title: Discriminative Dictionary Learning With Motion Weber Local Descriptor for Violence Detection

abstract: Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in developing an algorithm that can detect violence in surveillance videos with high performance. In this paper, following our recently proposed idea of motion Weber local descriptor (WLD), we make two major improvements and propose a more effective and efficient algorithm for detecting violence from motion images. First, we propose an improved WLD (IWLD) to better depict low-level image appearance information, and then extend the spatial descriptor IWLD by adding a temporal component to capture local motion information and hence form the motion IWLD. Second, we propose a modified sparse-representation-based classification model to both control the reconstruction error of coding coefficients and minimize the classification error. Based on the proposed sparse model, a class-specific dictionary containing dictionary atoms corresponding to the class labels is learned using class labels of training samples. With this learned dictionary, not only the representation residual but also the representation coefficients become discriminative. A classification scheme integrating the modified sparse model is developed to exploit such discriminative information. The experimental results on three benchmark data sets have demonstrated the superior performance of the proposed approach over the state of the arts.


title: Revisiting Complex Moments For 2D Shape Representation and Image Normalization

abstract: When comparing 2D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem. In the paper, we show how to uniquely define the orientation of an arbitrary 2D shape, in terms of what we call its Principal Moments. We show that a small subset of these moments suffice to represent the underlying 2D shape and propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize grey-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating the method with real images.


title: Comparison of different designs of cylindrical printed quadrifilar Helix antennas

abstract: INMARSAT allocated frequencies are 1.525 GHz to 1.559 GHz as downlink and 1.625 GHz to 1.6605 GHz as uplink GPS allocated frequencies 1.57542 GHz in L 1 band. In this paper we design two types of Helix antenna as PQHA to operate in allocated frequencies for INMARSAT and GPS application. We used for their optimum design Electromagnetic simulations and were verified by fabrication and VSWR measurement.


title: Measuring Retail Company Performance Using Credit Scoring Techniques-YC Hu and J Ansell 1 Measuring Retail Company Performance Using Credit Scoring Techniques

abstract: Abstract This paper discusses models for evaluating credit risk in relation to the retailing industry. Hunt’s [Hunt, S.D., 2000. A General Theory of Competition. Sage Publications Inc., California] Resource–Advantage Theory of Competition is used as a basis for variable selection, given the theory’s relevancy to retail competition. The study focuses on the US retail market. Four standard credit scoring methodologies: Naive Bayes, Logistic Regression, Recursive Partitioning and Artificial Neural Network, are compared with Sequential Minimal Optimization (SMO), using a sample of 195 healthy companies and 51 distressed firms over five time periods from 1994 to 2002. The five methodologies performed well in predicting default particularly one year before financial distress. Prediction remained sound even five years before distress with accuracy rates above 78% and AUROC values above 0.79. No single methodology, however, had the best classification ability across different time scales and variable sets. External environmental influences exist, but these influences are weak. In terms of similarity with Moody’s ranking, both SMO and logistic regression models are better than the neural network model, with SMO being slightly better than logistic regression.


title: Swarm-Intelligence Tuned Current Reduction for Power-Assisted Steering Control in Electric Vehicles

abstract: In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle need to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm-intelligence technique namely particle swarm optimization (PSO) and ant colony optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PID-PSO and PID-ACO are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm-intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method.


title: Minimal models for Kaehler threefolds

abstract: Let X be a compact Kaehler threefold that is not uniruled. We prove that X has a minimal model.


GAKE: Graph Aware Knowledge Embedding


title: Multimodal Deep Convolutional Neural Network for Audio-Visual Emotion Recognition

abstract: Emotion recognition is a challenging task because of the emotional gap between subjective emotion and the low-level audio-visual features. Inspired by the recent success of deep learning in bridging the semantic gap, this paper proposes to bridge the emotional gap based on a multimodal Deep Convolution Neural Network (DCNN), which fuses the audio and visual cues in a deep model. This multimodal DCNN is trained with two stages. First, two DCNN models pre-trained on large-scale image data are fine-tuned to perform audio and visual emotion recognition tasks respectively on the corresponding labeled speech and face data. Second, the outputs of these two DCNNs are integrated in a fusion network constructed by a number of fully-connected layers. The fusion network is trained to obtain a joint audio-visual feature representation for emotion recognition. Experimental results on the RML audio-visual database demonstrates the promising performance of the proposed method. To the best of our knowledge, this is an early work fusing audio and visual cues in DCNN for emotion recognition. Its success guarantees further research in this direction.


title: Dynamic response for motion capture animation

abstract: Human motion capture embeds rich detail and style which is difficult to generate with competing animation synthesis technologies. However, such recorded data requires principled means for creating responses in unpredicted situations, for example reactions immediately following impact. This paper introduces a novel technique for incorporating unexpected impacts into a motion capture-driven animation system through the combination of a physical simulation which responds to contact forces and a specialized search routine which determines the best plausible re-entry into motion library playback following the impact. Using an actuated dynamic model, our system generates a physics-based response while connecting motion capture segments. Our method allows characters to respond to unexpected changes in the environment based on the specific dynamic effects of a given contact while also taking advantage of the realistic movement made available through motion capture. We show the results of our system under various conditions and with varying responses using martial arts motion capture as a testbed.


title: A Cooperative Algorithm for Stereo Matching and Occlusion Detection

abstract: Presents a stereo algorithm for obtaining disparity maps with occlusion explicitly detected. To produce smooth and detailed disparity maps, two assumptions that were originally proposed by Marr and Poggio (1976, 1979) are adopted: uniqueness and continuity. That is, the disparity maps have a unique value per pixel and are continuous almost everywhere. These assumptions are enforced within a three-dimensional array of match values in disparity space. Each match value corresponds to a pixel in an image and a disparity relative to another image. An iterative algorithm updates the match values by diffusing support among neighboring values and inhibiting others along similar lines of sight. By applying the uniqueness assumption, occluded regions can be explicitly identified. To demonstrate the effectiveness of the algorithm, we present the processing results from synthetic and real image pairs, including ones with ground-truth values for quantitative comparison with other methods.


title: The cognitive task analysis methods for job and task design: review and reappraisal

abstract: This paper reviews and reappraises the current research on the cognitive task analysis methodology for job or task design and analysis. Specifically, it classifies the current cognitive task analysis methods for job or task design and analysis, sorts out commonalities and differences among all these cognitive task analysis methodology for job and task design and analysis by conducting pros and cons comparisons, and provides guidelines in selecting cognitive task analysis methods for job and task design and analysis. Moreover, based on the current literature review, a validated human-centered information-processing model for cognitive task performance was developed based on human information processing theory. This new model focuses on identifying all cognitive aspects of human performance in technical work, with the goal of assisting job (re)design to increase human job performance.


title: Screening for mild cognitive impairment: a systematic review

abstract: Objective ::: Patients with mild cognitive impairment account for a significant number of referrals to old age psychiatry services and specialist memory clinics. The cognitive evaluation of such patients is commonly restricted to brief dementia screens, with no consideration to their suitability for assessing MCI. Here, we review the utility of such cognitive screens for MCI and provide an overview of validated instruments. ::: ::: Methods ::: We identified papers published after Petersen and colleagues 1999 MCI criteria (Petersen et al., 1999) and examining face-to-face cognitive screening for MCI from publication databases using combinations of the search terms ‘mild cognitive impairment’ and ‘cognitive screening’. We also combined the former search with the names of 39 screening tests recently identified in a relevant review (Cullen et al., 2007). ::: ::: Results ::: Fifteen cognitive screening instruments were identified, 11 cover a restricted range of cognitive domains. High sensitivity and specificity for MCI relative to healthy controls were reported for two comprehensive and two noncomprehensive screening instruments, adequate test-retest and inter-rater reliability for only one of these. With the exception of three studies, sample sizes were universally small (i.e. n ≤ 100), and prognostic values were reported for only two of the identified 15 screening measures. Sensitivities of the full domain measures were universally high, but information about their specificity against psychiatric and non-progressive neurological conditions and predictive validity is lacking. ::: ::: Conclusion ::: Several cognitive screening instruments afford the clinician the ability to detect MCI, early AD, and in some cases non-AD dementia, but they cannot currently be used to make reliable inferences about the course and eventual outcome of MCI. Copyright © 2009 John Wiley & Sons, Ltd.


title: Citizen adoption of electronic government initiatives

abstract: Electronic government, or e-government, increases the convenience and accessibility of government services and information to citizens. Despite the benefits of e-government - increased government accountability to citizens, greater public access to information, and a more efficient, cost-effective government - the success and acceptance of e-government initiatives, such as online voting and license renewal, are contingent upon citizens' willingness to adopt this innovation. In order to develop "citizen-centered" e-government services that provide citizens with accessible, relevant information and quality services that are more expedient than traditional "brick and mortar" transactions, government agencies must first understand the factors that influence citizen adoption of this innovation. This study integrates constructs from the technology acceptance model (TAM), diffusions of innovation theory (DOI) and Web trust model to form a parsimonious, yet comprehensive model of factors that influence citizen adoption of electronic government initiatives. The findings and implications of this study are discussed in the paper.


title: Cloning and Sequencing of a 27.8-kb Nucleotide Sequence of the 79°—81° Region of the Bacillus subtilis Genome Containing the sspE Locus

abstract: The nucleotide sequence of a 27830-bp DNA segment in the 79°--81° region of the Bacillus subtilis genome has been determined. This region contains 29 complete ORFs including the sspE gene, which encodes a small acid-soluble spore protein gamma and locates on the one side terminal of our assigned region. A homo logy search for the products deduced from the 29 ORFs revealed that nine of them exhibit significant similarity to known proteins, e.g. proteins involved in an iron uptake system, a multidrug resistance protein, a chloramphenicol resistance protein, epoxide hydrolase. adenine glycosylase. and a glucose- 1-dehydrogenase homolog.


title: Prospective evaluation of the use of Mitchell shoes and dynamic abduction brace for idiopathic clubfeet.

abstract: Ponseti treatment for clubfoot has been successful, but recurrence continues to be an issue. After correction, patients are typically braced full time with a static abduction bar and shoes. Patient compliance with bracing is a modifiable risk factor for recurrence. We hypothesized that the use of Mitchell shoes and a dynamic abduction brace would increase compliance and thereby reduce the rate of recurrence. A prospective, randomized trial was carried out with consecutive patients treated for idiopathic clubfeet from 2008 to 2012. After casting and tenotomy, patients were randomized into either the dynamic or static abduction bar group. Both groups used Mitchell shoes. Patient demographics, satisfaction, and compliance were measured with self-reported questionnaires throughout follow-up. Thirty patients were followed up, with 15 in each group. Average follow-up was 18.7 months (range 3-40.7 months). Eight recurrences (26.7%) were found, with four in each group. Recurrences had a statistically significant higher number of casts and a longer follow-up time. Mean income, education level, patient-reported satisfaction and compliance, and age of caregiver tended to be lower in the recurrence group but were not statistically significant. No differences were found between the two brace types. Our study showed excellent patient satisfaction and reported compliance with Mitchell shoes and either the dynamic or static abduction bar. Close attention and careful education should be directed towards patients with known risk factors or difficult casting courses to maximize brace compliance, a modifiable risk factor for recurrence.


Survey Analysis of Machine Learning Methods for Natural Language Processing for MBTI Personality Type Prediction


title: Electronic Theses and Dissertations: Developing Standards and Changing Practices for Libraries and Universities

abstract: 1. Authors' Preface 2. ETDs: From the Beginning 3. The Development and Assessment of ETD Initiatives 4. Historical Perspectives on the Treatment and Cataloging of Print Theses and Dissertations 5. Cataloging and Treatment of ETDs 6. Issues of Name Authority Control in the Cataloging of Theses and Dissertations 7. Theses/Dissertations and ETD Cataloging: An Annotated Bibliography


title: Joint Sentiment and Emotion Classification with Integer Linear Programming

abstract: As two foundational tasks in sentiment analysis, sentiment classification and emotion classification have been considered separately and studied independently in the literature. In this paper, we put forward an Integer Linear Programming (ILP)-driven joint learning approach to leveraging the relationship between these two tasks. Empirical study verifies the appropriateness and effectiveness of our proposed approach to joint sentiment and emotion classification.


title: 6LoWPAN: a study on QoS security threats and countermeasures using intrusion detection system approach

abstract: Fuelled to bring the Internet of Things concept to real life, the Internet Engineering Task Force is working on 6LoWPAN, in which the standard allows a vast number of smart objects to be deployed in local wireless sensor networks (WSNs) using the huge address space of IPv6 for data and information harvesting through the Internet. From the security point of view, 6LoWPAN/WSN will be open to security threats from the local network itself and the Internet. Cryptography techniques applied as the front line of defence or deterrent can easily be broken because of the weak secure nature of LoWPAN devices and the wireless environment. Compromised nodes could lead to insider attacks without being detected by any cryptography checking. An intrusion detection system (IDS) is, primarily needed as a second line of defence to monitor the network operations and raise an alarm in case of any anomaly. This paper analyses potential security threats in 6LoWPAN and reviews the current countermeasures, in particular, the IDS-based solutions for countering insider/internal threats. Additionally, it discovers three novel QoS-related security threats, namely rank attack, local repair attack, and resource depleting attack, which are more seriously affecting the routing protocol for low-power and lossy network, the routing protocol used to establish 6LoWPAN network topology. A new two-layer IDS concept is introduced as a countermeasure method for securing the routing protocol for low-power and lossy network-built network topology from the internal QoS attacks. Potential research works are also presented to provide baseline reference to researchers in this field. Copyright © 2012 John Wiley & Sons, Ltd.


title: Ultra low latency market data feed on IBM PowerENTM

abstract: Financial Market IT solutions increasingly depend on ultra low latency message processing and target microseconds latencies in order to provide traders with a competitive advantages over their peers. Some solutions are available on the market, ranging from general purpose systems with advanced network cards to specialized hardware solutions based on FPGA. The novel IBM PowerENTM "Edge of Network" processor integrates network interfaces with functional accelerators and multi-threaded cores. This paper describes the design, implementation and performance evaluation of a Market Data Feed solution based on the PowerEN processor. The paper details the SoC mechanism designed to reduce latency and increase throughput and shows how these can be used to build a Market Data Feed solutions that achieves performance figures usually obtainable only by special hardware. This prototype described achieves an average latency of 6.7 μs for an OPRA v2 feed and is able to process 1M updates per second on 2 hardware thread, achieving more than 16M updates per second on a single chip solution without consuming all the resources and thus being able to also run customer software.


title: Towards Pervasive Augmented Reality: Context-Awareness in Augmented Reality

abstract: Augmented Reality is a technique that enables users to interact with their physical environment through the overlay of digital information. While being researched for decades, more recently, Augmented Reality moved out of the research labs and into the field. While most of the applications are used sporadically and for one particular task only, current and future scenarios will provide a continuous and multi-purpose user experience. Therefore, in this paper, we present the concept of Pervasive Augmented Reality, aiming to provide such an experience by sensing the user’s current context and adapting the AR system based on the changing requirements and constraints. We present a taxonomy for Pervasive Augmented Reality and context-aware Augmented Reality, which classifies context sources and context targets relevant for implementing such a context-aware, continuous Augmented Reality experience. We further summarize existing approaches that contribute towards Pervasive Augmented Reality. Based our taxonomy and survey, we identify challenges for future research directions in Pervasive Augmented Reality.


title: Patient perceptions of physician empathy, satisfaction with physician, interpersonal trust, and compliance

abstract: Objectives: This study was designed to investigate psycho- metric properties of the Jefferson Scale of Patient Percep- tions of Physician Empathy (JSPPPE), and to examine correlations between its scores and measures of overall satisfaction with physicians, personal trust, and indicators of patient compliance. Methods: Research participants included 535 out-patients (between 18-75 years old, 66% female). A survey was mailed to participants which included the JSPPPE (5-item), a scale for measuring overall satisfaction with the primary care physician (10-item), and demographic questions. Patients were also asked about compliance with their physician's recommendation for preventive tests (colonos- copy, mammogram, and PSA for age and gender appropri- ate patients). Results: Factor analysis of the JSPPPE resulted in one prominent component. Corrected item-total score correla- tions ranged from .88 to .94. Correlation between scores of the JSPPPE and scores on the patient satisfaction scale was 0.93. Scores of the JSPPPE were highly correlated with measures of physician-patient trust (r >.73). Higher scores of the JSPPPE were significantly associated with physicians' recommendations for preventive tests (colonoscopy, mammogram, and PSA) and with compliance rates which were > .80). Cronbach's coefficient alpha for the JSPPPE ranged from .97 to .99 for the total sample and for patients in different gender and age groups. Conclusions: Empirical evidence supported the psychomet- rics of the JSPPPE, and confirmed significant links with patients' satisfaction with their physicians, interpersonal trust, and compliance with physicians' recommendations. Availability of this psychometrically sound instrument will facilitate empirical research on empathy in patient care in different countries.


title: Multiview Video Transmission Over Underwater Acoustic Path

abstract: Existing studies on multiview video streaming are proposed for terrestrial environments. They can be classified into request-reply and all camera transmission models, while both models suffer from a long switching delay and low video quality in an underwater acoustic path. The implementation of multiview video transmission in an underwater acoustic path requires reduction of the switching delay and the maintenance of high video quality. To this end, we propose a video transmission scheme, namely, Dolphin. Dolphin comprises time-shifted slot assignment, stochastic transmission, correlation-based compensation, and greedy transmission. Dolphin assigns asymmetric and time-shifted transmission slots to an encoder node and a user node according to a propagation delay. Stochastic transmission sends a potential camera's video that will be the most likely to be played back by the user before playback. If the stochastic transmission fails, the video frames of the user's desired camera are encoded with the mispredicted video frames to prevent switching delay from increasing. Moreover, greedy transmission sends all potential video frames, which are potentially displayed by a user, and determines rate allocation of the video frames based on the display probability. Evaluations using Mitsubishi Electric Research Laboratories' benchmark test sequences demonstrated that Dolphin achieved a short switching delay with a slight degradation in video quality irrespective of communication distance and acoustic path quality. For example, Dolphin decreases the switching delay by 92.7% compared with an existing request-reply model with 0.4-dB quality degradation at a communication distance of 300 m when a user tends to gaze at one camera.


title: A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management

abstract: Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) module can be cast as a continuous Markov Decision Process (MDP) and trained through Reinforcement Learning (RL). Several RL models have been investigated over recent years. However, the lack of a common benchmarking framework makes it difficult to perform a fair comparison between different models and their capability to generalise to different environments. Therefore, this paper proposes a set of challenging simulated environments for dialogue model development and evaluation. To provide some baselines, we investigate a number of representative parametric algorithms, namely deep reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and compare them to a non-parametric model, GP-SARSA. Both the environments and policy models are implemented using the publicly available PyDial toolkit and released on-line, in order to establish a testbed framework for further experiments and to facilitate experimental reproducibility.


title: A manipulation motion planner for dual-arm industrial manipulators

abstract: In this paper, we propose a general manipulation planner for dual-arm industrial manipulators. According to the context, the planner automatically determines whether both arms have to be used simultaneously or not. The approach is based on (i) the extension of an object placement algorithm previously developed in [23], and (ii) the introduction of several types of re-grasping motions dedicated to dual-arm manipulators. Such motions induce a special topological structure in the manipulation space that can be captured into a manipulation graph. The graph is then used to solve the manipulation problem by a simple graph search algorithm. After searching for a solution path, we further consider optimizing the path by minimizing the number of re-grasps. The effectiveness of the approach is demonstrated on the dual-arm manipulator HiroNX working in a realistic factory environment.


title: Which fMRI clustering gives good brain parcellations?

abstract: Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subjects images, parcellation approaches use brain activity (e.g. found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and K-means clustering algorithms. We show that in general Ward’s clustering performs better than alternative methods with regards to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.


title: Eratosthenes sieve based key-frame extraction technique for event summarization in videos

abstract: The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. It is a herculean task to manage access to video content in real time where humongous amount of audiovisual recorded data is generated every second. In this paper we propose an Eratosthenes Sieve based key-frame extraction approach for video summarization (VS) which can work better for real-time applications. Here, Eratosthenes Sieve is used to generate sets of all Prime number frames and nonprime number frames up to total N frames of a video. k-means clustering procedure is employed on these sets to extract the key–frames quickly. Here, the challenge is to find the optimal set of clusters, achieved by employing Davies-Bouldin Index (DBI). DBI a cluster validation technique which allows users with free parameter based VS approach to choose the desired number of key-frames without incurring additional computational costs. Moreover, our proposed approach includes likes of both local and global perspective videos. The method strongly enhances clustering procedure performance trough engagement of Eratosthenes Sieve. Qualitative and quantitative evaluation and complexity computation are done in order to compare the performances of the proposed model and state-of-the-art models. Experimental results on two benchmark datasets with various types of videos exhibit that the proposed methods outperform the state-of-the-art models on F-measure.


title: Learning in POMDPs with Monte Carlo Tree Search

abstract: The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but constructing an accurate POMDP model is difficult. Bayes-Adaptive Partially Observable Markov Decision Processes (BA-POMDPs) extend POMDPs to allow the model to be learned during execution. BA-POMDPs are a Bayesian RL approach that, in principle, allows for an optimal trade-off between exploitation and exploration. Unfortunately, BA-POMDPs are currently impractical to solve for any non-trivial domain. In this paper, we extend the Monte-Carlo Tree Search method POMCP to BA-POMDPs and show that the resulting method, which we call BA-POMCP, is able to tackle problems that previous solution methods have been unable to solve. Additionally, we introduce several techniques that exploit the BA-POMDP structure to improve the efficiency of BA-POMCP along with proof of their convergence.


Automatic email response suggestion for support departments within a university


title: Particle Swarm Optimization-Based Unequal and Fault Tolerant Clustering Protocol for Wireless Sensor Networks

abstract: Clustering is one of most efficient energy saving techniques for maximizing network lifetime in wireless sensor networks. In the multi-hop approach, cluster heads (CHs) close to the base station deplete their energy very quickly due to high inter-cluster relay traffic load, causing the hot spot problem. Thus, a clustering protocol is required to be energy efficient as well as fault tolerant. This paper presents a particle swarm optimization (PSO)-based unequal and fault tolerant clustering protocol referred as PSO-UFC. The proposed protocol addresses imbalanced clustering and fault tolerance issues in the existing energy-balanced unequal clustering (EBUC) protocol for the long-run operation of the network. To solve the imbalanced clustering problem, the PSO-UFC protocol utilizes unequal clustering mechanism to balance intra-cluster and inter-cluster energy consumption between the Master CHs (MCHs). Also, in PSO-UFC protocol the network connectivity is restored by electing an extra CH called Surrogate CH due to sudden failure of MCH. The obtained simulation results demonstrate that the PSO-UFC protocol prolongs the network lifetime against EBUC, PSO-C, and LEACH-C protocols.


title: A Comparative Study of Ontology Languages and Tools

abstract: There are many languages and tools for constructing ontologies. In this paper we survey and compare different ontology languages and tools by the aid of an evaluation framework. A semiotic framework is adopted to aid the evaluation. We hope the evluation results can be used in helping user to choose suitable language and tool in the task of onotloy building.


title: A Disk-Packing Algorithm for an Origami Magic Trick

abstract: We present an algorithm for a magic trick. Given a polygon with holesP , our algorithm determines a folding of a rectangular sheet of paper such that a single straight cut suffices to cut out P .


title: Substructure similarity search in graph databases

abstract: Advanced database systems face a great challenge raised by the emergence of massive, complex structural data in bioinformatics, chem-informatics, and many other applications. The most fundamental support needed in these applications is the efficient search of complex structured data. Since exact matching is often too restrictive, similarity search of complex structures becomes a vital operation that must be supported efficiently.In this paper, we investigate the issues of substructure similarity search using indexed features in graph databases. By transforming the edge relaxation ratio of a query graph into the maximum allowed missing features, our structural filtering algorithm, called Grafil, can filter many graphs without performing pairwise similarity computations. It is further shown that using either too few or too many features can result in poor filtering performance. Thus the challenge is to design an effective feature set selection strategy for filtering. By examining the effect of different feature selection mechanisms, we develop a multi-filter composition strategy, where each filter uses a distinct and complementary subset of the features. We identify the criteria to form effective feature sets for filtering, and demonstrate that combining features with similar size and selectivity can improve the filtering and search performance significantly. Moreover, the concept presented in Grafil can be applied to searching approximate non-consecutive sequences, trees, and other complicated structures as well.


title: Sitting posture affects pelvic floor muscle activity in parous women: An observational study

abstract: Question Do different sitting postures require different levels of pelvic floor and abdominal muscle activity in healthy women? Design Observational study. Participants Eight parous women with no pelvic floor dysfunction. Outcome measures Bilateral activity of pelvic floor muscles (assessed vaginally) and two abdominal muscles, obliquus internus abdominis and obliquus externus abdominis, during three sitting postures. Results There was a significant increase in pelvic floor muscle activity from slump supported sitting (mean 7.2% maximal voluntary contraction, SD 4.8) to both upright unsupported sitting (mean 12.6% maximal voluntary contraction, SD 7.8) ( p = 0.01) and very tall unsupported sitting (mean 24.3% maximal voluntary contraction, SD 14.2) ( p = 0.004). Activity in both abdominal muscles also increased but did not reach statistical significance. Conclusion Both unsupported sitting postures require greater pelvic floor muscle activity than the supported sitting posture.


title: A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM

abstract: Many model compression techniques of Deep Neural Networks (DNNs) have been investigated, including weight pruning, weight clustering and quantization, etc. Weight pruning leverages the redundancy in the number of weights in DNNs, while weight clustering/quantization leverages the redundancy in the number of bit representations of weights. They can be effectively combined in order to exploit the maximum degree of redundancy. However, there lacks a systematic investigation in literature towards this direction. ::: In this paper, we fill this void and develop a unified, systematic framework of DNN weight pruning and clustering/quantization using Alternating Direction Method of Multipliers (ADMM), a powerful technique in optimization theory to deal with non-convex optimization problems. Both DNN weight pruning and clustering/quantization, as well as their combinations, can be solved in a unified manner. For further performance improvement in this framework, we adopt multiple techniques including iterative weight quantization and retraining, joint weight clustering training and centroid updating, weight clustering retraining, etc. The proposed framework achieves significant improvements both in individual weight pruning and clustering/quantization problems, as well as their combinations. For weight pruning alone, we achieve 167x weight reduction in LeNet-5, 24.7x in AlexNet, and 23.4x in VGGNet, without any accuracy loss. For the combination of DNN weight pruning and clustering/quantization, we achieve 1,910x and 210x storage reduction of weight data on LeNet-5 and AlexNet, respectively, without accuracy loss. Our codes and models are released at the link this http URL


title: Analyzing Gene Expression Time-Courses

abstract: Measuring gene expression over time can provide important insights into basic cellular processes. Identifying groups of genes with similar expression time-courses is a crucial first step in the analysis. As biologically relevant groups frequently overlap, due to genes having several distinct roles in those cellular processes, this is a difficult problem for classical clustering methods. We use a mixture model to circumvent this principal problem, with hidden Markov models (HMMs) as effective and flexible components. We show that the ensuing estimation problem can be addressed with additional labeled data partially supervised learning of mixtures - through a modification of the expectation-maximization (EM) algorithm. Good starting points for the mixture estimation are obtained through a modification to Bayesian model merging, which allows us to learn a collection of initial HMMs. We infer groups from mixtures with a simple information-theoretic decoding heuristic, which quantifies the level of ambiguity in group assignment. The effectiveness is shown with high-quality annotation data. As the HMMs we propose capture asynchronous behavior by design, the groups we find are also asynchronous. Synchronous subgroups are obtained from a novel algorithm based on Viterbi paths. We show the suitability of our HMM mixture approach on biological and simulated data and through the favorable comparison with previous approaches. A software implementing the method is freely available under the GPL from http://ghmm.org/gql.


ARM Virtualization: Performance and Architectural Implications


Tremor suppression in wrist joint using active force control method


title: Ultra Dense Networks: The New Wireless Frontier for Enabling 5G Access

abstract: The extreme traffic load that future wireless networks are expected to accommodate requires a rethinking of the system design. The initial estimations indicate that, unlike the evolutionary path of previous cellular generations that was based on spectral efficiency improvements, the most substantial amount of future system performance gains will be obtained by means of network infrastructure densification. By increasing the density of operator-deployed infrastructure elements along with incorporation of user-deployed access nodes (ANs) and mobile user devices acting as infrastructure prosumers, having one or more ANs exclusively dedicated to each user is expected to become feasible, introducing the ultradense network (UDN) paradigm. Although it is clear that UDNs are able to take advantage of the significant benefits provided by proximal transmissions and increased spatial reuse of system resources, large node density and irregular deployment introduce new challenges, mainly due to the interference environment characteristics that are vastly different from previous cellular deployments. This article attempts to provide insights on fundamental issues related to UDN deployment, such as determining the infrastructure density required to support the given traffic load requirements and the benefits of network-wise coordination, demonstrating the potential of UDNs for fifth-generation (5G) wireless networks.


title: Wideband circularly polarized antenna for GPS/Glonass

abstract: In this paper, a novel circularly polarized, single-feed microstrip antenna is presented. The operating frequency can be controlled by adjusting the S-slot arm lengths and the feed point. The design has a reduced antenna size compared to the conventional circularly polarized microstrip antenna at a given operating frequency. Further more, the circularly polarized radiation quality is excellent over the entire upper hemisphere. The simulated 10dB return loss bandwidths is about 10% (1500M∼1655M), which covers the global positioning system and Glonass.


title: Reinforcement learning improves behaviour from evaluative feedback

abstract: Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.


title: A CUSUM framework for detection of space–time disease clusters using scan statistics

abstract: Several methods for timely detection of emerging clusters of diseases have recently been proposed. We focus our attention on one of the most popular types of method; a scan statistic. Different ways of constructing space-time scan statistics based on surveillance theory are presented. We bridge the ideas from space-time disease surveillance, public health surveillance and industrial quality control and show that previously suggested space-time scan statistics methods can be fitted into a general CUSUM framework. Crucial differences between the methods studied are due to different assumptions about the spatial process. An example is the specification of the spatial regions of interest for a possible cluster, another is the increased rate to be detected within a cluster. We evaluate the detection ability of the methods considering the possibility of a cluster emerging at any time during the surveillance period. The methods are applied to the detection of an increased incidence of Tularemia in Sweden.


title: PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

abstract: We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436) images. Our models are available on this https URL.


title: Initial management of Parkinson's disease

abstract: Parkinson's disease is one of the most common neurodegenerative disorders seen in the United States and United Kingdom. The disease is characterised by two processes-cellular degeneration and the resulting biochemical deficiency of dopamine. Although these processes are inter-related, they are approached separately in the clinical setting. Currently, no proven neuroprotective or disease modifying treatment is available for Parkinson's disease. Several agents can be used to treat the motor symptoms associated with dopamine deficiency, and it is important to choose wisely when starting treatment. Drugs can have mild, moderate, or high potency, and the patient's goals, comorbidities, and the short and long term implications of choosing a specific agent should be taken into account when selecting the appropriate agent. Non-motor symptoms, such as depression, fatigue, and disorders of sleep and wakefulness, also need to be evaluated and treated. Research is under way to deliver dopaminergic therapy more effectively, but studies aimed at slowing or stopping disease progression have not shown promise.


title: Guest editors' introduction to the special issue: why is there a resource-based view? Toward a theory of competitive heterogeneity

abstract: Although the resource-based view (RBV) is one of the most popular and fruitful areas of strategy research, it often perplexes scholars from other disciplines. Also, it is unclear whether strategy scholars themselves agree on the RBV's basic issues and premises. To clarify the contribution of the RBV, in this paper we elaborate its basic tenets and relate them to other current research on interfirm performance variance. We suggest a more expansive discussion of sustained differences among firms and thereby develop a broad theory of competitive heterogeneity. Copyright © 2003 John Wiley & Sons, Ltd.


title: In-Water Habitat Restoration and Juvenile Salmonid Stranding in the Lower Columbia River

abstract: The U.S. Army Corps of Engineers (COE) is considering using an eroded area in the Columbia River estuary to dispose of dredged material from the navigation channel. We conducted baseline biological surveys in July and September 1992 to quantify benthic invertebrate and fishery resources and sediment characteristics associated with the eroded, deep subtidal area and an adjacent shallow subtidal habitat (control area). The goal of the project is to create stable shallow subtidal habitat in the eroded area, in return increase benthic invertebrate productivity and fish utilization of this area. Juvenile salmonids can become stranded on lower Columbia River beaches when deep-draft vessels wakes washes them onto a beach. Many beaches that the COE uses for dredged- material disposal have been documented. The type of ship traffic and physical attributes of each beach (slope, width, and shape) were measured from late June to September 1992 and the amount of juvenile stranding quantified. The goal was to identify the factors that contribute to juvenile salmonid stranding and suggest remedial actions (changes in beach configuration or dredged material placement) to reduce stranding.


title: 322 Update TRENDS in Cognitive Sciences Vol.11 No.8 Letters Why are VMPFC patients more utilitarian? A dual-process theory of moral judgment explains

abstract: Koenigs, Young and colleagues [1] recently tested patients with emotion-related damage in the ventromedial prefrontal cortex (VMPFC) using moral dilemmas used in previous neuroimaging studies [2,3]. These patients made unusually utilitarian judgments (endorsing harmful actions that promote the greater good). My collaborators and I have proposed a dual-process theory of moral judgment [2,3] that we claim predicts this result. In a Research Focus article published in this issue of Trends in Cognitive Sciences, Moll and de Oliveira-Souza [4] challenge this interpretation.


title: A High-Throughput LDPC Decoder Architecture With Rate Compatibility

abstract: This paper presents a high-throughput decoder architecture for rate-compatible (RC) low-density parity-check (LDPC) codes which supports arbitrary code rates between the rate of mother code and 1. Puncturing techniques are applied to produce different rates for quasi-cyclic (QC) LDPC codes with dual-diagonal parity structure. Simulation results show that our selected puncturing scheme only introduces the BER performance degradation of less than 0.2 dB, compared with the dedicated codes for different rates specified in the IEEE 802.16e (WiMax) standard. Subsequently, parallel layered decoding architecture (PLDA) is employed for high-throughput decoder design. While the original PLDA is lack of rate flexibility, the problem is solved gracefully by incorporating the puncturing scheme. As a case study, an RC-LDPC decoder based on the rate-1/2 WiMax LDPC code is implemented in the CMOS 65-nm process. The clock frequency is 1.1 GHz, and the synthesis core area is 1.96 mm2. The decoder can achieve an input throughput of 1.28 Gb/s at ten iterations and supports any rate between 1/2 and 1.


The first principles investigation of structural , electronic , mechanical and lattice dynamical properties of the B and N doped M 2 AX type MAX phases


title: Detection, localization, and tracking of unauthorized UAS and Jammers

abstract: Small unmanned aircraft systems (UASs) are expected to take major roles in future smart cities, for example, by delivering goods and merchandise, potentially serving as mobile hot spots for broadband wireless access, and maintaining surveillance and security. Although they can be used for the betterment of the society, they can also be used by malicious entities to conduct physical and cyber attacks to infrastructure, private/public property, and people. Even for legitimate use-cases of small UASs, air traffic management (ATM) for UASs becomes of critical importance for maintaining safe and collusion-free operation. Therefore, various ways to detect, track, and interdict potentially unauthorized drones carries critical importance for surveillance and ATM applications. In this paper, we will review techniques that rely on ambient radio frequency signals (emitted from UASs), radars, acoustic sensors, and computer vision techniques for detection of malicious UASs. We will present some early experimental and simulation results on radar-based range estimation of UASs, and receding horizon tracking of UASs. Subsequently, we will overview common techniques that are considered for interdiction of UASs.


Sociolinguistic Survey Report of Boro (Shinasha)


title: Pneumatic muscle actuators: musculature for an anthropomorphic robot arm

abstract: This paper considers the development of a bi-muscular pneumatic muscle actuator (PMA) system, for use on a 7-DOF anthropomorphic robot arm. Initially this actuator system is put into context with regard to other pneumatic-based designs. The modelling of the PMA is then considered to show the feasibility of the system for good control. The control of the system is explored via adaptive pole-placement techniques for position and PID controllers. Experimental results indicate that accurate position control of /spl plusmn/0.5/spl deg/ is possible at operating pressures up to 800 kPa. Contractile forces in excess of 1000 N have been produced from a device weighing less than 50 g. These devices have been operated for periods of up to 12 months at 4-8hrs/day showing good reliability and robustness.


title: On Active Contour Models and Balloons

abstract: Abstract The use of energy-minimizing curves, known as “snakes,” to extract features of interest in images has been introduced by Kass, Witkin & Terzopoulos ( Int. J. Comput. Vision 1, 1987, 321–331). We present a model of deformation which solves some of the problems encountered with the original method. The external forces that push the curve to the edges are modified to give more stable results. The original snake, when it is not close enough to contours, is not attracted by them and straightens to a line. Our model makes the curve behave like a balloon which is inflated by an additional force. The initial curve need no longer be close to the solution to converge. The curve passes over weak edges and is stopped only if the edge is strong. We give examples of extracting a ventricle in medical images. We have also made a first step toward 3D object reconstruction, by tracking the extracted contour on a series of successive cross sections.


title: SISR: System for integrating semantic relatedness and similarity measures

abstract: Semantic similarity and relatedness measures have increasingly become core elements in the recent research within the semantic technology community. Nowadays, the search for efficient meaning-centered applications that exploit computational semantics has become a necessity. Researchers, have therefore, become increasingly interested in the development of a model that can simulate the human thinking process and capable of measuring semantic similarity/relatedness between lexical terms, including concepts and words. Knowledge resources are fundamental to quantify semantic similarity or relatedness and achieve the best expression for the semantics content. No fully developed system that is able to centralize these approaches is currently available for the research and industrial communities. In this paper, we propose a System for Integrating Semantic Relatedness and similarity measures, SISR, which aims to provide a variety of tools for computing the semantic similarity and relatedness. This system is the first to treat the topic of computing semantic relatedness with a view of integrating different key stakeholders in a parameterized way. As an instance of the proposed architecture, we propose WNetSS which is a Java API allowing the use of a wide WordNet-based semantic similarity measures pertaining to different categories including taxonomic-based, features-based and IC-based measures. It is the first API that allows the extraction of the topological parameters from the WordNet “is a” taxonomy which are used to express the semantics of concepts. Moreover, an evaluation module is proposed to assess the reproducibility of the measures accuracy that can be evaluated according to 10 widely used benchmarks through the correlations coefficients.


title: HDFS scalability: the limits to growth

abstract: The Hadoop distributed file system (HDFS) is an open source system currently being used in situations where massive amounts of data need to be processed. Based on experience with the largest deployment of HDFS, I provide an analysis of how the amount of RAM of a single namespace server correlates with the storage capacity of Hadoop clusters, outline the advantages of the single-node namespace server architecture for linear performance scaling, and establish practical limits of growth for this architecture. This study may be applicable to issues with other distributed file systems.


title: Self-organized adaptation of a simple neural circuit enables complex robot behaviour

abstract: Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Current robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors via a simple neural control circuit, thereby generating 11 basic behavioural patterns (e.g., orienting, taxis, self-protection, various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and additionally enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom.


Learning path planning using genetic algorithm approach


title: Aggression, Social Psychology of

abstract: For over seven decades social psychological theories advanced understanding of aggressive behavior. The most recent major model – the General Aggression Model (GAM) – integrates prior theories, thereby encompassing the broadest range of aggressive phenomena. GAM is built on research about factors within a person that predispose them to aggression; factors from the environment that trigger aggression; and the underlying biological, neurocognitive, and psychological processes. This article summarizes historical and modern social psychological theories of aggression, key research methodologies and findings, and challenges of studying violence and aggression in society. It concludes by noting areas for future social psychological research of aggression. Human aggression is a social behavior, and whilst it has been studied from many perspectives, it is theoretical models and empirical research from the field of social psychology that have provided the strongest framework from which to understand it. This article focuses on the contribution of social psychologists to the understanding of human aggression, providing first some key definitions, then major theories (both classic and contemporary) and a brief summary of social psychological approaches to the study of aggressive behavior. An overview of research findings is presented, including those describing factors within a person that increase the likelihood they will aggress, situational cues that can trigger aggression, internal psychological processes that underlie an instance of aggressive behavior, and processes that increase trait aggressiveness. We conclude by suggesting a ‘risk factor’ framework for understanding societal violence and noting directions for future research.


title: The intrinsic functional organization of the brain is altered in autism

abstract: Abstract In higher functioning individuals with autism, a striking disparity exists between impaired social and emotional abilities and relatively preserved sustained attention and goal-directed cognitive abilities. As these two functional domains appear to map onto two distinct large-scale brain networks, the Task-Negative Network and the Task-Positive Network, respectively, we examined their intrinsically defined functional organization in individuals with autism. Using resting functional connectivity MRI (fcMRI), we found that, in autism, there was altered functional organization of the network involved in social and emotional processing, but no group difference in the functional organization of the network involved in sustained attention and goal-directed cognition. We suggest that these findings might serve to relate the seemingly disparate strengths and weaknesses of the autistic behavioral, perceptual, and cognitive phenotype into a tractable neurofunctional framework. These results also highlight the usefulness of resting fcMRI for studying the brain in neuropsychiatric and neurodevelopmental disorders.


title: A new trend in automotive software: AUTOSAR concept

abstract: This article sheds light on one important aspect of the automotive industry: the AUTomotive Open System ARchitecture (AUTOSAR) concept that is the basis for development of the new modules inside of the automotive systems and represents the newest worldwide automotive trend. First part of the article presents the reasons why such a standard is needed and mandatory in the current financial and technical contexts. The second part presents the vision and the architecture of the concept. For a better understanding of the theoretical aspects presented above, last part of the article presents a lower complexity example about the way in which AUTOSAR modules shall be designed and the working method.


Design and Simulation of PD , PID and Fuzzy Logic Controller for Industrial Application


title: Very linear ramp-generators for high resolution ADC BIST and calibration

abstract: Two very linear ramp-generator designs are presented. The circuits are to be used in high-resolution ADC built-in-self-test (BIST) and on-chip calibration. The first design is used to charge a capacitor by a small current, which is linear enough to test 14-bit ADCs. The second design is in a relaxation oscillator architecture. It is linear enough to test up to 12-bit ADCs. The two designs have been fabricated in CMOS 2 /spl mu/m and 1.2 /spl mu/m processes separately.


title: Why Are You Sharing Others’ Tweets?: The Impact of Argument Quality and Source Credibility on Information Sharing Behavior

abstract: Twitter, a 140-character microblogging social networking service, has garnered attention from researchers and practitioners due to its considerable potential for information diffusion. Prior studies on Twitter typically focused on how user traits or relationships in a network affect information diffusion. However, few studies have been conducted on how posted messages in the service influence this phenomenon. Thus, this paper focuses on posted messages (a.k.a. “tweets”) and how they affect individuals’ information sharing behaviors on Twitter. A model for investigating tweet sharing behavior on Twitter is proposed based on dual-process theory and on social cognitive theory. Results from a preliminary test show that individuals’ perceptions of the argument quality and source credibility of a received tweet play a major role in their information sharing behavior via the perceived level of usefulness of the information and self-efficacy in regard to the sharing of a received tweet. Additionally, the existence of external links in a tweet moderates the impact of argument quality on users’ attitudes toward received tweets.


title: A framework for easing the development of applications embedding answer set programming

abstract: Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge. Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts. In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.


A Do-It-All-Cipher for RFID: Design Requirements


title: Nonlinear trajectory generation for the Caltech Multi-Vehicle Wireless Testbed

abstract: The Caltech Multi-Vehicle Wireless Testbed (MVWT) is a platform designed to explore theoretical advances in multi-vehicle coordination and control, networked control systems and high confidence distributed computation. The contribution of this report is to present simulation and experimental results on the generation and implementation of optimal trajectories for the MVWT vehicles. The vehicles are nonlinear, spatially constrained and their input controls are bounded. The trajectories are generated using the NTG software package developed at Caltech. Minimum time trajectories and the application of Model Predictive Control (MPC) are investigated.


title: Citation chain aggregation: an interaction model to support citation cycling

abstract: Citation chaining is a powerful means of exploring the academic literature. Starting from just one or two known relevant items, a naive researcher can cycle backwards and forwards through the citation graph to generate a rich overview of key works, authors and journals relating to their topic. Whilst online citation indexes greatly facilitate this process, the size and complexity of the search space can rapidly escalate. In this paper, we propose a novel interaction model called citation chain aggregation (CCA). CCA employs a simple three-list view which highlights the overlaps that occur between the first-generation relations of known relevant items. As more relevant articles are identified, differences in the frequencies of citations made by or to unseen articles provide strong relevance feedback cues. The benefits of this technique are illustrated using a simple case study.


TopicShop: enhanced support for evaluating and organizing collections of Web sites


title: Skin, fascias, and scars: symptoms and systemic connections

abstract: Every element or cell in the human body produces substances that communicate and respond in an autocrine or paracrine mode, consequently affecting organs and structures that are seemingly far from each other. The same also applies to the skin. In fact, when the integrity of the skin has been altered, or when its healing process is disturbed, it becomes a source of symptoms that are not merely cutaneous. The skin is an organ, and similar to any other structure, it has different functions in addition to connections with the central and peripheral nervous system. This article examines pathological responses produced by scars, analyzing definitions and differences. At the same time, it considers the subcutaneous fascias, as this connective structure is altered when there is a discontinuous cutaneous surface. The consequence is an ample symptomatology, which is not limited to the body area where the scar is located, such as a postural or trigeminal disorder.


Label embedding for text recognition


title: CVAP: Validation for Cluster Analyses

abstract: Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work. We present a visual cluster validation tool, the Cluster Validity Analysis Platform (CVAP), to facilitate cluster validation. The CVAP provides necessary methods (e.g., many validity indices, several clustering algorithms and procedures) and an analysis environment for clustering, evaluation of clustering results, estimation of the number of clusters, and performance comparison among different clustering algorithms. It can help users accomplish their clustering tasks faster and easier and help achieve good clustering quality when there is little prior knowledge about the cluster structure of a data set.


title: Cross-domain security of cyber-physical systems

abstract: The interaction between the cyber domain and the physical domain components and processes can be leveraged to enhance the security of the cyber-physical system. In order to do so, we must first analyze various cyber domain and physical domain information flows, and characterize the relation between them using model functions. In this paper, we present a notion of cross-domain security of cyber-physical systems, whereby we present a security analysis framework that can be used for generating novel cross-domain attack models, attack detection methods, etc. We demonstrate how information flows such as discrete domain signal flows and continuous domain energy flows in the cyber and physical domain can be used to generate model functions using data-driven estimation, and use this model functions for performing various cross-domain security analysis. We also demonstrate the practical applicability of the cross-domain security analysis framework using the cyber-physical manufacturing system as a case study.


title: Information exchange in wireless networks with network coding and physical-layer broadcast

abstract: Abstract—We show that mutual exchange of independentinformation between two nodes in a wireless network can be efﬁ-ciently performed by exploiting network coding and the physical-layer broadcast property offered by the wireless medium. Theproposed approach improves upon conventional solutions thatseparate the processing of the two unicast sessions, correspondingto information transfer along one direction and the oppositedirection. We propose a distributed scheme that obviates theneed for synchronization and is robust to random packet lossand delay, and so on. The scheme is simple and incurs minoroverhead. I. I NTRODUCTION In this paper, we investigate the mutual exchange of inde-pendent information between two nodes in a wireless network.Let us name the two nodes in consideration a and b, respec-tively. Consider a packet-based communication network withall packets of equal size. The basic problem is very simple: awants to transmit a sequence of packets {X 1 (n)} to b andb wants to transmit a sequence of packets {X


title: Miniature antenna for IoT devices using LoRa technology

abstract: In this paper, the way to design different shapes of miniature antenna for LoRa devices are presented. With dimension of 0.06λ×0.1λ, the proposed antennas are printed on 34×80×0.8 mm3 FR-4 board. The simulation and experiment results show a good agreement with expectations of the electrical small antennas that operate at 868 MHz frequency band. Moreover, the changeable geometry of the designs while keeping a good performance proves that miniature antennas can be created to be more appealing as logos or figures.


title: FTR-18: Collecting rumours on football transfer news

abstract: This paper describes ongoing work on the creation of a multilingual rumour dataset on football transfer news, FTR-18. Transfer rumours are continuously published by sports media. They can both harm the image of player or a club or increase the player's market value. The proposed dataset includes transfer articles written in English, Spanish and Portuguese. It also comprises Twitter reactions related to the transfer rumours. FTR-18 is suited for rumour classification tasks and allows the research on the linguistic patterns used in sports journalism.


title: In Defense of Active Part Selection for Fine-Grained Classification

abstract: Fine-grained classification is a recognition task where subtle differences distinguish between different classes. To tackle this classification problem, part-based classification methods are mostly used. Partbased methods learn an algorithm to detect parts of the observed object and extract local part features for the detected part regions. In this paper we show that not all extracted part features are always useful for the classification. Furthermore, given a part selection algorithm that actively selects parts for the classification we estimate the upper bound for the fine-grained recognition performance. This upper bound lies way above the current state-of-the-art recognition performances which shows the need for such an active part selection method. Though we do not present such an active part selection algorithm in this work, we propose a novel method that is required by active part selection and enables sequential part-based classification. This method uses a support vector machine (SVM) ensemble and allows to classify an image based on arbitrary number of part features. Additionally, the training time of our method does not increase with the amount of possible part features. This fact allows to extend the SVM ensemble with an active part selection component that operates on a large amount of part feature proposals without suffering from increasing training time.


title: Learning Nonlinear Functions Using Regularized Greedy Forest

abstract: We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss. In contrast to these traditional boosting algorithms that treat a tree learner as a black box, the method we propose directly learns decision forests via fully-corrective regularized greedy search using the underlying forest structure. Our method achieves higher accuracy and smaller models than gradient boosting on many of the datasets we have tested on.


Applications of Combinatorial Designs to Communications, Cryptography, and Networking


title: Assessment of megakaryocyte migration and chemotaxis.

abstract: Cell migration is a highly integrated multistep process that plays an essential role during development and disease. Megakaryocytes (MKs) are specialized precursor cells that produce platelets and release them into the circulation. MK migration from the proliferative osteoblastic niche within the bone marrow (BM) environment to the capillary-rich vascular niche is an essential step for platelet production. Among the chemokines that may play a central role in cell migration, the stromal cell-derived factor 1α (SDF1α) also known as CXCL12 has been described to act as a potent chemoattractant for MKs. This biological effect is mediated by the SDF1α receptor CXCR4 (Fusin), which is expressed on haematopoietic stem cells, MKs and platelets. The Dunn chemotaxis chamber in conjunction with the time-lapse microscopy is a powerful tool that enables the user to observe directly the morphological response of cells to chemoattractant in real time. This chapter describes the Dunn chemotaxis chamber to study the migration of primary BM-derived MKs in response to a gradient of SDF1α. In combination with genetically modified mice, this provides a powerful approach to directly investigate the role of specific proteins in MK migration and chemotaxis.


title: Promise and perils of Dynamic Sensitivity control in IEEE 802.11ax WLANs

abstract: Dynamic sensitivity control (DSC) is being discussed within the new IEEE 802.11ax task group as one of the potential techniques to improve the system performance for next generation Wi-Fi in high capacity and dense deployment environments, e.g. stadiums, conference venues, shopping malls, etc. However, there appears to be lack of consensus regarding the adoption of DSC within the group. This paper reports on investigations into the performance of the baseline DSC technique proposed in the IEEE 802.11ax task group under realistic scenarios defined by the task group. Simulations were carried out and the results suggest that compared with the default case (no DSC), the use of DSC may lead to mixed results in terms of throughput and fairness with the gain varying depending on factors like inter-AP distance, node distribution, node density and the DSC margin value. Further, we also highlight avenues for mitigating the shortcomings of DSC found in this study.


title: An object detector based on multiscale sliding window search using a fully pipelined binarized CNN on an FPGA

abstract: An object detection problem consists of two problems: one is classification of detected object category and the other is localization. Frame object detection is used in an embedded vision systems, such as a robot, an automobile, a security camera, and a drone. These applications require high-performance computation and low-power consumption by an inexpensive device. This paper proposes multiscale sliding window based object detector using a fully pipelined binarized deep convolutional neural network (BCNN) on an FPGA. It consists of a sliding window part, a fully pipelined BCNN classifier, and an ARM processing unit for detection. Duplicate detections were filtered by using a non-maximum suppression algorithm running on the ARM processor. We propose the fully pipelined layers for the BCNN and its architecture for FPGA realization. Since the proposed BCNN circuit uses on-chip memories on the FPGA, its throughput is higher than a GPU based one with practical recognition accuracy. We trained the VGG11 based BCNN using the KITTI vision benchmark for the car detection scenario. Then, we implemented the proposed object detector on the Xilinx Inc. Zynq UltraScale+ MPSoC zcu102 evaluation board. The GPU based object detectors were too slow for the realtime application requirement (HD frame rate), with the exception of YOLOv2. As compared with the GPU implementation of YOLOv2, the proposed FPGA detector had higher recognition accuracy and lower power consumption. Compared with the YOLOv2, the proposed FPGA one is higher with respect to recognition accuracy, and its power consumption is lower than the GPU based YOLOv2. Thus, the FPGA based object detector suitable for the embedded realtime applications.


title: Transitive node similarity: predicting and recommending links in signed social networks

abstract: Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content. OSNs recommend new friends to registered users based on local features of the graph (i.e., based on the number of common friends that two users share). However, OSNs do not exploit the whole structure of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we define a basic node similarity measure that captures effectively local graph features (i.e., by measuring proximity between nodes). We exploit global graph features (i.e., by weighting paths that connect two nodes) introducing transitive node similarity. We also derive variants of our method that apply to different types of networks (directed/undirected and signed/unsigned). We perform extensive experimental comparison of the proposed method against existing recommendation algorithms using synthetic and real data sets (Facebook, Hi5 and Epinions). Our experimental results show that our FriendTNS algorithm outperforms other approaches in terms of accuracy and it is also time efficient. Finally, we show that a significant accuracy improvement can be gained by using information about both positive and negative edges.


title: Can we Evaluate the Quality of Generated Text?

abstract: Evaluating the output of NLG systems is notoriously difficult, and performing assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality have been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propose that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text quality.


title: Karyological analyses of 33 species of the tribe Ophiopogoneae (Liliaceae) from Southwest China

abstract: Chromosome numbers and karyotypes of 26 Ophiopogon species, 2 Liriope species and 5 Peliosanthes species of the family Liliaceae from Southwest China, were investigated. The study revealed a detailed picture of chromosome features and their pattern of karyotype variation in Ophiopogoneae. Karyotype asymmetry in different species and different populations of the same species varied greatly due to different locality conditions. Our analyses may support the separately monophyly of Ophiopogon, Liriope and Peliosanthes.


title: Learning to predict where to look in interactive environments using deep recurrent q-learning

abstract: Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e.g., sandwich making and playing the video games). In this paper, we leverage Reinforcement Learning (RL) to highlight task-relevant locations of input frames. We propose a soft attention mechanism combined with the Deep Q-Network (DQN) model to teach an RL agent how to play a game and where to look by focusing on the most pertinent parts of its visual input. Our evaluations on several Atari 2600 games show that the soft attention based model could predict fixation locations significantly better than bottom-up models such as Itti-Kochs saliency and Graph-Based Visual Saliency (GBVS) models.


title: Market Inclusion Through Enhanced Eco-Management in Bulgarian Farms

abstract: Enhancing environmental performance is a prospective way for increasing market inclusion, effectiveness and sustainability of small-scale farms in Eastern Europe and Central Asia. Nevertheless, with very few exceptions there are no studies on forms, factors and efficiency of ‘eco-driven’ market inclusion of smallholders in modern supply chains in Bulgaria. This paper presents the findings of a first large scale study on state, driving forces, and efficiency of market inclusion of farms through enhanced eco-management in Bulgaria. First, we identify diverse forms of eco-management in farms with different sizes, market orientation, product specialization, and location. Second, we assess costs, effects, and efficiency of eco-management, including the extent it contributes to increased market inclusion of farms. Third, we specify critical personal, economic, contractual, institutional, etc., factors of managerial choice in the eco-active farms. Finally, we estimate perspectives of expansion of this particular mode of market inclusion, and suggest directions for improving public policies and business strategies of smallholders.


title: Clustering cancer gene expression data: a comparative study

abstract: Background ::: The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context.


title: WordNet2Vec: Corpora Agnostic Word Vectorization Method

abstract: A complex nature of big data resources demands new methods for structuring especially for textual content. WordNet is a good knowledge source for comprehensive abstraction of natural language as its good implementations exist for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism WordNet2Vec is proposed in the paper. It creates vectors for each word from WordNet. These vectors encapsulate general position - role of a given word towards all other words in the natural language. Any list or set of such vectors contains knowledge about the context of its component within the whole language. Such word representation can be easily applied to many analytic tasks like classification or clustering. The usefulness of the WordNet2Vec method was demonstrated in sentiment analysis, i.e. classification with transfer learning for the real Amazon opinion textual dataset.


title: A Qualitative and Quantitative Evaluation of 8 Clear Sky Models

abstract: We provide a qualitative and quantitative evaluation of eight clear sky models used in Computer Graphics. We compare the models with each other as well as with measurements and with a reference model from the physics community. After a short summary of the physics of the problem, we present the measurements and the reference model, and how we “invert” it to get the model parameters. We then give an overview of each CG model, and detail its scope, its algorithmic complexity, and its results using the same parameters as in the reference model. We also compare the models with a perceptual study. Our quantitative results confirm that the less simplifications and approximations are used to solve the physical equations, the more accurate are the results. We conclude with a discussion of the advantages and drawbacks of each model, and how to further improve their accuracy.


title: An Efficient Message Authentication Scheme for Vehicular Communications

abstract: In this paper, we introduce a novel roadside unit (RSU)-aided message authentication scheme named RAISE, which makes RSUs responsible for verifying the authenticity of messages sent from vehicles and for notifying the results back to vehicles. In addition, RAISE adopts the k- anonymity property for preserving user privacy, where a message cannot be associated with a common vehicle. In the case of the absence of an RSU, we further propose a supplementary scheme, where vehicles would cooperatively work to probabilistically verify only a small percentage of these message signatures based on their own computing capacity. Extensive simulations are conducted to validate the proposed scheme. It is demonstrated that RAISE yields a much better performance than previously reported counterparts in terms of message loss ratio (LR) and delay.


title: A rough sets based approach to feature selection

abstract: Feature selection techniques aim at reducing the number of unnecessary features in classification rules. The features are measured by their necessity in heuristic feature selection techniques. Rough set theory has been used to define the necessity of features in literature. We propose a new rough set based feature selection approach called Parameterized Average Support Heuristic (PASH). The PASH considers the overall quality of the potential set of rules. It selects features causing high average support of rules over all decision classes. In addition, the PASH arms with parameters that are used to adjust the level of approximation.


title: Privacy Risks in Recommender Systems

abstract: Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries.


title: Worldwide trends in childhood overweight and obesity

abstract: OBJECTIVES ::: Obesity has become a global epidemic but our understanding of the problem in children is limited due to lack of comparable representative data from different countries, and varying criteria for defining obesity. This paper summarises the available information on recent trends in child overweight and obesity prevalence. ::: ::: ::: METHODS ::: PubMed was searched for data relating to trends over time, in papers published between January 1980 and October 2005. Additional studies identified by citations in retrieved papers and by consultation with experts were included. Data for trends over time were found for school-age populations in 25 countries and for pre-school populations in 42 countries. Using these reports, and data collected for the World Health Organization's Burden of Disease Program, we estimated the global prevalence of overweight and obesity among school-age children for 2006 and likely prevalence levels for 2010. ::: ::: ::: RESULTS ::: The prevalence of childhood overweight has increased in almost all countries for which data are available. Exceptions are found among school-age children in Russia and to some extent Poland during the 1990s. Exceptions are also found among infant and pre-school children in some lower-income countries. Obesity and overweight has increased more dramatically in economically developed countries and in urbanized populations. ::: ::: ::: CONCLUSIONS ::: There is a growing global childhood obesity epidemic, with a large variation in secular trends across countries. Effective programs and policies are needed at global, regional and national levels to limit the problem among children.


title: Rhinoplasty: a practical guide for surgical planning.

abstract: A method for determining the aesthetically proportioned nasal length, tip projection, and radix projection in any given face is described. The proportioned nasal length is two-thirds (0.67) the midfacial height and exactly equal to chin vertical. Tip projection is two-thirds (0.67) the surgically planned or ideal nasal length. Radix projection, measured from the junction of the nasal bones with the orbit, is one-third (0.33) the ideal nasal length. The preferred clinical reference for measuring radix projection is the plane of the corneal surface; the radix projects 0.28 times the ideal nasal length from this surface (range: 9-14 mm). These-dimensional relationships were confirmed from direct clinical measurements taken from 87 models and subsequently applied in 126 consecutive rhinoplasties. The significance of this dimensional approach to rhinoplasty lies in the fact that planned nasal dimensions are based on facial measurements that allow the nose to vary in size directly with the face. Furthermore, it removes the dorsum as the primary focus in dimensional assessment. Rather, the dorsal prominence may be consistently described relative to a plane connecting the "ideal" radix and tip.


title: Impact of probing procedure on flip chip reliability

abstract: Probe-after-bump is the primary probing procedure for flip chip technology, since it does not directly contact the bump pad, and involves a preferred under bump metallurgy (UBM) step coverage on the bump pads. However, the probe-after-bump procedure suffers from low throughputs and high cost. It also delays the yield feedback to the fab, and makes difficult clarification of the accountability of the low yield bumped wafer between the fab and the bumping house. The probe-before-bump procedure can solve these problems, but the probing tips may over-probe or penetrate the bump pads, leading to poor UBM step coverage, due to inadequate probing conditions or poor probing cards. This work examines the impact of probing procedure on flip chip reliability, using printing and electroplating bumpings on aluminum and copper pads. Bump height, bump shear strength, die shear force, UBM step coverage, and reliability testing are used to determine the influence of probing procedure on flip chip reliability. The experimental results reveal that bump quality and reliability test in the probe-before-bump procedure, under adequate probing conditions, differ slightly from the corresponding items in the probe-after-bump procedure. UBM gives superior step coverage of probe marks in both probe-before-bump and probe-after-bump procedures, implying that UBM achieves greater adhesion and barrier function between the solder bump and the bump pad. Both printing and electroplating bump processes slightly influence all evaluated items. The heights of probe marks on the copper pads are 40–60% lower than those on the aluminum pads, indicating that the copper pad enhances UBM step coverage. This finding reveals that adequate probing conditions of the probe-before-bump procedure are suited to sort flip chip wafers and do not significantly affect bump height, bump shear strength, die shear force, or flip chip reliability.


title: PDF-TREX: An Approach for Recognizing and Extracting Tables from PDF Documents

abstract: This paper presents PDF-TREX, an heuristic approach for table recognition and extraction from PDF documents.The heuristics starts from an initial set of basic content elements and aligns and groups them, in bottom-up way by considering only their spatial features, in order to identify tabular arrangements of information. The scope of the approach is to recognize tables contained in PDF documents as a 2-dimensional grid on a Cartesian plane and extract them as a set of cells equipped by 2-dimensional coordinates. Experiments, carried out on a dataset composed of tables contained in documents coming from different domains, shows that the approach is well performing in recognizing table cells.The approach aims at improving PDF document annotation and information extraction by providing an output that can be further processed for understanding table and document contents.


title: Design, Analysis and Optimization of A Slotted Microstrip Patch Antenna Array at Frequency 5.25 GHz for WLAN-SDMA System

abstract: The design of array antenna is vital study for today's Wireless communication system to achieve higher gain, highly directional beam and also to counteract the effect of fading while signal propagates through various corrupted environments. In this study, the design and analysis of a (2x2) microstrip patch antenna array is introduced. It is designed to function in the 5.25 GHz which corresponds to IEEE 802.11a (VSWR -9.5dB) and antenna gain of 13.88 dBi have been achieved. Both numerical and experimental studies have been carried out to optimize the distance between the antenna elements. The measured results have a good agreement with that of simulated results. The numerical study has been done by using Zeland make IE3D electromagnetic simulator and measurements have been done with the help of Agilent make N5230A, network analyzer. Index Terms: Antenna array, Antenna gain, Slotted microstrip patch, Fringing field, Wireless LAN, IE3D electromagnetic simulator, antenna feed point, Array optimization.


title: Answer Extraction for Why Arabic Questions Answering Systems: EWAQ

abstract: With the increasing amount of web information, questions answering systems becomes very important to allow users to access to direct answers for their requests. This paper presents an Arabic Questions Answering Systems based on entailment metrics. The type of questions which this paper focuses on is why questions. There are many reasons lead us to develop this system: generally, the lack of Arabic Questions Answering Systems and scarcity Arabic Questions Answering Systems which focus on why questions. The goal of the proposed system in this research is to extract answers from re-ranked retrieved passages which are retrieved by search engines. This system extracts the answer only to why questions. This system is called by EWAQ: Entailment based Why Arabic Questions Answering. Each answer is scored with entailment metrics and ranked according to their scores in order to determine the most possible correct answer. EWAQ is compared with search engines: yahoo, google and this http URL, the well-established web-based Questions Answering systems, using manual test set. In EWAQ experiments, it is showed that the accuracy is increased by implementing the textual entailment in re-raking the retrieved relevant passages by search engines and deciding the correct answer. The obtained results show that using entailment based similarity can help significantly to tackle the why Answer Extraction module in Arabic language.


title: Design and analysis of Interleaved Boost Converter for renewable energy source

abstract: Renewable energy is derived from natural resources that are replenished constantly. The commonly used renewable energy systems include photovoltaic cells and fuel cells. A suitable DC-DC converter is proposed for highly efficient renewable energy systems. Interleaved Boost Converter (TBC) topology is discussed in this paper for renewable energy applications. The advantages of interleaved boost converter compared to the classical boost converter are low input current ripple, high efficiency, faster transient response, reduced electromagnetic emission and improved reliability. Three cases of interleaved boost converter have been considered and analysed. Two-phase IBC's with (i) the front end inductors magnetically coupled (ii) uncoupled inductors and (iii) inversely coupled inductors performance have been analyzed and compared. The output voltage ripple, input current ripple and inductor current ripple of the three types of converters are compared. The waveforms of input, inductor current ripple and output voltage ripple are obtained using MATLAB/SIMULINK. The design equations for IBC have been presented. Using the results obtained from simulation the best of the three IBC is inferred.


title: Models for Interactive Narrative actions

abstract: In Interactive Storytelling, authors need to conceive events in an indirect way, which differs from traditional storytelling that assumes to pre-define a linear order of narrated events. Actions of characters are to be described including the whole acting situation as conditions before and after the action. This concept is compared with narrative theory and illustrated by a practical authoring example. The goal is to find general conceptual models and a vocabulary for authors in Interactive Narrative.


title: Mitral valve replacement following a failed MitraClip procedure.

abstract: While mitral valve surgery remains the gold standard for mitral regurgitation (MR), recent innovative and less invasive procedures like percutaneous MitraClip insertion make treatment options open to patients with end-stage dilated cardiomyopathy and poor left ventricular function, since such a cohort of patients invariably represents a high surgical risk. Enthusiasts of this procedure advocate the use of MitraClip as a primary procedure for patients with Type 1 MR and end-stage cardiomyopathy. Valve repair could be reserved for those patients with ongoing regurgitation following MitraClip insertion. We describe a patient treated by MitraClip insertion in whom the unsuccessful mid-term result necessitated surgery. In this patient, damage to the mitral valve from the MitraClip insertion produced a central leaflet perforation, which precluded repair, and thereby, the patient received a mechanical valve replacement. The enthusiasm for a less invasive initial approach in such patients must be balanced against the risk of failure of the primary procedure so that the patient is not denied the prospect of repair in the first instance.