title: Classifier systems and genetic algorithms

abstract: Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of competing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems.


Deep Recurrent Convolutional Neural Networks for Classifying P300 BCI signals.


title: Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes.

abstract: We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to error $\varepsilon$ in total variation distance. This improves both the known upper bounds and lower bounds for this problem. For mixtures of axis-aligned Gaussians, we show that $\tilde{O}(k d / \varepsilon^2)$ samples suffice, matching a known lower bound. Moreover, these results hold in the agnostic-learning/robust-estimation setting as well, where the target distribution is only approximately a mixture of Gaussians. ::: The upper bound is shown using a novel technique for distribution learning based on a notion of `compression.' Any class of distributions that allows such a compression scheme can also be learned with few samples. Moreover, if a class of distributions has such a compression scheme, then so do the classes of products and mixtures of those distributions. The core of our main result is showing that the class of Gaussians in $\mathbb{R}^d$ admits a small-sized compression scheme.


title: Finite-Approximation-Error-Based Optimal Control Approach for Discrete-Time Nonlinear Systems

abstract: In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite-horizon discrete-time nonlinear systems with finite approximation errors. The idea is to use an iterative ADP algorithm to obtain the iterative control law that makes the iterative performance index function reach the optimum. When the iterative control law and the iterative performance index function in each iteration cannot be accurately obtained, the convergence conditions of the iterative ADP algorithm are obtained. When convergence conditions are satisfied, it is shown that the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some mild assumptions. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.


title: Interoceptive predictions in the brain

abstract: Intuition suggests that perception follows sensation and therefore bodily feelings originate in the body. However, recent evidence goes against this logic: interoceptive experience may largely reflect limbic predictions about the expected state of the body that are constrained by ascending visceral sensations. In this Opinion article, we introduce the Embodied Predictive Interoception Coding model, which integrates an anatomical model of corticocortical connections with Bayesian active inference principles, to propose that agranular visceromotor cortices contribute to interoception by issuing interoceptive predictions. We then discuss how disruptions in interoceptive predictions could function as a common vulnerability for mental and physical illness.


title: The cognitive neuroscience of constructive memory: remembering the past and imagining the future

abstract: Episodic memory is widely conceived as a fundamentally constructive, rather than reproductive, process that is prone to various kinds of errors and illusions. With a view towards examining the functions served by a constructive episodic memory system, we consider recent neuropsychological and neuroimaging studies indicating that some types of memory distortions reflect the operation of adaptive processes. An important function of a constructive episodic memory is to allow individuals to simulate or imagine future episodes, happenings and scenarios. Since the future is not an exact repetition of the past, simulation of future episodes requires a system that can draw on the past in a manner that flexibly extracts and recombines elements of previous experiences. Consistent with this constructive episodic simulation hypothesis, we consider cognitive, neuropsychological and neuroimaging evidence showing that there is considerable overlap in the psychological and neural processes involved in remembering the past and imagining the future.


title: Exercise‐induced neuroplasticity: Balance training increases cortical thickness in visual and vestibular cortical regions

abstract: Abstract Physical exercise has been shown to induce structural plasticity in the human brain and to enhance cognitive functions. While previous studies focused on aerobic exercise, suggesting a link between increased cardiorespiratory fitness and exercise-induced neuroplasticity, recent findings have suggested that whole-body exercise with minor metabolic demands elicits beneficial effects on brain structure as well. In the present study, we tested if balance training, challenging the sensory-motor system and vestibular self-motion perception, induces structural plasticity. Thirty-seven healthy adults aged 19–65 years were randomly assigned to either a balance training or a relaxation training group. All participants exercised twice a week for 12 weeks. Assessments before and after the training included a balance test and the acquisition of high-resolution T1-weighted images to analyze morphological brain changes. Only the balance group significantly improved balance performance after training. Cortical thickness was increased in the superior temporal cortex, in visual association cortices, in the posterior cingulate cortex, in the superior frontal sulcus, and in the precentral gyri in the balance group, compared to the relaxation group. Moreover, there was evidence that the balance training resulted in decreased putamen volume. Improved balance performance correlated with the increase of precentral cortical thickness and the decrease in putamen volume. The results suggest that balance training elicits neuroplasticity in brain regions associated with visual and vestibular self-motion perception. As these regions are known for their role in spatial orienting and memory, stimulating visual-vestibular pathways during self-motion might mediate beneficial effects of physical exercise on cognition.


title: Practice exams make perfect: incorporating course resource use into an early warning system

abstract: Early Warning Systems (EWSs) are being developed and used more frequently to aggregate multiple sources of data and provide timely information to stakeholders about students in need of academic support. As these systems grow more complex, there is an increasing need to incorporate relevant and real-time course-related information that could be predictors of a student's success or failure. This paper presents an investigation of how to incorporate students' use of course resources from a Learning Management System (LMS) into an existing EWS. Specifically, we focus our efforts on understanding the relationship between course resource use and a student's final course grade. Using ten semesters of LMS data from a requisite Chemistry course, we categorized course resources into four categories. We used a multinomial logistic regression model with semester fixed-effects to estimate the relationship between course resource use and the likelihood that a student receives an "A" or "B" in the course versus a "C." Results suggest that students who use Exam Preparation or Lecture resources to a greater degree than their peers are more likely to receive an "A" or "B" as a final grade. We discuss the implications of our results for the further development of this EWS and EWSs in general.


title: Vectorized Bloom filters for advanced SIMD processors

abstract: Analytics are at the core of many business intelligence tasks. Efficient query execution is facilitated by advanced hardware features, such as multi-core parallelism, shared-nothing low-latency caches, and SIMD vector instructions. Only recently, the SIMD capabilities of mainstream hardware have been augmented with wider vectors and non-contiguous loads termed gathers. While analytical DBMSs minimize the use of indexes in favor of scans based on sequential memory accesses, some data structures remain crucial. The Bloom filter, one such example, is the most efficient structure for filtering tuples based on their existence in a set and its performance is critical when joining tables with vastly different cardinalities. We introduce a vectorized implementation for probing Bloom filters based on gathers that eliminates conditional control flow and is independent of the SIMD length. Our techniques are generic and can be reused for accelerating other database operations. Our evaluation indicates a significant performance improvement over scalar code that can exceed 3X when the Bloom filter is cache-resident.


title: Urban Sensing Using Mobile Phone Network Data: A Survey of Research

abstract: The recent development of telecommunication networks is producing an unprecedented wealth of information and, as a consequence, an increasing interest in analyzing such data both from telecoms and from other stakeholders' points of view. In particular, mobile phone datasets offer access to insights into urban dynamics and human activities at an unprecedented scale and level of detail, representing a huge opportunity for research and real-world applications. This article surveys the new ideas and techniques related to the use of telecommunication data for urban sensing. We outline the data that can be collected from telecommunication networks as well as their strengths and weaknesses with a particular focus on urban sensing. We survey existing filtering and processing techniques to extract insights from this data and summarize them to provide recommendations on which datasets and techniques to use for specific urban sensing applications. Finally, we discuss a number of challenges and open research areas currently being faced in this field. We strongly believe the material and recommendations presented here will become increasingly important as mobile phone network datasets are becoming more accessible to the research community.


Achieving high cache hit ratios for CDN memory caches with size-aware admission


title: Unmanned surface vehicles: An overview of developments and challenges

abstract: Abstract With growing worldwide interest in commercial, scientific, and military issues associated with both oceans and shallow waters, there has been a corresponding growth in demand for the development of unmanned surface vehicles (USVs) with advanced guidance, navigation and control (GNC) capabilities. This paper presents a comprehensive literature review of recent progress in USVs development. The paper first provides an overview of both historical and recent USVs development, along with some fundamental definitions. Next, existing USVs GNC approaches are outlined and classified according to various criteria, such as their applications, methodologies, and challenges. Finally, more general challenges and future directions of USVs towards more practical GNC capabilities are highlighted.


title: Holistic Approach to Fraud Management in Health Insurance

abstract: Fraud present an immense problem for health insurance companies and the only way to fight fraud is by using specialized fraud management systems. The current research community focussed great efforts on different fraud detection techniques while neglecting other also important activities of fraud management. We propose a holistic approach that focuses on all 6 activities of fraud management, namely, (1) deterrence, (2) prevention, (3) detection, (4) investigation, (5) sanction and redress, and (6) monitoring. The main contribution of the paper are 15 key characteristics of a fraud management system, which enable construction of a fraud management system that provides effective and efficient support to all fraud management activities. We base our research on literature review, interviews with experts from different fields, and a case study. The case study provides additional confirmation to expert opinions, as it puts our holistic framework into practice.


title: Towards a Big Data Architecture for Facilitating Cyber Threat Intelligence

abstract: Internet and organizational network security is still threatened by devastating malicious activities. Given the continuous escalation of such attacks in terms of their frequency, sophistication and stealthiness, it is of paramount importance to generate effective cyber threat intelligence that aim at inferring, attributing, characterizing and mitigating such misdemeanors. Nevertheless, such imperative tasks are partially impeded by the lack of approaches that can produce prompt and accurate actionable intelligence by investigating various network traffic sources. In this paper, we propose and evaluate a big data architecture that is rooted in real-time network traffic processing, distributed messaging and scalable data storage. The key innovation behind the proposed architecture is that it automates the analysis of heterogeneous network data, allowing the focus to remain on devising effective cyber threat intelligence analytics, rather than being hindered by data management, aggregation, reconciliation and formatting. Empirical evaluations investigating the application of machine learning analytics by exploiting the artifacts of the proposed architecture and by using 100 GB of real network traffic, indeed demonstrate the practicality, effectiveness, and addedvalue of the proposed architecture.


title: The design and implementation of Zap: a system for migrating computing environments

abstract: We have created Zap, a novel system for transparent migration of legacy and networked applications. Zap provides a thin virtualization layer on top of the operating system that introduces pods, which are groups of processes that are provided a consistent, virtualized view of the system. This decouples processes in pods from dependencies to the host operating system and other processes on the system. By integrating Zap virtualization with a checkpoint-restart mechanism, Zap can migrate a pod of processes as a unit among machines running independent operating systems without leaving behind any residual state after migration. We have implemented a Zap prototype in Linux that supports transparent migration of unmodified applications without any kernel modifications. We demonstrate that our Linux Zap prototype can provide general-purpose process migration functionality with low overhead. Our experimental results for migrating pods used for running a standard user's X windows desktop computing environment and for running an Apache web server show that these kinds of pods can be migrated with subsecond checkpoint and restart latencies.


title: Processes and patterns in transitions and system innovations : Refining the co-evolutionary multi-level perspective

abstract: This article investigates transitions at the level of societal functions (e.g., transport, communication, housing). Societal functions are fulfilled by sociotechnical systems, which consist of a cluster of aligned elements, e.g., artifacts, knowledge, markets, regulation, cultural meaning, infrastructure, maintenance networks and supply networks. Transitions are conceptualised as system innovations, i.e., a change from one sociotechnical system to another. The article describes a co-evolutionary multi-level perspective to understand how system innovations come about through the interplay between technology and society. The article makes a new step as it further refines the multi-level perspective by distinguishing characteristic patterns: (a) two transition routes, (b) fit–stretch pattern, and (c) patterns in breakthrough.


title: Digital stethoscope: technology update

abstract: Cardiovascular disease (CVD) is recognized as the leading cause of mortality throughout the world. About one-third of global mortality is attributable to CVD. In addition to clinical presentation, specific clinical exam findings can assist in treating and preventing CVD. CVD may initially manifest as pulmonary pathology, and thus, accurate cardiopulmonary auscultation is paramount to establishing accurate diagnosis. One of the most powerful tools available to physicians is the stethoscope. The stethoscope first emerged in the year 1818, invented by a French physician, René Laennec. Since then, the initial modest monaural wooden tube has evolved into a sophisticated digital device. This paper provides an analysis of the evolution of the stethoscope as well as highlights the advancement made by the modern digital stethoscope including the application of this tool in advancing care for patients suffering from CVD.


title: Broadband radial waveguide power amplifier using a spatial power combining technique

abstract: A broadband radial waveguide power amplifier has been designed and fabricated using a spatial power dividing/combining technique. A simple electromagnetic model of this power-dividing/combining structure has been developed. Analysis based on equivalent circuits gives the design formula for perfect power-dividing/ combining circuits. The measured small-signal gain of the eight-device power amplifier is 12 - 16.5 dB over a broadband from 7 to 15 GHz. The measured maximum output power at 1-dB compression is 28.6 dBm at 10 GHz, with a power-combining efficiency of about 91%. Furthermore, the performance degradation of this power amplifier because of device failures has also been measured.


title: Boosting Convolutional Features for Robust Object Proposals

abstract: Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many categories, in particular for cluttered scenes and occlusion. Modern detection algorithms like Regions with CNNs (Girshick et al., 2014) rely on Selective Search (Uijlings et al., 2013) to propose regions which with high probability represent objects, where in turn CNNs are deployed for classification. Selective Search represents a family of sophisticated algorithms that are engineered with multiple segmentation, appearance and saliency cues, typically coming with a significant run-time overhead. Furthermore, (Hosang et al., 2014) have shown that most methods suffer from low reproducibility due to unstable superpixels, even for slight image perturbations. Although CNNs are subsequently used for classification in top-performing object-detection pipelines, current proposal methods are agnostic to how these models parse objects and their rich learned representations. As a result they may propose regions which may not resemble high-level objects or totally miss some of them. To overcome these drawbacks we propose a boosting approach which directly takes advantage of hierarchical CNN features for detecting regions of interest fast. We demonstrate its performance on ImageNet 2013 detection benchmark and compare it with state-of-the-art methods.


title: Trajectory tracking control for hovering and acceleration maneuver of Quad Tilt Rotor UAV

abstract: The ability to transition between vertical and horizontal flight is of great importance to convertible unmanned aerial vehicles. A novel convertible UAV-a Quad Tilt Rotor UAV (QTR UAV) was designed, which could operate as a quad rotor as well as an airplane by means of transition maneuver. This paper focuses on the trajectory tracking control for the hovering mode and acceleration maneuver which is the basis for the transition maneuver. First, a prototype QTR UAV was constructed to prove the concept and validate the proposed layout, which demonstrated good stability and control characteristics under manual control. Then dynamic model for full flight profile was constructed according to the first principle. A control strategy was scheduled for the transition maneuver. A nonlinear controller was designed based on dynamic inversion method. The effectiveness of the proposed control methodology was shown through numerical simulations.


title: Disturbance Effect on Time-Dependent Yield Stress Measurement of Bentonite Suspensions

abstract: Disturbance effect on time-dependent yield stress measurement of bentonite suspension was investigated using vane and cone and plate geometry. With cone and plate geometry, the disturbance occurs as a result of the large strains the suspensions are subjected to when placed on the lower plate and when the cone is lowered to the testing position before the actual shear stress is applied. As an alternative, vane ge- ometry has been used to measure yield stress in bentonite suspension to (1) minimize the placing disturbance, and (2) eliminate the loading disturb- ance. Even though a minimal amount of disturbance was still induced by vane geometry, it was assumed that the amount of disturbance was small enough to be ignored compared to that with cone and plate geometry. Yield stresses of bentonite suspensions at different weight fractions and vari- ous resting times were determined from stress ramp tests. Linear relationships were developed between yield stresses measured with the different geometries. Disturbance rapidly increased up to approximately 50 % with resting times, then converged at long resting times (60 % at 10 days). This indicates that cone and plate geometry significantly underestimated the actual yield stress of the tested material because of disturbance. Based on the quantified disturbance, a method to predict the undisturbed yield stress from disturbed one was proposed. The observations were interpreted with structural kinetics.


title: A 2.4 GHz gain-boosted CMOS amplifier using multibias points for improved linearization

abstract: A highly linear two stage CMOS amplifier using 130 nm technology is presented. A linearization technique is employed using multibias points through a combination of bulk bias control and floating bulk devices to achieve significant improvement of intercept points. Forward gain boosting is ensured by utilizing a cascode topology followed by a common source stage while maintaining a low ceiling for noise in the S band. Including a bias controlled feedback path between the stages further improves the architecture's expected gain compression. The final design reports 27.6 dB simulated power gain with third order input intercept of 9.92 dBm and noise figure of 1.22 dB at the center frequency. Input and output impedances have been matched with the help of passive appendages to ensure −24 dB input return loss and −27.5 dB output return loss, respectively. Optimized radio frequency parameters are achieved along with a linearity upgrade which is better than reported CMOS amplifiers.


title: Freudian Dream Theory, Dream Bizarreness, and the Disguise-Censor Controversy

abstract: One particular area of contention in discussions of Freudian dream theory and its relation to the neuroscientific evidence is the notion of “disguise-censorship” and its relation to dream bizarreness. The discussion to date, however, has neglected the conceptual basis of repression and disguise-censorship, and this paper aims to clarify the role of repression in dreaming and its contribution to dream bizarreness. An analysis of disguise-censorship and repression reveals two competing accounts in Freud’s theory. Freud’s account of the “dream-censor”, acting as an agency intentionally disguising cognitive content, is found to be problematic. However, Freud’s alternative account of repression, in terms of cognitive inhibition instigated by motivational conflict, is developed and discussed in relation to neural inhibition. On this view, dream bizarreness arises, in part, through interdrive competition preventing direct expression of wishes and the subsequent formation of substitute aims. Resolution of certain...


title: Neural Structural Correspondence Learning for Domain Adaptation

abstract: Domain adaptation, adapting models from domains rich in labeled training data to domains poor in such data, is a fundamental NLP challenge. We introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural networks. Particularly, our model is a three-layer neural network that learns to encode the nonpivot features of an input example into a low-dimensional representation, so that the existence of pivot features (features that are prominent in both domains and convey useful information for the NLP task) in the example can be decoded from that representation. The low-dimensional representation is then employed in a learning algorithm for the task. Moreover, we show how to inject pre-trained word embeddings into our model in order to improve generalization across examples with similar pivot features. On the task of cross-domain product sentiment classification (Blitzer et al., 2007), consisting of 12 domain pairs, our model outperforms both the SCL and the marginalized stacked denoising autoencoder (MSDA, (Chen et al., 2012)) methods by 3.77% and 2.17% respectively, on average across domain pairs.


title: Chapter 2 INFORMATION EXTRACTION FROM TEXT

abstract: Information extraction is the task of finding structured information from unstructured or semi-structured text. It is an important task in text mining and has been extensively studied in various research communities including natural language processing, information retrieval and Web mining. It has a wide range of applications in domains such as biomedical literature mining and business intelligence. Two fundamental tasks of information extraction are named entity recognition and relation extraction. The former refers to finding names of entities such as people, organizations and locations. The latter refers to finding the semantic relations such as FounderOf and HeadquarteredIn between entities. In this chapter we provide a survey of the major work on named entity recognition and relation extraction in the past few decades, with a focus on work from the natural language processing community.


title: Solar powered smart ultrasonic insects repellent with DTMF and manual control for agriculture

abstract: The paper represents a smart gadget to avert the calamitous effects of pest bugs in the farming land as well as in household commodities. In this paper, development and performance analysis of smart ultrasonic insects repellent device has been demonstrated. This device is able to generate different types of frequencies which are very helpful to repel different types of insects. It can be controlled from any distance with DTMF (Dual Tone Multi Frequency) technology. Besides, manual control with night mode via LDR (Light Dependent Resistor) has also been employed. The device would be charged through solar energy system which is a cost effective approach. A power amplifier along with a microcontroller known as Arduino Uno as well as solar charger circuit regulator and solar panel are used. The implementation of this device in farmland has shown a great reduction of bugs attack compared to the other land which doesn't have one.


Commentary / Nell : Cruelty ’ s rewards : The gratifications of perpetrators and spectators


title: Tracking Recurring Concepts with Meta-learners

abstract: This work address data stream mining from dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current model, and start learning a new decision model, Nevertheless, it is not rare for the concepts from history to reappear, for example seasonal changes. In this work we present method that memorizes learnt decision models whenever a concept drift is signaled. The system uses meta-learning techniques that characterize the domain of applicability of previous learnt models. The meta-learner can detect re-occurrence of contexts and take pro-active actions by activating previous learnt models. The main benefit of this approach is that the proposed meta-learner is capable of selecting similar historical concept, if there is one, without the knowledge of true classes of examples.


title: Application-level security for ROS-based applications

abstract: While the topic of security in industrial applications has gained some momentum in recent years, there are still severe security vulnerabilities which are actively exploited for attacks. The robot operating system (ROS) is expected to further grow in usage and to be used in many industrial applications. Analysis, however, shows that it lacks several security enhancements in order to make it suitable for industrial use. In its current state, false data and commands can be injected posing a possible safety risk for the resulting product and humans in the production. In addition, data may be eavesdropped and used by outsiders to gain insight into the production process. In this paper we propose a security architecture intended for use on top of ROS on the application level. We use a dedicated authorization server to ensure that only valid nodes are part of the application. Cryptographic methods ensure data confidentiality and integrity. We show in a demonstration with a collaborative robot how our architecture can be used to secure a ROS-based application.


title: Posterior cruciate ligament tears: functional and postoperative rehabilitation

abstract: PURPOSE ::: Historically, the results of posterior cruciate ligament (PCL) reconstructions are not as favourable as anterior cruciate ligament (ACL) reconstructions, and it is well recognized that nonoperative treatment and postoperative rehabilitation for PCL injuries must be altered compared to those for ACL injuries. The purpose of this article was to review current peer-reviewed PCL rehabilitation programmes and to recommend a nonoperative and postoperative programme based on basic science and published outcomes studies. ::: ::: ::: METHODS ::: To discover the current practices being used to rehabilitate PCL injuries, we conducted a search of PubMed with the terms "posterior cruciate ligament" and "rehabilitation" from 1983 to 2011. All articles within the reference lists of these articles were also examined to determine their rehabilitation programmes. ::: ::: ::: RESULTS ::: A review of peer-reviewed PCL rehabilitation protocols revealed that the treatment of PCL injuries depends on the timing and degree of the injury. Rehabilitation should focus on progressive weight bearing, preventing posterior tibial subluxation and strengthening of the quadriceps muscles. General principles of proper PCL rehabilitation, whether nonoperative or postoperative, should include early immobilization (when necessary), prone passive range of motion to prevent placing undue stress on grafts or healing tissue, and progression of rehabilitation based on biomechanical, clinical, and basic science research. ::: ::: ::: CONCLUSIONS ::: An optimal set of guidelines for the nonoperative or postoperative management of PCL injuries has not yet been defined or agreed upon. Based on the current review study, suggested guidelines are proposed. ::: ::: ::: LEVEL OF EVIDENCE ::: IV.


title: When apache spark meets FPGAs: a case study for next-generation DNA sequencing acceleration

abstract: FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks like Apache Spark? To provide a generalized methodology and insights for efficient integration, we conduct an in-depth analysis of challenges at single-thread, single-node multi-thread, and multi-node levels, and propose solutions including batch processing and the FPGA-as-a-Service framework to address them. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1,000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.


title: Organizational performance effects of ERP systems usage: The impact of post-implementation changes ☆

abstract: This paper examines the long-term financial performance effects of ERP system changes/revisions for firms that have previously reported ERP adoptions. The study is motivated by the mixed results of recent studies examining the financial effects of ERP systems and by studies which demonstrate that ERP implementations are modular in nature and thus may yield organizational performance gains or losses as a result of discrete changes that might be initiated after the initial system implementation. This study empirically examines the extent to which discrete changes to ERP systems over a post-implementation time-frame impact on firms' ability to deliver long-run financial performance. It further examines whether the timing and nature of system transformation during the post-implementation period presents a significant moderating condition of ERP performance outcomes. We specifically consider the ERP-adopting firms previously examined by Nicolaou [Nicolaou, A.I. 2004a. Firm performance effects in relation to the implementation and use of enterprise resource planning systems. Journal of Information Systems 18 (Fall): 79–105] and track them for changes/revisions in the form of enhancements, upgrades, abandonments and switches during the post-implementation period. Two research hypotheses are developed in the study which posit that both the nature and the timing of system implementation changes represent significant conditions for ERP post-implementation success. ::: ::: From the original sample of 247 firms used in Nicolaou [Nicolaou, A.I. 2004a. Firm performance effects in relation to the implementation and use of enterprise resource planning systems. Journal of Information Systems 18 (Fall): 79–105], a total of 83 firms were identified to report 182 discrete changes during their ERP system post-implementation period. The results suggest that, in general, subsequent changes in ERP systems often help resolve or surface implementation issues that affect subsequent use of and success from the use of such systems. Specific findings indicate that ERP-adopting firms, which initiate early enhancements in the form of either add-ons or upgrades, may enjoy superior differential financial performance in comparison to other ERP-adopting firms' differential performance. Late enhancements and both early and late abandonments lead to apparent differential performance deterioration for the ERP-adopting firms. These findings support the theoretical propositions developed in the study The study's findings provide an important contribution to the ERP systems success literature and present important insights for future research in this area.


title: Phase Retrieval via Sparse Wirtinger Flow

abstract: Abstract Phase retrieval (PR) problem is an inverse problem which arises in various applications. Based on the Wirtinger flow method, an algorithm utilizing the sparsity priority called SWF (Sparse Wirtinger Flow) is proposed in this paper to deal with the PR problem. Firstly, the support of the signal is estimated besides the initialization is evaluated based on this support. Then the evaluation is updated by the hard-thresholding method from this initialization. We prove that for any k -sparse signal with length n , SWF has a linear convergence with O ( k 2 log n ) phaseless Gaussian random measurements. To get e accuracy, the computational complexity of SWF is O ( k 3 n log n log 1 e ) . Numerical tests also demonstrate that SWF has a higher recovery rate than other algorithms compared especially when we have no prior information about sparsity k . Moreover, SWF is robust to the noise.


title: Neural network based detection of hard exudates in retinal images

abstract: Diabetic retinopathy (DR) is an important cause of visual impairment in developed countries. Automatic recognition of DR lesions in fundus images can contribute to the diagnosis of the disease. The aim of this study is to automatically detect one of these lesions, hard exudates (EXs), in order to help ophthalmologists in the diagnosis and follow-up of the disease. We propose an algorithm which includes a neural network (NN) classifier for this task. Three NN classifiers were investigated: multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM). Our database was composed of 117 images with variable colour, brightness, and quality. 50 of them (from DR patients) were used to train the NN classifiers and 67 (40 from DR patients and 27 from healthy retinas) to test the method. Using a lesion-based criterion, we achieved a mean sensitivity (SE"l) of 88.14% and a mean positive predictive value (PPV"l) of 80.72% for MLP. With RBF we obtained SE"l=88.49% and PPV"l=77.41%, while we reached SE"l=87.61% and PPV"l=83.51% using SVM. With an image-based criterion, a mean sensitivity (SE"i) of 100%, a mean specificity (SP"i) of 92.59% and a mean accuracy (AC"i) of 97.01% were obtained with MLP. Using RBF we achieved SE"i=100%, SP"i=81.48% and AC"i=92.54%. With SVM the image-based results were SE"i=100%, SP"i=77.78% and AC"i=91.04%.


title: “I'm More in Balance”: A Qualitative Study of Yoga for Patients with Chronic Neck Pain

abstract: Abstract Objectives: This study investigated the perceived influence of yoga on body perception and psychosocial aspects of life for patients with chronic neck pain. Design: This qualitative study was conducted using semistandardized interviews. Setting: The interventions and interviews took place in a referral center's research department. Subjects: Eighteen (18) patients with chronic nonspecific neck pain were recruited from a larger randomized controlled trial of yoga for chronic neck pain. Interventions: Participants attended 90 minutes of Iyengar yoga once a week for 9 weeks. Outcome measures: Participants completed a drawing of their neck and shoulder regions to reflect their subjective body perceptions before and after their yoga program. Semistandardized interviews were used to explore their body perception, emotional status, everyday life and coping skills, as well as any perceived changes in these dimensions postparticipation. An interdisciplinary group analyzed the study data using content anal...


title: BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music

abstract: Fundamental frequency (F 0 ) is one of the essential features in many acoustic related applications. Although numerous F 0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F 0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F 0 value among several F 0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F 0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.


title: Dynamic models of human motion

abstract: This paper describes experiments in human motion understanding, defined here as estimation of the physical state of the body (the Plant) combined with interpretation of that part of the motion that cannot be predicted by the plant alone (the Behavior). The described behavior system operates in conjunction with a real-time, fully-dynamic, 3-D person tracking system that provides a mathematically concise formulation for incorporating a wide variety of physical constraints and probabilistic influences. The framework takes the form of a non-linear recursive filter that enables pixel-level, probabilistic processes to take advantage of the contextual knowledge encoded in the higher-level models. Results are shown that demonstrate both qualitative and quantitative gains in tracking performance.


title: Graph Querying Meets HCI: State of the Art and Future Directions

abstract: Querying graph databases has emerged as an important research problem for real-world applications that center on large graph data. Given the syntactic complexity of graph query languages (e.g., SPARQL, Cypher), visual graph query interfaces make it easy for non-expert users to query such graph data repositories. In this tutorial, we survey recent developments in the emerging area of visual graph querying paradigm that bridges traditional graph querying with human computer interaction (HCI). We discuss manual and data-driven visual graph query interfaces, various strategies and guidance for constructing graph queries visually, interleaving processing of graph queries and visual actions, and visual exploration of graph query results. In addition, the tutorial suggests open problems and new research directions. In summary, in this tutorial we review and summarize the research thus far into HCI and graph querying in the database community, giving researchers a snapshot of the current state of the art in this topic, and future research directions.


title: ADACOR: A holonic architecture for agile and adaptive manufacturing control

abstract: In the last decades significant changes in the manufacturing environment have been noticed: moving from a local economy towards a global economy, with markets asking for products with higher quality at lower costs, highly customised and with short life cycle. In these circumstances, the challenge is to develop manufacturing control systems with intelligence capabilities, fast adaptation to the environment changes and more robustness against the occurrence of disturbances. This paper presents an agile and adaptive manufacturing control architecture that addresses the need for the fast reaction to disturbances at the shop floor level, increasing the agility and flexibility of the enterprise, when it works in volatile environments. The proposed architecture introduces an adaptive control that balances dynamically between a more centralised structure and a more decentralised one, allowing combining the global production optimisation with agile reaction to unexpected disturbances.


title: Dynamic itemset counting and implication rules for market basket data

abstract: We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating “implication rules,” which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed by synthetic data, can dramatically affect the performance of the system and the form of the results.


title: Association between Facebook addiction, self-esteem and life satisfaction: A cross-sectional study

abstract: In recent years, many research efforts have been focused on investigation of potential connection between social networking and mental health issues. Particularly important and controversial remains the association between Facebook use, self-esteem and life satisfaction. In our cross-sectional study, on a sample of 381 Facebook users, we tested the existence and strength of this relationship using Bergen Facebook Addiction Scale (BFAS), Facebook Intensity Scale (FBI), Rosenberg's Self-Esteem Scale (SES), and Satisfaction With Life Scale (SWLS). With k-means cluster analysis, we divided the sample into 3 groups: ordinary, intensive, and addicted Facebook users. The results of our study show that ordinary Facebook users differ statistically in self-esteem and life satisfaction from both addicted and intensive users. Facebook addiction was in relation with lower self-esteem. Facebook addiction was also negatively related to life satisfaction. These results are in accordance with the previously published findings of other authors in the fields of social networking psychology and psychiatry. We investigated the relationship between Facebook use, self-esteem and life satisfaction.The study was done on a sample of 381 Facebook users.Facebook addiction was negatively related to satisfaction with life.The results are in accordance with the previously published findings.


title: Consciousness and the brainstem

abstract: In the first part of this article we summarize a theoretical framework and a set of hypotheses aimed at accounting for consciousness in neurobiological terms. The basic form of consciousness, core consciousness is placed in the context of life regulation; it is seen as yet another level of biological processing aimed at ensuring the homeostatic balance of a living organism; and the representation of the current organism state within somato-sensing structures is seen as critical to its development. Core consciousness is conceived as the imaged relationship of the interaction between an object and the changed organism state it causes. In the second part of the article we discuss the functional neuroanatomy of nuclei in the brainstem reticular formation because they constitute the basic set of somato-sensing structures necessary for core consciousness and its core self to emerge. The close relationship between the mechanisms underlying cortical activation and the bioregulatory mechanisms outlined here is entirely compatible with the classical idea that the reticular formation modulates the electrophysiological activity of the cerebral cortex. However, in the perspective presented here, that modulation is placed in the setting of the organism’s homeostatic regulation. q 2001 Elsevier Science B.V. All rights reserved.


4G- A NEW ERA IN WIRELESS TELECOMMUNICATION


title: Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks

abstract: While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps and show that the depth estimation task can be effectively tackled within an adversarial learning framework. Specifically, we propose a deep generative network that learns to predict the correspondence field i.e. the disparity map between two image views in a calibrated stereo camera setting. The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other. Extensive experiments on the publicly available datasets KITTI and Cityscapes demonstrate the effectiveness of the proposed model and competitive results with state of the art methods. The code and trained model are available on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.


title: Named Entity Corpus Construction using Wikipedia and DBpedia Ontology

abstract: In this paper, we propose a novel method to automatically build a named entity corpus based on the DBpedia ontology. Since most of named entity recognition systems require time and effort consuming annotation tasks as training data. Work on NER has thus for been limited on certain languages like English that are resource-abundant in general. As an alternative, we suggest that the NE corpus generated by our proposed method, can be used as training data. Our approach introduces Wikipedia as a raw text and uses the DBpedia data set for named entity disambiguation. Our method is language-independent and easy to be applied to many different languages where Wikipedia and DBpedia are provided. Throughout the paper, we demonstrate that our NE corpus is of comparable quality even to the manually annotated NE corpus.


Few-Shot Semantic Segmentation with Prototype Learning


title: Online Initialization and Automatic Camera-IMU Extrinsic Calibration for Monocular Visual-Inertial SLAM

abstract: Most of the existing monocular visual-inertial SLAM techniques assume that the camera-IMU extrinsic parameters are known, therefore these methods merely estimate the initial values of velocity, visual scale, gravity, biases of gyroscope and accelerometer in the initialization stage. However, it's usually a professional work to carefully calibrate the extrinsic parameters, and it is required to repeat this work once the mechanical configuration of the sensor suite changes slightly. To tackle this problem, we propose an online initialization method to automatically estimate the initial values and the extrinsic parameters without knowing the mechanical configuration. The biases of gyroscope and accelerometer are considered in our method, and a convergence criteria for both orientation and translation calibration is introduced to identify the convergence and to terminate the initialization procedure. In the three processes of our method, an iterative strategy is firstly introduced to iteratively estimate the gyroscope bias and the extrinsic orientation. Secondly, the scale factor, gravity, and extrinsic translation are approximately estimated without considering the accelerometer bias. Finally, these values are further optimized by a refinement algorithm in which the accelerometer bias and the gravitational magnitude are taken into account. Extensive experimental results show that our method achieves competitive accuracy compared with the state-of-the-art with less calculation.


title: Analysis of sequences generated by ELCA-type Cellular Automata targeting noise generation

abstract: The Equal Length Cellular Automata (ELCA) are suggested as a mean for generating pseudo-random sequences. For supporting this application, an analysis of the randomness of the generated sequences is performed for a variety of automata, using several analysis methods. The results of the analysis are presented and discussed. In addition, the complexity of the generation method is compared with other types of pseudo-chaotic generators. Potentiality of usage in noise generating application targeting security of data in microsystems is envisaged.


title: Landmark-based fisher vector representation for video-based face verification

abstract: Unconstrained video-based face verification is a challenging problem because of dramatic variations in pose, illumination, and image quality of each face in a video. In this paper, we propose a landmark-based Fisher vector representation for video-to-video face verification. The proposed representation encodes dense multi-scale SIFT features extracted from patches centered at detected facial landmarks, and face similarity is computed with the distance measure learned from joint Bayesian metric learning. Experimental results demonstrate that our approach achieves significantly better performance than other competitive video-based face verification algorithms on two challenging unconstrained video face dataseis, Multiple Biometric Grand Challenge (MBGC) and Face and Ocular Challenge Series (FOCS).


title: Motion artefact correction for capacitive ECG measurement

abstract: Applications for capacitive ECG measurement that have been demonstrated range from clinical applications like body surface potential mapping to monitoring devices for personal health care. One common drawback in all systems is the susceptibility to motion artefacts caused by relative movement between the capacitive electrodes and the patient. In this paper we describe the setup and experiments that were used to verify the model that simulates time domain behavior of a capacitive electrode. Finally, we show that artefact correction is possible for real world capacitive ECG signals using this model, even for motion artefacts that are within the ECG spectrum.


title: Age of language acquisition and cortical language organization in multilingual patients undergoing awake brain mapping

abstract: OBJECTIVEMost knowledge regarding the anatomical organization of multilingualism is based on aphasiology and functional imaging studies. However, the results have still to be validated by the gold standard approach, namely electrical stimulation mapping (ESM) during awake neurosurgical procedures. In this ESM study the authors describe language representation in a highly specific group of 13 multilingual individuals, focusing on how age of acquisition may influence the cortical organization of language.METHODSThirteen patients who had a high degree of proficiency in multiple languages and were harboring lesions within the dominant, left hemisphere underwent ESM while being operated on under awake conditions. Demographic and language data were recorded in relation to age of language acquisition (for native languages and early- and late-acquired languages), neuropsychological pre- and postoperative language testing, the number and location of language sites, and overlapping distribution in terms of language...


Variance of Spinal Osteoporosis Induced by Dexamethasone and Methylprednisolone and its Associated Mechanism


title: Public health implications of meat production and consumption.

abstract: The high level of meat and saturated fat consumption in the USA and other high-income countries exceeds nutritional needs and contributes to high rates of chronic diseases such as cardiovascular disease, diabetes mellitus and some cancers. Affluent citizens in middle- and low-income countries are adopting similar high-meat diets and experiencing increased rates of these same chronic diseases. The industrial agricultural system, now the predominant form of agriculture in the USA and increasingly world-wide, has consequences for public health owing to its extensive use of fertilisers and pesticides, unsustainable use of resources and environmental pollution. In industrial animal production there are public health concerns surrounding feed formulations that include animal tissues, arsenic and antibiotics as well as occupational health risks and risks for nearby communities. It is of paramount importance for public health professionals to become aware of and involved in how our food is produced.


title: Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

abstract: Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.


title: Twitter under crisis: can we trust what we RT?

abstract: In this article we explore the behavior of Twitter users under an emergency situation. In particular, we analyze the activity related to the 2010 earthquake in Chile and characterize Twitter in the hours and days following this disaster. Furthermore, we perform a preliminary study of certain social phenomenons, such as the dissemination of false rumors and confirmed news. We analyze how this information propagated through the Twitter network, with the purpose of assessing the reliability of Twitter as an information source under extreme circumstances. Our analysis shows that the propagation of tweets that correspond to rumors differs from tweets that spread news because rumors tend to be questioned more than news by the Twitter community. This result shows that it is posible to detect rumors by using aggregate analysis on tweets.


title: Conditional Probability Models for Deep Image Compression

abstract: Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the latter challenge and propose a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder. The main idea is to directly model the entropy of the latent representation by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto-encoder. During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation. Our experiments show that this approach, when measured in MS-SSIM, yields a state-of-the-art image compression system based on a simple convolutional auto-encoder.


title: PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents

abstract: This paper proposes PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word's occurrences into a biased PageRank.


title: Brain plasticity and functional losses in the aged: scientific bases for a novel intervention.

abstract: Aging is associated with progressive losses in function across multiple systems, including sensation, cognition, memory, motor control, and affect. The traditional view has been that functional decline in aging is unavoidable because it is a direct consequence of brain machinery wearing down over time. In recent years, an alternative perspective has emerged, which elaborates on this traditional view of age-related functional decline. This new viewpoint — based upon decades of research in neuroscience, experimental psychology, and other related fields — argues that as people age, brain plasticity processes with negative consequences begin to dominate brain functioning. Four core factors — reduced schedules of brain activity, noisy processing, weakened neuromodulatory control, and negative learning — interact to create a self-reinforcing downward spiral of degraded brain function in older adults. This downward spiral might begin from reduced brain activity due to behavioral change, from a loss in brain function driven by aging brain machinery, or more likely from both. In aggregate, these interrelated factors promote plastic changes in the brain that result in age-related functional decline. This new viewpoint on the root causes of functional decline immediately suggests a remedial approach. Studies of adult brain plasticity have shown that substantial improvement in function and/or recovery from losses in sensation, cognition, memory, motor control, and affect should be possible, using appropriately designed behavioral training paradigms. Driving brain plasticity with positive outcomes requires engaging older adults in demanding sensory, cognitive, and motor activities on an intensive basis, in a behavioral context designed to reengage and strengthen the neuromodulatory systems that control learning in adults, with the goal of increasing the fidelity, reliability, and power of cortical representations. Such a training program would serve a substantial unmet need in aging adults. Current treatments directed at age-related functional losses are limited in important ways. Pharmacological therapies can target only a limited number of the many changes believed to underlie functional decline. Behavioral approaches focus on teaching specific strategies to aid higher order cognitive functions, and do not usually aspire to fundamentally change brain function. A brain-plasticity-based training program would potentially be applicable to all aging adults with the promise of improving their operational capabilities. We have constructed such a brain-plasticity-based training program and conducted an initial randomized controlled pilot study to evaluate the feasibility of its use by older adults. A main objective of this initial study was to estimate the effect size on standardized neuropsychological measures of memory. We found that older adults could learn the training program quickly, and could use it entirely unsupervised for the majority of the time required. Pre- and posttesting documented a significant improvement in memory within the training group (effect size 0.41, p<0.0005), with no significant within-group changes in a time-matched computer using active control group, or in a no-contact control group. Thus, a brain-plasticity-based intervention targeting normal age-related cognitive decline may potentially offer benefit to a broad population of older adults.


title: Virtual reality in the treatment of spider phobia: a controlled study

abstract: This study explored whether virtual reality (VR) exposure therapy was effective in the treatment of spider phobia. We compared a treatment condition vs. a waiting list condition in a between group design with 23 participants. Participants in the VR treatment group received an average of four one-hour exposure therapy sessions. VR exposure was effective in treating spider phobia compared to a control condition as measured with a Fear of Spiders questionnaire, a Behavioural Avoidance Test (BAT), and severity ratings made by the clinician and an independent assessor. Eighty-three percent of patients in the VR treatment group showed clinically significant improvement compared with 0% in the waiting list group, and no patients dropped out. This study shows that VR exposure can be effective in the treatment of phobias.


title: Addressing User Experience and Societal Impact in a User Study with a Humanoid Robot

abstract: Using a preliminary exploratory case study the presented work investigates the feasibility of methods for the evaluation of user experience factors in human-humanoid interaction as well as measurements of societal impact in user studies. The case study is based on two tasks participants had to perform with the HRP-2 robot. The robot was controlled by the participants via speech commands to pick up (task1) and put down (task2) an object onto another place. Main goal of the case study was to explore the methodological concept on how to measure novice users’ experiences during the collaboration with the humanoid robot HRP-2 via speech commands and if the general attitude towards robotics changes because of the interaction with the robot. To address users’ experiences during the user study, participants were asked after each task to state their thoughts and feelings they had during the interaction (retrospective think aloud). Furthermore, participants were interviewed by means of two validated standardized questionnaires (NARS and AttrakDiff) and an especially developed questionnaire. The preliminary results show that retrospective think aloud is a good way to gather qualitative data on users’ experiences. Furthermore a final interview on societal impact of humanoid robots gave insights into how novice users imagine a future society with robots.


title: Juvenile Delinquency: Analysis of Risk and Protective Factors Using Quantitative and Qualitative Methods

abstract: ABSTRACT Several factors are associated to delinquency. It is difficult to change or to reduce risk factors, therefore, the perspective of protective factors promotion appears as an alternative or a complementary approach for the risk behaviour prevention. The present study was developed using mixed methods in order to analyse risk and protective factors in juvenile delinquency. First, a quantitative study, using structural equation modelling with 300 juvenile offenders, was conducted in order to develop an explanatory model of delinquency. Secondly, a qualitative study, using a "focal groups" procedure, was also conducted with 24 juvenile offenders, aiming at knowing their perceptions regarding risk and protective factors for delinquency. Several different risk and protective factors were found. The quantitative study showed that, "substance use" was the most salient risk factor for juvenile delinquency while "positive relations in school context" was the most salient protective factor. The qualitative results confirmed these results. Implications to selective and universal preventive interventions are discussed. KEYWORDS: delinquency, risk and protective factors, preventive intervention. INTRODUCTION Delinquent and disruptive behaviors include different forms of aggression and violence. These behaviors are associated, on one hand, to illicit drug-use and traffic, urban, racial, and xenophobic violence (Delles, 2001; Uniao Europeia, 2001); on the other hand, these behaviors are associated with anxiety and mood disorders, and other behavioral disorders (Frick, Bodin, & Barry, 2000; Hill, 2002; Teplin, Abram, McClelland, Dulcan, & Mericle, 2002). Delinquency is increasing in some countries, and is increasing in females, while the age of juvenile offenders decreases (Loeber, Farrington, & Petechuck, 2003; Uniao Europeia, 2001). Delinquency has also several social adverse outcomes, like disadvantages regarding social integration and economic independency (Fergusson & Horwood, 1999; Moffitt, Caspi, Harrington, & Milne, 2002; Werner & Smith, 2001), and other costs for society related to medical, rehabilitation, and custody costs (DiClemente, Hansen, & Ponton, 1996). Therefore, it becomes important to know the determinants of these behaviors during adolescence, and in different life contexts. Only knowing the factors that place the young people at risk, and the ones that protect them from potential problems, it is possible to draw up preventive interventions. The research developed on this topic shows that these behaviors have several different determinants but also share common determinants There is a diversity of causes or paths that can lead to a certain outcome, but there is also a diversity of effects that a single factor can produce (Thornberry, Ireland, & Smith, 2001). This is valid for risk factors, but also for protective factors. It is difficult to change or to reduce risk factors therefore, the perspective of protective factors promotion appears as an alternative or a complementary approach for the risk behavior prevention (Matos, et al., 2003; Matos & Simoes, 2003). Research in this area found several factors associated with delinquent and disruptive behavior: (1) Individual (stress, genetic vulnerability, physiological, psychological, cognitive, or behavioral) characteristics (Barkauskiene & Bieliauskaite, 2002; Frick, et al., 2000; Hill, 2002; Matos & Simoes, 2003; McBurnett, Naguib, & Brown, 2000; O'Connor, Neiderhiser, Reiss, Hetherington, & Plomin, 1998); (2) Peer relationships, that draw attention to the influence of socializing with peers with similar antisocial behaviors (Hill, 2002; Lahey, Gordon, Loeber, Stouthamer-Loeber, & Farrington, 1999; Shoemaker, 1996; Tiet & Huizinga, 2002); (3) Social that highlight the influence of socioeconomic and social organization features on antisocial behavior development, and (4) Situational that underline the opportunities to crime practice as the main determinant of delinquency (Cloward & Ohlin, 1960; Farrington, 1998; Hill, 2002; Shoemaker, 1996). …


title: Adaptive boosting for SAR automatic target recognition

abstract: The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature


Sleep Apnea in a patient with Becker Muscular Dystrophy


title: Transformerless multicellular dc-dc converter for highly efficient next generation dc distribution system

abstract: A transformerless multicellular dc-dc converter has been newly proposed. The capacitively isolated dc-dc cell converters have been applied to develop the multicellular converter without using high frequency transformers. The high voltage transformation ratio is achieved by the ISOP (Input Series Output Parallel) connection topology of the cell converters and the realization of the highly efficient cell converter is expected because of no transformers. A 144 V-24 V, 200 W prototype of the multicellular dc-dc converter using 3 cell converters based on the capacitive isolation has been developed, and the feasibility of the transformerless multicellular converter has been confirmed.


title: Topology-Based Recommendation of Users in Micro-Blogging Communities

abstract: Nowadays, more and more users share real-time news and information in micro-blogging communities such as Twitter, Tumblr or Plurk. In these sites, information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows, named followees. With the increasing number of registered users in this kind of sites, finding relevant and reliable sources of information becomes essential. The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation. To address this problem, we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources. Experimental evaluation conducted with a group of users is reported, demonstrating the potential of the approach.


title: Enumeration of time series motifs of all lengths

abstract: Time series motifs are repeated patterns in long and noisy time series. Motifs are typically used to understand the dynamics of the source because repeated patterns with high similarity evidentially rule out the presence of noise. Recently, time series motifs have also been used for clustering, summarization, rule discovery and compression as features. For all such purposes, many high quality motifs of various lengths are desirable and thus, originates the problem of enumerating motifs for a wide range of lengths. Existing algorithms find motifs for a given length. A trivial way to enumerate motifs is to run one of the algorithms for the whole range of lengths. However, such parameter sweep is computationally infeasible for large real datasets. In this paper, we describe an exact algorithm, called MOEN, to enumerate motifs. The algorithm is an order of magnitude faster than the naive algorithm. The algorithm frees us from re-discovering the same motif at different lengths and tuning multiple data-dependent parameters. The speedup comes from using a novel bound on the similarity function across lengths and the algorithm uses only linear space unlike other motif discovery algorithms. We describe three case studies in entomology and activity recognition where MOEN enumerates several high quality motifs.


Vulnerabilities of Encrypted IoT Traffic


title: Learned local descriptors for recognition and matching

abstract: A magnetic head is provided with an apex portion having a first and second chamfered portion, both formed in a C core. The first chamfered portion has a first apex angle alpha at a position corresponding to a predetermined gap depth. The first chamfered portion also has a predetermined apex length. The second chamfered portion is contiguous to the first chamfered portion and has second apex angle beta which is smaller than the first apex angle alpha . The first apex angle alpha is greater than or equal to 70 DEG and less than 80 DEG .


title: Development and natural history of mood disorders

abstract: Abstract To expand and accelerate research on mood disorders, the National Institute of Mental Health (NIMH) developed a project to formulate a strategic research plan for mood disorder research. One of the areas selected for review concerns the development and natural history of these disorders. The NIMH convened a multidisciplinary Workgroup of scientists to review the field and the NIMH portfolio and to generate specific recommendations. To encourage a balanced and creative set of proposals, experts were included within and outside this area of research, as well as public stakeholders. The Workgroup identified the need for expanded knowledge of mood disorders in children and adolescents, noting important gaps in understanding the onset, course, and recurrence of early-onset unipolar and bipolar disorder. Recommendations included the need for a multidisciplinary research initiative on the pathogenesis of unipolar depression encompassing genetic and environmental risk and protective factors. Specifically, we encourage the NIMH to convene a panel of experts and advocates to review the findings concerning children at high risk for unipolar depression. Joint analyses of existing data sets should examine specific risk factors to refine models of pathogenesis in preparation for the next era of multidisciplinary research. Other priority areas include the need to assess the long-term impact of successful treatment of juvenile depression and known precursors of depression, in particular, childhood anxiety disorders. Expanded knowledge of pediatric-onset bipolar disorder was identified as a particularly pressing issue because of the severity of the disorder, the controversies surrounding its diagnosis and treatment, and the possibility that widespread use of psychotropic medications in vulnerable children may precipitate the condition. The Workgroup recommends that the NIMH establish a collaborative multisite multidisciplinary Network of Research Programs on Pediatric-Onset Bipolar Disorder to achieve a better understanding of its causes, course, treatment, and prevention. The NIMH should develop a capacity-building plan to ensure the availability of trained investigators in the child and adolescent field. Mood disorders are among the most prevalent, recurrent, and disabling of all illnesses. They are often disorders of early onset. Although the NIMH has made important strides in mood disorders research, more data, beginning with at-risk infants, children, and adolescents, are needed concerning the etiology and developmental course of these disorders. A diverse program of multidisciplinary research is recommended to reduce the burden on children and families affected with these conditions.


title: General Purpose Database Summarization

abstract: In this paper, a message-oriented architecture for large database summarization is presented. The summarization system takes a database table as input and produces a reduced version of this table through both a rewriting and a generalization process. The resulting table provides tuples with less precision than the original but yet are very informative of the actual content of the database. This reduced form can be used as input for advanced data mining processes as well as some specific application presented in other works. We describe the incremental maintenance of the summarized table, the system capability to directly deal with XML database systems, and finally scalability which allows it to handle very large datasets of a million record.


title: Random Projection Clustering on Streaming Data

abstract: Clustering streaming data has gained importance in recent years due to an expanding opportunity to discover knowledge in widely available data streams. As streams are potentially evolving and unbounded sequence of data objects, clustering algorithms capable of performing fast and incremental processing of data points are necessary. This paper presents a method of clustering high-dimensional data streams using approximate methods called streamingRPHash. streamingRPHash combines random projections with locality-sensitivity hashing to construct a high-performance clustering method. streamingRPHash is amenable to distributed processing frameworks such as Map-Reduce, and also has the benefits of constrained overall complexity growth. This paper describes streamingRPHash algorithm and its various configurations. The clustering performance of streamingRPHash is compared to several alternatives. Experimental results show that streamingRPHash has comparable clustering accuracy and substantially lower runtime and memory usage.


title: A Confidence-Based Approach for Balancing Fairness and Accuracy

abstract: We study three classical machine learning algorithms in the context of algorithmic fairness: adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain the high accuracy of these learning algorithms while reducing the degree to which they discriminate against individuals because of their membership in a protected group. ::: Our first contribution is a method for achieving fairness by shifting the decision boundary for the protected group. The method is based on the theory of margins for boosting. Our method performs comparably to or outperforms previous algorithms in the fairness literature in terms of accuracy and low discrimination, while simultaneously allowing for a fast and transparent quantification of the trade-off between bias and error. ::: Our second contribution addresses the shortcomings of the bias-error trade-off studied in most of the algorithmic fairness literature. We demonstrate that even hopelessly naive modifications of a biased algorithm, which cannot be reasonably said to be fair, can still achieve low bias and high accuracy. To help to distinguish between these naive algorithms and more sensible algorithms we propose a new measure of fairness, called resilience to random bias (RRB). We demonstrate that RRB distinguishes well between our naive and sensible fairness algorithms. RRB together with bias and accuracy provides a more complete picture of the fairness of an algorithm.


title: Clustering with Multi-Layer Graphs: A Spectral Perspective

abstract: Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of the multi-layer graph for improved clustering of the vertices compared to using layers independently. We propose two novel methods, which are based on joint matrix factorization and graph regularization framework respectively, to efficiently combine the spectrum of the multiple graph layers, namely the eigenvectors of the graph Laplacian matrices. In each case, the resulting combination, which we call a"joint spectrum"of multiple graphs, is used for clustering the vertices. We evaluate our approaches by simulations with several real world social network datasets. Results demonstrate the superior or competitive performance of the proposed methods over state-of-the-art technique and common baseline methods, such as co-regularization and summation of information from individual graphs.


title: Special Operations Forces Language and Culture Needs Assessment: Cultural Awarenes and Knowledge Training

abstract: Abstract : This study is one component of the Special Operations Forces (SOF) Language and Culture Needs Assessment Project. The larger study consisted of 23 focus groups conducted across the SOF community and an issue-oriented web-based survey. This report documents the current state of culture training in the SOF community from the perspectives of SOF operators and leaders. SOF operators described participating in a variety of culture training experiences. Essentially, there does not appear to be consistency in the type of culture training received. For example, some operators and leaders described formal training events (e.g., immersion experiences, classroom training) whereas others described informal training events (e.g., on-the-job experience). Additionally, the format (e.g., varying training lengths) and perceived effectiveness of culture training also varied. SOF operators and leaders provided suggestions for culture training, including culture training length, when it should occur, and how its importance should be promoted in the community.


title: A review of data complexity measures and their applicability to pattern classification problems

abstract: It is widely accepted that the empirical behavior of classifiers strongly depends on available data. For a given problem, it is rather dicult to guess which classifier will provide the best performance. Traditional experimental studies consist of presenting accuracy of a set of classifiers on a small number of problems, without analyzing why a classifier outperforms other classification algorithms. Recently, some researchers have tried to characterize data complexity and relate it to classifier performance. In this paper, we present a review of data complexity measures in the framework of pattern classification and discuss possible applications to a number of practical problems.


title: Detection and Recognition of Painted Road Surface Markings

abstract: A method for the automatic detection and recognition of text and symbols painted on the road surface is ::: ::: presented. Candidate regions are detected as maximally stable extremal regions (MSER) in a frame which ::: ::: has been transformed into an inverse perspective mapping (IPM) image, showing the road surface with the ::: ::: effects of perspective distortion removed. Detected candidates are then sorted into words and symbols, before ::: ::: they are interpreted using separate recognition stages. Symbol-based road markings are recognised using ::: ::: histogram of oriented gradient (HOG) features and support vector machines (SVM). Text-based road signs are ::: ::: recognised using a third-party optical character recognition (OCR) package, after application of a perspective ::: ::: correction stage. Matching of regions between frames, and temporal fusion of results is used to improve ::: ::: performance. The proposed method is validated using a data-set of videos, and achieves F-measures of 0.85 ::: ::: for text characters and 0.91 for symbols.


title: Online detection of freezing of gait in Parkinson's disease patients: a performance characterization

abstract: Freezing of gait (FOG) is a common gait deficit in advanced Parkinson's disease (PD). It is often a cause of falls, interferes with daily activities and significantly impairs quality of life. PD patients can be assisted by auditory cueing. In daily life cueing should be automatic during gait freeze. This paper describes our ambulatory research platform for context-aware online FOG detection and auditory cueing. The system analyzes frequency components of body motion to detect FOG and provides a metronome sound until the patient resumes walking. We characterize the sensitivity and specificity of the system as functions of: sensor placement and orientation, walking style and algorithm parameters. We have performed a study with ten PD patients, which have worn our system performing several walking tasks. Over 8h of data has been recorded and 237 FOG events have been identified by professional physiotherapists in a post-video analysis. The system detected the FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. We show that the theoretical maximum performance of this algorithm with patient specific optimal parameter sets is 88.6% sensitivity and 92.8% specificity. By separating the patients into saccadic and smooth walker with separate feature sets a detection accuracy of 85.9% sensitivity and 90.9% specificity was measured. ::: ::: The vertical axis of the sensor at the knee is the best sensor position and orientation for FOG detection. However the detection performance is relatively insensitive to the location and orientation, showing the robustness of the algorithm.


title: Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes

abstract: The paper offers a new methodology for modelling, recognition and understanding of electroencephalography (EEG) spatio-temporal data measuring complex cognitive brain processes during mental tasks. The key element is that mental tasks are performed through complex spatio-temporal brain processes and they can be better understood only if we model properly the spatio-/spectro temporal data that measures these processes. The proposed methodology is based on a recently proposed novel spiking neural network architecture, called NeuCube as a general framework for spatio-temporal brain data modelling. The methodology is demonstrated on benchmark cognitive EEG data. The new approach leads to a faster data processing, improved accuracy of the EEG data classification and improved understanding of this data and the cognitive processes that generated it. The paper concluded that the new methodology is worth exploring further on other spatio-temporal data, measuring complex cognitive brain processes, aiming at using this method for the development of the next generation of brain-computer interfaces and systems for early diagnosis of degenerative brain disease, such as Alzheimer's Disease (AD), and for personalised neuro-rehabilitation systems.


title: Effects of humor and laughter on psychological functioning, quality of life, health status, and pulmonary functioning among patients with chronic obstructive pulmonary disease: A preliminary investigation

abstract: OBJECTIVE ::: Previous research indicates the beneficial effects of humor among healthy adults. Little is known about the physical and psychological effects of sense of humor and laughter among patients with chronic obstructive pulmonary disease (COPD). ::: ::: ::: METHODS ::: Patients with COPD (n = 46; mean age ± SD, 66.9 ± 9.9 years) completed assessments of sense of humor, depression, anxiety, quality of life, and recent illness. A subset of patients (n = 22) completed a laughter induction study and were randomly assigned to view either a humorous or a neutral video. Pulmonary function, mood state, and dyspnea were assessed before and after the video. ::: ::: ::: RESULTS ::: Sense of humor was associated with fewer symptoms of depression and anxiety and an enhanced quality of life. However, the induction of laughter led to lung hyperinflation. ::: ::: ::: CONCLUSION ::: Sense of humor among patients with COPD is associated with positive psychological functioning and enhanced quality of life, but laughing aloud may cause acute deterioration in pulmonary function secondary to worsened hyperinflation.


title: The status, challenges, and future of additive manufacturing in engineering

abstract: Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a "positive feedback loop" effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration. The fundamental attributes and challenges/barriers of Additive Manufacturing (AM).The evolution of research on AM with a focus on engineering capabilities.The affordances enabled by AM such as geometry, material and tools design.The developments in industry, intellectual property, and education-related aspects.The important future trends of AM technologies.


Modeling and Simulation Frameworks for Cloud Computing Environment : A Critical Evaluation


title: Performance evaluation of OFDM-based 256- and 1024-QAM in multipath fading propagation conditions

abstract: The use of higher-order modulation is an important approach for increasing data rates within a limited bandwidth. In this paper, we demonstrate the performance of the orthogonal frequency division multiplexing (OFDM)-based 256- and 1024-QAM in multipath fading propagation conditions by link-level simulation, as well as under static propagation condition. The BER performance is investigated for two types of propagation models, i.e., “extended pedestrian A” and “extended vehicular A” models, specified by 3GPP as parameters of the coding rates. Using these results, we validate the application of OFDM-based 256- and 1024-QAM to mobile communication systems.


title: Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach

abstract: We propose a generalized framework for influence maximization in large-scale, time evolving networks. Many real-life influence graphs such as social networks, telephone networks, and IP traffic data exhibit dynamic characteristics, e.g., the underlying structure and communication patterns evolve with time. Correspondingly, we develop a dynamic framework for the influence maximization problem, where we perform effective local updates to quickly adjust the top-k influencers, as the structure and communication patterns in the network change. We design a novel N-Family approach (N=1, 2, 3, ...) based on the maximum influence arborescence (MIA) propagation model with approximation guarantee of (1-1/e). We then develop heuristic algorithms by extending the N-Family approach to other information propagation models (e.g., independent cascade, linear threshold) and influence maximization algorithms (e.g., CELF, reverse reachable sketch). Based on a detailed empirical analysis over several real-world, dynamic, and large-scale networks, we find that our proposed solution, N-Family improves the updating time of the top-k influencers by 1-2 orders of magnitude, compared to state-of-the-art algorithms, while ensuring similar memory usage and influence spreads.


title: Lingual nerve injury after third molar removal: Unilateral atrophy of fungiform papillae

abstract: Background: Pain and sensory changes due to lingual nerve injury are one of the most common alterations that follow surgical removal of third molar. They are usually transient but other less common complications, such as the atrophy of fungiform papillae, have an uncertain prognosis. Case Description: We report a case of a 34-year-old woman who presented a unilateral lingual atrophy of fungiform papillae after third molar extraction accompanied by severe dysesthesia that altered her daily life significantly during the following months and how this complication evolved over time. We conducted a literature review on the different factors that can lead to a lingual nerve injury. Clinical Implications: The clinical evolution of temporary and permanent somatosensitve injuries is an important fact to take into consideration during the postoperative management because it will indicate the lesion prognosis.


title: Improving the accuracy of EKF-based visual-inertial odometry

abstract: In this paper, we perform a rigorous analysis of EKF-based visual-inertial odometry (VIO) and present a method for improving its performance. Specifically, we examine the properties of EKF-based VIO, and show that the standard way of computing Jacobians in the filter inevitably causes inconsistency and loss of accuracy. This result is derived based on an observability analysis of the EKF's linearized system model, which proves that the yaw erroneously appears to be observable. In order to address this problem, we propose modifications to the multi-state constraint Kalman filter (MSCKF) algorithm [1], which ensure the correct observability properties without incurring additional computational cost. Extensive simulation tests and real-world experiments demonstrate that the modified MSCKF algorithm outperforms competing methods, both in terms of consistency and accuracy.


title: Coverage and Capacity Analysis of LTE-M and NB-IoT in a Rural Area

abstract: The 3GPP has introduced the LTE-M and NB-IoT User Equipment categories and made amendments to LTE release 13 to support the cellular Internet of Things. The contribution of this paper is to analyze the coverage probability, the number of supported devices, and the device battery life in networks equipped with either of the newly standardized technologies. The study is made for a site specific network deployment of a Danish operator, and the simulation is calibrated using drive test measurements. The results show that LTE-M can provide coverage for 99.9 % of outdoor and indoor devices, if the latter is experiencing 10 dB additional loss. However, for deep indoor users NB-IoT is required and provides coverage for about 95 % of the users. The cost is support for more than 10 times fewer devices and a 2-6 times higher device power consumption. Thus both LTE-M and NB- IoT provide extended support for the cellular Internet of Things, but with different trade- offs.