title: Dense interest features for video processing

abstract: We propose two novel feature detection methods for action recognition, based on the dense interest points described by Tuytelaars [1]. The first one is an extension of dense interest points to three dimensions. In the second one, trajectories are constructed starting from dense interest points. We present an analysis of the properties of these methods and conclude that both give higher classification accuracies than dense sampling when less features are used.


An Integrated Approach of Automatic Road Extraction and Evaluation From Remotely Sensed Imagery


title: Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks

abstract: Previous algorithms for supervised sequence learning are based on dynamic recurrent networks. This paper describes an alternative class of gradient-based systems consisting of two feedforward nets that learn to deal with temporal sequences using fast weights: The first net learns to produce context-dependent weight changes for the second net whose weights may vary very quickly. The method offers the potential for STM storage efficiency: A single weight (instead of a full-fledged unit) may be sufficient for storing temporal information. Various learning methods are derived. Two experiments with unknown time delays illustrate the approach. One experiment shows how the system can be used for adaptive temporary variable binding.


title: The TPTP Problem Library

abstract: This paper provides a detailed description of the CNF part of the TPTP Problem Library for automated theorem-proving systems. The library is available via the Internet and forms a common basis for development and experimentation with automated theorem provers. This paper explains the motivations and reasoning behind the development of the TPTP (thus implicitly explaining the design decisions made) and describes the TPTP contents and organization. It also provides guidelines for obtaining and using the library, summary statistics about release v1.2.1, and an overview of the tptp2X utility program. References for all the sources of TPTP problems are provided.


title: Spatio-temporal factorization of log data for understanding network events

abstract: Understanding the impacts and patterns of network events such as link flaps or hardware errors is crucial for diagnosing network anomalies. In large production networks, analyzing the log messages that record network events has become a challenging task due to the following two reasons. First, the log messages are composed of unstructured text messages generated by vendor-specific rules. Second, network equipment such as routers, switches, and RADIUS severs generate various log messages induced by network events that span across several geographical locations, network layers, protocols, and services. In this paper, we have tackled these obstacles by building two novel techniques: statistical template extraction (STE) and log tensor factorization (LTF). STE leverages a statistical clustering technique to automatically extract primary templates from un- structured log messages. LTF aims to build a statistical model that captures spatial-temporal patterns of log messages. Such spatial- temporal patterns provide useful insights into understanding the impacts and root cause of hidden network events. This paper first formulates our problem in a mathematical way. We then validate our techniques using massive amount of network log messages collected from a large operating network. We also demonstrate several case studies that validate the usefulness of our technique. I. INTRODUCTION


title: Encrypting Multiple Images Using Visual Secret Sharing Scheme

abstract: Visual Cryptography is an encryption technique where a secret image is cryptographically encoded into n meaningless share images. A basic model for visual cryptography for natural images was proposed by Naor and Shamir. Security has gained a lot of importance as information technology is widely used. Cryptography refers to the study of mathematical techniques and related aspects of Information Security like data confidentiality, data Integrity, and of data authentication. Visual cryptography is a process where a secret image is encrypted into shares which refuse to divulge information about the original secret image. Its strength is a fact that the decryption of the secret image is through human visual system without computation. This paper provides a formulation of encryption for multiple secret images, which is a generalization of the existing ones, and also a general method of constructing visual secret sharing schemes encrypting multiple secret images.


title: Monitoring Intake Gestures using Sensor Fusion (Microsoft Kinect and Inertial Sensors) for Smart Home Tele-Rehab Setting

abstract: Smart home technologies help post-stroke patients complete activities of daily living (ADL) independently, while saving their time, money, and extra effort. The patients are otherwise required to visit rehabilitation clinics for formal care. Toward this goal, we present our approach to spot specific $'/¶s of eating and drinking in a home setting. We fuse inertial and Microsoft Kinect sensors WRPRQLWRUWKHSDWLHQWV� intake gestures including fine cutting, loading food, and maneuvering the food to the mouth. For both sides of the body, we measured (i) position of the wrist, elbow, and shoulder; (ii) angular displacements at the elbow and shoulder joints; and (iii) acceleration of the spoon/fork/cup which are held by the subject. The use of Kinect allows distinguishing between healthy and paralyzed body sides which is a common problem in tele-rehab. The system was tested successfully on healthy subjects; because stroke-patients show slower motion in a shorter range, the system would serve them at least equally well.


title: Glutamine Restores Tight Junction Protein Claudin‐1 Expression in Colonic Mucosa of Patients With Diarrhea‐Predominant Irritable Bowel Syndrome

abstract: Background:Recent studies showed that patients with diarrhea-predominant irritable bowel syndrome (IBS-D) had an increased intestinal permeability as well as a decreased expression of tight junctions. Glutamine, the major substrate of rapidly dividing cells, is able to modulate intestinal permeability and tight junction expression in other diseases. We aimed to evaluate, ex vivo, glutamine effects on tight junction proteins, claudin-1 and occludin, in the colonic mucosa of patients with IBS-D. Materials and Methods: Twelve patients with IBS-D, diagnosed with the Rome III criteria, were included (8 women/4 men, aged 40.7 ± 6.9 years). Colonic biopsy specimens were collected and immediately incubated for 18 hours in culture media with increasing concentrations of glutamine from 0.6–10 mmol/L. Claudin-1 and occludin expression was then measured by immunoblot, and concentrations of cytokines were assessed by multiplex technology. Claudin-1 expression was affected by glutamine (P < .05, analysis of variance). ...


title: The use of relative operating characteristic (ROC) curves in test performance evaluation.

abstract: Abstract Relative (or receiver) operating characteristic (ROC) curves are a graphical representation of the relationship between sensitivity and specificity of a laboratory test over all possible diagnostic cutoff values. Laboratory medicine has been slow to adopt the use of ROC curves for the analysis of diagnostic test performance. In this tutorial, we discuss the advantages and limitations of the ROC curve for clinical decision making in laboratory medicine. We demonstrate the construction and statistical uses of ROC analysis, review its published applications in clinical pathology, and comment on its role in the decision analytic framework in laboratory medicine.


title: Fairy tale corpus organization using latent semantic mapping and an item-to-item top-n recommendation algorithm

abstract: In this paper we present a fairy tale corpus that was semantically organized and tagged. The proposed method uses latent semantic mapping to represent the stories and a top-n item-to-item recommendation algorithm to define clusters of similar stories. Each story can be placed in more than one cluster and stories in the same cluster are related to the same concepts. The results were manually evaluated regarding the groupings as perceived by human judges. The evaluation resulted in a precision of 0.81, a recall of 0.69, and an f-measure of 0.75 when using tf*idf for word frequency. Our method is topicand language-independent, and, contrary to traditional clustering methods, automatically defines the number of clusters based on the set of documents. This method can be used as a setup for traditional clustering or classification. The resulting corpus will be used for recommendation purposes, although it can also be used for emotion extraction, semantic role extraction, meaning extraction, text classification, among others.


title: Convex Regularization for High-Dimensional Tensor Regression

abstract: In this paper we present a general convex optimization approach for solving high-dimensional tensor regression problems under low-dimensional structural assumptions. We consider using convex and \emph{weakly decomposable} regularizers assuming that the underlying tensor lies in an unknown low-dimensional subspace. Within our framework, we derive general risk bounds of the resulting estimate under fairly general dependence structure among covariates. Our framework leads to upper bounds in terms of two very simple quantities, the Gaussian width of a convex set in tensor space and the intrinsic dimension of the low-dimensional tensor subspace. These general bounds provide useful upper bounds on rates of convergence for a number of fundamental statistical models of interest including multi-response regression, vector auto-regressive models, low-rank tensor models and pairwise interaction models. Moreover, in many of these settings we prove that the resulting estimates are minimax optimal.


title: Mobile Botnet Attacks - an Emerging Threat: Classification, Review and Open Issues

abstract: The rapid development of smartphone technologies have resulted in the evolution of mobile botnets. The implications of botnets have inspired attention from the academia and the industry alike, which includes vendors, investors, hackers, and researcher community. Above all, the capability of botnets is uncovered through a wide range of malicious activities, such as distributed denial of service (DDoS), theft of business information, remote access, online or click fraud, phishing, malware distribution, spam emails, and building mobile devices for the illegitimate exchange of information and materials. In this study, we investigate mobile botnet attacks by exploring attack vectors and subsequently present a well-defined thematic taxonomy. By identifying the significant parameters from the taxonomy, we compared the effects of existing mobile botnets on commercial platforms as well as open source mobile operating system platforms. The parameters for review include mobile botnet architecture, platform, target audience, vulnerabilities or loopholes, operational impact, and detection approaches. In relation to our findings, research challenges are then presented in this domain.


title: Critical Factors Influencing E-Banking Service Adoption in Jordanian Commercial Banks: A Proposed Model

abstract: The primary purpose of this current research is to conduct an investigation into the factors affecting the acceptance of E-Banking by clients who have access to the internet and elicit the opinion of non-internet users about E-Banking. To develop a theoretical framework model for the current study, a literature review relating to the technology acceptance model (TAM) and its various adaptations is being undertaken. The E-Banking has been chosen as an innovative service, whose possible adoption by customers will be investigated. It is believed that the successful adoption of E-Banking will be cost and convenience beneficial for all stakeholders in the financial sector. The current research model purposes an extension to the technology acceptance/ adoption models that account essential factor influence E-Banking services adoption in the Jordanian commercial banks.


title: Neuroimaging parameters in early open spina bifida detection. Further benefit in first trimester screening

abstract: Objective: Morphological investigation of the central nervous system (CNS) in fetuses with positive markers for open spina bifida (OSB) detection, visualized by ultrasound during the first trimester of pregnancy. Materials and Methods: Data from fetuses that underwent routine first trimester ultrasound scan in our center during September 2007–March 2011 and presented abnormal aspects of the fourth ventricle, also referred as intracranial translucency (IT), provided the morphological support to evaluate CNS features. A neuro-histological study of posterior cerebral fossa illustrated anatomical features of the structures involved in the sonographic first trimester detection of neural tube defects. Results: Abnormal IT aspects were found in OSB cases examined in the first trimester, but also in other severe cerebral abnormalities. Brain stem antero-posterior diameter (BS) and brain stem to occipital bone (BSOB) ratio may be more specific for OSB detection. Correlations between histological aspects of posterior brain fossa and ultrasound standard assessment have been made; highlighting the anatomical features involved by the new techniques developed for OSB early detection. Conclusions: Preliminary results show that modern sonographic protocols are capable to detect abnormalities in the morphometry of the posterior brain. First trimester fourth ventricle abnormalities should be followed by careful CNS evaluation because are likely to appear in OSB affected fetuses, but also in other CNS severe anomalies; in such cases, normal BS and BSOB ratio may serve as indirect argument for spine integrity, if specificity is confirmed in large series of fetuses.


title: Sum-product networks for modeling activities with stochastic structure

abstract: This paper addresses recognition of human activities with stochastic structure, characterized by variable spacetime arrangements of primitive actions, and conducted by a variable number of actors. We demonstrate that modeling aggregate counts of visual words is surprisingly expressive enough for such a challenging recognition task. An activity is represented by a sum-product network (SPN). SPN is a mixture of bags-of-words (BoWs) with exponentially many mixture components, where subcomponents are reused by larger ones. SPN consists of terminal nodes representing BoWs, and product and sum nodes organized in a number of layers. The products are aimed at encoding particular configurations of primitive actions, and the sums serve to capture their alternative configurations. The connectivity of SPN and parameters of BoW distributions are learned under weak supervision using the EM algorithm. SPN inference amounts to parsing the SPN graph, which yields the most probable explanation (MPE) of the video in terms of activity detection and localization. SPN inference has linear complexity in the number of nodes, under fairly general conditions, enabling fast and scalable recognition. A new Volleyball dataset is compiled and annotated for evaluation. Our classification accuracy and localization precision and recall are superior to those of the state-of-the-art on the benchmark and our Volleyball datasets.


title: Effect of Topical Steroid (0.05% Clobetasol Propionate) Treatment in Children With Severe Phimosis

abstract: 88 patients (28.4%) after treatment. Patients who had a history of balanoposthitis, smegma, ballooning of the prepuce, or UTI showed significantly poorer improvement in preputial retraction (p＜0.001, p＜0.001, p＜0.001, and p=0.02, respectively) and phimotic ring disappearance (p＜0.001, p=0.001, p＜0.001, and p=0.001, respectively) after treatment. No significant local or systemic side effects were associated with the administration of topical steroids. Conclusions: Topical application of 0.05% clobetasol propionate cream and skin stretching is a safe, simple, and effective procedure with no significant side effects for severe phimosis in prepubertal boys.


title: Parameter Matching Method for Hybrid Power System of Hydraulic Excavator

abstract: In order to improve the efficiency of hybrid power system applied in hydraulic excavator, a parameter matching method was developed to optimize the power system. Structure, working condition and control strategy of the hybrid power system applied in hydraulic excavator were analyzed. Then, a parameter matching method based on optimization object functions and constraint condition functions was proposed. The parameters of the main components, such as engine, electric motor, generator and capacitor of the power system were matched using this method. The matching results were analyzed using the built hybrid power hydraulic excavator model. The results show that the system efficiency is improved significantly after parameter is matched and the main components meet the demand of working condition.


title: Progressive failure analysis of pin joints in composite laminates

abstract: A three-dimensional, non-linear finite element code including large displacement theory, isotropic and orthotropic material properties and a contact algorithm based on transformation matrix was developed to analyze the failure of composite to metal joints. A progressive damage model was implemented to predict the extent and the failure modes of the internal damage in composite laminates as a function of applied load and to stimulate the three-dimensional response of composite laminates from initial loading to final collapse. Hashin failure criteria was used together with the fiber-matrix shear failure and Yeh delamination criterion and material properties were degraded according to the mode of failure. ::: ::: The progressive damage algorithm was verified using a clustered cross-ply [06/906]s laminate with a hole under compressive in-plane load. The results determined by Hashin failure criterion were found to be in good agreement with the experimental results with a deviation of as low as 2.6% from the average. ::: ::: Pinned joint failure analysis of same cross-ply [06/906]s laminate was conducted using two different geometries and net-tension, bearing, shear-out and mixed modes of failure of the joined was examined according to the progression of the damage.


title: On Happiness and Human Potentials: A Review of Research on Hedonic and Eudaimonic Well-Being

abstract: ▪ Abstract Well-being is a complex construct that concerns optimal experience and functioning. Current research on well-being has been derived from two general perspectives: the hedonic approach, which focuses on happiness and defines well-being in terms of pleasure attainment and pain avoidance; and the eudaimonic approach, which focuses on meaning and self-realization and defines well-being in terms of the degree to which a person is fully functioning. These two views have given rise to different research foci and a body of knowledge that is in some areas divergent and in others complementary. New methodological developments concerning multilevel modeling and construct comparisons are also allowing researchers to formulate new questions for the field. This review considers research from both perspectives concerning the nature of well-being, its antecedents, and its stability across time and culture.


A scoping review to identify organizational and education theories relevant for interprofessional practice and education October


title: Precision CMOS current reference with process and temperature compensation

abstract: This paper presents a new first-order temperature compensated CMOS current reference. To achieve a compact architecture able to operate under low voltage with low power consumption, it is based on a self-biasing beta multiplier current generator. Compensation against temperature is achieved by using instead of an ordinary resistor two triode transistors in parallel, acting as a negative and a positive temperature coefficient resistor, that generate a proportional to absolute temperature and a complementary to absolute temperature current which can be directly added to attain a temperature compensated current. Programmability is included to adjust the temperature coefficient and the reference current magnitude over process variations. Results for a 0.18 μm CMOS implementation show that the proposed 500 nA reference operate with supplies down to 1.2 V accomplishing over a (-40 to +120°C) range temperature drifts below 120 ppm/°C.


title: Deep Learning Based Forecasting of Critical Infrastructure Data

abstract: Intelligent monitoring and control of critical infrastructure such as electric power grids, public water utilities and transportation systems produces massive volumes of time series data from heterogeneous sensor networks. Time Series Forecasting (TSF) is essential for system safety and security, and also for improving the efficiency and quality of service delivery. Being highly dependent on various external factors, the observed system behavior is usually stochastic, which makes the next value prediction a tricky and challenging task that usually needs customized methods. In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods. Our approach takes advantage of extracting features at different time scales, which improves accuracy without compromising reliability in comparison with the state-of-the-art methods. Our experimental evaluation using real-world SCADA data from a municipal water management system shows that our proposed method outperforms the baseline methods evaluated here.


title: SystemT: a system for declarative information extraction

abstract: As applications within and outside the enterprise encounter increasing volumes of unstructured data, there has been renewed interest in the area of information extraction (IE) -- the discipline concerned with extracting structured information from unstructured text. Classical IE techniques developed by the NLP community were based on cascading grammars and regular expressions. However, due to the inherent limitations of grammarbased extraction, these techniques are unable to: (i) scale to large data sets, and (ii) support the expressivity requirements of complex information tasks. At the IBM Almaden Research Center, we are developing SystemT, an IE system that addresses these limitations by adopting an algebraic approach. By leveraging well-understood database concepts such as declarative queries and costbased optimization, SystemT enables scalable execution of complex information extraction tasks. In this paper, we motivate the SystemT approach to information extraction. We describe our extraction algebra and demonstrate the effectiveness of our optimization techniques in providing orders of magnitude reduction in the running time of complex extraction tasks.


title: Model-driven interoperability: engineering heterogeneous IoT systems

abstract: Interoperability remains a significant burden to the developers of Internet of Things systems. This is because resources and APIs are dynamically composed; they are highly heterogeneous in terms of their underlying communication technologies, protocols and data formats, and interoperability tools remain limited to enforcing standards-based approaches. In this paper, we propose model-based engineering methods to reduce the development effort towards ensuring that complex software systems interoperate with one another. Lightweight interoperability models can be specified in order to monitor and test the execution of running software so that interoperability problems can be quickly identified, and solutions put in place. A graphical model editor and testing tool are also presented to highlight how a visual model improves upon textual specifications. We show using case-studies from the FIWARE Future Internet Service domain that the software framework can support non-expert developers to address interoperability challenges.


title: Quality Factors of Test Cases: A Systematic Literature Review

abstract: The use of high quality test cases enable the ::: detection of software defect which eventually helps in ensuring the quality of software before being released to end users. Unfortunately, at the moment, the criteria of good test cases are still vague without any specific model to measure the quality of the test cases. Therefore, this study aims to identify the criteria of good test cases based on the findings from studies conducted within the years 2010 to 2018. The Systematic Literature Review (SLR) by Kitchenham approach was adapted in order to comprehensively identify the related criteria. From the review, a total of 310 related articles were found from the IEEE Xplore, ACM Digital Library, and Science Direct databases. The search was then narrowed down using specific key-words and as a result the number relevant articles ended up to 14.From the review of these articles, 30 quality factors of the test cases were identified. These quality factors were further examined, categorized and finalized to be included as the quality factors of test cases evaluation metrics.


title: The influences of electronic word‐of‐mouth message appeal and message source credibility on brand attitude

abstract: Purpose – Based on message source theory, the purpose of this paper is to use positive electronic word‐of‐mouth (eWOM) settings to examine the influence of message source credibility on brand attitude (brand trust, brand affection, and purchase intention). The current study also uses the elaboration likelihood model to examine the moderating effect of product involvement on the relationships between message appeals (rational vs emotional) and brand attitude, as well as between message source credibility and brand attitude.Design/methodology/approach – Data were collected via internet questionnaires. Based on the internet investigation and pretest, this study chose the stimuli for research products, virtual brands, eWOM, and message source credibility. Via a 2 (notebooks vs shampoo) *2 (appeal: rational vs emotional) experimental design, 211 effective samples were collected to verify the hypotheses of this study.Findings – The results, for both notebooks and shampoo, are: the positive eWOM message with hig...


title: Multi-label Learning with Emerging New Labels

abstract: Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously unseen instances. In order to maintain good predictive performance in this environment, a multi-label learning method must have the ability to detect and classify those instances with emerging new labels. To this end, we propose a new approach called Multi-label learning with Emerging New Labels (MuENL). It builds models with three functions: classify instances on currently known labels, detect the emergence of a new label in new instances, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. Our empirical evaluation shows the effectiveness of MuENL.


TRACKING NETWORK DYNAMICS : A SURVEY OF DISTANCES AND SIMILARITY METRICS


title: Autoencoder Node Saliency: Selecting Relevant Latent Representations

abstract: Abstract The autoencoder is an artificial neural network that performs nonlinear dimension reduction and learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component analysis (PCA). While both methods use weight vectors for linear transformations, the autoencoder does not come with any indication similar to the eigenvalues in PCA that are paired with eigenvectors. We propose a novel autoencoder node saliency method that examines whether the features constructed by autoencoders exhibit properties related to known class labels. The supervised node saliency ranks the nodes based on their capability of performing a learning task. It is coupled with the normalized entropy difference (NED). We establish a property for NED values to verify classifying behaviors among the top ranked nodes. By applying our methods to real datasets, we demonstrate their ability to provide indications on the performing nodes and explain the learned tasks in autoencoders.


title: eCushion: A Textile Pressure Sensor Array Design and Calibration for Sitting Posture Analysis

abstract: Sitting posture analysis is widely applied in many daily applications in biomedical, education, and health care domains. It is interesting to monitor sitting postures in an economic and comfortable manner. Accordingly, we present a textile-based sensing system, called Smart Cushion, which analyzes the sitting posture of human being accurately and non-invasively. First, we introduce the electrical textile sensor and its electrical characteristics, such as offset, scaling, crosstalk, and rotation. Second, we present the design and implementation of the Smart Cushion system. Several effective techniques have been proposed to improve the recognition rate of sitting postures, including sensor calibration, data representation, and dynamic time warping-based classification. Last, our experimental results show that the recognition rate of our Smart Cushion system is in excess of 85.9%.


title: Global-Local Contrast Enhancement

abstract: Using global contrast enhancement, low contrast image can be improved in its quality globally. The enhanced output image, with such type of enhancement, may not have the noise and ringing artifacts. However, it may have over-exposure on some parts of the image and under-exposure on some other parts of the image when too high contrast gain occurs. Besides these, the enhanced output image may lack of local details. On the other hand, using local contrast enhancement, the local details of an image can be better defined. However, local contrast enhancement may produce the output image with noise and ringing artifact when too much contrast gain occurs. Besides these, it may be poor in global contrast. For some images, applying the local contrast enhancement along with global contrast enhancement is much better than that of global contrast enhancement only or local contrast enhancement only.


Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure


title: The Burrows–Wheeler Transform for Block Sorting Text Compression: Principles and Improvements

abstract: A recent development in text compression is a 'block sorting' algorithm which permutes the input text according to a special sort procedure and then processes the permuted text with Move-ToFront (MTF) and a final statistical compressor. The technique combines good speed with excellent compression performance. This paper investigates the fundamental operation of the algorithm and presents some improvements based on that analysis. Although block sorting is clearly related to previous compression techniques, it appears that it is best described by techniques derived from work by Shannon on the prediction and entropy of English text. A simple model is developed which relates the compression to the proportion of zeros after the MTF stage.


title: From IT Leveraging Competence to Competitive Advantage in Turbulent Environments: The Case of New Product Development

abstract: A burning question for information systems (IS) researchers and practitioners is whether and how IT can build a competitive advantage in turbulent environments. To address this question, this study focuses on the business process level of analysis and introduces the construct of IT leveraging competence---the ability to effectively use IT functionalities. This construct is conceptualized in the context of new product development (NPD). IT leveraging competence is shown to indirectly influence competitive advantage in NPD through two key mediating links: functional competencies (the ability to effectively execute operational NPD processes) and dynamic capabilities (the ability to reconfigure functional competencies to address turbulent environments). Environmental turbulence is also shown to moderate the process by which IT leveraging competence influences competitive advantage in NPD. Empirical data were collected from 180 NPD managers. ::: ::: Through the construct of IT leveraging competence, the study shows that the effective use of IT functionalities, even generic functionalities, by business units can help build a competitive advantage. The study also shows that the strategic effect of IT leveraging competence is more pronounced in higher levels of environmental turbulence. This effect is not direct: It is fully mediated by both dynamic capabilities and functional competencies. Taken together, these findings suggest that IS researchers should look beyond the direct effects of firm-level IT infrastructures and focus their attention on how business units can leverage IT functionalities to better reconfigure and execute business processes. In turbulent environments, focusing on these aspects is even more vital.


title: MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem

abstract: In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.


title: Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system

abstract: The stock market is an important component in the current economic market. And stock price prediction has recently garnered significant interest among investment brokers, individual investors and researchers. In general, stock market is very complex nonlinear dynamic system. Accordingly, accurate prediction of stock market is a very challenging task, owing to the inherent noisy environment and high volatility related to outside factors. In this paper, we focus on deep learning method to achieve high precision in stock market forecast. And a deep belief networks(DBNs), which is a kind of deep learning algorithm model, coupled with stock technical indicators(STIs) and two-dimensional principal component analysis((2D)2PCA) is introduced as a novel approach to predict the closing price of stock market. A comparison experiment is also performed to evaluate this model.


title: A novel method for detecting lips, eyes and faces in real time

abstract: This paper presents a real-time face detection algorithm for locating faces in images and videos. This algorithm finds not only the face regions, but also the precise locations of the facial components such as eyes and lips. The algorithm starts from the extraction of skin pixels based upon rules derived from a simple quadratic polynomial model. Interestingly, with a minor modification, this polynomial model is also applicable to the extraction of lips. The benefits of applying these two similar polynomial models are twofold. First, much computation time are saved. Second, both extraction processes can be performed simultaneously in one scan of the image or video frame. The eye components are then extracted after the extraction of skin pixels and lips. Afterwards, the algorithm removes the falsely extracted components by verifying with rules derived from the spatial and geometrical relationships of facial components. Finally, the precise face regions are determined accordingly. According to the experimental results, the proposed algorithm exhibits satisfactory performance in terms of both accuracy and speed for detecting faces with wide variations in size, scale, orientation, color, and expressions.


title: Labelled data collection for anomaly detection in wireless sensor networks

abstract: Security of wireless sensor networks (WSN) is an important research area in computer and communications sciences. Anomaly detection is a key challenge in ensuring the security of WSN. Several anomaly detection algorithms have been proposed and validated recently using labeled datasets that are not publicly available. Our group proposed an ellipsoid-based anomaly detection algorithm but demonstrated its performance using synthetic datasets and real Intel Berkeley Research Laboratory and Grand St. Bernard datasets which are not labeled with anomalies. This approach requires manual assignment of the anomalies' positions based on visual estimates for performance evaluation. In this paper, we have implemented a single-hop and multi-hop sensor-data collection network. In both scenarios we generated real labeled data for anomaly detection and identified different types of anomalies. These labeled sensor data and types of anomalies are useful for research, such as machine learning, and this information will be disseminated to the research community.


title: Tracking Control of 3-Wheels Omni-Directional Mobile Robot Using Fuzzy Azimuth Estimator

abstract: Home service robot are not working in the fixed task such as industrial robot, because they are together with human in the same indoor space, but have to do in much more flexible and various environments. Most of them are developed on the base of the wheel-base mobile robot in the same method as a vehicle robot for factory automation. In these days, for holonomic system characteristics, omni-directional wheels are used in the mobile robot. A holonomicrobot, using omni-directional wheels, is capable of driving in any direction. But trajectory control for omni-directional mobile robot is not easy. Especially, azimuth control which sensor uncertainty problem is included is much more difficult. This paper develops trajectory controller of 3-wheels omni-directional mobile robot using fuzzy azimuth estimator. A trajectory controller for an omni-directional mobile robot, which each motor is controlled by an individual PID law to follow the speed command from inverse kinematics, needs a precise sensing data of its azimuth and exact estimation of reference azimuth value. It has imprecision and uncertainty inherent to perception sensors for azimuth. In this paper, they are solved by using fuzzy logic inference which can be used straightforward to perform the control of the mobile robot by means of the fuzzy behavior-based scheme already existent in literature. Finally, the good performance of the developed mobile robot is confirmed through live tests of path control task.


title: Examining the relationships between challenge and threat cognitive appraisals and coaching behaviours in football coaches

abstract: ABSTRACTPrevious research demonstrates that sports coaching is a stressful activity. This article investigates coaches’ challenge and threat cognitive appraisals of stressful situations and their impact on coaching behaviour, using Blascovich and Mendes’ (2000) biopsychosocial model as a theoretical framework. A cross-sectional correlational design was utilised to examine the relationships between irrational beliefs (Shortened general attitude and belief scale), challenge and threat appraisals (Appraisal of life events scale), and coaching behaviours (Leadership scale for sports) of 105 professional football academy coaches. Findings reveal significant positive associations between challenge appraisals and social support, and between threat appraisals and autocratic behaviour, and a significant negative association between threat appraisals and positive feedback. Results also show that higher irrational beliefs are associated with greater threat, and lesser challenge cognitive appraisals. However, no asso...


title: A piecewise hole filling algorithm in reverse engineering

abstract: Abstract While scanning a complex part in reverse engineering, it is not possible to acquire all part of the scanned surface. Data are inevitably missing due to the complexity of the scanned part or imperfect scanning process. Missing scanned data cause holes in the created triangular mesh, so that a hole-free mesh model is prerequisite for fitting watertight surfaces. Although a number of hole filling algorithms have been investigated, they enable to fill holes only on the smooth regions of a model. They are not always robust in the regions of high curvature. This paper proposes a novel methodology that can automatically fill complex polygonal holes with a piecewise manner. It incrementally splits a complex hole into several simple holes with respect to the 3D shape of the hole boundary, and then it consecutively fills each divided simple hole with planar triangulation method until the entire complex hole is firmly closed. Finally smoothing and subdivision techniques are applied for enhancing the hole triangles. The newly created vertices and triangles are added to their respective lists and the topology information is updated. The method has proven to be robust and effective from the result of test with a variety of complex holes. Examples are given and discussed to validate the methodology.


title: Control flow analysis

abstract: Any static, global analysis of the expression and data relationships in a program requires a knowledge of the control flow of the program. Since one of the primary reasons for doing such a global analysis in a compiler is to produce optimized programs, control flow analysis has been embedded in many compilers and has been described in several papers. An early paper by Prosser [5] described the use of Boolean matrices (or, more particularly, connectivity matrices) in flow analysis. The use of “dominance” relationships in flow analysis was first introduced by Prosser and much expanded by Lowry and Medlock [6]. References [6,8,9] describe compilers which use various forms of control flow analysis for optimization. Some recent developments in the area are reported in [4] and in [7]. ::: The underlying motivation in all the different types of control flow analysis is the need to codify the flow relationships in the program. The codification may be in connectivity matrices, in predecessor-successor tables, in dominance lists, etc. Whatever the form, the purpose is to facilitate determining what the flow relationships are; in other words to facilitate answering such questions as: is this an inner loop?, if an expression is removed from the loop where can it be correctly and profitably placed?, which variable definitions can affect this use? ::: In this paper the basic control flow relationships are expressed in a directed graph. Various graph constructs are then found and shown to codify interesting global relationships.


Why a Special Issue on the Golden Section Hypothesis? An Introduction


title: Comparative Study on Load Balancing Techniques in Cloud Computing

abstract: The present era has witnessed tremendous growth of the internet and various applications that are running over it. Cloud computing is the internet based technology, emphasizing its utility and follows pay-as-you-go model, hence became so popular with high demanding features. Load balancing is one of the interesting and prominent research topics in cloud computing, which has gained a large attention recently. Users are demanding more services with better results. Many algorithms and approaches are proposed by various researchers throughout the world, with the aim of balancing the overall workload among given nodes, while attaining the maximum throughput and minimum time. In this paper, various proposed algorithms addressing the issue of load balancing in Cloud Computing are analyzed and compared to provide a gist of the latest approaches in this research area.


title: The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation

abstract: Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.


title: Privacy Preservation in Big Data

abstract: Big data has brought a revolution in the world of data analytics. Data that was discarded a few years back is now considered a powerful asset. Big data is now being extensively used for knowledge discovery by all sectors of society. It is produced by almost all digital processes and is stored, shared on web. This reliance of big data on web model poses serious security concerns. Traditional security methods cannot be applied to big data due to its large volume, variety and volume. Also since big data contains person specific information, privacy is a major security concern. The three important privacy preservation methods are: data anonymization, notice and consent and differential privacy. In this paper we discuss these privacy preservation methods for big data and how differential privacy is a better solution for big data privacy.


title: Lethal endomyocarditis caused by chronic “Krokodil” intoxication

abstract: "Krokodil" is a home-made opioid drug obtained by synthesizing desomorphine from codeine and combining it with other low-cost additives. Initially introduced in the former Soviet countries, it was then imported to Western Europe as a heroin substitute. To our knowledge, this is the first report of an Italian case of lethal krokodil abuse, that occurred in a 39-year-old man, who died suddenly after transportation to the Emergency Department (ED) for hyperthermia associated with sweating, dyspnoea and tachycardia. Post-mortem examination revealed extensive necrotic ulcerative lesions on the forearms, and autopsy showed a hypertrophic heart with ample endocardial vegetation on the aortic valve and patency of the foramen ovale. Histopathological examination of the heart showed ulcero-vegetative lesions of the aortic valve with an abscess on the annulus and extension to the periaortic adipose tissue, as well as diffuse myocardial interstitial inflammatory neutrophilic infiltrates. Toxicological analysis demonstrated a desomorphine metabolite in urine. On the basis of all these findings the cause of death was ruled to be congestive heart failure caused by endocarditis and myocarditis, correlated with chronic abuse of krokodil.


title: A Novel Dual-Band 3-dB Branch-Line Coupler Design With Controllable Bandwidths

abstract: This paper presents, for the first time, the design and implementation of a dual-band branch-line coupler with unequal operating bandwidths. The control of bandwidths is accomplished by the use of dual-band quarter-wavelength impedance transformer with controllable phase slopes over the two designated frequency bands. The adjustment of phase slopes is attained by the proper selection of the circuit parameters of the transformer. For design purposes, closed-form expressions are derived for the determination of the electrical lengths and line impedances of the resulting coupler. For experimental verification, both simulated and measured results of dual-band couplers designed to operate at 1 and 2 GHz are shown.


title: Memristor-Based Material Implication (IMPLY) Logic: Design Principles and Methodologies

abstract: Memristors are novel devices, useful as memory at all hierarchies. These devices can also behave as logic circuits. In this paper, the IMPLY logic gate, a memristor-based logic circuit, is described. In this memristive logic family, each memristor is used as an input, output, computational logic element, and latch in different stages of the computing process. The logical state is determined by the resistance of the memristor. This logic family can be integrated within a memristor-based crossbar, commonly used for memory. In this paper, a methodology for designing this logic family is proposed. The design methodology is based on a general design flow, suitable for all deterministic memristive logic families, and includes some additional design constraints to support the IMPLY logic family. An IMPLY 8-bit full adder based on this design methodology is presented as a case study.


title: Malware Analysis, Clustering and Classification: A Literature Review

abstract: Malware is major security threat on the Internet now-a-days. Anti-Virus companies receive large number of malware samples every day. Malware samples are classified and grouped for further analysis. There are different type of malware analysis, clustering and classification methods available. The purpose of this study is to examine the available literature on malware analysis, clustering and classification.


title: Gist: Efficient Data Encoding for Deep Neural Network Training

abstract: Modern deep neural networks (DNNs) training typically relies on GPUs to train complex hundred-layer deep networks. A significant problem facing both researchers and industry practitioners is that, as the networks get deeper, the available GPU main memory becomes a primary bottleneck, limiting the size of networks it can train. In this paper, we investigate widely used DNNs and find that the major contributors to memory footprint are intermediate layer outputs (feature maps). We then introduce a framework for DNN-layer-specific optimizations (e.g., convolution, ReLU, pool) that significantly reduce this source of main memory pressure on GPUs. We find that a feature map typically has two uses that are spread far apart temporally. Our key approach is to store an encoded representation of feature maps for this temporal gap and decode this data for use in the backward pass; the full-fidelity feature maps are used in the forward pass and relinquished immediately. Based on this approach, we present Gist, our system that employs two classes of layer-specific encoding schemes -- lossless and lossy -- to exploit existing value redundancy in DNN training to significantly reduce the memory consumption of targeted feature maps. For example, one insight is by taking advantage of the computational nature of back propagation from pool to ReLU layer, we can store the intermediate feature map using just 1 bit instead of 32 bits per value. We deploy these mechanisms in a state-of-the-art DNN framework (CNTK) and observe that Gist reduces the memory footprint to upto 2X across 5 state-of-the-art image classification DNNs, with an average of 1.8X with only 4% performance overhead. We also show that further software (e.g., CuDNN) and hardware (e.g., dynamic allocation) optimizations can result in even larger footprint reduction (upto 4.1X).


title: An Approach to Balance Maintenance Costs and Electricity Consumption in Cloud Data Centers

abstract: We target the problem of managing the power states of the servers in a Cloud Data Center (CDC) to jointly minimize the electricity consumption and the maintenance costs derived from the variation of power (and consequently of temperature) on the servers’ CPU. More in detail, we consider a set of virtual machines (VMs) and their requirements in terms of CPU and memory across a set of Time Slot (TSs). We then model the consumed electricity by taking into account the VMs processing costs on the servers, the costs for transferring data between the VMs, and the costs for migrating the VMs across the servers. In addition, we employ a material-based fatigue model to compute the maintenance costs needed to repair the CPU, as a consequence of the variation over time of the server power states. After detailing the problem formulation, we design an original algorithm, called Maintenance and Electricity Costs Data Center (MECDC), to solve it. Our results, obtained over several scenarios from a real CDC, show that MECDC largely outperforms two reference algorithms, which instead either target the load balancing or the energy consumption of the servers.


title: Education at University and Industry 4.0

abstract: Modern students will work in the “Industry 4.0” and create digital economy of Russia. Digital economy is based on the infrastructure organization of production, which is based on the network interaction of production and technology. The world infrastructure of transport, trading, finance provides a technological organization of production and consumers. Industry 4.0 begins with the creation of redundant infrastructure networks - Industrial Internet of Things (IIoT). Today, university education corresponds to the processing form of the technological organization. The infrastructure form of the organization of educational process is necessary. Information technologies supporting the entire training process and representing it on the Internet, do not exist. A new methodology of educational process is suggested. Training in the training process, lectures, seminars, laboratory works are organized according to the logical fulfillment of the target works. The task of the teacher is the design of the target works and the analysis of the result. The simulation training system is a form of the activity organization that is directed to the development of participants and obtaining a result. The result will be a course work as a part of the project in the simulation system. Students act in specially developed problem, training and developing situations. Practical formation in the field of wireless communications technologies is carried out on the basis of equipment of software defined radio (SDR) or universal software radio peripheral (USRP) systems as a combination of hardware and software platforms for creating prototypes of real radio systems. Laboratory and research works are performed on the basis of this firmware radio system (SDR) in the remote Internet access Mode. This conforms to the principles of the Industry4.0. The project is carried out under the control of the teacher. The activity of each student is monitored. The Internet provides individual activities and simulation, communication, scheduling, and group activity analysis. As a result, there is no knowledge of the sum of knowledge, and the integral technical culture, which is a criterion of the education.


Mobile Sensing and Machine Learning for Identifying Driving Safety Profiles


title: A self-tuning system based on application Profiling and Performance Analysis for optimizing Hadoop MapReduce cluster configuration

abstract: One of the most widely used frameworks for programming MapReduce-based applications is Apache Hadoop. Despite its popularity, however, application developers face numerous challenges in using the Hadoop framework, which stem from them having to effectively manage the resources of a MapReduce cluster, and configuring the framework in a way that will optimize the performance and reliability of MapReduce applications running on it. This paper addresses these problems by presenting the Profiling and Performance Analysis-based System (PPABS) framework, which automates the tuning of Hadoop configuration settings based on deduced application performance requirements. The PPABS framework comprises two distinct phases called the Analyzer, which trains PPABS to form a set of equivalence classes of MapReduce applications for which the most appropriate Hadoop config- uration parameters that maximally improve performance for that class are determined, and the Recognizer, which classifies an incoming unknown job to one of these equivalence classes so that its Hadoop configuration parameters can be self-tuned. The key research contributions in the Analyzer phase includes modifications to the well-known k - means + + clustering and Simulated Annealing algorithms, which were required to adapt them to the MapReduce paradigm. The key contributions in the Recognizer phase includes an approach to classify an unknown, incoming job to one of the equivalence classes and a control strategy to self-tune the Hadoop cluster configuration parameters for that job. Experimental results comparing the performance improvements for three different classes of applications running on Hadoop clusters deployed on Amazon EC2 show promising results.


title: Big Data and Cloud Computing Issues

abstract: Big data is a key concept that cannot be overlooked in the IT world considering the prominent increase in data, and data related services, it is important to explore this field and look at ways to improve data service delivery especially in the cloud. Cloud computing on the other hand helps in tackling the issue of storage and data service. This research focus on the two key concept big data and cloud computing and some of the issues and challenges that are inherent with the deployment of cloud services and big data. The shows study that security challenges is among the most prominent issue in cloud and big data services. The plumbing issue and some other issues such as the issue of the cost to run cloud services in handling big data were observed. Also the issues of service level agreement which gives an organization the assurance of enjoying all services rendered by the organization running the cloud services. After considering some of the issues associated with big data and cloud computing, some solution were suggested towards improving the two key concepts which will go a long way in increasing the adoption rate of cloud computing by organizations. It is important for organizations to consider the nature of how their data will grow in the future before deploying any cloud service in their business.


title: Applying Decision Trees to Gene Expression Data from DNA Microarrays: A Leukemia Case Study ∗

abstract: Analyzing gene expression data is a challenging task since the large number of features against the shortage of available examples can be prone to overfitting. In order to avoid this pitfall and achieve high performance, some approaches construct complex classifiers, using new or well-established strate- gies. The main objective of this communication is to construct classifiers that can be human readable as well as robust in performance in microarray data us- ing decision trees. Using one well-known leukemia dataset, a publicly available gene expression classification problem, we show the feasibility of decision trees on microarray data. Summarizing our results, we have obtained simple decision trees with performance comparable to related work.


title: Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

abstract: Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we investigate interactive semantic parsing, where the agent can ask the user clarification questions to resolve ambiguities via a multi-turn dialogue, on an important type of programs called “If-Then recipes.” We develop a hierarchical reinforcement learning (HRL) based agent that significantly improves the parsing performance with minimal questions to the user. Results under both simulation and human evaluation show that our agent substantially outperforms non-interactive semantic parsers and rule-based agents.1


title: Research through design as a method for interaction design research in HCI

abstract: For years the HCI community has struggled to integrate design in research and practice. While design has gained a strong foothold in practice, it has had much less impact on the HCI research community. In this paper we propose a new model for interaction design research within HCI. Following a research through design approach, designers produce novel integrations of HCI research in an attempt to make the right thing: a product that transforms the world from its current state to a preferred state. This model allows interaction designers to make research contributions based on their strength in addressing under-constrained problems. To formalize this model, we provide a set of four lenses for evaluating the research contribution and a set of three examples to illustrate the benefits of this type of research.


title: Flexible Multimodal Tactile Sensing System for Object Identification

abstract: This work presents results towards realizing a flexible multimodal tactile sensing system for object identification. Using polymer substrates and simple fabrication, robust devices are made that can identify objects based on texture, temperature, as well as material properties such as hardness and thermal conductivity. These capabilities are possible using signal processing techniques and physical models along with individual sensing structures inspired by the specialization found in biological skin. These structures are used to sense various object parameters, and array-wide processing to identify texture using a Maximum Likelihood decision rule. 80% texture classification is achieved. In blind object identification tests, over 90% correct identification was achieved by measurement of material properties.


title: Categorised ethical guidelines for large scale mobile HCI

abstract: The recent rise in large scale trials of mobile software using 'app stores' has moved current researcher practice beyond available ethical guidelines. By surveying this recent and growing body of literature, as well as established professional principles adopted in psychology, we propose a set of ethical guidelines for large scale HCI user trials. These guidelines come in two parts: a set of general principles and a framework into which individual app store-based trials can be assessed and ethical concerns exposed. We categorise existing literature using our scheme, and explain how researchers could use our framework to classify their future user trials to determine ethical responsibility, and the steps required to meet these obligations.


Cancer Biology and Signal Transduction GSK 3 Inhibitors Regulate MYCN mRNA Levels and Reduce Neuroblastoma Cell Viability through Multiple Mechanisms , Including p 53 and Wnt Signaling


title: The intensity of work recovery in low back pain.

abstract: The intensity of work recovery in LBP has been studied using the National Health Register. Nine hundred forty men, 40-47 years old, were selected randomly from the census register of the city of Göteborg, Sweden. Sickness absence data were obtained from the Health Register, in which all sickness absence from age 16 is recorded. The rate of return to work decreases as expected with an increase in absence period. Different rates were found for different diagnoses, however, with low return intensities in patients with sciatica compared with those with back pain, ie, return to work was slower in patients with sciatica. Men with manual work had a significantly longer average sickness absence than white-collar workers. The intensity of work recovery was lower in blue-collar workers during the first 20 days of absence, while the reverse was true after 20 days of sickness absence, ie, the white-collar workers who were absent more than 20 days had a slower rate of recovery than blue-collar workers who had been absent for 20 days. Data as presented here can be used to study the effect of intervention (for example, manual therapy) on the natural course of work recovery. It also can be used, as above, to study differences in sickness absence patterns between different diagnoses and work groups.


title: Simulation of cathode ray tube

abstract: The Cathode Ray Tube (CRT) experiment performed by J. J. Thomson, is one of the most well-known physical experiments, which led to the discovery of electrons. The experiment could also describe characteristic properties, essentially, its affinity towards positive charge, and its charge to mass ratio. This paper describes the simulation of J. J. Thomson's Cathode Ray Tube experiment. The major contribution of this work is the new approach for modelling this experiment, with a great deal of accuracy and precision, using the equations of physical laws to describe the motion of the electrons. The motion of the electrons can be manipulated and recorded by the user, by assigning different values to the experimental parameters. This can be used as a good learning tool by the needy.


title: Combining background subtraction algorithms with convolutional neural network

abstract: Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder–decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies.


title: Status report: specifying javascript with ML

abstract: The Ecma TC39-TG1 working group is using ML as the specification language for the next generation of JavaScript, the popular programming language for browser-based web applications. This "definitional interpreter" serves many purposes: a high-level and readable specification language, an executable and testable specification, a reference implementation, and an aid in driving the design process. We describe the design and specification of JavaScript and our experience so far using Standard ML for this purpose.


title: XPED2: A Passive Exoskeleton with Artificial Tendons

abstract: Wearable exoskeletons might reduce human effort during walking. However, many of the current exoskeletons rely on heavy actuators and/or external power supplies; this has a negative impact on their efficiency and operation range. As an alternative, (quasi)passive exoskeletons have been developed. One of the proposed passive exoskeleton concepts is the exotendon concept of van den Bogert [1]. In this concept, long elastic cables span multiple joints. The cables can temporarily store and transfer energy between joints. In simulation, the average absolute joint torque can be reduced by 71%. The simulations are based on the hypotheses 1) that the exoskeleton does not influence the joint angles and 2) the total joint torques and a reduction in the human joint torques results in a reduction in the metabolic cost of walking. The goal of this article is to experimentally evaluate the exotendon concept and test the hypotheses underlying it. We implemented the exotendon concept in a lightweight exoskeleton. Experimental results show that the exotendons indeed reduced the average absolute joint torques. However, the exotendons also influenced the joint kinematics, and the metabolic cost of walking did not decrease. Therefore, the underlying assumptions of the exotendon concept are invalid. We also found that, in practice, the amount of support given by the exotendons is limited to about 35% of the theoretical optimal support. For higher levels of support, the motion is hindered and the support is experienced as uncomfortable by the users of the exoskeleton.


title: Spectrum and Power Allocation for Vehicular Communications With Delayed CSI Feedback

abstract: Channel state information (CSI) at the base station (BS) is critical to resource allocation design for wireless networks, but it is hard to obtain accurate CSI in a high mobility vehicular environment. In this letter, we study the spectrum and power allocation problem in device-to-device-enabled vehicular networks, where CSI of vehicular links is only reported to the BS periodically. We maximize the sum throughput of all vehicle-to-infrastructure (V2I) links while guaranteeing the reliability of each vehicle-to-vehicle (V2V) link with the delayed CSI feedback. We propose a low-complexity algorithm to find the optimal spectrum sharing strategy among V2I and V2V links and properly adjust their transmit powers. Its desirable performance is confirmed by computer simulation.


title: Extraction of High-Resolution Frames from Video Sequences

abstract: The human visual system appears to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. While the mechanisms in the human visual system that do this are unknown, the effect is not too surprising given that temporally adjacent frames in a video sequence contain slightly different, but unique, information. This paper addresses the use of both the spatial and temporal information present in a short image sequence to create a single high-resolution video frame. A novel observation model based on motion compensated subsampling is proposed for a video sequence. Since the reconstruction problem is ill-posed, Bayesian restoration with a discontinuity-preserving prior image model is used to extract a high-resolution video still given a short low-resolution sequence. Estimates computed from a low-resolution image sequence containing a subpixel camera pan show dramatic visual and quantitative improvements over bilinear, cubic B-spline, and Bayesian single frame interpolations. Visual and quantitative improvements are also shown for an image sequence containing objects moving with independent trajectories. Finally, the video frame extraction algorithm is used for the motion-compensated scan conversion of interlaced video data, with a visual comparison to the resolution enhancement obtained from progressively scanned frames.


From AADL to Timed Automaton-A Verification Approach


title: An Accurate Design Algorithm for LLC Resonant Converters—Part II

abstract: This paper proposes a new approach to optimally design LLC converters. The core of the proposed approach is an accurate algorithm that can find all the possible designs satisfying the peak-gain requirement. Designers can conveniently evaluate several design results and find the optimal one for their respective applications. Part II of this paper discusses design considerations and procedure using the proposed algorithm. Case studies reveal different design tradeoffs in different application scenarios. A 384-V nominal 300-V minimum input voltage range 12-V/50-A output LLC converter prototype with 97.3% peak efficiency is built to verify the proposed design algorithm.


Fully Automatic Brain Tumor Segmentation from Multiple MR Sequences using Hidden Markov Fields and Variational


Design, Materials, and Mechanobiology of Biodegradable Scaffolds for Bone Tissue Engineering


title: Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments

abstract: Abstract The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently its bidirectional version has also been introduced in the literature known as Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT* and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed for complex cluttered environments. IB-RRT* utilizes the bidirectional trees approach and introduces intelligent sample insertion heuristic for fast convergence to the optimal path solution using uniform sampling heuristics. The proposed algorithm is evaluated theoretically and experimental results are presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental results demonstrate the superior efficiency of IB-RRT* in comparison with RRT* and B-RRT in complex cluttered environments.


title: The Neuroscience of Spontaneous Thought: An Evolving, Interdisciplinary Field

abstract: An often-overlooked characteristic of the human mind is its propensity to wander. Despite growing interest in the science of mind-wandering, most studies operationalize mind-wandering by its task-unrelated contents, which may be orthogonal to the processes constraining how thoughts are evoked and unfold over time. In this chapter, we emphasize the importance of incorporating such processes into current definitions of mind-wandering, and proposing that mind-wandering and other forms of spontaneous thought (such as dreaming and creativity) are mental states that arise and transition relatively freely due to an absence of constraints on cognition. We review existing psychological, philosophical, and neuroscientific research on spontaneous thought through the lens of this framework, and call for additional research into the dynamic properties of the mind and brain.


title: Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

abstract: Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.


title: Semantic Video Segmentation by Gated Recurrent Flow Propagation

abstract: Semantic video segmentation is challenging due to the sheer amount of data that needs to be processed and labeled in order to construct accurate models. In this paper we present a deep, end-to-end trainable methodology for video segmentation that is capable of leveraging the information present in unlabeled data, besides sparsely labeled frames, in order to improve semantic estimates. Our model combines a convolutional architecture and a spatiotemporal transformer recurrent layer that is able to temporally propagate labeling information by means of optical flow, adaptively gated based on its locally estimated uncertainty. The flow, the recognition and the gated temporal propagation modules can be trained jointly, end-to-end. The temporal, gated recurrent flow propagation component of our model can be plugged into any static semantic segmentation architecture and turn it into a weakly supervised video processing one. Our experiments in the challenging CityScapes and Camvid datasets, and for multiple deep architectures, indicate that the resulting model can leverage unlabeled temporal frames, next to a labeled one, in order to improve both the video segmentation accuracy and the consistency of its temporal labeling, at no additional annotation cost and with little extra computation.


title: A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter

abstract: Sentiment analysis and opinion mining in social networks present nowadays a hot topic of research. However, most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented toward the binary classification (i.e., classification into “positive” and “negative”) or the ternary classification (i.e., classification into “positive,” “negative,” and “neutral”) of texts. In this paper, we propose a novel approach that, in addition to the aforementioned tasks of binary and ternary classifications, goes deeper in the classification of texts collected from Twitter and classifies these texts into multiple sentiment classes. While in this paper, we limit our scope to seven different sentiment classes, the proposed approach is scalable and can be run to classify texts into more classes. We first introduce SENTA, our tool built to help users select out of a wide variety of features the ones that fit the most for their application, to run the classification, through an easy-to-use graphical user interface. We then use SENTA to run our own experiments of multi-class classification. Our experiments show that the proposed approach can reach up to 60.2% accuracy on the multi-class classification. Nevertheless, the approach proves to be very accurate in binary classification and ternary classification: in the former case, we reach an accuracy of 81.3% for the same data set used after removing neutral tweets, and in the latter case, we reached an accuracy of classification equal to 70.1%.


title: JaTeCS an open-source JAva TExt Categorization System

abstract: JaTeCS is an open source Java library that supports research on automatic text categorization and other related problems, such as ordinal regression and quantification, which are of special interest in opinion mining applications. It covers all the steps of an experimental activity, from reading the corpus to the evaluation of the experimental results. As JaTeCS is focused on text as the main input data, it provides the user with many text-dedicated tools, e.g.: data readers for many formats, including the most commonly used text corpora and lexical resources, natural language processing tools, multi-language support, methods for feature selection and weighting, the implementation of many machine learning algorithms as well as wrappers for well-known external software (e.g., SVM_light) which enable their full control from code. JaTeCS support its expansion by abstracting through interfaces many of the typical tools and procedures used in text processing tasks. The library also provides a number of "template" implementations of typical experimental setups (e.g., train-test, k-fold validation, grid-search optimization, randomized runs) which enable fast realization of experiments just by connecting the templates with data readers, learning algorithms and evaluation measures.


title: BraMBLe: a Bayesian multiple-blob tracker

abstract: Blob trackers have become increasingly powerful in recent years largely due to the adoption of statistical appearance models which allow effective background subtraction and robust tracking of deforming foreground objects. It has been standard, however, to treat background and foreground modelling as separate processes-background subtraction is followed by blob detection and tracking-which prevents a principled computation of image likelihoods. This paper presents two theoretical advances which address this limitation and lead to a robust multiple-person tracking system suitable for single-camera real-time surveillance applications. The first innovation is a multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects. This likelihood function has a rigorous mathematical basis: it is adapted from the theory of Bayesian correlation, but uses the assumption of a static camera to create a more specific background model while retaining a unified approach to background and foreground modelling. Second we introduce a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time. We show how a particle filter can be used to perform joint inference on both the number of objects present and their configurations. Finally we demonstrate that our system runs comfortably in real time on a modest workstation when the number of blobs in the scene is small.


title: CNN-based thermal infrared person detection by domain adaptation

abstract: Imaging sensors capturing the surroundings of an autonomous vehicle are vital for its understanding of the environment. While thermal infrared cameras promise improved bad weather and nighttime robustness compared with standard RGB cameras, detecting objects, such as persons, in thermal infrared imagery is a tough problem because image resolution and quality is typically far lower, especially for low-cost sensors. Currently, deep learning based object detection frameworks offer an impressive performance on high-quality images. However, applying them to low-quality data in a different spectral range causes significant performance drops. This work proposes a strategy to make use of elaborate CNN-based object detector frameworks which are pre-trained on visual RGB images. Two key steps are undertaken: First, an appropriate preprocessing strategy for the IR data is suggested which transforms the IR data as close as possible to the RGB domain. This allows pre-trained RGB features to be effective on the novel domain. Second, the remaining domain gap is addressed by fine-tuning the pre-trained CNN on a limited set of thermal IR data. Different IR preprocessing options are explored, each addressing a different aspect of the domain gap between thermal IR and RGB data. Examples include dynamic range, blur or contrast. Because no preprocessing can cover all aspects alone, providing preprocessing combinations to the CNN allows addressing more than one aspect at once and further improves the results. Experiments indicate significant person detection improvements on the public KAIST dataset with the optimized preprocessing strategy.


title: Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

abstract: In the developing technology, crime reduction is one of the major and complex processes due to the various techniques and minimum amount of crime-related data. The traditional method is difficult to identify the crime activities with effective manner due to the minimum data. So, this paper introduces the novel big data and soft computing techniques for recognizing the crime activities with effective manner. Initially, the crime activities-related data have been collected from the various resources present in the big data. From the collected data, the inconsistent data and missing values are eliminated by applying the incremental mean normalization method. After that, the similar crime data have been clustered with the help of the fireflies-based fuzzy cognitive map neural networks which help to predict the crime activity-related features with effective manner. Finally, the prediction process is done by using the enhanced associative neural networks approach. The efficiency of the system is evaluated with the help of the experimental results and discussions in terms of the precision, recall, accuracy.


title: Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets

abstract: Quantum computers can be used to address molecular structure, materials science and condensed matter physics problems, which currently stretch the limits of existing high-performance computing resources. Finding exact numerical solutions to these interacting fermion problems has exponential cost, while Monte Carlo methods are plagued by the fermionic sign problem. These limitations of classical computational methods have made even few-atom molecular structures problems of practical interest for medium-sized quantum computers. Yet, thus far experimental implementations have been restricted to molecules involving only Period I elements. Here, we demonstrate the experimental optimization of up to six-qubit Hamiltonian problems with over a hundred Pauli terms, determining the ground state energy for molecules of increasing size, up to BeH2. This is enabled by a hardware-efficient variational quantum eigensolver with trial states specifically tailored to the available interactions in our quantum processor, combined with a compact encoding of fermionic Hamiltonians and a robust stochastic optimization routine. We further demonstrate the flexibility of our approach by applying the technique to a problem of quantum magnetism. Across all studied problems, we find agreement between experiment and numerical simulations with a noisy model of the device. These results help elucidate the requirements for scaling the method to larger systems, and aim at bridging the gap between problems at the forefront of high-performance computing and their implementation on quantum hardware.


title: Past, present and future of mobile payments research: A literature review

abstract: The mobile payment services markets are currently under transition with a history of numerous tried and failed solutions, and a future of promising but yet uncertain possibilities with potential new technology innovations. At this point of the development, we take a look at the current state of the mobile payment services market from a literature review perspective. We review prior literature on mobile payments, analyze the various factors that impact mobile payment services markets, and suggest directions for future research in this still emerging field. To facilitate the analysis of literature, we propose a framework of four contingency and five competitive force factors, and organize the mobile payment research under the proposed framework. Consumer perspective of mobile payments as well as technical security and trust are best covered by contemporary research. The impacts of social and cultural factors on mobile payments, as well as comparisons between mobile and traditional payment services are entirely uninvestigated issues. Most of the factors outlined by the framework have been addressed by exploratory and early phase studies.


title: Mr-compatible biopsy needle with enhanced tip force sensing

abstract: We describe an instrumented biopsy needle that provides physicians the capability to sense interaction forces directly at the tip of the needle's inner stylet. The sensors consist of optical fiber Bragg gratings (FBGs), and are unaffected by electromagnetic fields; hence the needle is suitable for MR-guided procedures. In comparison to previous instrumented needles that measure bending strains, the new design has additional sensors and a series of micro-machined holes at the tip. The holes increase strain sensitivity to axial forces, without significantly reducing the stiffness or strength. Axial loads of 10 mN can be detected with flat response from 0-200 Hz. A comparison of the dynamic forces measured with the needle's sensors and those obtained using an external force/torque sensor at the base shows that the enhanced tip sensitivity is particularly noticeable when there is significant friction along the needle sheath.


title: High speed coreless axial flux permanent magnet motor with printed circuit board winding

abstract: High speed gearless and compact motors rotating at speeds above 10000 rpm are finding many applications nowadays. Design of the high speed motor is indeed a technological challenge. This paper presents novel design and analysis of a coreless axial flux permanent magnet motor for high speed, low power application. The proposed motor is designed to rotate at a speed of 30000 rpm. To achieve high reliability of the motor at this speed, the torque ripple and axial pull in the axial flux machine should be minimized. For this purpose, coreless stator design is preferred. Furthermore, a novel multilayer Printed Circuit Board (PCB) stator is proposed to obtain uniform distribution in phase winding which minimizes harmonics in the back emf of the motor. In order to improve the air gap flux density in the coreless design, halbach array based rotor is used. The proposed axial flux motor has light, yet strong, well-balanced halbach rotor. The novel multilayer PCB stator has made the motor highly efficient and reliable. MagNet 3-D is used for the Finite Element Analysis (FEA) and optimization of the motor and the results are presented.


title: Force Sensing and Manipulation Strategy in Robot-Assisted Microinjection on Zebrafish Embryos

abstract: Robot-assisted microinjection has attracted considerable attention from both engineering and biological communities, due to its advantages of high precision and throughput. This paper addresses two issues in the development of enable technologies for robot-assisted microinjection. One is the microforce measurement during the embryo-injection process. A new injection force-sensing scheme based on a simply supported beam structure is reported. The model combining mechanoelectrical transduction of the beam and the polyvinylidene fluoride sensing material is theoretically investigated. The other issue to be addressed is the manipulation strategy to mimic human-injection operation, which is based on force sensing and control techniques. A set of microinjection experiments on zebrafish embryos is performed to demonstrate the effectiveness of the proposed robot-assisted manipulation methodology. Experimental results show that satisfied survival rate of the injected cells can be obtained, which is comparable to manual operation by human expert.


title: Artificial Intelligence Techniques in Medicine and Health Care

abstract: Now-a-days, researchers are increasingly looking into new and innovative techniques with the help of information technology to overcome the rapid surge in health care costs facing the community. Research undertaken in the past has shown that artificial intelligence (AI) tools and techniques can aid in the diagnosis of disease states and assessment of treatment outcomes. This has been demonstrated in a number of areas, including: help with medical decision support system, classification of heart disease from electrocardiogram (ECG) waveforms, identification of epileptic seizure from electroencephalogram (EEG) signals, ophthalmology to detect glaucoma disease, abnormality in movement pattern (gait) recognition for rehabilitation and potential falls risk minimization, assisting functional electrical stimulation (FES) control in rehabilitation setting of spinal cord injured patients, and clustering of medical images (Begg et al., 2003; Garrett et al., 2003; Masulli et al., 1998; Papadourokis et al., 1998; Silva & Silva, 1998). Recent developments in information technology and AI tools, particularly in neural networks, fuzzy logic and support vector machines, have provided the necessary support to develop highly efficient automated diagnostic systems. Despite plenty of future challenges, these new advances in AI tools hold much promise for future developments in AI-based approaches in solving medical and health-related problems. This article is organized as follows: Following an overview of major AI techniques, a brief review of some of the applications of AI in health care is provided. Future challenges and directions in automated diagnostics are discussed in the summary and conclusion sections.


title: Philosophy and Logic in Search of the Polish Tradition

abstract: Preface. Part I: Truth. A Minimalist Critique of Tarski on Truth P. Horwich. Is Strong Correspondence (Theory of Truth) Possible? P. Materna. Content and Likeness Definitions of Truthlikeness I. Niiniluoto. Absolute Truth in a Changing World P. Simons. Part II: Logic and Semantics. On the Epistemology of Game-theoretical Semantics J. Hintikka. Can Majority be Supervalued? K. Kijania-Placek. On Belnap's Branching Space-Times T. Placek. Partially Interpreted Henkin Quantifiers G. Sandu. Tarski and Lesniewski on Languages with Meaning versus Languages without Use G. Sundholm. Part III: History of Logic and Philosophy. Ajdukiewicz on Language Change and Truth T. Czarnecki. Alfred Tarski and a Watershed Meeting in Logic: Cornell, 1957 S. Feferman. On Thinking about the Mental and the Physical H. Hochberg. Bernard Bolzano's 'Wissenschaftslehre' and Polish Analytical Philosophy between 1894 and 1935 W. Kunne. Dispositions, Their Bases and Correlates - Meinong's Analysis K. Mulligan. Reactions to the Discovery of the Incompleteness Phenomenon R. Murawski. Truthmakers, Truthbearers and the Objectivity of Truth A. RojszczakDAGGER, B. Smith. The Extension of the Concept Abolished? Reflexions on a Fregean Dilemma C. Thiel. Selected Bibliography of Jan Wolenski's Works. Index of Names.


English to Tamil Transliteration using WEKA


title: How deep learning extracts and learns leaf features for plant classification

abstract: Abstract Plant identification systems developed by computer vision researchers have helped botanists to recognize and identify unknown plant species more rapidly. Hitherto, numerous studies have focused on procedures or algorithms that maximize the use of leaf databases for plant predictive modeling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. In this paper, we learn useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gain intuition of the chosen features based on a Deconvolutional Network (DN) approach. We report somewhat unexpected results: (1) different orders of venation are the best representative features compared to those of outline shape, and (2) we observe multi-level representation in leaf data, demonstrating the hierarchical transformation of features from lower-level to higher-level abstraction, corresponding to species classes. We show that these findings fit with the hierarchical botanical definitions of leaf characters. Through these findings, we gained insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems. The source code and models are available at: https://github.com/cs-chan/Deep-Plant .


title: Autopsy findings in asphyxia in medical bed rails.

abstract: Lethal asphyxial entrapment between bedrails and mattresses is a well-recognized clinical event although there are few descriptions of autopsy findings. This convenience sample series of 29 deaths shows the infrequency of petechiae and laryngeal crush injuries and subtlety of soft-tissue trauma findings in such cases. Petechiae, plethora, and venous congestion may be noted in extremities that are pinned by rails in a manner that obstructs venous return. Medical examiners must mainly rely on eyewitness descriptions of the how the person was found and try to correlate this position with scanty signs of trauma.


title: Real-time tessellation of terrain on graphics hardware

abstract: Synthetic terrain is a key element in many applications, which can lessen the sense of realism if it is not handled correctly. We propose a new technique for visualizing terrain surfaces by tessellating them on the GPU. The presented algorithm introduces a new adaptive tessellation scheme for managing the level of detail of the terrain mesh, avoiding the appearance of t-vertices that can produce visually disturbing artifacts. Previous solutions exploited the geometry shader's capabilities to tessellate meshes from scratch. In contrast, we reuse the already calculated data to minimize the operations performed in the shader units. This feature allows us to increase performance through smart refining and coarsening. Finally, we also propose a framework to manage large DEMs as height maps.


title: Approximate Bayesian Computation (ABC) in practice

abstract: Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied.