title: C 1-continuous Terrain Reconstruction from Sparse Contours

abstract: Contour lines from topographic maps are still the most common form of elevation data for the Earth’s surface and in the case of historical landscapes, they often are the only available source of information. In this paper we present a new contour interpolation method that solves this bivariate problem by considering univariate curve interpolation along the approximate gradient directions of the unknown surface. For a point between two contours the height value is computed with Hermite interpolation based on the shortest distances to the contours and height and derivative information at the contours. The surfaces generated are C except at terrain characteristics such as ridges and valleys which are reconstructed as sharp features. The method also faithfully reconstructs summits, pits, and saddles and is especially well-suited for sparse sets of contours. The approach allows for an efficient numerical implementation as we demonstrate with a number of examples.


title: Mobile phone messaging for preventive health care

abstract: Background ::: Preventive health care promotes health and prevents disease or injuries by addressing factors that lead to the onset of a disease, and by detecting latent conditions to reduce or halt their progression. Many risk factors for costly and disabling conditions (such as cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases) can be prevented, yet healthcare systems do not make the best use of their available resources to support this process. Mobile phone messaging applications, such as Short Message Service (SMS) and Multimedia Message Service (MMS), could offer a convenient and cost-effective way to support desirable health behaviours for preventive health care. ::: ::: Objectives ::: To assess the effects of mobile phone messaging interventions as a mode of delivery for preventive health care, on health status and health behaviour outcomes. ::: ::: Search methods ::: We searched: the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library 2009, Issue 2), MEDLINE (OvidSP) (January 1993 to June 2009), EMBASE (OvidSP) (January 1993 to June 2009), PsycINFO (OvidSP) (January 1993 to June 2009), CINAHL (EbscoHOST) (January 1993 to June 2009), LILACS (January 1993 to June 2009) and African Health Anthology (January 1993 to June 2009). ::: ::: We also reviewed grey literature (including trial registers) and reference lists of articles. ::: ::: Selection criteria ::: We included randomised controlled trials (RCTs), quasi-randomised controlled trials (QRCTs), controlled before-after (CBA) studies, and interrupted time series (ITS) studies with at least three time points before and after the intervention. We included studies using SMS or MMS as a mode of delivery for any type of preventive health care. We only included studies in which it was possible to assess the effects of mobile phone messaging independent of other technologies or interventions. ::: ::: Data collection and analysis ::: Two review authors independently assessed all studies against the inclusion criteria, with any disagreements resolved by a third review author. Study design features, characteristics of target populations, interventions and controls, and results data were extracted by two review authors and confirmed by a third author. Primary outcomes of interest were health status and health behaviour outcomes. We also considered patients’ and providers’ evaluation of the intervention, perceptions of safety, health service utilisation and costs, and potential harms or adverse effects. Because the included studies were heterogeneous in type of condition addressed, intervention characteristics and outcome measures, we did not consider that it was justified to conduct a meta-analysis to derive an overall effect size for the main outcome categories; instead, we present findings narratively. ::: ::: Main results ::: We included four randomised controlled trials involving 1933 participants. ::: ::: For the primary outcome category of health, there was moderate quality evidence from one study that women who received prenatal support via mobile phone messages had significantly higher satisfaction than those who did not receive the messages, both in the antenatal period (mean difference (MD) 1.25, 95% confidence interval (CI) 0.78 to 1.72) and perinatal period (MD 1.19, 95% CI 0.37 to 2.01). Their confidence level was also higher (MD 1.12, 95% CI 0.51 to 1.73) and anxiety level was lower (MD -2.15, 95% CI -3.42 to -0.88) than in the control group in the antenatal period. In this study, no further differences were observed between groups in the perinatal period. There was low quality evidence that the mobile phone messaging intervention did not affect pregnancy outcomes (gestational age at birth, infant birth weight, preterm delivery and route of delivery). ::: ::: For the primary outcome category of health behaviour, there was moderate quality evidence from one study that mobile phone message reminders to take vitamin C for preventive reasons resulted in higher adherence (risk ratio (RR) 1.41, 95% CI 1.14 to 1.74). There was high quality evidence from another study that participants receiving mobile phone messaging support had a significantly higher likelihood of quitting smoking than those in a control group at 6 weeks (RR 2.20, 95% CI 1.79 to 2.70) and at 12 weeks follow-up (RR 1.55, 95% CI 1.30 to 1.84). At 26 weeks, there was only a significant difference between groups if, for participants with missing data, the last known value was carried forward. There was very low quality evidence from one study that mobile phone messaging interventions for self-monitoring of healthy behaviours related to childhood weight control did not have a statistically significant effect on physical activity, consumption of sugar-sweetened beverages or screen time. ::: ::: For the secondary outcome of acceptability, there was very low quality evidence from one study that user evaluation of the intervention was similar between groups. There was moderate quality evidence from one study of no difference in adverse effects of the intervention, measured as rates of pain in the thumb or finger joints, and car crash rates. ::: ::: None of the studies reported the secondary outcomes of health service utilisation or costs of the intervention. ::: ::: Authors' conclusions ::: We found very limited evidence that in certain cases mobile phone messaging interventions may support preventive health care, to improve health status and health behaviour outcomes. However, because of the low number of participants in three of the included studies, combined with study limitations of risk of bias and lack of demonstrated causality, the evidence for these effects is of low to moderate quality. The evidence is of high quality only for interventions aimed at smoking cessation. Furthermore, there are significant information gaps regarding the long-term effects, risks and limitations of, and user satisfaction with, such interventions.


title: Predicting the fix time of bugs

abstract: Two important questions concerning the coordination of development effort are which bugs to fix first and how long it takes to fix them. In this paper we investigate empirically the relationships between bug report attributes and the time to fix. The objective is to compute prediction models that can be used to recommend whether a new bug should and will be fixed fast or will take more time for resolution. We examine in detail if attributes of a bug report can be used to build such a recommender system. We use decision tree analysis to compute and 10-fold cross validation to test prediction models. We explore prediction models in a series of empirical studies with bug report data of six systems of the three open source projects Eclipse, Mozilla, and Gnome. Results show that our models perform significantly better than random classification. For example, fast fixed Eclipse Platform bugs were classified correctly with a precision of 0.654 and a recall of 0.692. We also show that the inclusion of postsubmission bug report data of up to one month can further improve prediction models.


title: SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis

abstract: This paper describes the system developed at LIF for the SemEval-2016 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system extends the Convolutional Neural Networks (CNN) state of the art approach. We initialize the input representations with embeddings trained on different units: lexical, partof-speech, and sentiment embeddings. Neural networks for each input space are trained separately, and then the representations extracted from their hidden layers are concatenated as input of a fusion neural network. The system ranked 2nd at SemEval-2016 and obtained an average F1 of 63.0%.


title: The Alternaria genomes database: a comprehensive resource for a fungal genus comprised of saprophytes, plant pathogens, and allergenic species

abstract: BackgroundAlternaria is considered one of the most common saprophytic fungal genera on the planet. It is comprised of many species that exhibit a necrotrophic phytopathogenic lifestyle. Several species are clinically associated with allergic respiratory disorders although rarely found to cause invasive infections in humans. Finally, Alternaria spp. are among the most well known producers of diverse fungal secondary metabolites, especially toxins.DescriptionWe have recently sequenced and annotated the genomes of 25 Alternaria spp. including but not limited to many necrotrophic plant pathogens such as A. brassicicola (a pathogen of Brassicaceous crops like cabbage and canola) and A. solani (a major pathogen of Solanaceous plants like potato and tomato), and several saprophytes that cause allergy in human such as A. alternata isolates. These genomes were annotated and compared. Multiple genetic differences were found in the context of plant and human pathogenicity, notably the pro-inflammatory potential of A. alternata. The Alternaria genomes database was built to provide a public platform to access the whole genome sequences, genome annotations, and comparative genomics data of these species. Genome annotation and comparison were performed using a pipeline that integrated multiple computational and comparative genomics tools. Alternaria genome sequences together with their annotation and comparison data were ported to Ensembl database schemas using a self-developed tool (EnsImport). Collectively, data are currently hosted using a customized installation of the Ensembl genome browser platform.ConclusionRecent efforts in fungal genome sequencing have facilitated the studies of the molecular basis of fungal pathogenicity as a whole system. The Alternaria genomes database provides a comprehensive resource of genomics and comparative data of an important saprophytic and plant/human pathogenic fungal genus. The database will be updated regularly with new genomes when they become available. The Alternaria genomes database is freely available for non-profit use at http://alternaria.vbi.vt.edu.


title: How to train a compact binary neural network with high accuracy

abstract: How to train a binary neural network (BinaryNet) with both high compression rate and high accuracy on large scale datasets? We answer this question through a careful analysis of previous work on BinaryNets, in terms of training strategies, regularization, and activation approximation. Our findings first reveal that a low learning rate is highly preferred to avoid frequent sign changes of the weights, which often makes the learning of BinaryNets unstable. Secondly, we propose to use PReLU instead of ReLU in a BinaryNet to conveniently absorb the scale factor for weights to the activation function, which enjoys high computation efficiency for binarized layers while maintains high approximation accuracy. Thirdly, we reveal that instead of imposing L2 regularization, driving all weights to zero which contradicts with the setting of BinaryNets, we introduce a regularization term that encourages the weights to be bipolar. Fourthly, we discover that the failure of binarizing the last layer, which is essential for high compression rate, is due to the improper output range. We propose to use a scale layer to bring it to normal. Last but not least, we propose multiple binarizations to improve the approximation of the activations. The composition of all these enables us to train BinaryNets with both high compression rate and high accuracy, which is strongly supported by our extensive empirical study.


title: Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power

abstract: Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients from either one or two single-channel or bipolar channel intra-cranial or scalp electroencephalogram (EEG) recordings with low hardware complexity. Spectral power features are extracted and their ratios are computed. For each channel, a total of 44 features including 8 absolute spectral powers, 8 relative spectral powers and 28 spectral power ratios are extracted every two seconds using a 4-second window with a 50% overlap. These features are then ranked and selected in a patient-specific manner using a two-step feature selection. Selected features are further processed by a second-order Kalman filter and then input to a linear support vector machine (SVM) classifier. The algorithm is tested on the intra-cranial EEG (iEEG) from the Freiburg database and scalp EEG (sEEG) from the MIT Physionet database. The Freiburg database contains 80 seizures among 18 patients in 427 hours of recordings. The MIT EEG database contains 78 seizures from 17 children in 647 hours of recordings. It is shown that the proposed algorithm can achieve a sensitivity of 100% and an average false positive rate (FPR) of 0.0324 per hour for the iEEG (Freiburg) database and a sensitivity of 98.68% and an average FPR of 0.0465 per hour for the sEEG (MIT) database. These results are obtained with leave-one-out cross-validation where the seizure being tested is always left out from the training set. The proposed algorithm also has a low complexity as the spectral powers can be computed using FFT. The area and power consumption of the proposed linear SVM are 2 to 3 orders of magnitude less than a radial basis function kernel SVM (RBF-SVM) classifier. Furthermore, the total energy consumption of a system using linear SVM is reduced by 8% to 23% compared to system using RBF-SVM.


title: Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

abstract: Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO.


title: Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch Shadowing

abstract: Intel has introduced a hardware-based trusted execution environment, Intel Software Guard Extensions (SGX), that provides a secure, isolated execution environment, or enclave, for a user program without trusting any underlying software (e.g., an operating system) or firmware. Researchers have demonstrated that SGX is vulnerable to a page-fault-based attack. However, the attack only reveals page-level memory accesses within an enclave. ::: ::: In this paper, we explore a new, yet critical, side-channel attack, branch shadowing, that reveals fine-grained control flows (branch granularity) in an enclave. The root cause of this attack is that SGX does not clear branch history when switching from enclave to nonenclave mode, leaving fine-grained traces for the outside world to observe, which gives rise to a branch-prediction side channel. However, exploiting this channel in practice is challenging because 1) measuring branch execution time is too noisy for distinguishing fine-grained controlflow changes and 2) pausing an enclave right after it has executed the code block we target requires sophisticated control. To overcome these challenges, we develop two novel exploitation techniques: 1) a last branch record (LBR)-based history-inferring technique and 2) an advanced programmable interrupt controller (APIC)-based technique to control the execution of an enclave in a finegrained manner. An evaluation against RSA shows that our attack infers each private key bit with 99.8% accuracy. Finally, we thoroughly study the feasibility of hardware-based solutions (i.e., branch history flushing) and propose a software-based approach that mitigates the attack.


title: Localization and map-building of mobile robot based on RFID sensor fusion system

abstract: In this paper, we propose an improved localization system for an indoor mobile robot using RFID (Radio Frequency IDentification) system and wheel encoders. Nowadays, RFID technology is widely used in the robot field. We investigate recent RFID localization system based on (passive) tag-floor for mobile robot, and analyze the problems and limitation of previous researches. First, RFID and wheel encoder localization systempsilas uncertainty, which may result in inaccurate location data, is modeled. And then, the algorithm for estimating each uncertainty is proposed for localization. Finally, a proposed algorithm successfully demonstrated through simulation experiments conducted under certain assumption.


title: Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures

abstract: The electricity industry is now at the verge of a new era—an era that promises, through the evolution of the existing electrical grids to smart grids, more efficient and effective power management, better reliability, reduced production costs, and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around not only the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the smart grid and the smart home, which we present as an integral part of the smart grid. Based on several scenarios, we aim to present some of the most representative threats to the smart home/smart grid environment. The threats detected are categorized according to specific security goals set for the smart home/smart grid environment, and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes this paper.


title: Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system

abstract: Abstract In this study, three popular signal processing techniques (Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the decomposition of Electroencephalography (EEG) Signals in Brain Computer Interface (BCI) system for a classification task. Publicly available BCI competition III dataset IVa, a multichannel 2-class motor-imagery dataset, was used for this purpose. Multiscale Principal Component Analysis method was applied for the purpose of noise removal. In addition, different sets of features were formed to examine the effect of a particular group of features. The parameter selection process for signal decomposition methods was thoroughly explained as well. Our results show that the combination of Multiscale Principal Component Analysis de-noising and higher order statistics features extracted from wavelet packet decomposition sub-bands resulted in highest average classification accuracy of 92.8%. Our study is one among very few that provides a comprehensive comparison between signal decomposition methods in combination with higher order statistics in classification of BCI signals. In addition, we stressed the importance of higher frequency ranges in improving the classification task for EEG signals in Brain Computer Interface Systems. Obtained results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair. It can also further enhance the current rehabilitation therapies where appropriate feedback is delivered once the individual executes the correct movement. In that way, motor rehabilitation outcomes may improve over time.


title: Supervised and Unsupervised Discretization of Continuous Features

abstract: Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify defining characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised discretization method, to entropy-based and purity-based methods, which are supervised algorithms. We found that the performance of the Naive-Bayes algorithm significantly improved when features were discretized using an entropy-based method. In fact, over the 16 tested datasets, the discretized version of Naive-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm significantly improved if features were discretized in advance; in our experiments, the performance never significantly degraded, an interesting phenomenon considering the fact that C4.5 is capable of locally discretizing features.


title: Inclined planes optimization algorithm in optimal architecture of MLP neural networks

abstract: In this paper an Inclined Planes Optimization algorithm, is used to optimize the performance of the multilayer perceptron. Indeed, the performance of the neural network depends on its parameters such as the number of neurons in the hidden layer and the connection weights. So far, most research has been done in the field of training the neural network. In this paper, a new algorithm optimization is presented in optimal architecture for data classification. Neural network training is done by backpropagation (BP) algorithm and optimization the architecture of neural network is considered as independent variables in the algorithm. The results in three classification problems have shown that a neural network resulting from these methods have low complexity and high accuracy when compared with results of Particle Swarm Optimization and Gravitational Search Algorithm.


Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks


title: Duplex Metric Learning for Image Set Classification

abstract: Image set classification has attracted much attention because of its broad applications. Despite the success made so far, the problems of intra-class diversity and inter-class similarity still remain two major challenges. To explore a possible solution to these challenges, this paper proposes a novel approach, termed duplex metric learning (DML), for image set classification. The proposed DML consists of two progressive metric learning stages with different objectives used for feature learning and image classification, respectively. The metric learning regularization is not only used to learn powerful feature representations but also well explored to train an effective classifier. At the first stage, we first train a discriminative stacked autoencoder (DSAE) by layer-wisely imposing a metric learning regularization term on the neurons in the hidden layers and meanwhile minimizing the reconstruction error to obtain new feature mappings in which similar samples are mapped closely to each other and dissimilar samples are mapped farther apart. At the second stage, we discriminatively train a classifier and simultaneously fine-tune the DSAE by optimizing a new objective function, which consists of a classification error term and a metric learning regularization term. Finally, two simple voting strategies are devised for image set classification based on the learnt classifier. In the experiments, we extensively evaluate the proposed framework for the tasks of face recognition, object recognition, and face verification on several commonly-used data sets and state-of-the-art results are achieved in comparison with existing methods.


title: Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking

abstract: The Joint Probabilistic Data Association (JPDA) filter for multiple object tracking is based on the assumption that at most one measurement originates from a target object. However, with the development of high-resolution sensors, it is often the case that multiple spatially distributed detections are obtained from a single object. To tackle this emerging data association challenge, this paper presents a JPDA method based on the Poisson spatial measurement model for extended objects. As the constraint that one target gets at most one measurement is relaxed, the marginal association probabilities can be obtained with linear complexity in the number of measurements and targets. The proposed method is compared to a partition-based multiple extended object tracking algorithm.


title: Fully Integrated 94-GHz Dual-Polarized TX and RX Phased Array Chipset in SiGe BiCMOS Operating up to 105 °C

abstract: 94-GHz dual-polarized phased array transmitter and receiver ICs in 130 nm BiCMOS technology are reported. The transmitter IC integrates 16 transmitter front ends with two independent outputs, a 1-to-16 power splitter, and an intermediate frequency (IF)-to-RF up-converter. The receiver IC integrates 32 front ends, two separate 16-to-1 power combiners, and two RF-to-IF down-converters, which can either support a 32-element phased array or a 16-element polarimetric phased array if connected to 16 dual-polarized antennas. Both transmitter and receiver ICs include IF/baseband circuitry, a frequency synthesizer with continuous lock detection, a temperature sensor, and digital circuitry, including serial interface and front-end memory. On-wafer TX measurements at 94 GHz taken at 25 °C show an IF-to-RF conversion gain of 35 dB, oP1dB of 4 dBm, $P_{\text {SAT}}$ of 7.8 dBm per channel with a total power consumption of 3 W. On-wafer RX measurements at 94 GHz and 25 °C show a maximum RF-to-IF array conversion gain of 40 dB and an noise figure (NF) of 6 dB. The total RX power consumption varies from 3 to 4.6 W as it is configured from its minimum to its maximum RF front-end gain settings. RF phase shifting elements used in both TX and RX demonstrate a 1.6° phase resolution with 360° tuning range and gain variation (rms) lower than 0.5 dB. Phase-invariant gain tunability in TX and RX front ends has been achieved for independent tapering and beam steering, demonstrating the measured phase variation within ±0.5° for 10-dB gain tuning. Both TX and RX ICs are functional up to 105 °C, maintaining $P_{\text {SAT}}>6.5$ dBm per channel in TX and NF $\times $ 5.6 mm.


title: ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

abstract: Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in employing them in resource-limited applications. In this work, we propose to compress deep models by using channel-wise convolutions, which replace dense connections among feature maps with sparse ones in CNNs. Based on this novel operation, we build light-weight CNNs known as ChannelNets. ChannelNets use three instances of channel-wise convolutions; namely group channel-wise convolutions, depth-wise separable channel-wise convolutions, and the convolutional classification layer. Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy. Notably, our work represents the first attempt to compress the fully-connected classification layer, which usually accounts for about 25% of total parameters in compact CNNs. Along this new direction, we investigate the behavior of our proposed convolutional classification layer and conduct detailed analysis. Based on our in-depth analysis, we further propose convolutional classification layers without weight-sharing. This new classification layer achieves a good trade-off between fully-connected classification layers and the convolutional classification layer. Experimental results on the ImageNet dataset demonstrate that ChannelNets achieve consistently better performance compared to prior methods.


title: A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training

abstract: Abstract Background Stroke is one of the most common disorders among the elderly. A practical problem in stroke rehabilitation systems is that how to separate motor imagery patterns from electroencephalographic (EEG) recordings. There is a sharp decline in performance of these systems when classical algorithms, such as Common Spatial Pattern (CSP), are directly applied on stroke patients. New method We propose a tensor-based scheme to detect motor imagery EEG patterns in spatial–spectral–temporal domain directly from multidimensional EEG constructed by wavelet transform method. Discriminative motor imagery EEG patterns are obtained by Fisher score strategy. Furthermore, the most contributed channel groups and frequency bands are selected from these patterns and utilized as prior knowledge for the following motor imagery tasks. Results We evaluate our scheme based on EEG datasets recorded from stroke patients. The results show that our method outperforms five other traditional methods in both online and offline recognition performance. Comparison with existing methods Unlike the existing methods, motor imagery EEG patterns in spatial–spectral–temporal domain are simultaneously obtained by our method, preserving the structural information of the multi-channel time-varying EEG. Conclusions Our scheme is encouraged to be transferred to some other practical rehabilitation applications for its better performance.


title: A clinician-administered observation and corresponding caregiver interview capturing DSM-5 sensory reactivity symptoms in children with ASD

abstract: The SAND provides a novel method to characterize sensory reactivity symptoms based on DSM-5 criteria for ASD. This is the first known sensory assessment that combines a clinician-administered observation and caregiver interview to optimally capture sensory phenotypes characteristic of individuals with neurodevelopmental disorders. The SAND offers a beneficial new tool for both research and clinical purposes and has the potential to meaningfully enhance gold-standard assessment of ASD.


title: Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect

abstract: This paper summarizes the recent progress we have made for the computer vision technologies in physical therapy with the accessible and affordable devices. We first introduce the remote health coaching system we build with Microsoft Kinect. Since the motion data captured by Kinect is noisy, we investigate the data accuracy of Kinect with respect to the high accuracy motion capture system. We also propose an outlier data removal algorithm based on the data distribution. In order to generate the kinematic parameter from the noisy data captured by Kinect, we propose a kinematic filtering algorithm based on Unscented Kalman Filter and the kinematic model of human skeleton. The proposed algorithm can obtain smooth kinematic parameter with reduced noise compared to the kinematic parameter generated from the raw motion data from Kinect.


title: Towards Building a Knowledge Base of Monetary Transactions from a News Collection

abstract: We address the problem of extracting structured representations of economic events from a large corpus of news articles, using a combination of natural language processing and machine learning techniques. The developed techniques allow for semi-automatic population of a financial knowledge base, which, in turn, may be used to support a range of data mining and exploration tasks. The key challenge we face in this domain is that the same event is often reported multiple times, with varying correctness of details. We address this challenge by first collecting all information pertinent to a given event from the entire corpus, then considering all possible representations of the event, and finally, using a supervised learning method, to rank these representations by the associated confidence scores. A main innovative element of our approach is that it jointly extracts and stores all attributes of the event as a single representation (quintuple). Using a purpose-built test set we demonstrate that our supervised learning approach can achieve 25% improvement in F1-score over baseline methods that consider the earliest, the latest or the most frequent reporting of the event.


title: Yangians and quantum loop algebras

abstract: Let \(\mathfrak{g }\) be a complex, semisimple Lie algebra. Drinfeld showed that the quantum loop algebra \(U_\hbar (L\mathfrak g )\) of \(\mathfrak{g }\) degenerates to the Yangian \({Y_\hbar (\mathfrak g )}\). We strengthen this result by constructing an explicit algebra homomorphism \(\Phi \) from \(U_\hbar (L\mathfrak g )\) to the completion of \({Y_\hbar (\mathfrak g )}\) with respect to its grading. We show moreover that \(\Phi \) becomes an isomorphism when \({U_\hbar (L\mathfrak g )}\) is completed with respect to its evaluation ideal. We construct a similar homomorphism for \(\mathfrak{g }=\mathfrak{gl }_n\) and show that it intertwines the actions of \(U_\hbar (L\mathfrak gl _{n})\) and \(Y_\hbar (\mathfrak gl _{n})\) on the equivariant \(K\)-theory and cohomology of the variety of \(n\)-step flags in \({\mathbb{C }}^d\) constructed by Ginzburg–Vasserot.


title: BundleFusion: Real-Time Globally Consistent 3D Reconstruction Using On-the-Fly Surface Reintegration

abstract: Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic applications. However, scalability brings challenges of drift in pose estimation, introducing significant errors in the accumulated model. Approaches often require hours of offline processing to globally correct model errors. Recent online methods demonstrate compelling results but suffer from (1) needing minutes to perform online correction, preventing true real-time use; (2) brittle frame-to-frame (or frame-to-model) pose estimation, resulting in many tracking failures; or (3) supporting only unstructured point-based representations, which limit scan quality and applicability. We systematically address these issues with a novel, real-time, end-to-end reconstruction framework. At its core is a robust pose estimation strategy, optimizing per frame for a global set of camera poses by considering the complete history of RGB-D input with an efficient hierarchical approach. We remove the heavy reliance on temporal tracking and continually localize to the globally optimized frames instead. We contribute a parallelizable optimization framework, which employs correspondences based on sparse features and dense geometric and photometric matching. Our approach estimates globally optimized (i.e., bundle adjusted) poses in real time, supports robust tracking with recovery from gross tracking failures (i.e., relocalization), and re-estimates the 3D model in real time to ensure global consistency, all within a single framework. Our approach outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness. Our framework leads to a comprehensive online scanning solution for large indoor environments, enabling ease of use and high-quality results.1


title: A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

abstract: This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naive Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).


title: A Review of Data Mining Techniques

abstract: Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.


title: Design of a High-Performance System for Secure Image Communication in the Internet of Things

abstract: Image or video exchange over the Internet of Things (IoT) is a requirement in diverse applications, including smart health care, smart structures, and smart transportations. This paper presents a modular and extensible quadrotor architecture and its specific prototyping for automatic tracking applications. The architecture is extensible and based on off-the-shelf components for easy system prototyping. A target tracking and acquisition application is presented in detail to demonstrate the power and flexibility of the proposed design. Complete design details of the platform are also presented. The designed module implements the basic proportional–integral–derivative control and a custom target acquisition algorithm. Details of the sliding-window-based algorithm are also presented. This algorithm performs $20\times $ faster than comparable approaches in OpenCV with equal accuracy. Additional modules can be integrated for more complex applications, such as search-and-rescue, automatic object tracking, and traffic congestion analysis. A hardware architecture for the newly introduced Better Portable Graphics (BPG) compression algorithm is also introduced in the framework of the extensible quadrotor architecture. Since its introduction in 1987, the Joint Photographic Experts Group (JPEG) graphics format has been the de facto choice for image compression. However, the new compression technique BPG outperforms the JPEG in terms of compression quality and size of the compressed file. The objective is to present a hardware architecture for enhanced real-time compression of the image. Finally, a prototyping platform of a hardware architecture for a secure digital camera (SDC) integrated with the secure BPG (SBPG) compression algorithm is presented. The proposed architecture is suitable for high-performance imaging in the IoT and is prototyped in Simulink. To the best of our knowledge, this is the first ever proposed hardware architecture for SBPG compression integrated with an SDC.


title: Developing an embodied gait on a compliant quadrupedal robot

abstract: Incorporating the body dynamics of compliant robots into their controller architectures can drastically reduce the complexity of locomotion control. An extreme version of this embodied control principle was demonstrated in highly compliant tensegrity robots, for which stable gait generation was achieved by using only optimized linear feedback from the robot's sensors to its actuators. The morphology of quadrupedal robots has previously been used for sensing and for control of a compliant spine, but never for gait generation. In this paper, we successfully apply embodied control to the compliant, quadrupedal Oncilla robot. As initial experiments indicated that mere linear feedback does not suffice, we explore the minimal requirements for robust gait generation in terms of memory and nonlinear complexity. Our results show that a memoryless feedback controller can generate a stable trot by learning the desired nonlinear relation between the input and the output signals. We believe this method can provide a very useful tool for transferring knowledge from open loop to closed loop control on compliant robots.


Algorithm For Path Planning of Robotic Soccer Player


title: Calcium entry and α-synuclein inclusions elevate dendritic mitochondrial oxidant stress in dopaminergic neurons.

abstract: The core motor symptoms of Parkinson9s disease (PD) are attributable to the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNc). Mitochondrial oxidant stress is widely viewed a major factor in PD pathogenesis. Previous work has shown that activity-dependent calcium entry through L-type channels elevates perinuclear mitochondrial oxidant stress in SNc dopaminergic neurons, providing a potential basis for their selective vulnerability. What is less clear is whether this physiological stress is present in dendrites and if Lewy bodies, the major neuropathological lesion found in PD brains, exacerbate it. To pursue these questions, mesencephalic dopaminergic neurons derived from C57BL/6 transgenic mice were studied in primary cultures, allowing for visualization of soma and dendrites simultaneously. Many of the key features of in vivo adult dopaminergic neurons were recapitulated in vitro . Activity-dependent calcium entry through L-type channels increased mitochondrial oxidant stress in dendrites. This stress progressively increased with distance from the soma. Examination of SNc dopaminergic neurons ex vivo in brain slices verified this pattern. Moreover, the formation of intracellular α-synuclein Lewy-body-like aggregates increased mitochondrial oxidant stress in perinuclear and dendritic compartments. This stress appeared to be extramitochondrial in origin, because scavengers of cytosolic reactive oxygen species or inhibition of NADPH oxidase attenuated it. These results show that physiological and proteostatic stress can be additive in the soma and dendrites of vulnerable dopaminergic neurons, providing new insight into the factors underlying PD pathogenesis.


title: A vector space search engine for Web services

abstract: As Web services increasingly become important in distributed computing, some of the flaws and limitations of this technology become more and more obvious. One of this flaws is the discovery of Web services through common methods. Research has been pursued in the field of "semantic Web services". This research is driven by the idea, to describe the functionality of Web services as accurately as possible and to create programs automatically out of already existing Web services. In this paper we discuss a new method for discovery and analysis of Web services. Our approach uses a vector space search engine to index descriptions of already composed services. Rather than generating or automatically composing applications, this approach provides developers with a valuable utility to browse repositories based on already existing information. Furthermore, we propose some additional modifications to extract the maximum amount of semantics from existing service definition repositories.


title: ficient Implementation of Reid's Multiple ypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking

abstract: An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the k-best hypotheses are determined in polynomial time using an algorithm due to Murly (1968). The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination, and continuation are demonstrated together with the latter's capability to provide low level support of temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 51 frames. Each corner is tracked using a simple linear Kalman filter and any data association uncertainty is resolved by the MHT. Kalman filter parameter estimation is discussed, and experimental results show that the algorithm is robust to errors in the motion model. An investigation of the performance of the algorithm as a function of look-ahead (tree depth) indicates that high accuracy can be obtained for tree depths as shallow as three. Experimental results suggest that a real-time MHT solution to the motion correspondence problem is possible for certain classes of scenes.


title: Estimation of charges and critical velocity of plastic particles in triboelectric separator

abstract: Two important parameters of triboelectric separation of plastic particles are analyzed: the distribution of their electric charges and their critical velocity (velocity, at which the trajectories of falling particles are not longer significantly influenced by the applied electric field). The analysis is performed by a combination of experimental and theoretical procedures and the complete methodology is illustrated with a typical example.


title: A Real-Time Face Detector Based on an End-to-End CNN

abstract: Convolutional neural networks with deeply trained make a significant performance improvement in face detection. However, the major shortcomings, i.e. need of high computational cost and slow calculation, make the existing CNN-based face detectors impractical in many applications. In this paper, a real-time approach for face detection was proposed by utilizing a single end-to-end deep neural network with multi-scale feature maps, multi-scale prior aspect ratios as well as confidence rectification. Multi-scale feature maps overcome the difficulties of detecting small face, and meanwhile, multiscale prior aspect ratios reduce the computing cost and the confidence rectification, which is in line with the biological intuition and can further improve the detection rate. Evaluated on the public benchmark, FDDB, the proposed algorithm, gained a performance as good as the state-of-the-art CNNbased methods, however, with much faster speed.


title: Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors

abstract: This investigation is one in a series of studies that address the possibility of stroke rehabilitation using robotic devices to facilitate “adaptive training.” Healthy subjects, after training in the presence of systematically applied forces, typically exhibit a predictable “after-effect.” A critical question is whether this adaptive characteristic is preserved following stroke so that it might be exploited for restoring function. Another important question is whether subjects benefit more from training forces that enhance their errors than from forces that reduce their errors. We exposed hemiparetic stroke survivors and healthy age-matched controls to a pattern of disturbing forces that have been found by previous studies to induce a dramatic adaptation in healthy individuals. Eighteen stroke survivors made 834 movements in the presence of a robot-generated force field that pushed their hands proportional to its speed and perpendicular to its direction of motion — either clockwise or counterclockwise. We found that subjects could adapt, as evidenced by significant after-effects. After-effects were not correlated with the clinical scores that we used for measuring motor impairment. Further examination revealed that significant improvements occurred only when the training forces magnified the original errors, and not when the training forces reduced the errors or were zero. Within this constrained experimental task we found that error-enhancing therapy (as opposed to guiding the limb closer to the correct path) to be more effective than therapy that assisted the subject.


Proofs of Work for Blockchain Protocols.


title: bandicoot: a python toolbox for mobile phone metadata

abstract: bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license.


title: High-Definition Occupational Therapy: HD OT

abstract: This article was taken from Dr. Florence Clark�s address given at the AOTA Annual Conference & Expo, held in Orlando, Florida, April 29�May 2, 2010.


Using Stanford NER and Illinois NER to Detect Malay Named Entity Recognition


title: A surface reconstruction algorithm for topology optimization

abstract: For mechanical structural design, topology optimization is often utilized. During this process, a topologically optimized model must be converted into a parametric CAD solid model. The key point of conversion is that a discretized shape of a topologically optimized model must be smoothed, but features such as creases and corners must be retained. Thus, a surface reconstruction algorithm to produce the parametric CAD solid model from a topologically optimized model is proposed in this paper. Our presented algorithm consists of three parts: (1) an enclosed isosurface geometry from which the topologically optimized model is generated, (2) features detected and (3) the parametric CAD solid model reconstructed as biquartic surface splines. In order to generate an enclosed isosurface model effectively, we propose an algorithm based upon the marching cubes method to detect elements intersected by an isosurface. After generating an enclosed isosurface model, we produce biquartic surface splines. By applying our algorithm to an enclosed isosurface model, it is possible to produce smoothed biquartic surface splines with features retained. Some examples are shown and the effectiveness of our algorithm is discussed in this paper.


title: Understanding the content and features of open data portals in American cities

abstract: Abstract In this paper, we present the results of research on features and content of open data portals in American cities. Five scales are developed to categorize and describe these portals: the Open Data Portal Index (ODPI), Data Content Index (DCI), a compilation of the two (Overall Index), the Number of Datasets and Number of Datasets per 100,000. Regression models explaining variation between cities on these scales indicate city population as an important influence, along with participation in a regional consortium. More variation could be explained in the number of datasets model (79.8%) than in any other model. Overall, results indicate portals are in a very early stage of development and need a great deal of work to improve user help and analysis features as well as inclusion of features to help citizens understand the data, such as more charting and analysis.


title: A Low-RCS and High-Gain Circularly Polarized Antenna With a Low Profile

abstract: A low-radar-cross-section and high-gain broadband circularly polarized (CP) antenna with a low profile was specially designed using metasurface concepts. A polarization conversion metasurface is designed and used as the radiator of the proposed CP antenna. It is shown that a good axial ratio and high gain can be obtained over a broad band. Three kinds of artificial magnetic conductors are arranged around the polarization conversion metasurface. By making the discrepancies of the reflection phases among the artificial magnetic conductors and polarization conversion metasurface to be 90° in turn, the monostatic radar cross section of the proposed antenna is reduced in a wide frequency band covering the operation frequency band of the CP antenna. Good agreement between the experimental and simulation results is obtained.


title: Applications of Clifford's Geometric Algebra

abstract: We survey the development of Clifford's geometric algebra and some of its engineering applications during the last 15 years. Several recently developed applications and their merits are discussed in some detail. We thus hope to clearly demonstrate the benefit of developing problem solutions in a unified framework for algebra and geometry with the widest possible scope: from quantum computing and electromagnetism to satellite navigation, from neural computing to camera geometry, image processing, robotics and beyond.


title: How to derive priorities in AHP: a comparative study

abstract: A heated discussion has arisen over the “best” priorities derivation method. Using a Monte Carlo simulation this article compares and evaluates the solutions of four AHP ratio scaling methods: the right eigenvalue method, the left eigenvalue method, the geometric mean and the mean of normalized values. Matrices with different dimensions and degree of impurities are randomly constructed. We observe a high level of agreement between the different scaling techniques. The number of ranking contradictions increases with the dimension of the matrix and the inconsistencies. However, these contradictions affect only close priorities. Copyright Springer-Verlag 2006


title: Perceived Benefits of Incorporating Yoga into Classroom Teaching: Assessment of the Effects of “Yoga Tools for Teachers”

abstract: With rising health issues among children and adolescents in America such as obesity and diabetes, getting physically active becomes ever more important. Yoga, as an ancient system of exercise, has a great potential to teach children to be mindful of factors that impact their health and improve their total well-being. The purpose of this study was to examine the perceived benefits of incorporating yoga-based activities into classroom teaching as a result of implementing the Yoga Ed. Tools for Teachers program. One hundred and three physical education and classroom teachers were trained by certified Yoga Ed. instructors for two days. These teachers, in turn, implemented the yoga-based activities for 5 - 15 minutes daily for a year. At the completion of this period, questionnaires from 550 parents and 661 students as well as 103 teachers were analyzed. Triangulation of the data provided solid evidence suggesting that yoga-based activities produced perceived benefits in such areas as mental well-being, social well-being, physical well-being, and daily behaviors. The data analyses also revealed barriers teachers encountered during implementation and what they did to overcome these barriers. The results were discussed with regard to their future implications for yoga programs appropriate for schools in the United States (US).


title: Combined task and motion planning through an extensible planner-independent interface layer

abstract: The need for combined task and motion planning in robotics is well understood. Solutions to this problem have typically relied on special purpose, integrated implementations of task planning and motion planning algorithms. We propose a new approach that uses off-the-shelf task planners and motion planners and makes no assumptions about their implementation. Doing so enables our approach to directly build on, and benefit from, the vast literature and latest advances in task planning and motion planning. It uses a novel representational abstraction and requires only that failures in computing a motion plan for a high-level action be identifiable and expressible in the form of logical predicates at the task level. We evaluate the approach and illustrate its robustness through a number of experiments using a state-of-the-art robotics simulator and a PR2 robot. These experiments show the system accomplishing a diverse set of challenging tasks such as taking advantage of a tray when laying out a table for dinner and picking objects from cluttered environments where other objects need to be re-arranged before the target object can be reached.


title: Druid: a real-time analytical data store

abstract: Druid is an open source data store designed for real-time exploratory analytics on large data sets. The system combines a column-oriented storage layout, a distributed, shared-nothing architecture, and an advanced indexing structure to allow for the arbitrary exploration of billion-row tables with sub-second latencies. In this paper, we describe Druid's architecture, and detail how it supports fast aggregations, flexible filters, and low latency data ingestion.


on Digital Dental Radiographic Images for Disease Identification and Classification


title: Selectivity and direct visualization of carbon dioxide and sulfur dioxide in a decorated porous host.

abstract: Understanding the mechanism by which porous solids trap harmful gases such as CO(2) and SO(2) is essential for the design of new materials for their selective removal. Materials functionalized with amine groups dominate this field, largely because of their potential to form carbamates through H(2)N(δ(-))···C(δ(+))O(2) interactions, thereby trapping CO(2) covalently. However, the use of these materials is energy-intensive, with significant environmental impact. Here, we report a non-amine-containing porous solid (NOTT-300) in which hydroxyl groups within pores bind CO(2) and SO(2) selectively. In situ powder X-ray diffraction and inelastic neutron scattering studies, combined with modelling, reveal that hydroxyl groups bind CO(2) and SO(2) through the formation of O=C(S)=O(δ(-))···H(δ(+))-O hydrogen bonds, which are reinforced by weak supramolecular interactions with C-H atoms on the aromatic rings of the framework. This offers the potential for the application of new 'easy-on/easy-off' capture systems for CO(2) and SO(2) that carry fewer economic and environmental penalties.


title: A new approach to sample size calculation for reference interval studies

abstract: A new criterion is proposed for determining the sample size required for a study performed for the purpose of establishing reference intervals. The basic idea behind the criterion is to compare the empirical coverage (i.e. the probability content) of the reference region obtained from the sample with its target value (e.g. 95 per cent) and to set suitable limits delta1, delta2 to the difference between both quantities which must not be exceeded with sufficiently large probability beta (e.g. beta=90 per cent). For the most frequently used parametric and distribution-free methods of estimating univariate reference limits, implicit formulae are derived relating the sample size to the design parameters delta1, delta2 and beta. For symmetric specification of (delta1, delta2), explicit approximation formulae for the computation of n are given. Exact values obtained by means of suitable numerical techniques are presented in a set of tables covering specifications of delta1, delta2 and beta which can be recommended for real applications. The tables can be used both for one- and two-sided reference intervals.


title: A Comparison Between Etymon-and Word-Based Chinese Sign Language Recognition Systems

abstract: Hitherto, one major challenge to sign language recognition is how to develop approaches that scale well with increasing vocabulary size. In large vocabulary speech recognition realm, it is effective to use phonemes instead of words as the basic units. This idea can be used in large vocabulary Sign Language recognition, too. In this paper, Etyma are defined to be the smallest unit in a sign language, that is, a unit that has some meaning and distinguishes one sign from the others. They can be seen as phonemes in Sign Language. Two approaches to large vocabulary Chinese Sign Language recognition are discussed in this paper. One uses etyma and the other uses whole signs as the basic units. Two CyberGloves and a Pohelmus 3-D tracker with three receivers positioned on the wrist of CyberGlove and the back are used as input device. Etymon- and word- based recognition systems are introduced, which are designed to recognize 2439 etyma and 5100 signs. And then the experimental results of these two systems are given and analyzed.


title: Semi-supervised learning for named entity recognition using weakly labeled training data

abstract: The shortage of the annotated training data is still an important challenge to building many Natural Language Process (NLP) tasks such as Named Entity Recognition. NER requires a large amount of training data with a high degree of human supervision whereas there is not enough labeled data for every language. In this paper, we use an unlabeled bilingual corpora to extract useful features from transferring information from resource-rich language toward resource-poor language and by using these features and a small training data, make a NER supervised model. Then we utilize a graph-based semi-supervised learning method that trains a CRF-based supervised classifier using that labeled data and uses high-confidence predictions on the unlabeled data to expand the training set and improve efficiency of NER model with the new training set.


title: Measuring and Characterizing Nutritional Information of Food and Ingestion Content in Instagram

abstract: Social media sites like Instagram have emerged as popular platforms for sharing ingestion and dining experiences. However research on characterizing the nutritional information embedded in such content is limited. In this paper, we develop a computational method to extract nutritional information, specifically calorific content from Instagram food posts. Next, we explore how the community reacts specifically to healthy versus non-healthy food postings. Based on a crowdsourced approach, our method was found to detect calorific content in posts with 89% accuracy. We further show the use of Instagram as a platform where sharing of moderately healthy food content is common, and such content also receives the most support from the community.


title: PowerLyra: differentiated graph computation and partitioning on skewed graphs

abstract: Natural graphs with skewed distribution raise unique challenges to graph computation and partitioning. Existing graph-parallel systems usually use a "one size fits all" design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab), or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX). In this paper, we argue that skewed distribution in natural graphs also calls for differentiated processing on high-degree and low-degree vertices. We then introduce PowerLyra, a new graph computation engine that embraces the best of both worlds of existing graph-parallel systems, by dynamically applying different computation and partitioning strategies for different vertices. PowerLyra further provides an efficient hybrid graph partitioning algorithm (hybrid-cut) that combines edge-cut and vertex-cut with heuristics. Based on PowerLyra, we design locality-conscious data layout optimization to improve cache locality of graph accesses during communication. PowerLyra is implemented as a separate computation engine of PowerGraph, and can seamlessly support various graph algorithms. A detailed evaluation on two clusters using graph-analytics and MLDM (machine learning and data mining) applications show that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real-world and synthetic graphs accordingly, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid-cut to GraphX further confirms the efficiency and generality of PowerLyra.


title: Stereo vision in blind navigation assistance

abstract: Visual impairment and blindness caused by infectious diseases has been greatly reduced, but increasing numbers of people are at risk of age-related visual impairment. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage because appropriate information about the surrounding environment is not available. With the recent advances in inclusive technology it is possible to extend the support given to people with visual impairment during their mobility. In this context we propose a system, named SmartVision, whose global objective is to give blind users the ability to move around in unfamiliar environments, whether indoor or outdoor, through a user friendly interface. This paper is focused mainly in the development of the computer vision module of the SmartVision system.


title: A 3D coupled thermo-visco-elastic shear-lag formulation for the prediction of residual stresses in photovoltaic modules after lamination

abstract: Evaluation of the residual stress distribution arising from lamination of photovoltaic (PV) modules is important to address thermomechanically induced failure of PV modules during service. In view of the fact that PV modules contain several Silicon cells, modelling the thermo-mechanical response of PV laminates during cooling after lamination is computationally challenging. Due to the coupling between the thermal and the mechanical fields, the stress state experienced by each silicon cell in a module varies from one position to another. Here, a novel 3D coupled thermo-visco-elastic shear-lag model is proposed to determine the stress distribution in a PV module after lamination. To enhance the prediction of stress distribution in the laminate, viscoelastic properties of the EVA encapsulant are taken into account by using an asymptotic model which is stable for small and large time steps of strain increments. The results for a simulated mini-module show that residual stresses vary significantly from point to point inside the PV module.


title: A literature survey on load–frequency control for conventional and distribution generation power systems

abstract: In this paper an extensive literature review on load–frequency control (LFC) problem in power system has been highlighted. The various configuration of power system models and control techniques/strategies that concerns to LFC issues have been addressed in conventional as well as distribution generation-based power systems. Further, investigations on LFC challenges incorporating storage devices BESS/SMES, FACTS devices, wind–diesel and PV systems etc have been discussed too.


title: Physics of the Negative Resistance in the Avalanche $I{-}V$ Curve of Field Stop IGBTs: Collector Design Rules for Improved Ruggedness

abstract: In this paper, we investigate the avalanche behavior of field-stop insulated gate bipolar transistors (IGBTs) by means of analytical and theoretical considerations, supported by ad hoc numerical simulations. A physical explanation of the presence of negative differential resistance branches in the avalanche I-V curve of the IGBT is presented and design criteria are derived to reduce and eventually eliminate this effect.


title: Inferring Visualization Task Properties, User Performance, and User Cognitive Abilities from Eye Gaze Data

abstract: Information visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities, and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to the individual user. To this end, this article presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict properties of the user's visualization task; the user's performance (in terms of predicted task completion time); and the user's individual cognitive abilities, such as perceptual speed, visual working memory, and verbal working memory. We provide a detailed analysis of different eye gaze feature sets, as well as over-time accuracies. We show that these predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are then discussed with a view to designing visualization systems that can adapt to the individual user in real time.


title: Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation

abstract: In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained sequential characteristics over context information. Besides, existing methods often directly take the feature vectors derived from context information as auxiliary input, which is difficult to fully exploit the structural patterns in context information for learning preference representations. To address above issues, we propose a novel Taxonomy-aware Multi-hop Reasoning Network, named TMRN, which integrates a basic GRU-based sequential recommender with an elaborately designed memory-based multi-hop reasoning architecture. For enhancing the reasoning capacity, we incorporate taxonomy data as structural knowledge to instruct the learning of our model. We associate the learning of user preference in sequential recommendation with the category hierarchy in the taxonomy. Given a user, for each recommendation, we learn a unique preference representation corresponding to each level in the taxonomy based on her/his overall sequential preference. In this way, the overall, coarse-grained preference representation can be gradually refined in different levels from general to specific, and we are able to capture the evolvement and refinement of user preference over the taxonomy, which makes our model highly explainable. Extensive experiments show that our proposed model is superior to state-of-the-art baselines in terms of both effectiveness and interpretability.


title: Single-Image Depth Perception in the Wild

abstract: This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.


title: Translating Constructivism into Instructional Design: Potential and Limitations

abstract: Instructional designers are expected to be familiar with the epistemological underpinnings of several theories and their consequences on the process of instruction. Constructivism is the dominant theory of the last decade and supports construction of knowledge by the individual. This paper discusses the basic principles underlying constructivism, particularly active, collaborative and authentic learning. Application of these principles on the process - analysis, development, evaluation - of instructional design poses certain challenges with regards to issues such as pre-specification of knowledge, authentic evaluation and learner control. Most of the problems are attributed to the fact that constructivism is a learning theory and not an instructional-design theory. Therefore, instructional designers must attempt to translate constructivism into instructional design through a more pragmatic approach that focuses on the principles of moderate - rather than extreme - constructivism and makes use of emergent technology tools. This shift could facilitate the development of more situated, experiential, meaningful and cost-effective learning environments.


title: Multi-view modeling to support embedded systems engineering in SysML

abstract: Embedded systems engineering problems often involve many domains, each with their own experts and tools. To help these experts with analysis and decision making in their domain, it is desirable to present them with a view of the system that is tailored to their particular task. In this paper, a model integration framework is demonstrated to address issues associated with multi-view modeling. The Systems Modeling Language (OMG SysMLTM) is used as a general language to represent a common model for the system as well as the dependencies between the different domain-specific tools and languages. To maintain consistency between these domain-specific views, model transformations are defined that map the interdependent constructs to and from a common SysML model. The approach is illustrated bymeans of amechatronic design problem involving views in multiple domain-specific tools, namely EPLAN FluidTM (to create production ready layouts) and Modelica® (for dynamic system analysis).


title: Brainstorms: Philosophical Essays on Mind and Psychology

abstract: A collection of 17 essays exploring the central issues of the philosophy of the mind, and human interaction with psychology and evolutionary biology. This book questions the relationship between psychology and morality as well as exploring the concept of human intentionality. It argues that intentional attributes such as desires, goals, beliefs and knowledge are purely mechanistic. The author also considers the meaning of mental imagery, sensations, pain and other puzzling aspects of consciousness. Central to the discussion of the book is the question of whether psychology can support a vision of humans as moral agents, free to choose what they do and responsible for their actions.


TR 03-002 Item-Based Top-N Recommendation Algorithms


title: Design of Proportional Integral Derivative (PID) controller for impedance-source inverter

abstract: This paper presents the design of voltage mode control for impedance-source inverter using Proportional Integral Derivative (PID) controller. The proposed controller controls the output AC voltage by regulating the DC-link voltage at the input side. Mathematical model of the inverter is developed and the control parameters are design via Bode plot. Simulation under source and load disturbances is carried out to validate the design. From the results, it was shown that the proposed controller is capable to regulate the AC output voltage well.


title: Energy efficient hybrid satellite terrestrial 5G networks with software defined features

abstract: In order to improve the manageability and adaptability of future 5G wireless networks, the software orchestration mechanism, named software defined networking (SDN) with control and user plane (C/U-plane) decoupling, has become one of the most promising key techniques. Based on these features, the hybrid satellite terrestrial network is expected to support flexible and customized resource scheduling for both massive machinetype- communication (MTC) and high-quality multimedia requests while achieving broader global coverage, larger capacity and lower power consumption. In this paper, an end-to-end hybrid satellite terrestrial network is proposed and the performance metrics, e. g., coverage probability, spectral and energy efficiency (SE and EE), are analysed in both sparse networks and ultra-dense networks. The fundamental relationship between SE and EE is investigated, considering the overhead costs, fronthaul of the gateway (GW), density of small cells (SCs) and multiple quality-ofservice (QoS) requirements. Numerical results show that compared with current LTE networks, the hybrid system with C/U split can achieve approximately 40% and 80% EE improvement in sparse and ultra-dense networks respectively, and greatly enhance the coverage. Various resource management schemes, bandwidth allocation methods, and on-off approaches are compared, and the applications of the satellite in future 5G networks with software defined features are proposed.


title: Consumer Power: Evolution in the Digital Age

abstract: The predictions of growing consumer power in the digital age that predated the turn of the century were fueled by the rise of the Internet, then reignited by social media. This article explores the intersection of consumer behavior and digital media by clearly defining consumer power and empowerment in Internet and social media contexts and by presenting a theoretical framework of four distinct consumer power sources: demand-, information-, network-, and crowd-based power. Furthermore, we highlight technology's evolutionary role in the development of these power sources and discuss the nature of shifts in power from marketers to consumers in terms of each source. The framework organizes prior marketing literature on Internet-enabled consumer empowerment and highlights gaps in current research. Specific research questions are elaborated for each source of power outlining the agenda for future research areas.


title: Impact of SiC Devices on Hybrid Electric and Plug-in Hybrid Electric Vehicles

abstract: The application of SiC devices (as battery interface, motor controller, etc.) in a hybrid electric vehicle (HEV) will benefit from their high-temperature capability, high-power density, and high efficiency. Moreover, the light weight and small volume will affect the whole power train system in a HEV, and thus performance and cost. In this work, the performance of HEVs is analyzed using PSAT (powertrain system analysis tool, vehicle simulation software). Power loss models of a SiC inverter are incorporated into PSAT powertrain models in order to study the impact of SiC devices on HEVs. Two types of HEVs are considered. One is the 2004 Toyota Prius HEV, the other is a plug-in HEV (PHEV), whose powertrain architecture is the same as that of the 2004 Toyota Prius HEV. The vehicle-level benefits from the introduction of the SiC devices are demonstrated by simulations. Not only the power loss in the motor controller but also those in other components in the vehicle powertrain are reduced. As a result, the system efficiency is improved and the vehicles consume less energy and emit less harmful gases. It also makes it possible to improve the system compactness with simplified thermal management system. For the PHEV, the benefits are more distinct. Especially, the size of battery bank can be reduced for optimum design.


Minimising semantic drift with mutual exclusion bootstrapping


title: Effects of Multisensory Stimulation on a Sample of Institutionalized Elderly People With Dementia Diagnosis

abstract: Long-term effects of multisensory stimulation were assessed using a “Snoezelen” room on older residents with dementia. Thirty patients were randomly assigned to 3 groups: multisensory stimulation environment (MSSE) group, individualized activities (activity) group, and control group. The MSSE and activity groups participated in two 30-minute weekly individualized intervention sessions over 16 weeks. Pre-, mid-, posttrial, and 8-week follow-up behavior, mood, cognitive, and functional impairment in basic activities of daily living were registered. Items included in the physically nonaggressive behavior factor improved significantly in post- versus pretrial in the MSSE group compared to the activity group, with no significant differences between MSSE and control groups. The MSSE and activity groups demonstrated behavior improvements and higher scores on the Cohen-Mansfield agitation inventory, verbal agitated behavior factor, and Neuropsychiatric Inventory–Nursing Home, with no significant differences betwe...


title: Non-contact ECG monitoring for automotive application

abstract: This work reports on a sensing technique suitable for non-contact ECG monitoring inside a running car. Specifically, the physical principles of operation and the specific boundary conditions regarding automotive integration is presented. To eliminate the need for additional cabling, a wireless communication module based on the Bluetooth standard was added and used to transmit and store data on a measurement PC for off-line analysis. Finally, some results obtained during test drives on German city roads and highways are discussed.


title: Using SDN and reinforcement learning for traffic engineering in UbuntuNet Alliance

abstract: Software Defined Networking (SDN) provides opportunities for dynamic and flexible traffic engineering. This paper discusses how UbuntuNet Alliance National Research and Education Networks (NRENs) can improve bandwidth utilization and reduce inter-NREN latencies through implementation of SDN-based traffic engineering and applying network metrics in selection of inter-NREN paths. Additionally, the paper looks at the utility of applying Reinforcement Learning to path selection, using network data obtained through an SDN controller. Results from simulations using the UbuntuNet topology show an increase in total throughputs when multipath is employed. Furthermore, simulation results show that where latency is the key metric for computing rewards, lower latencies are achieved.


title: Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

abstract: Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.


title: The effects of emotional intelligence, age, work experience, and academic performance.

abstract: In recent years, emotional intelligence (EI) has been a popular topic of debate in the field of management. It has been praised as a successful predictor of job performance and leadership ability. Authors have also claimed that emotional intelligence predicts success at school. However, little empirical research has been conducted to test this assertion. In this study, the relationship between emotional intelligence, as measured by the Trait Emotional Intelligence Questionnaire Short Form (TEIQue SF) and academic performance were examined in a sample of undergraduate business students (N=193). Emotional intelligence was found to be positively associated with work experience. Despite this finding, emotional intelligence was not significantly associated with age. Global trait emotional intelligence was not significantly associated with academic achievement, however, students in the mid-range GPA had a significantly higher mean “well-being” factor score than students in the lower and higher-range GPA. Implications and recommendations for developing emotional intelligence in students are discussed.


An Efficient Data Deduplication based on Tar-format Awareness in Backup Applications


title: Design and Control of a Mobile Robot with an Articulated Body

abstract: Mobile robots having good terrain adaptability, sufficient payload capability, and high mobility are now urgently in de mand. In this paper, design of a practical mobile robot is attempted, with an inspection task in a nuclear reactor as a concrete objective for development. The configuration of the mobile robot is first discussed. A wheel with crawler track, legs, and a snake-like articulated body are shown to be three fundamental configurations. A hybrid configuration consist ing of an articulated body and a crawler track is most ade quate for the nuclear reactor robot because of its excellent terrain adaptability, sufficient payload capability, and high mobility. Design of the joint structure of the articulated body is discussed. Basic control problems such as signal process ing for tactile sensors and control of statically indeterminant forces are also investigated. A mechanical model KR I, a robot with six articulated body segments, 16 degrees of free dom, length 1391 mm, and weight 27.8 kg, is constructed and several experiments are done to demonstrate the basic mobility of the robot and to show the validity of introducing force control.


title: Towards an elastic distributed SDN controller

abstract: Distributed controllers have been proposed for Software Defined Networking to address the issues of scalability and reliability that a centralized controller suffers from. One key limitation of the distributed controllers is that the mapping between a switch and a controller is statically configured, which may result in uneven load distribution among the controllers. To address this problem, we propose ElastiCon, an elastic distributed controller architecture in which the controller pool is dynamically grown or shrunk according to traffic conditions and the load is dynamically shifted across controllers. We propose a novel switch migration protocol for enabling such load shifting, which conforms with the Openflow standard. We also build a prototype to demonstrate the efficacy of our design.


A Study on the Effect of Language Style Matching of a Review and the Whole Reviews on Perceived Customer Review Helpfulness


title: Is digital health technology empowering patients?

abstract: This brief commentary explores how Web 2.0 technology is leading to a new kind of digital health technology. It considers what this might mean for the patient–practitioner encounter; whether practitioners are signed up to the notion of shared decision-making, and whether patients feel empowered and confident to make decisions about their care


title: IMPLEMENTATION OF A PLDRO WITH A FRACTIONAL MULTIPLE FREQUENCY OF REFERENCE

abstract: A PLDRO (Phase Locked Dielectric Resonator Oscillator) with the output frequency of a fractional multiple of reference is proposed and implemented. The key element in the proposed PLDRO is an image rejection mixer placed between a VCDRO (Voltage Controlled Dielectric Resonator Oscillator) and SPD (Sampling Phase Detector). The image rejection mixer shifts the coupled signal from the VCDRO before the signal feeds the SPD. Therefore, the output frequency of the PLDRO can be realized such that it is not harmonically related with its reference frequency. The frequency divider and multiplier generate the IF frequency for the mixer from the reference frequency. The general PLL (Phase Locked Loop) design parameters such as the damping coe-cient and the natural frequency are derived for the proposed topology of the PLDRO. A 7.25GHz PLDRO with a 100MHz reference, intended for use as a local oscillator for a ka band Block-up Converter (BUC), is designed and measured. A BJT (Bipolar Junction Transistor) is used as an active component of the VCDRO and a modifled two micro-strip line coupled DR model is presented and used for frequency tuning range estimation. The measured phase noise at 10kHz/100kHz ofiset is 101dBc/Hz and 115dBc/Hz, respectively. The fabricated PLDRO size is 100mm by 105mm by 23mm including a 100MHz reference crystal oscillator.


title: Long range standoff speaker identification using laser Doppler vibrometer

abstract: Existing studies on speaker identification are mostly performed on telephone and microphone speech data, which are collected with subjects close to the sensor. For the first time, this study reports long range standoff automatic speaker identification experiments using laser Doppler vibrometer (LDV) sensor. The LDV sensor modality has the potential to extend the speech acquisition standoff distance far beyond microphone arrays to enable new capabilities in automatic audio and speech intelligence, surveillance, and reconnaissance (ISR). Five LDV speech corpuses, each consists of 630 speakers, are collected from the vibrations of a glass window, a metal plate, a plastic box, a wood slate, and a concrete wall, using Polytec LDV model OFV-505. The distance from the LDV sensor to the vibration targets is 50 feet. State of the art i-vector speaker identification experiments on this LDV speech data show great promise of this LDV long range acoustic sensing modality.


title: Noise optimization of an inductively degenerated CMOS low noise amplifier

abstract: This paper presents a technique for substantially reducing the noise of a CMOS low noise amplifier implemented in the inductive source degeneration topology. The effects of the gate induced current noise on the noise performance are taken into account, and the total output noise is strongly reduced by inserting a capacitance of appropriate value in parallel with the amplifying MOS transistor of the LNA. As a result, very low noise figures become possible already at very low power consumption levels.


title: Multi-Level Discovery of Deep Options

abstract: Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abstract state spaces. While recent work has proposed several techniques for automated option discovery, they do not scale to multi-level hierarchies and to expressive representations such as deep networks. We present Discovery of Deep Options (DDO), a policy-gradient algorithm that discovers parametrized options from a set of demonstration trajectories, and can be used recursively to discover additional levels of the hierarchy. The scalability of our approach to multi-level hierarchies stems from the decoupling of low-level option discovery from high-level meta-control policy learning, facilitated by under-parametrization of the high level. We demonstrate that using the discovered options to augment the action space of Deep Q-Network agents can accelerate learning by guiding exploration in tasks where random actions are unlikely to reach valuable states. We show that DDO is effective in adding options that accelerate learning in 4 out of 5 Atari RAM environments chosen in our experiments. We also show that DDO can discover structure in robot-assisted surgical videos and kinematics that match expert annotation with 72% accuracy.


title: Evolving Evolutionary Algorithms Using Linear Genetic Programming

abstract: A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.


title: Motion-based detection and tracking in 3D LiDAR scans

abstract: Robots are expected to operate autonomously in increasingly complex scenarios such as crowded streets or heavy traffic situations. Perceiving the dynamics of moving objects in the environment is crucial for safe and smart navigation and therefore a key enabler for autonomous driving. In this paper we present a novel model-free approach for detecting and tracking dynamic objects in 3D LiDAR scans obtained by a moving sensor. Our method only relies on motion cues and does not require any prior information about the objects. We sequentially detect multiple motions in the scene and segment objects using a Bayesian approach. For robustly tracking objects, we utilize their estimated motion models. We present extensive quantitative results based on publicly available datasets and show that our approach outperforms the state of the art.


title: Direct Measurement of Cognitive Load in Multimedia Learning

abstract: Cognitive load theory (CLT) is gaining increasing importance in the design and evaluation of instruction, both traditional and technology based. Although it is well understood as a theoretical construct, the measurement of cognitive load induced by instructional materials in general, and by multimedia instruction in particular, mainly relies on methods that are either indirect, subjective, or both. Integrating aspects of CLT, working memory research, and cognitive theories of multimedia learning, we describe the conceptual basis and practical implementation of a dual-task approach to the direct measurement of cognitive load in multimedia learning. This computer-based instrument provides a direct and objective measure that overcomes many of the shortcomings of other indirect and subjective methods that will enable researchers to validate empirically theoretical predictions of CLT.


title: Information-centric networking: The evolution from circuits to packets to content

abstract: Today’s information-centric networks (ICNs) represent a 100-year evolution of communication networks from circuit-switched networks to packet-switched networks to ICNs, sharing common features with both of these earlier network architectures, but having many unique characteristics of its own. We describe and survey ongoing research and identify challenges in the modeling, design and analysis of information-centric networks and protocols. We discuss performance modeling frameworks and challenges for ICNs, with a particular focus on content flowing through a network of caches, drawing analogies and distinctions from past research in both circuit-switched and packet-switched networks. We also survey the challenges and recent research results associated with finding content in a network of caches and managing the content in those caches. The challenges posed by mobility (of both the end users accessing content as well as content itself) are also discussed.


title: Toward robust early-warning models: A horse race, ensembles and model uncertainty

abstract: This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods. Further, we test various ensemble approaches to aggregating the information products of the built models, providing a more robust basis for measuring country-level vulnerabilities. Finally, we provide approaches to estimating model uncertainty in early-warning exercises, particularly model performance uncertainty and model output uncertainty. The approaches put forward in this paper are shown with Europe as a playground. Generally, our results show that the conventional statistical approaches are outperformed by more advanced machine learning methods, such as k-nearest neighbors and neural networks, and particularly by model aggregation approaches through ensemble learning.


title: Development of a theoretically derived model of resilience through concept analysis

abstract: AbstractBackground: Resilience refers to a dynamic process that results in adaptation in the context of significant adversity (Margalit 2004). The concept of resilience has been of interest to various professional groups for many years; however, it is only recently that the nursing profession has begun to recognise its potential contribution in diverse clinical contexts.Objective: First, to identify current theoretical and operational definitions of resilience and second, to identify and describe defining attributes of resilience.Methods: The method of inquiry was guided byWalker and Avant’s (1995) approach to concept analysis.Findings: From this analysis, a conceptual model of resilience postulates that the constructs of self-efficacy, hope and coping are defining attributes of resilience.Discussion: Resilience appears to be a process that can be developed at any time during lifespan, and thus is not an inherent characteristic of personality. Further, the development of resilience is based on the synergy...


Machine Learning Methods for Gender Identification