we devise policy gradient and actor-critic algorithms that (1) estimate such gradient, (2) update the policy in the descent direction, and (3) update the Lagrange multiplier in the ascent direction. For these algorithms we prove convergence to locally optimal policies. Finally, we demonstrate the effectiveness of our algorithms in an optimal stopping problem and an online marketing application.


title: W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video

abstract: Huge amounts of video are being recorded every day by surveillance systems. Since video is capable of recording and preserving an enormous amount of information which can be used in many applications, it is worth examining the degree of privacy loss that might occur due to public access to the recorded video. A fundamental requirement of privacy solutions is an understanding and analysis of the inference channels than can lead to a breach of privacy. Though inference channels and privacy risks are well studied in traditional data sharing applications (e.g., hospitals sharing patient records for data analysis), privacy assessments of video data have been limited to the direct identifiers such as people's faces in the video. Other important inference channels such as location (Where), time (When), and activities (What) are generally overlooked. In this paper we propose a privacy loss model that highlights and incorporates identity leakage through multiple inference channels that exist in a video due to what, when, and where information. We model the identity leakage and the sensitive information separately and combine them to calculate the privacy loss. The proposed identity leakage model is able to consolidate the identity leakage through multiple events and multiple cameras. The experimental results are provided to demonstrate the proposed privacy analysis framework.


title: Cross-Language Entity Linking

abstract: There has been substantial recent interest in aligning mentions of named entities in unstructured texts to knowledge base descriptors, a task commonly called entity linking. This technology is crucial for applications in knowledge discovery and text data mining. This paper presents experiments in the new problem of crosslanguage entity linking, where documents and named entities are in a different language than that used for the content of the reference knowledge base. We have created a new test collection to evaluate cross-language entity linking performance in twenty-one languages. We present experiments that examine issues such as: the importance of transliteration; the utility of cross-language information retrieval; and, the potential benefit of multilingual named entity recognition. Our best model achieves performance which is 94% of a strong monolingual baseline.


title: A Survey on Detection of Reasons Behind Infant Cry Using Speech Processing

abstract: Infant cry analysis is necessary to detect the reasons behind the cry. Infants can communicate through their cry only. They express their physical and emotional needs through their cry. The crying of an infant can be stopped if the reason behind their cry is known. Therefore, identifying the reason behind infant cry is very essential for medical follow up. In this paper, a literature survey is done to study, analyze, compare the existing approaches and their limitations. This survey helped us to propose a new approach for detecting the reasons behind Infant's cry. The proposed model will use input as the cry signal and from these signal patterns the unique features are extracted using MFCCs, LPCCs and Pitch, etc. These features will help discriminating the patterns from one another. These extracted features will be used to train Neural Network Multilayer classifier. This classifier will be used to identify multiple classes of reasons behind infant cry such as hunger, pain, sleep, discomfort, etc. Efforts will be made for achieving good accuracy results through this approach.


title: Compression of human body sequences using graph Wavelet Filter Banks

abstract: The next step in immersive communication beyond video from a single camera is object-based free viewpoint video, which is the capture and compression of a dynamic object such that it can be reconstructed and viewed from an arbitrary viewpoint. The moving human body is a particularly useful subclass of dynamic object for object-based free viewpoint video relevant to both telepresence and entertainment. In this paper, we compress moving human body sequences by applying recently developed Graph Wavelet Filter Banks to time-varying geometry and color signals living on a mesh representation of the human body. This model-based approach significantly outperforms state- of-the-art coding of the human body represented as ordinary depth plus color video sequences. I. INTRODUCTION


title: Characterizing Depression Issues on Sina Weibo

abstract: The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support individuals who experience depression. More than 394,000,000 postings were collected from China’s most popular social media website, Sina Weibo. 1000 randomly selected depression-related postings was coded and analyzed to learn the themes of these postings, and a text classifier was built to identify the postings indicating depression. The identified depressed users were compared with the general population on demographic characteristics, diurnal patterns, and patterns of emoticon usage. We found that disclosure of depression was the most popular theme; depression displayers were more engaged with social media compared to non-depression displayers, the depression postings showed geographical variations, depression displayers tended to be active during periods of leisure and sleep, and depression displayers used negative emoticons more frequently than non-depression displayers. This study offers a broad picture of depression references on China’s social media, which may be cost effectively developed to detect and help individuals who may suffer from depression disorders.


title: A novel ZVS DC/DC converter for high power applications

abstract: This paper presents a novel ZVS PWM DC/DC power converter for high power applications. By using two active switches on the secondary side of a transformer, the proposed converter achieves not only ZVS of the active switches in the entire load range, but also soft commutation of the output rectifier diodes. Simulation results and experimental results of a 2.8 kW 200 kHz DC/DC converter are presented.


title: Using a Heterogeneous Dataset for Emotion Analysis in Text

abstract: In this paper, we adopt a supervised machine learning approach to recognize six basic emotions (anger, disgust, fear, happiness, sadness and surprise) using a heterogeneous emotion-annotated dataset which combines news headlines, fairy tales and blogs. For this purpose, different features sets, such as bags of words, and N-grams, were used. The Support Vector Machines classifier (SVM) performed significantly better than other classifiers, and it generalized well on unseen examples.


title: Heart beat rate monitoring using optical sensors

abstract: The heartbeat is a unit for counting the intensity of the heart function, whose unit is the beats per minute (bpm). The heart has pacemaker cells, which, by creating electrical waves, these cells cause heart rhythm from the right atrium with its spread throughout the heart, blood pumping occurs. The human heart naturally breaks about 60 to 100 bpm, which can vary with different conditions. Any disorder in heartbeat is a type of disease in the heart that can be detected by human beings by measuring beats in different ways.We present a heart rate measuring device in this paper, which firstinfrared light is transmitted by the infrared sensor (IR LED TX) to the fingertip and then reflects light on a Photodiode RX sensorand converted to a suitable signal by the Signal Conditioning, and suitable signal sent to the microcontroller, finally, the heart rate is measured by processing and analysis. ::: ::: On the device, when the power button is pressed, the user places his fingertip on the sensors then the microcontroller key is pressed and then the heart rate is measured in 15 seconds and displayed on the LCD screen. Using this device, it can be measured easily and in a short time, heart rate with a non-invasive method without harm to the human body.


title: U.: Network motifs: simple building blocks of complex networks

abstract: Complex networks are studied across many fields of science. To uncover their structural design principles, we defined “network motifs,” patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This


title: Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks

abstract: IP traffic classification has been a vitally important topic that attracts persistent interest in the networking and machine learning communities for past decades. While there exist quite a number of works applying machine learning techniques to realize IP traffic classification, most works suffer from limitations like either heavily depending on handcrafted features or be only able to handle offline traffic classification. To get rid of the aforementioned weakness, in this paper, we propose our online Convolutional Neural Networks (CNNs) based traffic classification framework named Seq2Img. The basic idea is to employ a compact nonparametric kernel embedding based method to convert early flow sequences into images which fully capture the static and dynamic behaviors of different applications and avoid using handcrafted features that might cause loss of information. A CNN is then applied on the generated images to obtain traffic classification results. Experiments on real network traffic are conducted and encouraging results justify the efficacy of our proposed approach.


title: Reading during the composition of multi-sentence texts: an eye-movement study

abstract: Writers composing multi-sentence texts have immediate access to a visual representation of what they have written. Little is known about the detail of writers’ eye movements within this text during production. We describe two experiments in which competent adult writers’ eye movements were tracked while performing short expository writing tasks. These are contrasted with conditions in which participants read and evaluated researcher-provided texts. Writers spent a mean of around 13 % of their time looking back into their text. Initiation of these look-back sequences was strongly predicted by linguistically important boundaries in their ongoing production (e.g., writers were much more likely to look back immediately prior to starting a new sentence). 36 % of look-back sequences were associated with sustained reading and the remainder with less patterned forward and backward saccades between words (“hopping”). Fixation and gaze durations and the presence of word-length effects suggested lexical processing of fixated words in both reading and hopping sequences. Word frequency effects were not present when writers read their own text. Findings demonstrate the technical possibility and potential value of examining writers’ fixations within their just-written text. We suggest that these fixations do not serve solely, or even primarily, in monitoring for error, but play an important role in planning ongoing production.


title: Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks

abstract: We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm has the following advantages: (1) Our algorithm can not only point out the lesions in fundus color images, but also give the severity grades of DR. (2) By introducing an imbalanced weighting scheme, more attentions will be payed on lesion patches for DR grading, which significantly improves the performance of DR grading under the same implementation setup. In this study, we label 12, 206 lesion patches and re-annotate the DR grades of 23, 595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our lesion detection net achieves comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly enhance the capability of our DCNN-based DR grading algorithm.


Object matching on real-world problems


title: A context-sensitive-chunk BPTT approach to training deep LSTM/BLSTM recurrent neural networks for offline handwriting recognition

abstract: We propose a context-sensitive-chunk based back-propagation through time (BPTT) approach to training deep (bidirectional) long short-term memory ((B)LSTM) recurrent neural networks (RNN) that splits each training sequence into chunks with appended contextual observations for character modeling of offline handwriting recognition. Using short context-sensitive chunks in both training and recognition brings following benefits: (1) the learned (B)LSTM will model mainly local character image dependency and the effect of long-range language model information reflected in training data is reduced; (2) mini-batch based training on GPU can be made more efficient; (3) low-latency BLSTM-based handwriting recognition is made possible by incurring only a delay of a short chunk rather than a whole sentence. Our approach is evaluated on IAM offline handwriting recognition benchmark task and performs better than the previous state-of-the-art BPTT-based approaches.


Exploring ROI size in deep learning based lipreading