title: A systematic review on security in Process-Aware Information Systems - Constitution, challenges, and future directions

abstract: Display Omitted HighlightsSecurity in PAIS constitutes an interdisciplinary research area.Security is a multifaceted subject that covers technical and human aspects in PAIS.12 security controls are identified for Process-Aware Information Systems (PAIS).Security controls have centered on preventive countermeasures so far.Reaction, detection, and human orientation constitute promising research directions. ContextSecurity in Process-Aware Information Systems (PAIS) has gained increased attention in current research and practice. However, a common understanding and agreement on security is still missing. In addition, the proliferation of literature makes it cumbersome to overlook and determine state of the art and further to identify research challenges and gaps. In summary, a comprehensive and systematic overview of state of the art in research and practice in the area of security in PAIS is missing. ObjectiveThis paper investigates research on security in PAIS and aims at establishing a common understanding of terminology in this context. Further it investigates which security controls are currently applied in PAIS. MethodA systematic literature review is conducted in order to classify and define security and security controls in PAIS. From initially 424 papers, we selected in total 275 publications that related to security and PAIS between 1993 and 2012. Furthermore, we analyzed and categorized the papers using a systematic mapping approach which resulted into 5 categories and 12 security controls. ResultsIn literature, security in PAIS often centers on specific (security) aspects such as security policies, security requirements, authorization and access control mechanisms, or inter-organizational scenarios. In addition, we identified 12 security controls in the area of security concepts, authorization and access control, applications, verification, and failure handling in PAIS. Based on the results, open research challenges and gaps are identified and discussed with respect to possible solutions. ConclusionThis survey provides a comprehensive review of current security practice in PAIS and shows that security in PAIS is a challenging interdisciplinary research field that assembles research methods and principles from security and PAIS. We show that state of the art provides a rich set of methods such as access control models but still several open research challenges remain.


title: Querying RDF data with text annotated graphs

abstract: Scientists and casual users need better ways to query RDF databases or Linked Open Data. Using the SPARQL query language requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used, and URIs for entities of interest. Natural language query systems are a powerful approach, but current techniques are brittle in addressing the ambiguity and complexity of natural language and require expensive labor to supply the extensive domain knowledge they need. We introduce a compromise in which users give a graphical "skeleton" for a query and annotates it with freely chosen words, phrases and entity names. We describe a framework for interpreting these "schema-agnostic queries" over open domain RDF data that automatically translates them to SPARQL queries. The framework uses semantic textual similarity to find mapping candidates and uses statistical approaches to learn domain knowledge for disambiguation, thus avoiding expensive human efforts required by natural language interface systems. We demonstrate the feasibility of the approach with an implementation that performs well in an evaluation on DBpedia data.


title: Subspace clustering based on low rank representation and weighted nuclear norm minimization

abstract: Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces. Aiming at the subspace clustering problem, various subspace clustering algorithms have been proposed and low rank representation based subspace clustering is a very promising and efficient subspace clustering algorithm. Low rank representation method seeks the lowest rank representation among all the candidates that can represent the data points as linear combinations of the bases in a given dictionary. Nuclear norm minimization is adopted to minimize the rank of the representation matrix. However, nuclear norm is not a very good approximation of the rank of a matrix and the representation matrix thus obtained can be of high rank which will affect the final clustering accuracy. Weighted nuclear norm (WNN) is a better approximation of the rank of a matrix and WNN is adopted in this paper to describe the rank of the representation matrix. The convex program is solved via conventional alternation direction method of multipliers (ADMM) and linearized alternating direction method of multipliers (LADMM) and they are respectively refer to as WNNM-LRR and WNNM-LRR(L). Experimental results show that, compared with low rank representation method and several other state-of-the-art subspace clustering methods, WNNM-LRR and WNNM-LRR(L) can get higher clustering accuracy.


Color filter liquid-crystal-on-silicon microdisplays


title: Advanced Multifrequency Radar Instrumentation for Polar Research

abstract: This paper presents a radar sensor package specifically developed for wide-coverage sounding and imaging of polar ice sheets from a variety of aircraft. Our instruments address the need for a reliable remote sensing solution well-suited for extensive surveys at low and high altitudes and capable of making measurements with fine spatial and temporal resolution. The sensor package that we are presenting consists of four primary instruments and ancillary systems with all the associated antennas integrated into the aircraft to maintain aerodynamic performance. The instruments operate simultaneously over different frequency bands within the 160 MHz-18 GHz range. The sensor package has allowed us to sound the most challenging areas of the polar ice sheets, ice sheet margins, and outlet glaciers; to map near-surface internal layers with fine resolution; and to detect the snow-air and snow-ice interfaces of snow cover over sea ice to generate estimates of snow thickness. In this paper, we provide a succinct description of each radar and associated antenna structures and present sample results to document their performance. We also give a brief overview of our field measurement programs and demonstrate the unique capability of the sensor package to perform multifrequency coincidental measurements from a single airborne platform. Finally, we illustrate the relevance of using multispectral radar data as a tool to characterize the entire ice column and to reveal important subglacial features.


title: An efficient and principled method for detecting communities in networks

abstract: A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.


title: Practical steganalysis of digital images: state of the art

abstract: Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a great deal of attention both from law enforcement and the media. In our paper, we classify and review current stego-detection algorithms that can be used to trace popular steganographic products. We recognize several qualitatively different approaches to practical steganalysis - visual detection, detection based on first order statistics (histogram analysis), dual statistics methods that use spatial correlations in images and higher-order statistics (RS steganalysis), universal blind detection schemes, and special cases, such as JPEG compatibility steganalysis. We also present some new results regarding our previously proposed detection of LSB embedding using sensitive dual statistics. The recent steganalytic methods indicate that the most common paradigm in image steganography - the bit-replacement or bit substitution - is inherently insecure with safe capacities far smaller than previously thought.


title: An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

abstract: With the popularity of electrical vehicles, the lithium-ion battery industry is developing rapidly. To ensure battery safe usage and to reduce its average lifecycle cost, accurate state of charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper classifies the recently proposed online SOC estimation methods into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model-based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.


title: The relationship between sleep and behavior in autism spectrum disorder (ASD): a review

abstract: Although there is evidence that significant sleep problems are common in children with autism spectrum disorder (ASD) and that poor sleep exacerbates problematic daytime behavior, such relationships have received very little attention in both research and clinical practice. Treatment guidelines to help manage challenging behaviors in ASD fail to mention sleep at all, or they present a very limited account. Moreover, limited attention is given to children with low-functioning autism, those individuals who often experience the most severe sleep disruption and behavioral problems. This paper describes the nature of sleep difficulties in ASD and highlights the complexities of sleep disruption in individuals with low-functioning autism. It is proposed that profiling ASD children based on the nature of their sleep disruption might help to understand symptom and behavioral profiles (or vice versa) and therefore lead to better-targeted interventions. This paper concludes with a discussion of the limitations of current knowledge and proposes areas that are important for future research. Treating disordered sleep in ASD has great potential to improve daytime behavior and family functioning in this vulnerable population.


title: UStress: Understanding college student subjective stress using wrist-based passive sensing

abstract: Stress plays a major role in physical and emotional well-being, and is associated with several illnesses including depression, diabetes, and other chronic diseases. College student stress as a construct is important to detect in order to equip students in a timely manner with stress coping strategies. However, the lack of a passive sensing measure accepted as a gold standard impedes real time detection and treatment of stress. Many researchers are studying passive sensing of stress using wrist-worn sensors; however, this effort focuses on understanding the essential features of wrist-worn sensors in detecting stress, and how to best induce stress in a lab setting. Applying machine learning methods increasingly is making it feasible to validly infer in real time through passive sensing of physical features psychological states, such as stress. Given strong participant adherence to wrist-worn sensors, this paper focuses on analyzing the effect of replacing other body sensing platforms (e.g. chest-based heart rate) with their wrist-worn equivalent on stress prediction accuracy. Nine participants were equipped with multiple body sensors and were asked to wear a commercially available Android smartwatch, a custom-designed smartwatch equipped with a Galvanic Skin Response (GSR) sensor, a chest-based heart rate sensor, and a finger-based commercial GSR sensor. Based on participant self-reports, singing experiment showed greatest stress levels across participants. This paper further analyzes features for prediction from all sensors compared to wrist-worn only sensors. Using statistical features on one-minute fixed-time sub-divisions and correlation-based feature subset selection and a Random Forest model, the system is capable of detecting stress with 88.8% F-measure.


title: Inertial measurements of upper limb motion

abstract: We present an inertial sensor based monitoring system for measuring upper limb movements in real time. The purpose of this study is to develop a motion tracking device that can be integrated within a home-based rehabilitation system for stroke patients. Human upper limbs are represented by a kinematic chain in which there are four joint variables to be considered: three for the shoulder joint and one for the elbow joint. Kinematic models are built to estimate upper limb motion in 3-D, based on the inertial measurements of the wrist motion. An efficient simulated annealing optimisation method is proposed to reduce errors in estimates. Experimental results demonstrate the proposed system has less than 5% errors in most motion manners, compared to a standard motion tracker.


title: EMBARC: error model based adaptive rate control for vehicle-to-vehicle communications

abstract: Channel congestion is one of the major challenges for deployment of collision avoidance systems based on DSRC (Dedicated Short Range Communication) in large scale networks. If vehicles do not adapt to congestion conditions, DSRC transmissions could encounter extensive packet losses in areas of high vehicle density, leading to degradation in the performance of safety applications. In this paper, we propose a novel congestion control algorithm called Error Model Based Adaptive Rate Control (EMBARC) which adapts a vehicle's transmission rate as a function of channel load and vehicular dynamics. In particular, we extend Linear Integrated Message Rate Control (LIMERIC) algorithm's message rate adaptation with the capability to preemptively schedule messages based on the vehicle's movement. This leads to more transmission opportunities for vehicles with higher dynamics. The determination of a preemptive scheduling event is based on a novel suspected tracking error technique. Since LIMERIC maintains the channel load around a specific value, vehicles moving less dynamically will adapt to slightly reduced transmission rates in EMBARC. The extra transmit opportunities for highly dynamic vehicles reduce incidences of large tracking error compared to a pure LIMERIC approach. At the same time, EMBARC's use of adaptive rate control provides tracking error advantages over systems that transmit largely independent of channel load. We use simulations of a road with a winding segment to compare EMBARC with algorithms that do not take both channel load and vehicle dynamics into account. The results show that EMBARC has the best tracking accuracy among these algorithms over a wide range of node densities.


title: A molecular basis for the association of the HLA-DRB1 locus, citrullination, and rheumatoid arthritis

abstract: Rheumatoid arthritis (RA) is strongly associated with the human leukocyte antigen (HLA)- DRB1 locus that possesses the shared susceptibility epitope (SE) and the citrullination of self-antigens. We show how citrullinated aggrecan and vimentin epitopes bind to HLADRB1* 04:01/04. Citrulline was accommodated within the electropositive P4 pocket of HLA-DRB1*04:01/04, whereas the electronegative P4 pocket of the RA-resistant HLADRB1* 04:02 allomorph interacted with arginine or citrulline-containing epitopes. Peptide elution studies revealed P4 arginine-containing peptides from HLA-DRB1*04:02, but not from HLA-DRB1*04:01/04. Citrullination altered protease susceptibility of vimentin, thereby generating self-epitopes that are presented to T cells in HLA-DRB1*04:01+ individuals. Using HLA-II tetramers, we observed citrullinated vimentin- and aggrecan-specific CD4+ T cells in the peripheral blood of HLA-DRB1*04:01+ RA-affected and healthy individuals. In RA patients, autoreactive T cell numbers correlated with disease activity and were deficient in regulatory T cells relative to healthy individuals. These findings reshape our understanding of the association between citrullination, the HLA-DRB1 locus, and T cell autoreactivity in RA.


title: Building energy-efficient multi-level cell STT-MRAM based cache through dynamic data-resistance encoding

abstract: With attractive advantages like high density and low leakage, Spin-Transfer Torque Magnetoresistive RAM (STT-MRAM) is a promising candidate to replace conventional SRAM technology to build large-size and low-power on-chip caches. Multi-level cell (MLC) STT-MRAM, with a higher density, further improves the on-chip cache capacity for chip multiprocessor (CMP) systems. However, the notorious high write energy impedes the adoption of MLC STT-MRAM. In this paper, we focus on minimizing the energy consumption during MLC STT-MRAM write operations. Based on the strong dependency of write energy on data values, a dynamic encoding technique is proposed to map the most frequently appearing data patterns to the most energy-efficient resistance states at runtime. Our experimental results show that, compared with the existing static data mapping scheme, our technique reduces write energy by 12.4% on average and up to 25.4% for a typical MLC STT-MRAM cache.


title: Benchmarking Scalability and Elasticity of Distributed Database Systems

abstract: Distributed database system performance benchmarks are an important source of information for decision makers who must select the right technology for their data management problems. Since important decisions rely on trustworthy experimental data, it is necessary to reproduce experiments and verify the results. We reproduce performance and scalability benchmarking experiments of HBase and Cassandra that have been conducted by previous research and compare the results. The scope of our reproduced experiments is extended with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations, and an evaluation of Cassandra and HBase elasticity by measuring scaling speed and performance impact while scaling.


title: Strength Model of Self-Regulation as Limited Resource

abstract: The strength model of self-regulation holds that self-regulation operates by consuming a limited energy resource, thereby producing a state called ego depletion in which volition is curtailed because of low energy. We present our research program on ego depletion as well as much relevant work contributed by others. Challenges to the theory have emphasized allocation rather than depletion of resources, research participant expectations and obligations, changes in motivation and attention, beliefs and implicit theories, perceptions about depletion and vicarious depletion, glucose anomalies, and feelings of autonomy. We conclude that the theory needs revision and updating to accommodate the new findings, and we indicate the requisite changes. Furthermore, we conclude that the strength model is much better able than the rival accounts to explain all available evidence. Most of the rival accounts are compatible with it and indeed work best by sustaining the assumption that self-regulation relies on a limited resource.


title: The impact of user generated content on consumer based brand equity

abstract: Developed to address the challenges of the social web, this study aims to determine how ::: consumers’ involvement in user-generated content (UGC) affects brand equity. The ::: formation and testing of a conceptual model addresses a gap in the literature by ::: exploring links between UGC, involvement and consumer-based brand equity. Based on ::: previous research complemented by depth interviews with experts, co-creation, ::: empowerment, community and self-concept were selected to represent the domain of ::: UGC. The model was then empirically tested through data from 201 consumers ::: responding to an online survey. The results provide support for managerial initiatives ::: involving UGC campaigns for brand building. More specifically, the results indicate that ::: co-creation, community and self-concept have a positive impact on UGC involvement. ::: The data also supports that UGC involvement has a positive impact on consumer-based ::: brand equity. The contributions of these research findings provide empirical evidence ::: that has implications in building deeper relationships between consumers and brands.


Deep Learning Based Food Recognition


title: A separation theorem in property testing

abstract: Consider the following seemingly rhetorical question: Is it crucial for a property-tester to know the error parameter 2 in advance? Previous papers dealing with various testing problems, suggest that the answer may be no, as in these papers there was no loss of generality in assuming that 2 is given as part of the input, and is not known in advance. Our main result in this paper, however, is that it is possible to separate a natural model of property testing in which 2 is given as part of the input from the model in which 2 is known in advance (without making any computational hardness assumptions). To this end, we construct a graph property P, which has the following two properties: (i) There is no tester for P accepting 2 as part of the input, whose number of queries depends only on 2. (ii) For any fixed 2, there is a tester for P (that works only for that specific 2), which makes a constant number of queries. Interestingly, we manage to construct a separating property P, which is combinatorially natural as it can be expressed in terms of forbidden subgraphs and also computationally natural as it can be shown to belong to coNP . The main tools in this paper are efficiently constructible graphs of high girth and high chromatic number, a result about testing monotone graph properties, as well as basic ideas from the theory of recursive functions. Of independent interest is a precise characterization of the monotone graph properties that can be tested with 2 being part of the input, which we obtain as one of the main steps of the paper.


title: An Adversarial Approach to Hard Triplet Generation

abstract: While deep neural networks have demonstrated competitive results for many visual recognition and image retrieval tasks, the major challenge lies in distinguishing similar images from different categories (i.e., hard negative examples) while clustering images with large variations from the same category (i.e., hard positive examples). The current state-of-the-art is to mine the most hard triplet examples from the mini-batch to train the network. However, mining-based methods tend to look into these triplets that are hard in terms of the current estimated network, rather than deliberately generating those hard triplets that really matter in globally optimizing the network. For this purpose, we propose an adversarial network for Hard Triplet Generation (HTG) to optimize the network ability in distinguishing similar examples of different categories as well as grouping varied examples of the same categories. We evaluate our method on the real-world challenging datasets, such as CUB200-2011, CARS196, DeepFashion and VehicleID datasets, and show that our method outperforms the state-of-the-art methods significantly.


title: Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease

abstract: HighlightsFirst application of graph convolutional networks for brain analysis in populations.Graph based population model that leverages imaging and non‐imaging data.Experiments on two large and challenging databases: ABIDE and ADNI.Extensive evaluation of all the main components of the method.State of the art performance on both databases. Graphical abstract Figure. No caption available. ABSTRACT Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non‐imaging information as well as individual subject features simultaneously in disease classification tasks. Previous graph‐based approaches for supervised or unsupervised learning in the context of disease prediction solely focus on pairwise similarities between subjects, disregarding individual characteristics and features, or rather rely on subject‐specific imaging feature vectors and fail to model interactions between them. In this paper, we present a thorough evaluation of a generic framework that leverages both imaging and non‐imaging information and can be used for brain analysis in large populations. This framework exploits Graph Convolutional Networks (GCNs) and involves representing populations as a sparse graph, where its nodes are associated with imaging‐based feature vectors, while phenotypic information is integrated as edge weights. The extensive evaluation explores the effect of each individual component of this framework on disease prediction performance and further compares it to different baselines. The framework performance is tested on two large datasets with diverse underlying data, ABIDE and ADNI, for the prediction of Autism Spectrum Disorder and conversion to Alzheimer's disease, respectively. Our analysis shows that our novel framework can improve over state‐of‐the‐art results on both databases, with 70.4% classification accuracy for ABIDE and 80.0% for ADNI.


title: Word Representations: A Simple and General Method for Semi-Supervised Learning

abstract: If we take an existing supervised NLP system, a simple and general way to improve accuracy is to use unsupervised word representations as extra word features. We evaluate Brown clusters, Collobert and Weston (2008) embeddings, and HLBL (Mnih & Hinton, 2009) embeddings of words on both NER and chunking. We use near state-of-the-art supervised baselines, and find that each of the three word representations improves the accuracy of these baselines. We find further improvements by combining different word representations. You can download our word features, for off-the-shelf use in existing NLP systems, as well as our code, here: http://metaoptimize.com/projects/wordreprs/


title: Bayesian Online Multitask Learning of Gaussian Processes

abstract: Standard single-task kernel methods have recently been extended to the case of multitask learning in the context of regularization theory. There are experimental results, especially in biomedicine, showing the benefit of the multitask approach compared to the single-task one. However, a possible drawback is computational complexity. For instance, when regularization networks are used, complexity scales as the cube of the overall number of training data, which may be large when several tasks are involved. The aim of this paper is to derive an efficient computational scheme for an important class of multitask kernels. More precisely, a quadratic loss is assumed and each task consists of the sum of a common term and a task-specific one. Within a Bayesian setting, a recursive online algorithm is obtained, which updates both estimates and confidence intervals as new data become available. The algorithm is tested on two simulated problems and a real data set relative to xenobiotics administration in human patients.


Efficient mining of weighted association rules (WAR)


title: Cascade Cockcroft–Walton Voltage Multiplier Applied to Transformerless High Step-Up DC–DC Converter

abstract: This paper proposes a high step-up dc-dc converter based on the Cockcroft-Walton (CW) voltage multiplier without a step-up transformer. Providing continuous input current with low ripple, high voltage ratio, and low voltage stress on the switches, diodes, and capacitors, the proposed converter is quite suitable for applying to low-input-level dc generation systems. Moreover, based on the n-stage CW voltage multiplier, the proposed converter can provide a suitable dc source for an n + 1-level multilevel inverter. In this paper, the proposed control strategy employs two independent frequencies, one of which operates at high frequency to minimize the size of the inductor while the other one operates at relatively low frequency according to the desired output voltage ripple. A 200-W laboratory prototype is built for test, and both simulation and experimental results demonstrate the validity of the proposed converter.


title: Automatic cell nuclei segmentation and classification of breast cancer histopathology images

abstract: Abstract Breast cancer is the leading type of malignant tumor observed in women and the effective treatment depends on its early diagnosis. Diagnosis from histopathological images remains the "gold standard" for breast cancer. The complexity of breast cell histopathology (BCH) images makes reliable segmentation and classification hard. In this paper, an automatic quantitative image analysis technique of BCH images is proposed. For the nuclei segmentation, top-bottom hat transform is applied to enhance image quality. Wavelet decomposition and multi-scale region-growing (WDMR) are combined to obtain regions of interest (ROIs) thereby realizing precise location. A double-strategy splitting model (DSSM) containing adaptive mathematical morphology and Curvature Scale Space (CSS) corner detection method is applied to split overlapped cells for better accuracy and robustness. For the classification of cell nuclei, 4 shape-based features and 138 textural features based on color spaces are extracted. Optimal feature set is obtained by support vector machine (SVM) with chain-like agent genetic algorithm (CAGA). The proposed method was tested on 68 BCH images containing more than 3600 cells. Experimental results show that the mean segmentation sensitivity was 91.53% (±4.05%) and specificity was 91.64% (±4.07%). The classification performance of normal and malignant cell images can achieve 96.19% (±0.31%) for accuracy, 99.05% (±0.27%) for sensitivity and 93.33% (±0.81%) for specificity.


title: BreakDancer: an algorithm for high-resolution mapping of genomic structural variation

abstract: This software package provides genome-wide detection of structural variants (insertions, deletions, inversions and inter- and intrachromosomal translocations) from 50-base-pair paired-end reads. The sizes of the detected variants vary from 10 base pairs to 1 megabase pair.


title: Reduced volume of the cerebellar vermis in neuroleptic-naive schizophrenia

abstract: Abstract Background: Neuroimaging studies have suggested the possible role of the cerebellum in the pathophysiology of schizophrenia. However, no study has investigated the detailed structures of the cerebellum in patients without a history of neuroleptic medication. The objective of this study is to examine the volume of detailed structures of the cerebellum in neuroleptic-naive schizophrenic patients and to examine the relationship between cerebellar morphology and clinical symptoms. Methods: Magnetic resonance imaging scans were acquired from 20 male neuroleptic-naive schizophrenic patients and 20 healthy control subjects. We measured the volumes of the cerebrum, cerebellar hemisphere, cerebellar gray and white matter, and vermis. Symptoms were assessed with the Brief Psychiatric Rating Scale. Total Brief Psychiatric Rating Scale scores and subscale scores were used for analysis. Results: The volume of the vermis was significantly reduced in the schizophrenic group relative to the control group, whereas no significant differences were found in the volumes of other cerebellar structures and the cerebrum. Reduction in the vermal volume correlated with the total Brief Psychiatric Rating Scale Depression subscore and Paranoia subscore. Conclusions: This study indicates that the volume of the vermis is reduced in patients with schizophrenia, and reduction in vermal volume is suggested to be related to the pathophysiology of the disease.


title: Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver

abstract: The automation of the overtaking maneuver is considered to be one of the toughest challenges in the development of autonomous vehicles. This operation involves two vehicles (the overtaking and the overtaken) cooperatively driving, as well as the surveillance of any other vehicles that are involved in the maneuver. This operation consists of two lane changes-one from the right to the left lane of the road, and the other is to return to the right lane after passing. Lane-change maneuvers have been used to move into or out of a circulation lane or platoon; however, overtaking operations have not received much coverage in the literature. In this paper, we present an overtaking system for autonomous vehicles equipped with path-tracking and lane-change capabilities. The system uses fuzzy controllers that mimic human behavior and reactions during overtaking maneuvers. The system is based on the information that is supplied by a high-precision Global Positioning System and a wireless network environment. It is able to drive an automated vehicle and overtake a second vehicle that is driving in the same lane of the road.


title: Searching for Google’s Value: Using Prediction Markets to Forecast Market Capitalization Prior to an Initial Public Offering

abstract: We conducted prediction markets designed to forecast post-initial public offering (IPO) valuations before a particularly unique IPO: Google. The prediction markets forecast Google's post-IPO market capitalization relatively accurately. While Google's auction-based IPO price was 15.3% below the first-day closing market capitalization, the final prediction market forecast was only 4.0% above it. The forecast also accorded with the level of over-subscription in the IPO auction. Evidence available to both outsiders (from the prediction market forecasts) and insiders (through the orders in Google's auction) predicted similar degrees of underpricing. We argue that, with repetition, such markets could provide useful information for understanding the IPO process.


title: Forecasting with Many Predictors

abstract: Historically, time series forecasts of economic variables have used only a handful of predictor variables, while forecasts based on a large number of predictors have been the province of judgmental forecasts and large structural econometric models. The past decade, however, has seen considerable progress in the development of time series forecasting methods that exploit many predictors, and this chapter surveys these methods. The first group of methods considered is forecast combination (forecast pooling), in which a single forecast is produced from a panel of many forecasts. The second group of methods is based on dynamic factor models, in which the comovements among a large number of economic variables are treated as arising from a small number of unobserved sources, or factors. In a dynamic factor model, estimates of the factors (which become increasingly precise as the number of series increases) can be used to forecast individual economic variables. The third group of methods is Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model. The chapter also discusses empirical Bayes methods, in which the hyperparameters of the priors are estimated. An empirical illustration applies these different methods to the problem of forecasting the growth rate of the U.S. index of industrial production with 130 predictor variables.


title: Synergistic Effect of Green Tea Polyphenols on Their Protection Against FK506-Induced Cytotoxicity in Renal Cells

abstract: FK506 (tacrolimus) is a widely used immunosuppressant first employed in the management of rejection in organ transplantation, but now used for autoimmune disease. However, the nephrotoxicity induced by FK506 remains a serious clinical problem. We previously demonstrated that FK506 caused a significant increase in apoptosis of LLC-PK1 cells, a porcine proximal tubule cell line, but the addition of green tea extract and its polyphenolic components suppressed the cell death. Here, we examined the synergistic effect of tea polyphenols on the protection of FK506-induced cell death. The combined treatment with 5 μM (-)-epigallocatechin-gallate (EGCG) and 5 μM of (+)-catechin (C), (-)-epicatechin (EC), (-)-epigallocatechin (EGC) or (-)-epicatechin-gallate (ECG) reduced FK506-induced cytotoxicity in LLC-PK1. Similarly, the combined treatment with 5 μM EGC and 5 μM of C, EC, EGCG or ECG also reduced the cytotoxicity. These results showed that the co-treatments with EGCG and EGC, EGCG or ECG, and EGC and ECG have stronger synergistic effects on the protection of FK506-induced cell death. Furthermore, the combined treatment of EGCG (5 μM) and EGC (5 μM) showed a significant time-dependent suppression of the increased intracellular ROS levels 15 min after the addition of FK506, as well as on caspase activation. The results of these synergistic effects of the constituents of green tea extract suggest that its protective effects may reside in more than just one of its constituent.


title: Benefits and Challenges of System Prognostics

abstract: Prognostics is an engineering discipline utilizing in-situ monitoring and analysis to assess system degradation trends, and determine remaining useful life. This paper discusses the benefits of prognostics in terms of system life-cycle processes, such as design and development, production, operations, logistics support, and maintenance. Challenges for prognostics technologies from the viewpoint of both system designers and users will be addressed. These challenges include implementing optimum sensor systems and settings, selecting applicable prognostics methods, addressing prognostic uncertainties, and estimating the cost-benefit implications of prognostics implementation. The research opportunities are summarized as well.


title: Online dictionary learning for sparse coding

abstract: Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.


title: Security in wireless sensor networks

abstract: Wireless sensor networks (WSN) are envisioned to consist of large numbers of minimum capacity devices operating in demanding real-world environments. These issues, together with the inherent problems of wireless communication, make security an especially difficult problem. Many WSN also need to operate under real-time constraints. We discuss three routing protocols for WSN that have good real-time performance properties: SPEED, RAP and IGF. We suggest extensions to these protocols that improve their security properties. Denial of service is another key problem facing WSN. We discuss a highly decentralized algorithm for the detection and mapping of jammed regions of a WSN. The solution is robust to failures and can operate on partial information (a type of anytime algorithm). We conclude by considering several potential future directions for security solutions, including: secure groups based on relaxed semantics, exploiting physical properties, exploiting time validity intervals for data, and the interaction between security and privacy.


title: Design of Reversible Sequential Elements With Feasibility of Transistor Implementation

abstract: This paper presents the novel designs of reversible sequential circuits (latches and flip flops). The proposed reversible latches and flip flops are designed from reversible Fredkin, Feynman and Toffoli gates. Two new reversible gates called modified Fredkin gate (MFG) and modified Toffoli gate (MTG) are also proposed to design the optimized implementations. The proposed designs are better than the recently proposed ones in terms of number of reversible gates and garbage outputs. In order to reach towards the goal of transistor implementations of proposed reversible sequential circuits, transistor implementation of the existing Feynman gate, Fredkin gate, Toffoli gates as well as the proposed MTG and MFG are also proposed. The proposed transistor implementations are completely reversible in nature, i.e., suitable for both the forward and backward computation.


title: A Novel Multi-Layer Heuristic Model for Anti-Phishing

abstract: Phishing website detection1 is very important for e-banking and e-commerce users. Current detection methods for anti-phishing have proved to be well-performed in term of accuracy, recall rate and F-measure. However, increasingly complex phishing methods make it more necessary to optimize the detection scheme by timely recognizing new features and accurately choosing the optimal feature subset. To react changing phishing means and resolve feature updating issues, we propose a novel multi-layer heuristic anti-phishing model with feature selection algorithms and heuristic classification algorithms. Five feature selection algorithms are utilized to pre-process feature sets. Then four classification algorithms are applied to identify phishing websites and legitimate websites. Experimental results show that the proposed model utilizing information gain algorithm in procedure of feature subset selection and Random Tree algorithm in heuristic classification achieves 96% accuracy with less time cost.


title: Speech recognition with quaternion neural networks

abstract: Neural network architectures are at the core of powerful automatic speech recognition systems (ASR). However, while recent researches focus on novel model architectures, the acoustic input features remain almost unchanged. Traditional ASR systems rely on multidimensional acoustic features such as the Mel filter bank energies alongside with the first, and second order derivatives to characterize time-frames that compose the signal sequence. Considering that these components describe three different views of the same element, neural networks have to learn both the internal relations that exist within these features, and external or global dependencies that exist between the time-frames. Quaternion-valued neural networks (QNN), recently received an important interest from researchers to process and learn such relations in multidimensional spaces. Indeed, quaternion numbers and QNNs have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with up to four times less learning parameters than real-valued models. We propose to investigate modern quaternion-valued models such as convolutional and recurrent quaternion neural networks in the context of speech recognition with the TIMIT dataset. The experiments show that QNNs always outperform real-valued equivalent models with way less free parameters, leading to a more efficient, compact, and expressive representation of the relevant information.


title: A traceability attack against e-passports

abstract: Since 2004, many nations have started issuing “e-passports” containing an RFID tag that, when powered, broadcasts information. It is claimed that these passports are more secure and that our data will be protected from any possible unauthorised attempts to read it. In this paper we show that there is a flaw in one of the passport’s protocols that makes it possible to trace the movements of a particular passport, without having to break the passport’s cryptographic key. All an attacker has to do is to record one session between the passport and a legitimate reader, then by replaying a particular message, the attacker can distinguish that passport from any other. We have implemented our attack and tested it successfully against passports issued by a range of nations.


title: X-band 200W AlGaN/GaN HEMT for high power application

abstract: A 200 Watts GaN high electron mobility transistor (HEMT) has been developed for X-band applications. The device consists of four dies of 0.35 um-gate GaN HEMT of 16 mm gate periphery together with input and output 2-stage impedance transformers assembled into a low thermal resistance package. The developed GaN HEMT provides 204W output power and 12dB small signal gain at 9.3 GHz with power added efficiency of 32% under pulse condition at a duty of 10% with a pulse width of 100 usec.


title: An ethnographic study of copy and paste programming practices in OOPL

abstract: Although programmers frequently copy and paste code when they develop software, implications of common copy and paste (C&P) usage patterns have not been studied previously. We have conducted an ethnographic study in order to understand programmers' C&P programming practices and discover opportunities to assist common C&P usage patterns. We observed programmers using an instrumented Eclipse IDE and then analyzed why and how they use C&P operations. Based on our analysis, we constructed a taxonomy of C&P usage patterns. This paper presents our taxonomy of C&P usage patterns and discusses our insights with examples drawn from our observations. From our insights, we propose a set of tools that both can reduce software maintenance problems incurred by C&P and can better support the intents of commonly used C&P scenarios.


title: Student2student: arduino project-based learning

abstract: In this paper we report the work that jeKnowledge (Junior Empresa da Faculdade de Ciencias e Tecnologias da Universidade de Coimbra), a student-led initiative, has done in the 'jeKnowledge academy' Summer School to actively engage Portuguese high-school students in STEM education through hands-on projects based on the low-cost Arduino platform. F2F activities are complemented with tutorials and more advanced project suggestions in a blog. The course resulted in high degrees of satisfaction for both the students in the teaching role and the younger students and even influenced the Physics Department where it took place, which included Arduino workshops in the first-year syllabus of the Physics Engineering course.


title: Web-based personalized hybrid book recommendation system

abstract: Recommender Systems have been around for more than a decade now. Choosing what book to read next has always been a question for many. Even for students, deciding which textbook or reference book to read on a topic unknown to them is a big question. In this paper, we try to present a model for a web-based personalized hybrid book recommender system which exploits varied aspects of giving recommendations apart from the regular collaborative and content-based filtering approaches. Temporal aspects for the recommendations are incorporated. Also for users of different age, gender and country, personalized recommendations can be made on these demographic parameters. Scraping information from the web and using the information obtained from this process can be equally useful in making recommendations.


title: The Pulse of News in Social Media: Forecasting Popularity

abstract: News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.


title: Energy and Delay Tradeoff for Application Offloading in Mobile Cloud Computing

abstract: Recent work shows that offloading a mobile application from mobile devices to cloud servers can significantly reduce the energy consumption of mobile devices, thus extending the lifetime of mobile devices. However, previous work only considers the energy saving of mobile devices while ignoring the execution delay of mobile applications. To reduce the energy consumption of mobile devices, one may offload as many mobile applications as possible. However, offloading to cloud servers may incur a large execution delay because of the waiting time at the servers or the communication delay from the mobile devices to the servers. Thus, to balance the tradeoff between energy consumption and execution delay of mobile applications, it is necessary to determine whether the mobile application should be offloaded to the cloud server or run locally at the mobile devices. In this paper, we first formulate a joint optimization problem, which minimizes both the energy consumption at the mobile devices and the execution delay of mobile applications. We prove that the proposed problem is NP-hard. For a special case with unlimited residual energy at the mobile device and the same amount of resources required by each mobile application, we present a polynomial-time optimal solution. We also propose an efficient heuristic algorithm to solve the general case of the problem. Finally, simulation results demonstrate the effectiveness of the proposed scheme.


title: Palmprint verification based on robust line orientation code

abstract: In palmprint recognition field, orientation based approaches are thought to achieve the best results in terms of recognition rates. In this paper, we propose a novel orientation based scheme, in which three strategies, the modified finite Radon transform, enlarged training set and pixel to area matching, have been designed to further improve its performance. The experimental results of verification conducted on Hong Kong Polytechnic University Palmprint Database show that our approach has higher recognition rates and faster processing speed.


title: A Survey on Neutral-Point-Clamped Inverters

abstract: Neutral-point-clamped (NPC) inverters are the most widely used topology of multilevel inverters in high-power applications (several megawatts). This paper presents in a very simple way the basic operation and the most used modulation and control techniques developed to date. Special attention is paid to the loss distribution in semiconductors, and an active NPC inverter is presented to overcome this problem. This paper discusses the main fields of application and presents some technological problems such as capacitor balance and losses.


title: Large-Scale Cover Song Recognition Using the 2D Fourier Transform Magnitude

abstract: Large-scale cover song recognition involves calculating itemto-item similarities that can accommodate differences in timing and tempo, rendering simple Euclidean measures unsuitable. Expensive solutions such as dynamic time warping do not scale to million of instances, making them inappropriate for commercial-scale applications. In this work, we transform a beat-synchronous chroma matrix with a 2D Fourier transform and show that the resulting representation has properties that fit the cover song recognition task. We can also apply PCA to efficiently scale comparisons. We report the best results to date on the largest available dataset of around 18,000 cover songs amid one million tracks, giving a mean average precision of 3.0%.


End-to-End Deep Neural Network for Automatic Speech Recognition


title: The good side after stroke: ipsilateral sensory-motor function needs careful assessment.

abstract: Twenty subjects were examined 4-6 weeks after stroke to establish whether a sensory-motor ipsilateral deficit occurs early after stroke. Each underwent a timed test of repetitive side-to-side movement of both the upper and lower limbs ipsilateral to the cerebral infarct, and an assessment of motor disability using the Motor Assessment Scale. Results were compared with a group studied almost a year after their stroke, and with 41 age-matched healthy volunteers. There was a significantly worse performance (p < 0.005) on the right ipsilateral side, but not the left ipsilateral side, compared with normal volunteers, a finding similar to that of a group previously studied about a year after the stroke. There was no relationship between the severity of the motor deficit and performance of the side, possibly owing to reduction in cerebral activation as a result of a right hemispheric lesion. These observations have importance in rehabilitation and education as well as practical skills, including driving a car and maintaining balance.


title: Bayesian Compressive Sensing Using Laplace Priors

abstract: In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the sparsity of the unknown signal. We describe the relationship among a number of sparsity priors proposed in the literature, and show the advantages of the proposed model including its high degree of sparsity. Moreover, we show that some of the existing models are special cases of the proposed model. Using our model, we develop a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings. Unlike most existing CS reconstruction methods, the proposed algorithm is fully automated, i.e., the unknown signal coefficients and all necessary parameters are estimated solely from the observation, and, therefore, no user-intervention is needed. Additionally, the proposed algorithm provides estimates of the uncertainty of the reconstructions. We provide experimental results with synthetic 1-D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.


title: Geographic expansion of dengue: the impact of international travel.

abstract: Dengue has emerged as an international public health problem. Reasons for the resurgence of dengue in the tropics and subtropics are complex and include unprecedented urbanization with substandard living conditions, lack of vector control, virus evolution, and international travel. Of all these factors, urbanization has probably had the most impact on the amplification of dengue within a given country, and travel has had the most impact for the spread of dengue from country to country and continent to continent. Epidemics of dengue, their seasonality, and oscillations over time are reflected by the epidemiology of dengue in travelers. Sentinel surveillance of travelers could augment existing national public health surveillance systems.


title: Beyond Dwell Time: Estimating Document Relevance from Cursor Movements and other Post-click Searcher Behavior

abstract: Result clickthrough statistics and dwell time on clicked results have been shown valuable for inferring search result relevance, but the interpretation of these signals can vary substantially for different tasks and users. This paper shows that that post-click searcher behavior, such as cursor movement and scrolling, provides additional clues for better estimating document relevance. To this end, we identify patterns of examination and interaction behavior that correspond to viewing a relevant or non-relevant document, and design a new Post-Click Behavior (PCB) model to capture these patterns. To our knowledge, PCB is the first to successfully incorporate post-click searcher interactions such as cursor movements and scrolling on a landing page for estimating document relevance. We evaluate PCB on a dataset collected from a controlled user study that contains interactions gathered from hundreds of unique queries, result clicks, and page examinations. The experimental results show that PCB is significantly more effective than using page dwell time information alone, both for estimating the explicit judgments of each user, and for re-ranking the results using the estimated relevance.


title: PIC: Enable Large-Scale Privacy Preserving Content-Based Image Search on Cloud

abstract: Many cloud platforms emerge to meet urgent requirements for large-volume personal image store, sharing and search. Though most would agree that images contain rich sensitive information (e.g., People, location and event) and people's privacy concerns hinder their participation into untrusted services, today's cloud platforms provide little support for image privacy protection. Facing large-scale images from multiple users, it is extremely challenging for the cloud to maintain the index structure and schedule parallel computation without learning anything about the image content and indices. In this work, we introduce a novel system PIC: a Privacy-preserving Image search system on Cloud, which is a step towards feasible cloud services which provide secure content-based large-scale image search with fine-grained access control. Users can search on others' images if they are authorized by the image owners. Majority of the computationally intensive jobs are handled by the cloud, and a querier can now simply send the query and receive the result. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. Our security analysis and prototype system evaluation results show that PIC successfully protects the image privacy at a low cost of computation and communication.


title: Online Wavelet Transform-Based Control Strategy for UPQC Control System

abstract: In this paper, a new algorithm is proposed to estimate amplitude and phase angle (and frequency) of load currents and source voltages in the presence of harmonics and frequency oscillation. Moreover, a comprehensive control strategy is introduced to extract the compensating signals for the control of series and shunt converters of the unified power-quality conditioner (UPQC). The basic approach used in this algorithm is to extract the fundamental component of load current and source voltage by using wavelet-transform decomposition. Then, the amplitude, phase angle, and frequency of fundamental component are calculated using the well-known LS algorithm, which is relatively simple and fast. The voltage and current references of UPQC active power filters are obtained using the positive sequence of three-phase system voltages and load currents. The proposed control strategy can extract the reference currents and voltages of UPQC fast and accurately in the presence of harmonics and/or frequency oscillation. Simulation results have been presented to show improvement of the UPQC performance in the presence of frequency and amplitude variation condition and various forms of current and voltage distortion


title: What campus-based students think about the quality and benefits of e-learning

abstract: There is a trend in Irish universities to utilise the benefits of the e-learning as a mechanism to improve learning performance of campus-based students. Whilst traditional methods, such as face-to-face lectures, tutorials, and mentoring, remain dominant in the educational sector, universities are investing heavily in learning technologies, to facilitate improvements with respect to the quality of learning. The technology to support reuse and sharing of educational resources, or learning objects, is becoming more stable, with interoperability standards maturing. However, debate has raged about what constitutes effective use of learning technology. This research expands upon a study carried out in 2003 examining students' perceptions of e-learning in a large undergraduate accounting class environment. As a result, improvements were made to the instructional design of the course, to enable students to engage interactively with content. The subsequent study, reported in this paper, adopted a broad range of techniques to understand students' learning experience in depth. The findings of this research provide an insight into how these students really work and learn using technologies, if at all. It is hoped that our findings will improve the experience for both students and lecturers who engage in teaching and learning through this medium. © British Educational Communications and Technology Agency, 2005.


Examining the Effectiveness of IR Techniques for Document Image Retrieval


title: 40 Gb/s Transimpedance-AGC Amplifier and CDR Circuit for Broadband Data Receivers in 90 nm CMOS

abstract: High-speed front-end amplifiers and CDR circuits play critical roles in broadband data receivers as the former needs to perform amplification at high data rate and the latter has to retime the data with the extracted low-jitter clock. In this paper, the design and experimental results of 40 Gb/s transimpedance-AGC amplifier and CDR circuit are described. The transimpedance amplifier incorporates reversed triple-resonance networks (RTRNs) and negative feedback in a common-gate configuration. A mathematical model is derived to facilitate the design and analysis of the RTRN, showing that the bandwidth is extended by a larger factor compared to using the shunt-series peaking technique, especially in cases when the parasitic capacitance is dominated by the next stage. Operating at 40 Gb/s, the amplifier provides an overall gain of 2 kOmega and a differential output swing of 520 mVpp with for input spanning from to . The measured integrated input-referred noise is 3.3muArms. The half-rate CDR circuit employs a direction-determined rotary-wave quadrature VCO to solve the bidirectional-rotation problem in conventional rotary-wave oscillators. This guarantees the phase sequence while negligibly affecting the phase noise. With 40 Gb/s 231 - 1 PRBS input, the recovered clock jitter is and 0.7psrms. The retimed data exhibits 13.3 pspp jitter with BER . Fabricated in 90 nm digital CMOS technology, the overall amplifier consumes 75 mW and the CDR circuit consumes 48 mW excluding the output buffers, all from a 1.2 V supply.


title: Aplysia capsulin is localized to egg capsules and egg cordon sheaths and shares sequence homology with Drosophila dec-1 gene products

abstract: Differential library screening of an albumen gland cDNA library, Western blot analysis, protein expression, immunolocalization studies, comparative genomics, and secretion assays identified a major Aplysia californica albumen gland protein ('capsulin') that is localized to egg capsules and to the sheaths of the egg cordon. Capsulin shared sequence homology with eggshell proteins encoded by the Drosophila dec-1 gene. The 1790-amino acid A. californica precursor contains 17 repeat sequences that are flanked by basic residue processing sites. The numerous proteolytic processing sites may facilitate the breakdown of capsulin prior to when veliger larvae break out of egg capsules as free-swimming larvae. An Aplysia brasiliana capsulin repeat sequence was 97% identical to its A. californica homolog. Capsulin fragments were not detected in the eluates of egg cordons, suggesting that capsulin is not a candidate water-borne pheromone precursor.


title: Enhanced Privacy and Authentication: An Efficient and Secure Anonymous Communication for Location Based Service Using Asymmetric Cryptography Scheme

abstract: Past few years, the mobile technology and location based services have experienced a great increment in number of its users. The privacy issues related to these services are becoming main concerns because of the leakage of users' private information and contents. To prevent revelation of private information, many researchers have proposed several secure and authentication schemes which apply various technologies to provide integral security properties, such as symmetric encryption, digital signature, timestamp, etc. Unfortunately, some of these schemes still exhibit security and efficiency issues. In this research paper, we proposed an efficient and secure anonymous communication for location based service using asymmetric cryptography scheme over the wireless system was attempted missing some system detail. We also proposed the prevent user private information and secure communication by asymmetric cryptography scheme. We solved the wireless communication problem in A3 algorithm such as eavesdropping and this problem solved by asymmetric cryptography scheme because of its robustness against this type of attack by providing mutual authentication make the system more secure. Finally, performance and cost analysis show our scheme is more suitable for low-power and resource limited wireless system and thus availability for real implementation. According to our security analysis and performance, we can prove that our proposed asymmetric cryptography scheme is able to improve wireless communication system security and enhance efficiency in comparison to previous schemes.


title: Bobath Concept versus constraint-induced movement therapy to improve arm functional recovery in stroke patients: a randomized controlled trial

abstract: OBJECTIVE ::: To compare the effects of the Bobath Concept and constraint-induced movement therapy on arm functional recovery among stroke patients with a high level of function on the affected side. ::: ::: ::: DESIGN ::: A single-blinded, randomized controlled trial. ::: ::: ::: SETTING ::: Outpatient physiotherapy department of a stroke unit. ::: ::: ::: SUBJECTS ::: A total of 24 patients were randomized to constraint-induced movement therapy or Bobath Concept group. ::: ::: ::: INTERVENTION ::: The Bobath Concept group was treated for 1 hour whereas the constraint-induced movement therapy group received training for 3 hours per day during 10 consecutive weekdays. ::: ::: ::: MAIN MEASURES ::: Main measures were the Motor Activity Log-28, the Wolf Motor Function Test, the Motor Evaluation Scale for Arm in Stroke Patients and the Functional Independence Measure. ::: ::: ::: RESULTS ::: The two groups were found to be homogeneous based on demographic variables and baseline measurements. Significant improvements were seen after treatment only in the 'Amount of use' and 'Quality of movement' subscales of the Motor Activity Log-28 in the constraint-induced movement therapy group over the the Bobath Concept group (P = 0.003; P = 0.01 respectively). There were no significant differences in Wolf Motor Function Test 'Functional ability' (P = 0.137) and 'Performance time' (P = 0.922), Motor Evaluation Scale for Arm in Stroke Patients (P = 0.947) and Functional Independence Measure scores (P = 0.259) between the two intervention groups. ::: ::: ::: CONCLUSIONS ::: Constraint-induced movement therapy and the Bobath Concept have similar efficiencies in improving functional ability, speed and quality of movement in the paretic arm among stroke patients with a high level of function. Constraint-induced movement therapy seems to be slightly more efficient than the Bobath Concept in improving the amount and quality of affected arm use.


title: Positive Feedforward Control of Three-Phase Voltage Source Inverter for DC Input Bus Stabilization With Experimental Validation

abstract: A positive feedforward (PFF) controller is proposed as a new active approach to improve stability of a three-phase voltage source inverter. In particular, the proposed approach solves the source subsystem interaction problem, i.e., a system stability degradation which is commonly observed when the inverter is connected to a dc voltage source subsystem that presents finite Thevenin impedance. The implemented strategy is to combine the PFF control with the conventional negative feedback (NFB) control. When properly designed, the PFF control modifies the inverter input impedance in the frequency range where the subsystem interaction occurs. As a result, the PFF control stabilizes the dc bus voltage ensuring overall system stability, while allowing the NFB control to maintain good output voltage regulation performance. The approach results in greatly improved stability and damping of the dc bus voltage with a slight reduction of output FB control bandwidth. Complete small-signal models are presented on a dq rotating frame using g-parameter representation. The design criteria for the PFF controller as well as the tradeoff between dynamic output performance and stability improvement are discussed in detail. An extended analysis of stability based on minor loop gain and passivity concept at the dc input bus is also presented. The approach is validated by experimental results.


title: Phase-Based Methods for Heart Rate Detection Using UWB Impulse Doppler Radar

abstract: Ultra-wideband (UWB) pulse Doppler radars can be used for noncontact vital signs monitoring of more than one subject. However, their detected signals typically have low signal-to-noise ratio (SNR) causing significant heart rate (HR) detection errors, as the spurious harmonics of respiration signals and mixed products of respiration and heartbeat signals (that can be relatively higher than heartbeat signals) corrupt conventional fast Fourier transform spectrograms. In this paper, we extend the complex signal demodulation (CSD) and arctangent demodulation (AD) techniques previously used for accurately detecting the phase variations of reflected signals of continuous wave radars to UWB pulse radars as well. These detection techniques reduce the impact of the interfering harmonic signals, thus improving the SNR of the detected vital sign signals. To further enhance the accuracy of the HR estimation, a recently developed state-space method has been successfully combined with CSD and AD techniques and over 10 dB improvements in SNR is demonstrated. The implementation of these various detection techniques has been experimentally investigated and full error and SNR analysis of the HR detection are presented.


title: Concept Modeling and Its Application to Patent Modeling

abstract: This paper briefly introduces the notion of concept modeling, explore some existing techniques, and discuss their advantages and drawbacks. In an effort to find a general concept model, focus of our research was primarily on documents describing patents. Proposed is model for patents which allows efficient detection of individual concepts and relations among them, both within and across patent boundaries. This approach is based on a hybrid solution - employing existing conceptual indexing technique for extraction of individual concepts, and RDF/OWL descriptions for patent-specific data.


title: Consumer Choice for Quality and Sustainability in Seafood Products: Empirical Findings from United Kingdom

abstract: There has been a growing interest in recent years in the poten tial use of product differentiation (through eco-type labelling) as a means of promoting and rewarding the sustainable management and exploitation of fish stocks. This interest is marked by the growing literature on the topic, exploring both the concept and the key issues associated with it. It reflects a frustration among certain groups with the supply-side measures currently employed in fisheries management, which on their own have proven insufficient to counter the negative incentive structures characterising open-a ccess fisheries. The potential encapsulated by product differentiation has, however, yet to be tested in the market place. One of the debates that continues to accompany the concept is the nature and extent of the response of consumers to the introduction of labelled seafood products. Though differentiated seafood products are starting to come onto the market, we are still essentially dealing with a hypothetical market situation in terms of analysing consumer behaviour. Moving the debate from theoretical extrapolation to one of empirical evidence, this paper presents the preliminary empirical results of a study undertaken in the UK. The study aimed, amongst other things, to evaluate whether UK consumers are prepared to pay a premium for seafood products that are differentiated on the grounds that the fish is either of (a) high quality or (b) comes from a sustainably managed fishery. It also aimed to establish whether the quantity of fish products purchased would change. The results are presented in this paper.


title: Sensitivity Study of the Design of Unnotched and Notched Composite Laminates

abstract: A sensitivity study of the optimal design of composite laminates can aid in the understanding and improvement of the utilization of materials. One of the main ap proaches is to consider the fiber volume fraction of materials from the micromechanics point of view. This article adopts primarily the design point of view. Several examples with two material systems and two groups of laminates (unnotched and notched) are m- vestigated to show the property changes of the laminates due to variation of the fiber volume fraction, and the subsequent rankings of the laminates are given.


title: The Impact of Flow on Online Consumer Behavior

abstract: Previous research has acknowledged flow as a useful construct for explaining online consumer behavior. However, there is dearth of knowledge about what dimensions of flow and how they actually influence online consumer behavior as flow is difficult to conceptualize and measure. This research examines flow and its effects on online consumer behavior in a unified model which draws upon theory of planned behavior (TPB). The four important dimensions of flow (concentration, enjoyment, time distortion, telepresence) are explored in terms of their antecedent effects on online consumer behavior. Results of this empirical study show that flow influences online consumer behavior through several important latent constructs. Findings of this research not only extend the existing knowledge of flow and its antecedent effects on online consumer behavior but also provide new insights into how flow can be conceptualized and studied in the e-commerce setting.


title: Identification of Biomarkers for Endometriosis Using Clinical Proteomics

abstract: BACKGROUND ::: We investigated possible biomarkers for endometriosis (EM) using the ClinProt technique and proteomics methods. ::: ::: ::: METHODS ::: We enrolled 50 patients with EM, 34 with benign ovarian neoplasms and 40 healthy volunteers in this study. Serum proteomic spectra were generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS) combined with weak cationic exchange (WCX) magnetic beads. Possible biomarkers were analyzed by a random and repeat pattern model-validation method that we designed, and ClinProtools software, results were refined using online liquid chromatography-tandem MS. ::: ::: ::: RESULTS ::: We found a cluster of 5 peptides (4210, 5264, 2660, 5635, and 5904 Da), using 3 peptides (4210, 5904, 2660 Da) to discriminate EM patients from healthy volunteers, with 96.67% sensitivity and 100% specificity. We selected 4210 and 5904 m/z, which differed most between patients with EM and controls, and identified them as fragments of ATP1B4, and the fibrinogen alpha (FGA) isoform 1/2 of the FGA chain precursor, respectively. ::: ::: ::: CONCLUSIONS ::: ClinProt can identify EM biomarkers, which - most notably - distinguish even early-stage or minimal disease. We found 5 stable peaks at 4210, 5264, 2660, 5635, and 5904 Da as potential EM biomarkers, the strongest of which were associated with ATP1B4 (4210 Da) and FGA (5904 Da); this indicates that ATP1B4 and FGA are associated with EM pathogenesis.


title: A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification

abstract: Multi-label text classification (MLTC) aims to assign multiple labels to each sample in the dataset. The labels usually have internal correlations. However, traditional methods tend to ignore the correlations between labels. In order to capture the correlations between labels, the sequence-to-sequence (Seq2Seq) model views the MLTC task as a sequence generation problem, which achieves excellent performance on this task. However, the Seq2Seq model is not suitable for the MLTC task in essence. The reason is that it requires humans to predefine the order of the output labels, while some of the output labels in the MLTC task are essentially an unordered set rather than an ordered sequence. This conflicts with the strict requirement of the Seq2Seq model for the label order. In this paper, we propose a novel sequence-to-set framework utilizing deep reinforcement learning, which not only captures the correlations between labels, but also reduces the dependence on the label order. Extensive experimental results show that our proposed method outperforms the competitive baselines by a large margin.


title: The computational geometry algorithms library CGAL

abstract: The Computational Geometry Algorithms Library (CGAL) is an open source software library that provides industrial and academic users with easy access to reliable implementations of efficient geometric algorithms.


Discourse Analysis of Lyric and Lyric-Based Classification of Music


title: A small area 10-bit linear gamma DAC with voltage adder for large-sized active matrix flat panel displays

abstract: This paper proposes a 10-bit linear gamma digital-to-analog converter (DAC) of source driver ICs for large-sized active matrix flat panel displays (AMFPDs). The proposed DAC employs the voltage adder to reduce the area of source driver IC. The voltage adder not only reduces the size of 7-bit DAC to about half by adding the fixed one least significant bit (LSB) voltage but also quickly settles the output to the target voltage by its pre-charged output. To verify the performance of the proposed DAC, the simulation and layout are performed with a 55-inch full high-definition active matrix organic light emitting diodes (AMOLED) display. The simulation result of the proposed DAC shows INL of 0.98 LSB and DNL of 0.69 LSB. The proposed DAC uses only 57.3% of number of transistors compared to the conventional 10-bit DAC with a 3-bit embedded amplifier, which results in reducing the area of source driver IC for AMFPDs significantly.


title: Aligned Cluster Analysis for temporal segmentation of human motion

abstract: Temporal segmentation of human motion into actions is a crucial step for understanding and building computational models of human motion. Several issues contribute to the challenge of this task. These include the large variability in the temporal scale and periodicity of human actions, as well as the exponential nature of all possible movement combinations. We formulate the temporal segmentation problem as an extension of standard clustering algorithms. In particular, this paper proposes aligned cluster analysis (ACA), a robust method to temporally segment streams of motion capture data into actions. ACA extends standard kernel k-means clustering in two ways: (1) the cluster means contain a variable number of features, and (2) a dynamic time warping (DTW) kernel is used to achieve temporal invariance. Experimental results, reported on synthetic data and the Carnegie Mellon Motion Capture database, demonstrate its effectiveness.


title: Gender differences in the climate change communication on Twitter

abstract: Purpose – The purpose of this paper is to present a study about gender differences in the climate change communication on Twitter and in the use of affordances on Twitter. Design/methodology/approach – The data set consists of about 250,000 tweets and retweets for which the authors’ gender was identified. While content of tweets and hashtags used were analysed for common topics and specific contexts, the usernames that were proportionately more frequently mentioned by either male or female tweeters were coded according to the usernames’ stance in the climate change debate into convinced (that climate change is caused by humans), sceptics, neutrals and unclear groups, and according to the type or role of the user account (e.g. campaign, organization, private person). Findings – The results indicate that overall male and female tweeters use very similar language in their tweets, but clear differences were observed in the use of hashtags and usernames, with female tweeters mentioning significantly more campa...


title: A cookbook method for Persuasive Game Design

abstract: Despite the growing interest in persuasive game design, there have been few methods which cover the complete process of game design that designers could draw upon in their practice. In this paper, the Persuasive Game Design method(PGD) is presented as a non-directive approach for designing persuasive games including a practical hand-out. To better fit with the practical constraints encountered in game design, this method adopts a “cookbook” approach. A set of essential PGD components and tools are provided from which game designers can choose from, given their specific context and resources. Designers first consider the game design steps(“dishes”) to use in creating their game and in each step, select which components(“ingredients”) to take into account and tools(“utensils”) to use. The proposed method, based on our experience as persuasive game researchers and design practitioners, is further refined using feedback from professional game designers. The paper concludes with a case study illustrating how to put the meal into practice. Overall, the method provides a useful contribution to the existing research domain by combining knowledge from game theory, game design and design methodology to create a structured yet flexible approach which covers the complete persuasive game design process for researchers, students and practitioners.


title: Implementing Smith-Waterman Algorithm with Two-Dimensional Cache on GPUs

abstract: Finding regions of similarity between two data streams is a computational intensive and memory consuming problem, which refers to as sequence alignment for biological sequence. Smith-Waterman algorithm is an optimal method to find the local sequence alignment. It requires a large amount of computation and memory, and is also constrained by the memory access speed when accelerated by using Graphics Processing Units (GPUs). A new method to implement Smith-Waterman algorithm with two-dimensional cache is proposed, which aims at accelerating the first stage of Smith-Waterman algorithm and coalesced writing back the corresponding results to GPU global memory. Our proposal is implemented over NVIDIA Geforce GTX295 GPU, and compared with CUDASW++ 2.0. Experimental results show that our approach outperforms CUDASW++ 2.0 in the datasets chosen from NCBI.


title: A Fuzzy Similarity Based Concept Mining Model for Text Classification

abstract: Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. In this paper, a new Fuzzy Similarity Based Concept Mining Model (FSCMM) is proposed to classify a set of text documents into pre - defined Category Groups (CG) by providing them training and preparing on the sentence, document and integrated corpora levels along with feature reduction, ambiguity removal on each level to achieve high system performance. Fuzzy Feature Category Similarity Analyzer (FFCSA) is used to analyze each extracted feature of Integrated Corpora Feature Vector (ICFV) with the corresponding categories or classes. This model uses Support Vector Machine Classifier (SVMC) to classify correctly the training data patterns into two groups; i. e., + 1 and - 1, thereby producing accurate and correct results. The proposed model works efficiently and effectively with great performance and high - accuracy results.


title: Novel helical resonator filter structures

abstract: Helical resonator filters are well-known and widely used at lower microwave frequencies. Two novel helical resonator filter configurations for applications in wireless and satellite communication systems are described in this paper. This includes miniature filters utilizing single mode, high dielectric constant material loaded helical resonators, as well as higher order dual mode helical resonator loaded cavities. Both configurations can be used to realize advanced filters with non-adjacent couplings. The basic filter configurations are discussed and experimental results are presented.


4 D Printing for Freeform Surfaces : Design Optimization of Origami and Kirigami Structures


title: Signal Processing for Passive Radar Using OFDM Waveforms

abstract: Passive radar is a concept where illuminators of opportunity are used in a multistatic radar setup. New digital signals, like digital audio/video broadcast (DAB/DVB), are excellent candidates for this scheme, as they are widely available, can be easily decoded to acquire the noise-free signal, and employ orthogonal frequency division multiplex (OFDM). Multicarrier transmission schemes like OFDM use block channel equalization in the frequency domain, efficiently implemented as a fast Fourier transform, and these channel estimates can directly be used to identify targets based on Fourier analysis across subsequent blocks. In this paper, we derive the exact matched filter formulation for passive radar using OFDM waveforms. We then show that the current approach using Fourier analysis across block channel estimates is equivalent to the matched filter, based on a piecewise constant assumption on the Doppler-induced phase rotation in the time domain. We next present high-resolution algorithms based on the same assumption: first we implement MUSIC as a 2-D spectral estimator using spatial smoothing; then we use the new concept of compressed sensing to identify targets. We compare the new algorithms and the current approach using numerical simulation and experimental data recorded from a DAB network in Germany.


title: Twitter Hashtag Recommendation using Matrix Factorization

abstract: Twitter, one of the biggest and most popular microblogging Websites, has evolved into a powerful communication platform which allows millions of active users to generate huge volume of microposts and queries on a daily basis. To accommodate effective categorization and easy search, users are allowed to make use of hashtags, keywords or phrases prefixed by hash character, to categorize and summarize their posts. However, valid hashtags are not restricted and thus are created in a free and heterogeneous style, increasing difficulty of the task of tweet categorization. In this paper, we propose a low-rank weighted matrix factorization based method to recommend hashtags to the users solely based on their hashtag usage history and independent from their tweets' contents. We confirm using two-sample t-test that users are more likely to adopt new hashtags similar to the ones they have previously adopted. In particular, we formulate the problem of hashtag recommendation into an optimization problem and incorporate hashtag correlation weight matrix into it to account for the similarity between different hashtags. We finally leverage widely used matrix factorization from recommender systems to solve the optimization problem by capturing the latent factors of users and hashtags. Empirical experiments demonstrate that our method is capable to properly recommend hashtags.


title: Saliency detection for content-aware image resizing

abstract: Content aware image re-targeting methods aim to arbitrarily change image aspect ratios while preserving visually prominent features. To determine visual importance of pixels, existing re-targeting schemes mostly rely on grayscale intensity gradient maps. These maps show higher energy only at edges of objects, are sensitive to noise, and may result in deforming salient objects. In this paper, we present a computationally efficient, noise robust re-targeting scheme based on seam carving by using saliency maps that assign higher importance to visually prominent whole regions (and not just edges). This is achieved by computing global saliency of pixels using intensity as well as color features. Our saliency maps easily avoid artifacts that conventional seam carving generates and are more robust in the presence of noise. Also, unlike gradient maps, which may have to be recomputed several times during a seam carving based re-targeting operation, our saliency maps are computed only once independent of the number of seams added or removed.


title: Completing sparse and disconnected protein-protein network by deep learning

abstract: Background ::: Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge.


title: Sentiment analysis of tweets for the 2016 US presidential election

abstract: Twitter is a popular micro-blogging social media platform. For the 2016 US Presidential election, many people expressed their likes or dislikes for a particular presidential candidate. Our work's aim was to calculate the sentiment expressed by these tweets, and then compare this sentiment with polling data to see how much correlation they share. We used a lexicon and Naive Bayes Machine Learning Algorithm to calculate the sentiment of political tweets collected one-hundred days before the election. We used manually labeled tweets as well as automatically labeled tweets based on hashtag content/topic. Our results suggest that Twitter is becoming a more reliable platform in comparison to previous work. By focusing on tweets 43 days before the election (beginning with the first presidential debate), we found a correlation as high as 94% to polling data using a moving average smoothing technique.


title: Magnetic mechanisms of magnetic flux leakage nondestructive testing

abstract: The widely applied magnetic flux leakage (MFL) nondestructive testing for ferrous materials has been simply explained by “MFL escaping from defects and being induced is exactly the MFL testing principle,” with a rough application for evaluation. Hence, we analyze the “escape” course of MFL generation using magnetic refraction and explain the reason why the MFL technology really exists in practice. Furthermore, we also point out that the presently so-called MFL testing technique essentially hides a different signal component arising from bump-shaped defects features unlike the MFL caused by concave-shaped ones, explained by another forming mechanism.


title: Large Scale Kernel Learning using Block Coordinate Descent

abstract: We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points. Armed with this capability, we conduct a thorough comparison between the full kernel, the Nystr\"om method, and random features on three large classification tasks from various domains. Our results suggest that the Nystr\"om method generally achieves better statistical accuracy than random features, but can require significantly more iterations of optimization. Lastly, we derive new rates for block coordinate descent which support our experimental findings when specialized to kernel methods.


title: HapTip: Displaying Haptic Shear Forces at the Fingertips for Multi-Finger Interaction in Virtual Environments

abstract: The fingertips are one of the most important and sensitive parts of our body. They are the first stimulated areas of the hand when we interact with our environment. Providing haptic feedback to the fingertips in virtual reality could thus drastically improve perception and interaction with virtual environments. In this paper, we present a modular approach called HapTip to display such haptic sensations at the level of the fingertips. This approach relies on a wearable and compact haptic device able to simulate 2 Degree of Freedom (DoF) shear forces on the fingertip with a displacement range of +/- 2 mm. Several modules can be added and used jointly in order to address multi-finger and/or bimanual scenarios in virtual environments. For that purpose, we introduce several haptic rendering techniques to cover different cases of 3D interaction such as touching a rough virtual surface, or feeling the inertia or weight of a virtual object. In order to illustrate the possibilities offered by HapTip, we provide four use cases focused on touching or grasping virtual objects. To validate the efficiency of our approach, we also conducted experiments to assess the tactile perception obtained with HapTip. Our results show that participants can successfully discriminate the directions of the 2 DoF stimulation of our haptic device. We found also that participants could well perceive different weights of virtual objects simulated using two HapTip devices. We believe that HapTip could be used in numerous applications in virtual reality for which 3D manipulation and tactile sensations are often crucial, such as in virtual prototyping or virtual training.


title: Flexible Neural Representation for Physics Prediction

abstract: Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of detail. Inspired by this ability, we propose a hierarchical particle-based object representation that covers a wide variety of types of three-dimensional objects, including both arbitrary rigid geometrical shapes and deformable materials. We then describe the Hierarchical Relation Network (HRN), an end-to-end differentiable neural network based on hierarchical graph convolution, that learns to predict physical dynamics in this representation. Compared to other neural network baselines, the HRN accurately handles complex collisions and nonrigid deformations, generating plausible dynamics predictions at long time scales in novel settings, and scaling to large scene configurations. These results demonstrate an architecture with the potential to form the basis of next-generation physics predictors for use in computer vision, robotics, and quantitative cognitive science.


title: Low- and high-anxious hypermobile Ehlers–Danlos syndrome patients: comparison of psychosocial and health variables

abstract: Despite the frequent co-ocurrence of hypermobile Ehler-Danlos syndrome (hEDS) and pathological anxiety, little is known about the psychosocial and health implications of such comorbidity. Our aim was to explore the association between high levels of anxiety and psychosocial (catastrophizing, kinesiophobia, somatosensory amplification, social support and functioning), health (pain, fatigue, BMI, tobacco/alcohol use, depression, diagnosis delay, general health), and sociodemographic factors in people with hEDS. In this cross-sectional study, 80 hEDS patients were divided into two groups according to self-reported anxiety levels: low and high. Psychosocial, sociodemographic and health variables were compared between the groups. Forty-one participants reported a high level of anxiety (51.2%). No differences were found in the sociodemographic variables between high-anxious and low-anxious patients. The percentage of participants with severe fatigue and high depressive symptomatology was significantly higher in the high-anxious group (80.5 vs 56.4; 26.8 vs 12.8%, respectively). High-anxious hEDS patients also showed significantly higher levels of pain catastrophizing, somatosensory amplification as well as a poorer social functioning and general health. Multivariate analyses showed that somatosensory amplification, pain catastrophizing and poor social functioning are variables that increase the probability of belonging to the high-anxious group. Despite limitations, this first study comparing high-anxious versus low-anxious hEDS patients with respect to health aspects, highlight the importance of considering the psychosocial factors (many susceptible to modification), to improve the adjustment to this chronic condition and provide support to those affected through a biopsychosocial approach.