title: Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives

abstract: Long-term Web archives comprise Web documents gathered over longer time periods and can easily reach hundreds of terabytes in size. Semantic annotations such as named entities can facilitate intelligent access to the Web archive data. However, the annotation of the entire archive content on this scale is often infeasible. The most efficient way to access the documents within Web archives is provided through their URLs, which are typically stored in dedicated index files. The URLs of the archived Web documents can contain semantic information and can offer an efficient way to obtain initial semantic annotations for the archived documents. In this paper, we analyse the applicability of semantic analysis techniques such as named entity extraction to the URLs in a Web archive. We evaluate the precision of the named entity extraction from the URLs in the Popular German Web dataset and analyse the proportion of the archived URLs from 1,444 popular domains in the time interval from 2000 to 2012 to which these techniques are applicable. Our results demonstrate that named entity recognition can be successfully applied to a large number of URLs in our Web archive and provide a good starting point to efficiently annotate large scale collections of Web documents.


title: The Past, Present and Future of Text Classification

abstract: Despite over a century of research, the study of text classification is still chaotic. We examine the parameters at hand, and discuss some things that have been researched, and some things that have not.


title: Analysis of Blocking Probability in a Relay-based Cellular OFDMA Network

abstract: Relay deployment in orthogonal frequency division multiple access (OFDMA) based cellular networks helps in coverage extension and/or capacity improvement. To quantify capacity improvement, blocking probability of voice call is typically calculated using Erlang B formula. This calculation is based on the assumption that all users require same amount of resources to satisfy their rate requirement. However, in an OFDMA system, each user requires different number of subcarriers to meet its rate requirement. This resource requirement depends on the signal to interference ratio (SIR) experienced by a user. Therefore, the Erlang B formula can not be employed to compute blocking probability in an OFDMA network. In this paper, we determine an analytical expression to compute the blocking probability in a relay based cellular OFDMA network. We determine an expression of the probability distribution of the user's resource requirement based on its experienced SIR. Then, we classify the users into various classes depending upon their subcarrier requirement. We consider the system to be a multi-dimensional system with different classes and evaluate the blocking probability using the multi-dimensional Erlang loss formulas. This model is useful in the performance evaluation, design, planning of resources and call admission control in a relay based cellular OFDMA networks like long term evolution.


title: Design, modeling and control of a series elastic actuator for an assistive knee exoskeleton

abstract: This paper introduces the design, modeling and control of a novel inherently compliant actuator with the ability to manually reconfigure the level of stiffness (CompAct-ARS). CompAct-ARS is intended to be utilized in assistive devices for legs, which will aid users in different motion tasks such as walking, sit-to-stand movement and squatting and will demonstrate the benefits of passive compliance. The actuator modeling design and mechanics are introduced. Experimental and simulation data are used in order to determine the design specifications of the actuator for its use in a knee exoskeleton system. Finally an assistive control strategy is introduced that will allow the proposed system to provide motion assistance to the subject knee.


title: Quantifying skill relevance to job titles

abstract: Eliminating or reducing skill gaps in the job market is critical to putting people back to work, reducing the unemployment rate, and increasing the labor market participation rate. A key element in closing the skills gap is accurately identifying the mismatch between the skills expected by employers and those possessed by job seekers. In this study, our goal was to profile job titles by effectively quantifying the relevance of skills. We started by using a naive, frequency-based skill ranking approach, which resulted in the most generic skills ranked on the top. We then adapted a number of alternative metrics and compared their performances on a number of job titles. The outcome of this study can support CareerCoach, an analytical solution CareerBuilder has piloted to provide insights and data dashboards to job seekers.


title: Choice and Procrastination

abstract: Recent models of procrastination due to self-control problems assume that a procrastinator considers just one option and is unaware of her self-control problems. We develop a model where a person chooses from a menu of options and is partially aware of her self-control problems. This menu model replicates earlier results and generates new ones. A person might forego completing an attractive option because she plans to complete a more attractive but never-to-be-completed option. Hence, providing a non-procrastinator additional options can induce procrastination, and a person may procrastinate worse pursuing important goals than unimportant ones.


title: Conventions and Commitments in Distributed CSCW Groups

abstract: Conventions are necessary to establish in any recurrent cooperative arrangement. In electronic work, they are important so as to regulate the use of shared objects. Based on empirical results from a long-term study of a group cooperating in electronic work, I present examples showing that the group failed to develop normative convention behavior. These difficulties in forming conventions can be attributed to a long list of factors: the lack of clear precedents, different perspectives among group members, a flexible cooperation media, limited communication, the design process, and discontinuous cooperation. Further, I argue that commitments to the conventions were difficult, due to the conventions not reaching an acceptance threshold, uneven payoffs, and weak social influences. The empirical results call for a specific set of awareness information requirements to promote active learning about the group activity in order to support the articulation of conventions. The requirements focus on the role of feedback as a powerful mechanism for shaping and learning about group behavior.


title: The development of techniques for tissue culture of mango (Mangifera indica L.) var. Carabao and successful transfer of ex vitro-grafted plants to soil and the field

abstract: The mango, a virtually neglected fruit before the advent of nitrate-induced flowering, is now the third leading export fruit in the Philippines, with the ‘Carabao’ mango being the only variety exported. Delay or control of ripening, as well as the ability to program market availability, would further increase the value of mango. Prolonging its shelf life and improving other characteristics can be achieved through biotechnology, requiring a reliable tissue culture regeneration protocol, and a successful transfer-to-soil system. Between 2004 and 2008, immature fruits were collected and cultured in vitro. Primary somatic embryo (SE) induction ranged from 16–100% depending on the strain, collection time, and tree source. A single SE was required to initiate SE proliferation. When subcultured, the SEs proliferated, germinated, and produced plantlets. Initial shoot formation was 8–64%, while succeeding true leaf formation was reduced to 0–36%. In some cultures, abscission and browning were observed, but gas chromatography assays ruled out ethylene as the cause. Regenerated plants survived transfer to soil, but at low percentages. While it took 1 y to develop the tissue culture protocol, it took 3 y to develop the technique for successful transfer of plantlets to soil, and to the field. This is the first report, of a polyembryonic mango, variety Carabao, for which successful tissue culture and field transplantation systems were developed. The technology is equally important as a way to rapidly propagate uniform rootstocks of superior ‘Carabao’ strains or other varieties, for generation of variability, genetic transformation, and for germplasm conservation and exchange.


title: Cost-Effective Active Learning for Deep Image Classification

abstract: Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human effort. In this paper, we propose a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner. Our approach advances the existing AL methods in two aspects. First, we incorporate deep convolutional neural networks into AL. Through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. Second, we present a cost-effective sample selection strategy to improve the classification performance with less manual annotations. Unlike traditional methods focusing on only the uncertain samples of low prediction confidence, we especially discover the large amount of high-confidence samples from the unlabeled set for feature learning. Specifically, these high-confidence samples are automatically selected and iteratively assigned pseudolabels. We thus call our framework cost-effective AL (CEAL) standing for the two advantages. Extensive experiments demonstrate that the proposed CEAL framework can achieve promising results on two challenging image classification data sets, i.e., face recognition on the cross-age celebrity face recognition data set database and object categorization on Caltech-256.


title: A survey study of critical success factors in agile software projects in former Yugoslavia IT companies

abstract: Determining the factors that have an influence on the success of the software development projects has been the focus of extensive research for more than 30 years. In recent years agile methodology of software development has become the dominant one for all kinds of software development projects. In this paper we present the results of empirical study for determining critical factors that influence the success of agile software projects which we conducted among senior developers and project managers from IT companies located in the former Yugoslavia countries within South Eastern Europe (SEE) region. This study is inspired by the similar study conducted 5 years ago (Chow and Cao, 2008). With this study we were not able to confirm the model developed in the previous study. Moreover it disconfirmed not only part of the factors, but very much questioned the whole scheme. However, we were able to shed additional light regarding agile software development in former Yugoslavia countries from SEE region as a reference region for investigating outsourced projects done in agile way.


Palisaded Neutrophilic Granulomatous Dermatitis in a Patient with Systemic Sclerosis-Rheumatoid Arthritis Overlap Syndrome


title: Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach

abstract: The paper is offering a comparative study of major modeling and pattern discovery approaches applicable to the area of data analysis and decision support systems in general, and to the area of Bioinformatics and Medicine - in particular. Compared are inductive versus transductive reasoning, global, local, and personalised modeling, and all these approaches are illustrated on a case study of gene expression and clinical data related to cancer outcome prognosis. While inductive modeling is used to develop a model (function) from data on the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. A new method - WWKNN (weighted distance, weighted variables K-nearest neighbors), and a framework for the integration of global, local and personalised models for a single input vector are proposed. Integration of data (e.g. clinical and genetic) and of models (e.g. global, local and personalised) for a better pattern discovery, adaptation and accuracy of the results, are the major points of the paper.


title: Ground Metric Learning

abstract: Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in practice when this parameter is carefully chosen. To date, the only option available to practitioners to set the ground metric parameter was to rely on a priori knowledge of the features, which limited considerably the scope of application of transportation distances. We propose to lift this limitation and consider instead algorithms that can learn the ground metric using only a training set of labeled histograms. We call this approach ground metric learning. We formulate the problem of learning the ground metric as the minimization of the difference of two polyhedral convex functions over a convex set of distance matrices. We follow the presentation of our algorithms with promising experimental results on binary classification tasks using GIST descriptors of images taken in the Caltech-256 set.


title: EKF-SLAM for AUV navigation under probabilistic sonar scan-matching

abstract: This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach.


title: Automatic Language Identification fromWritten Texts An Overview

abstract: Language Identification is the task of automatically identifying the language(s) in which the content is written in a document (web page, text document). Due to the widespread use of internet, identification of languages has become an important preprocessing step for a number of applications such as machine translation, Part-of-Speech tagging, linguistic corpus creation, supporting low-density languages, accessibility of social media or user-generated content, search engines and information extraction in addition to processing multilingual documents. In a multilingual country like India, Language Identification has wider scope to bridge the digital divide between different language users. This paper presents a brief overview of the challenges involved in automatic language identification, existing methodologies and some of the tools available for language identification.


title: Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches

abstract: Summary The analysis of electroencephalography (EEG) recordings has attracted increasing interest in recent decades and provides the pivotal scientific tool for researchers to quantitatively study brain activity during sleep, and has extended our knowledge of the fundamental mechanisms of sleep physiology. Conventional EEG analyses are mostly based on Fourier transform technique which assumes linearity and stationarity of the signal being analyzed. However, due to the complex and dynamical characteristics of EEG, nonlinear approaches are more appropriate for assessing the intrinsic dynamics of EEG and exploring the physiological mechanisms of brain activity during sleep. Therefore, this article introduces the most commonly used nonlinear methods based on the concepts of fractals and entropy, and we review the novel findings from their clinical applications. We propose that nonlinear measures may provide extensive insights into brain activities during sleep. Further studies are proposed to mitigate the limitations and to expand the applications of nonlinear EEG analysis for a more comprehensive understanding of sleep dynamics.


title: An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition

abstract: Complex activity recognition is challenging due to the inherent uncertainty and diversity of performing a complex activity. Normally, each instance of a complex activity has its own configuration of atomic actions and their temporal dependencies. We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations. We also show that local temporal dependencies can be retained and are globally consistent in the resulting interval network. Moreover, network structure can be learned from empirical data. A new dataset of complex hand activities has been constructed and made publicly available, which is much larger in size than any existing datasets. Empirical evaluations on benchmark datasets as well as our in-house dataset demonstrate the competitiveness of our approach.


title: The evolution of synthetic aperture radar systems and their progression to the EOS SAR

abstract: The authors describe the evolution of the spaceborne imaging radar starting with the Seasat SAR, through the SIR-A, SIR-B, and SIR-C/X-SAR missions, to the Earth Observing System (Eos) SAR which is scheduled for launch as part of Eos in the late 1990s. A summary of the planned international missions, which may produce a permanent active microwave capability in space starting as early as 1991, is also presented, along with a description of the airborne systems which will be essential to the algorithm development and long-term calibration of the spaceborne data. A brief summary of the planetary missions utilizing SAR and a comparison of their imaging capabilities with those available on Earth are presented. >


title: Fusion and binarization of CNN features for robust topological localization across seasons

abstract: The extreme variability in the appearance of a place across the four seasons of the year is one of the most challenging problems in life-long visual topological localization for mobile robotic systems and intelligent vehicles. Traditional solutions to this problem are based on the description of images using hand-crafted features, which have been shown to offer moderate invariance against seasonal changes. In this paper, we present a new proposal focused on automatically learned descriptors, which are processed by means of a technique recently popularized in the computer vision community: Convolutional Neural Networks (CNNs). The novelty of our approach relies on fusing the image information from multiple convolutional layers at several levels and granularities. In addition, we compress the redundant data of CNN features into a tractable number of bits for efficient and robust place recognition. The final descriptor is reduced by applying simple compression and binarization techniques for fast matching using the Hamming distance. An exhaustive experimental evaluation confirms the improved performance of our proposal (CNN-VTL) with respect to state-of-the-art methods over varied long-term datasets recorded across seasons.


An improved model of semantic similarity based on lexical co-occurence


title: Beyond blacklists: learning to detect malicious web sites from suspicious URLs

abstract: Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper, we describe an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs. These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs. The resulting classifiers obtain 95-99% accuracy, detecting large numbers of malicious Web sites from their URLs, with only modest false positives.


title: Decision‐making processes following damage to the prefrontal cortex

abstract: Recent work has suggested an association between the orbitofrontal cortex in humans and practical decision making. The aim of this study was to investigate the profile of cognitive deficits, with particular emphasis on decision‐making processes, following damage to different sectors of the human prefrontal cortex. Patients with discrete orbitofrontal (OBF) lesions, dorsolateral (DL) lesions, dorsomedial (DM) lesions and large frontal lesions (Large) were compared with matched controls on three different decision‐making tasks: the Iowa Gambling Task and two recently developed tasks that attempt to fractionate some of the cognitive components of the Iowa task. A comprehensive battery including the assessment of recognition memory, working memory, planning ability and attentional set‐shifting was also administered. Whilst combined frontal patients were impaired on several of the tasks employed, distinct profiles emerged for each patient group. In contrast to previous data, patients with focal OBF lesions performed at control levels on the three decision‐making tasks (and the executive tasks), but showed some evidence of prolonged deliberation. DL patients showed pronounced impairment on working memory, planning, attentional shifting and the Iowa Gambling Task. DM patients were impaired at the Iowa Gambling Task and also at planning. The Large group displayed diffuse impairment, but were the only group to exhibit risky decision making. Methodological differences from previous studies of OBF patient groups are discussed, with particular attention to lesion laterality, lesion size and psychiatric presentation. Ventral and dorsal aspects of prefrontal cortex must interact in the maintenance of rational and ‘non‐risky’ decision making. ::: ::: Received April 11, 2001. Revised August 16, 2001. Second revision October 19, 2001. Accepted October 22, 2001


title: Supporting the Externalisation of Thinking in Criminal Intelligence Analysis

abstract: At the end of the criminal intelligence analysis process there are relatively well established and understood approaches to explicit externalisation and representation of thought that include theories of argumentation, narrative and hybrid approaches that include both of these. However the focus of this paper is on the little understood area of how to support users in the process of arriving at such representations from an initial starting point where little is given. The work is based on theoretical considerations and some initial studies with end users. In focusing on process we discuss the requirements of fluidity and rigor and how to gain traction in investigations, the processes of thinking involved including abductive, deductive and inductive reasoning, how users may use thematic sorting in early stages of investigation and how tactile reasoning may be used to externalize and facilitate reasoning in a productive way. In the conclusion section we discuss the issues raised in this work and directions for future work.


title: Hand Pose Estimation through Weakly-Supervised Learning of a Rich Intermediate Representation

abstract: We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to segmentation maps is learned in a semi/weakly-supervised way from two different datasets: (i) a synthetic dataset created through a rendering pipeline including densely labeled ground truth (pixelwise segmentations); and (ii) a dataset with real images for which ground truth joint positions are available, but not dense segmentations. Loss for training on real images is generated from a patch-wise restoration process, which aligns tentative segmentation maps with a large dictionary of synthetic poses. The underlying premise is that the domain shift between synthetic and real data is smaller in the intermediate representation, where labels carry geometric and topological meaning, than in the raw input domain. Experiments on the NYU dataset show that the proposed training method decreases error on joints over direct regression of joints from depth data by 15.7%.


title: Stair Descending Exercise Using a Novel Automatic Escalator: Effects on Muscle Performance and Health-Related Parameters

abstract: A novel automatic escalator was designed, constructed and used in the present investigation. The aim of the present investigation was to compare the effect of two repeated sessions of stair descending versus stair ascending exercise on muscle performance and health-related parameters in young healthy men. Twenty males participated and were randomly divided into two equal-sized groups: a stair descending group (muscle-damaging group) and a stair ascending group (non-muscle-damaging group). Each group performed two sessions of stair descending or stair ascending exercise on the automatic escalator while a three week period was elapsed between the two exercise sessions. Indices of muscle function, insulin sensitivity, blood lipid profile and redox status were assessed before and immediately after, as well as at day 2 and day 4 after both exercise sessions. It was found that the first bout of stair descending exercise caused muscle damage, induced insulin resistance and oxidative stress as well as affected positively blood lipid profile. However, after the second bout of stair descending exercise the alterations in all parameters were diminished or abolished. On the other hand, the stair ascending exercise induced only minor effects on muscle function and health-related parameters after both exercise bouts. The results of the present investigation indicate that stair descending exercise seems to be a promising way of exercise that can provoke positive effects on blood lipid profile and antioxidant status.


title: Stereo inverse perspective mapping: theory and applications

abstract: This paper discusses an extension to the inverse perspective mapping geometrical transform to the processing of stereo images and presents the calibration method used on the ARGO autonomous vehicle. The article features also an example of application in the automotive field in which the stereo inverse perspective mapping helps to speed up the process.


title: Intergenerational effect of schooling and childhood overweight

abstract: Prevalence of overweight among children is at the top of health policy agenda in many developed countries. We study the causal effect of mothers' schooling on children's body weight. We exploit the 1972 schooling reform in England and Wales, which raised the minimum school leaving age from Fifteen to sixteen. Our regression-discontinuity estimates use Health Survey for England (1998-2002) and show that the extra year of schooling for mothers induced by the reform significantly reduces their son's weight. There is only insignificant negative effect for daughters. Additionally, we do not find that mothers' schooling improves children's health behaviour (fruit and vegetable consumption; exercising).


A MODIFIED DECOMPOSED THEORY OF PLANNED BEHAVIOUR MODEL TO ANALYZE USER INTENTION TOWARDS DISTANCE-BASED ELECTRONIC TOLL COLLECTION SERVICES


Mammographic Diagnosis for Breast Cancer Biopsy Predictions Using Neural Network Classification Model and Receiver Operating Characteristic (ROC) Curve Evaluation


title: High-fidelity transmission of sensory information by single cerebellar mossy fibre boutons

abstract: Understanding the transmission of sensory information at individual synaptic connections requires knowledge of the properties of presynaptic terminals and their patterns of firing evoked by sensory stimuli. Such information has been difficult to obtain because of the small size and inaccessibility of nerve terminals in the central nervous system. Here we show, by making direct patch-clamp recordings in vivo from cerebellar mossy fibre boutons-the primary source of synaptic input to the cerebellar cortex-that sensory stimulation can produce bursts of spikes in single boutons at very high instantaneous firing frequencies (more than 700 Hz). We show that the mossy fibre-granule cell synapse exhibits high-fidelity transmission at these frequencies, indicating that the rapid burst of excitatory postsynaptic currents underlying the sensory-evoked response of granule cells can be driven by such a presynaptic spike burst. We also demonstrate that a single mossy fibre can trigger action potential bursts in granule cells in vitro when driven with in vivo firing patterns. These findings suggest that the relay from mossy fibre to granule cell can act in a 'detonator' fashion, such that a single presynaptic afferent may be sufficient to transmit the sensory message. This endows the cerebellar mossy fibre system with remarkable sensitivity and high fidelity in the transmission of sensory information.


title: Self-teaching adaptive dynamic programming for Gomoku

abstract: In this paper adaptive dynamic programming (ADP) is applied to learn to play Gomoku. The critic network is used to evaluate board situations. The basic idea is to penalize the last move taken by the loser and reward the last move selected by the winner at the end of a game. The results show that the presented program is able to improve its performance by playing against itself and has approached the candidate level of a commercial Gomoku program called 5-star Gomoku. We also examined the influence of two methods for generating games: self-teaching and learning through watching two experts playing against each other and presented the comparison results and reasons.


title: Strategic Attentive Writer for Learning Macro-Actions

abstract: We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement learning setting. The network builds an internal plan, which is continuously updated upon observation of the next input from the environment. It can also partition this internal representation into contiguous sub- sequences by learning for how long the plan can be committed to - i.e. followed without re-planing. Combining these properties, the proposed model, dubbed STRategic Attentive Writer (STRAW) can learn high-level, temporally abstracted macro- actions of varying lengths that are solely learnt from data without any prior information. These macro-actions enable both structured exploration and economic computation. We experimentally demonstrate that STRAW delivers strong improvements on several ATARI games by employing temporally extended planning strategies (e.g. Ms. Pacman and Frostbite). It is at the same time a general algorithm that can be applied on any sequence data. To that end, we also show that when trained on text prediction task, STRAW naturally predicts frequent n-grams (instead of macro-actions), demonstrating the generality of the approach.


title: The influence of diversity and experience on the effects of crowd size

abstract: One advantage of crowds over traditional teams is that crowds enable the assembling of a large number of individuals to address problems. The literature is unclear, however, about when crowd size leads to better outcomes. To better understand the effects of crowd size we conducted a study on the retention and performance of 4,317 articles in the WikiProject Film community. Results indicate that crowd composition, specifically diversity and experience, is vital to understanding when size leads to better retention and performance. Crowd size was positively related to retention and performance when crowds were high in diversity and experience. Retention was important to determining when crowd size led to better performance. Crowd size was positively related to performance when retention was low. Our results suggest that crowds benefit from their size when they are diverse, experienced, and have low retention rates.


title: A multi-block-matching approach for stereo

abstract: Block-Matching stereo is commonly used in applications with low computing resources in order to get some rough depth estimates. However, research on this simple stereo estimation technique has been very scarce since the advent of energy-based methods which promise a higher quality and a larger potential for further improvement. In the domain of intelligent vehicles, especially semi-global-matching (SGM) is widely spread due to its good performance and simple implementation. Unfortunately, the big downside of SGM is its large memory footprint because it is working on the full disparity space image. In contrast to this, local block-matching stereo is much more lean. In this paper, we will introduce a novel multi-block-matching scheme which tremendously improves the result of standard block-matching stereo while preserving the low memory-footprint and the low computational complexity. We tested our new multi-block-matching scheme on the KITTI stereo benchmark as well as on the new Middlebury stereo benchmark. For the KITTI benchmark we achieve results that even surpass the results of the best SGM implementations. For the new Middlebury benchmark we get results that are only slightly worse than state-of-the-art SGM implementations.


Robust logics


title: Blind Image Deconvolution of Linear Motion Blur

abstract: We present an efficient method to deblur images for information recognition. The method is successfully applied directly on mobile devices as a preprocessing phase to images of barcodes. Our main contribution is the fast identifaction of blur length and blur angle in the frequency domain by an adapted radon transform. As a result, the barcode recognition rate of the deblurred images has been increased significantly.


title: Autonomous Reverse Parking System Based on Robust Path Generation and Improved Sliding Mode Control

abstract: Some commercial vehicle models have been equipped with semiautonomous parking systems to a certain extent. However, gaps to fully automated solutions still exist, and cost considerations further constrain their acceptance among consumers. This paper proposes a low-cost vision-based approach to a fully self-reverse parking system. It consists of four key modules: a novel path-planning module ensures that a feasible path is available under any initial poses, which frees human intervention completely; a modified sliding mode controller on the steering wheel is designed for path following; image processing with Kalman state prediction provides consistent and real-time estimation on the vehicle pose; and a robust overall control scheme ensures that the vehicle can accurately park along the slot center line without intrusion into adjacent slots. Experimental results based on 216 on-field tests under different illumination conditions showed that the proposed system was able to accurately and consistently park the vehicle in all cases with a 4.71-cm RMS offset distance from the center line and a 1.24° RMS orientation deviation. With its easy setup and excellent performance, this system can be practically and robustly implemented to existing vehicles with minimal additional cost.


title: A statistical framework for fair predictive algorithms

abstract: Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived"neutrality"of computers. It is argued that because computer models do not hold personal prejudice, the predictions they produce will be equally free from prejudice. There is growing recognition that employing algorithms does not remove the potential for bias, and can even amplify it, since training data were inevitably generated by a process that is itself biased. In this paper, we provide a probabilistic definition of algorithmic bias. We propose a method to remove bias from predictive models by removing all information regarding protected variables from the permitted training data. Unlike previous work in this area, our framework is general enough to accommodate arbitrary data types, e.g. binary, continuous, etc. Motivated by models currently in use in the criminal justice system that inform decisions on pre-trial release and paroling, we apply our proposed method to a dataset on the criminal histories of individuals at the time of sentencing to produce"race-neutral"predictions of re-arrest. In the process, we demonstrate that the most common approach to creating"race-neutral"models-- omitting race as a covariate-- still results in racially disparate predictions. We then demonstrate that the application of our proposed method to these data removes racial disparities from predictions with minimal impact on predictive accuracy.


title: The non-convex Burer-Monteiro approach works on smooth semidefinite programs

abstract: Semidefinite programs (SDPs) can be solved in polynomial time by interior point methods, but scalability can be an issue. To address this shortcoming, over a decade ago, Burer and Monteiro proposed to solve SDPs with few equality constraints via rank-restricted, non-convex surrogates. Remarkably, for some applications, local optimization methods seem to converge to global optima of these non-convex surrogates reliably. Although some theory supports this empirical success, a complete explanation of it remains an open question. In this paper, we consider a class of SDPs which includes applications such as max-cut, community detection in the stochastic block model, robust PCA, phase retrieval and synchronization of rotations. We show that the low-rank Burer--Monteiro formulation of SDPs in that class almost never has any spurious local optima.


title: Leveraging temporal properties of news events for stock market prediction

abstract: Investors make decisions based on various factors, including consumer price index, price-earnings ratio, and also miscellaneous events reported by newspapers. In order to assist their decisions in a timely manner, many studies have been conducted to automatically analyze those information sources in the last decades. However, the majority of the efforts was made for utilizing numerical information, partly due to the difficulty to process natural language texts and to make sense of their temporal properties. This study sheds light on this problem by using deep learning, which has been attracting much attention in various areas of research including pattern mining and machine learning for its ability to automatically construct useful features from a large amount of data. Specifically, this study proposes an approach to market trend prediction based on a recurrent deep neural network to model temporal effects of past events. The validity of the proposed approach is demonstrated on the real-world data for ten Nikkei companies.


title: A digital envelope approach using attribute-based encryption for secure data exchange in IoT scenarios

abstract: The inclusion of the Big Data paradigm in our everyday life is giving rise to different IoT scenarios in which there is a continuous sensitive data sharing (e.g., remote healthcare or domestic automation). For such scenarios, the protection of such data is a key challenge to achieve the acceptance, by the end users, of new services and systems oriented to these scenarios. In order to address this issue, this work presents a novel approach combining the efficiency of symmetric cryptography and the flexibility and fine granularity of attribute-based cryptography, with the aim of carrying out secure information exchanges among different entities that make up this new IoT context.


title: Crocheted artificial tendons and ligaments for the anatomically correct testbed (ACT) hand

abstract: In the human hand, tendons and ligaments are important soft tissues that transfer forces from muscles to finger joints and coordinate the complex finger movements. However in conventional robotic hand research, their functions are often simplified by cables and linkages due to biological counterparts' complicated shapes and branching mechanism. In order to better simulate the neuromuscular control strategy of the human hand, we believe that it is important to replicate the salient features of the biological ligaments and tendons using artificial materials. To this end, we design and fabricate the artificial extensor hood for the anatomically correct testbed (ACT) hand via innovative crochet technique. The resulting artificial extensor hood resemble not only the shape and branching mechanism but also the varying fiber orientation in ligaments of its human counterparts. We successfully validate our design efficacy through finger movement experiments and quantitatively test the mechanical properties of the crocheted tendons under different branching conditions. We find that the crocheted ligament has a force-displacement behavior similar to that of the human hand and possesses a five times higher breaking strength on average.


title: Unsupervised Learning of Morphological Forests

abstract: This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edgewise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of tight morphological families. The resulting objective is solved using Integer Linear Programming (ILP) paired with contrastive estimation. We train the model by alternating between optimizing the local log-linear model and the global ILP objective. We evaluate our system on three tasks: root detection, clustering of morphological families and segmentation. Our experiments demonstrate that our model yields consistent gains in all three tasks compared with the best published results.


Modeling Patient Mortality from Clinical Text by Combining Topic Modeling and Ontological Feature Learning with Group Regularization


title: A news-topic recommender system based on keywords extraction

abstract: In recent years, internet news has become one of the most important channels for information acquisition, as more and more people read news through internet connected computers, tablets, and smart phones, etc. Owing to the constantly reproduced news, the number of online media increases dramatically and the volume of news also expands rapidly. Consequently, obtaining primary information from the internet is of great interest. This paper presents a news-topic recommender system based on keywords extraction. It is shown that the proposed system is very effective in acquiring specific topics within any specific period of time.


title: A case study on software risk analysis and planning in medical device development

abstract: Software failures in medical devices can lead to catastrophic situations. Therefore, it is crucial to handle software-related risks when developing medical devices, and there is a need for further analysis of how this type of risk management should be conducted. The objective of this paper is to collect and summarise experiences from conducting risk management with an organisation developing medical devices. Specific focus is put on the first steps of the risk management process, i.e. risk identification, risk analysis, and risk planning. The research is conducted as action research, with the aim of analysing and giving input to the organisation's introduction of a software risk management process. First, the method was defined based on already available methods and then used. The defined method focuses on user risks, based on scenarios describing the expected use of the medical device in its target environment. During the use of the method, different stakeholders, including intended users, were involved. Results from the case study show that there are challenging problems in the risk management process with respect to definition of the system boundary and system context, the use of scenarios as input to the risk identification, estimation of detectability during risk analysis, and action proposals during risk planning. It can be concluded that the risk management method has potential to be used in the development organisation, although future research is needed with respect to, for example, context limitation and how to allow for flexible updates of the product.


title: Vagal influence on working memory and attention

abstract: The aim of the present study was to investigate the effect of vagal tone on performance during executive and non-executive tasks, using a working memory and a sustained attention test. Reactivity to cognitive tasks was also investigated using heart rate (HR) and heart rate variability (HRV). Fifty-three male sailors from the Royal Norwegian Navy participated in this study. Inter-beat-intervals were recorded continuously for 5 min of baseline, followed by randomized presentation of a working memory test (WMT) based on Baddeley and Hitch's research (1974) and a continuous performance test (CPT). The session ended with a 5-min recovery period. High HRV and low HRV groups were formed based on a median split of the root mean squared successive differences during baseline. The results showed that the high HRV group showed more correct responses than the low HRV group on the WMT. Furthermore, the high HRV group showed faster mean reaction time (mRT), more correct responses and less error, than the low HRV group on the CPT. Follow-up analysis revealed that this was evident only for components of the CPT where executive functions were involved. The analyses of reactivity showed a suppression of HRV and an increase in HR during presentation of cognitive tasks compared to recovery. This was evident for both groups. The present results indicated that high HRV was associated with better performance on tasks involving executive function.


title: Data partitioning strategies for graph workloads on heterogeneous clusters

abstract: Large scale graph analytics are an important class of problem in the modern data center. However, while data centers are trending towards a large number of heterogeneous processing nodes, graph analytics frameworks still operate under the assumption of uniform compute resources. In this paper, we develop heterogeneity-aware data ingress strategies for graph analytics workloads using the popular PowerGraph framework. We illustrate how simple estimates of relative node computational throughput can guide heterogeneity-aware data partitioning algorithms to provide balanced graph cutting decisions. Our work enhances five online data ingress strategies from a variety of sources to optimize application execution for throughput differences in heterogeneous data centers. The proposed partitioning algorithms improve the runtime of several popular machine learning and data mining applications by as much as a 65% and on average by 32% as compared to the default, balanced partitioning approaches.


title: Teacher Retention and Attrition: A Review of the Literature

abstract: The teaching profession and the essential roles teachers play in building the human capital base of any nation cannot be underestimated. These essential roles are central to basic education, most especially in developing nations. There may however seem to be close substitutes for the roles of education managers in the areas of education delivering apparatus with programmed instructions. Apparently, in emerging economies such as Nigeria, teachers no doubt are still the key managers of knowledge. The main engine of economic growth in any country is through a well-trained and knowledge driven education workforce. Due to the persistent labour turnover in schools and high job dissatisfaction among teachers in Nigeria and other parts of the world, this study seeks to find out those factors that encourage teachers’ attrition. The study adopts an extensive review of literature such as conference papers, journal articles, internet sources, books, newspapers to find out the factors that determine teachers’ attrition and retention in schools particularly in Nigeria. The study concludes that for teachers to be retained in schools, certain management issues as they affect teacher education, must be thoroughly evaluated. The study further recommends that educational managers should be made to go through staff training and development programmes and curricula enhancement. This is necessary and important for teachers to be well equipped for the task ahead, provide adequate remuneration, welfare packages, proper work environment amongst others as may be obtainable in other professions. DOI: 10.5901/mjss.2016.v7n2s1p371


The prevalence of anemia and iron deficiency is more common in breastfed infants than their mothers in Bhaktapur, Nepal


title: Shader Metaprogramming

abstract: Modern graphics accelerators have embedded programmable components in the form of vertex and fragment shading units. Current APIs permit specification of the programs for these components using an assembly-language level interface. Compilers for high-level shading languages are available but these read in an external string specification, which can be inconvenient.It is possible, using standard C++, to define a high-level shading language directly in the API. Such a language can be nearly indistinguishable from a special-purpose shading language, yet permits more direct interaction with the specification of textures and parameters, simplifies implementation, and enables on-the-fly generation, manipulation, and specialization of shader programs. A shading language built into the API also permits the lifting of C++ host language type, modularity, and scoping constructs into the shading language without any additional implementation effort.


title: Design and implementation of a single-phase buck-boost inverter

abstract: In order to simplify the configuration of photovoltaic (PV) grid-connection system, this paper proposes to adopt a buck-boost dc-dc converter, and then develops a single-phase inverter via the connection with an H-bridge unfolding circuit with line-commutated. Depending on the conditions of dc input-voltage and ac output-voltage, the proposed circuit can work functionally as either a step-down or step-up inverter. It is suitable for the applications with wide voltage-variation range. Since only one switch is operated with high-frequency, the switching loss can be significantly reduced to improve the efficiency. Finally, a laboratory prototype with 110 V rms / 60 Hz output voltage is then simulated and implemented accordingly to verify the feasibility of the proposed inverter.


title: BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation

abstract: In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation. However, high annotation efforts and costs are commonly needed to acquire sufficient biomedical training data for DL models. To alleviate the burden of manual annotation, in this paper, we propose a new weakly supervised DL approach for biomedical image segmentation using boxes only annotation. First, we develop a method to combine graph search (GS) and DL to generate fine object masks from box annotation, in which DL uses box annotation to compute a rough segmentation for GS and then GS is applied to locate the optimal object boundaries. During the mask generation process, we carefully utilize information from box annotation to filter out potential errors, and then use the generated masks to train an accurate DL segmentation network. Extensive experiments on gland segmentation in histology images, lymph node segmentation in ultrasound images, and fungus segmentation in electron microscopy images show that our approach attains superior performance over the best known state-of-the-art weakly supervised DL method and is able to achieve (1) nearly the same accuracy compared to fully supervised DL methods with far less annotation effort, (2) significantly better results with similar annotation time, and (3) robust performance in various applications.


title: Defining neuromarketing: practices and professional challenges.

abstract: Neuromarketing has recently generated controversies concerning the involvement of medical professionals, and many key questions remain—ones that have potentially important implications for the field of psychiatry. Conflicting definitions of neuromarketing have been proposed, and little is known about the actual practices of companies, physicians, and scientists involved in its practice. This article reviews the history of neuromarketing and uses an exploratory survey of neuromarketing Web sites to illustrate ethical issues raised by this new field. Neuromarketing, as currently practiced, is heterogeneous, as companies are offering a variety of technologies. Many companies employ academicians and professionals, but few list their clients or fees. Media coverage of neuromarketing appears disproportionately high compared to the paucity of peer-reviewed reports in the field. Companies may be making premature claims about the power of neuroscience to predict consumer behavior. Overall, neuromarketing has impor...


title: Output controllability and optimal output control of state-dependent switched Boolean control networks

abstract: In the present paper, we investigate the output-controllability and optimal output control problems of a state-dependent switched Boolean control network. By using the semi-tensor product, the algebraic form of the system is obtained. Then, output-controllability problems of the system are discussed and some necessary and sufficient conditions are given. Next, the Mayer-type optimal output control issue is considered and an algorithm is provided to find out the control sequence. At last, an example is given to show the effectiveness of the main results.


Routing in Telecommunications Networks With "smart" Ant-Like Agents


title: An automated system for conversion of clinical notes into SNOMED clinical terminology

abstract: The automatic conversion of free text into a medical ontology can allow computational access to important information currently locked within clinical notes and patient reports. This system introduces a new method for automatically identifying medical concepts from the SNOMED Clinical Terminology in free text in near real time. The system presented consists of 3 modules; an Augmented Lexicon, term compositor and negation detector. The Augmented Lexicon indexes the SNOMED-CT terms, the term compositor finds qualification relationships between concepts and the negation detector identifies negative concepts. The system delivers the services through a variety of interfaces including direct programming access and web-based access. It is currently in use in a hospital environment to capture patient data response with SNOMED-CT codes in real time at the point of care. No strict evaluation has been done on the system to date, however preliminary results indicate performance within acceptable time and accuracy limits.


title: Outdoor augmented reality tracking using 3D city models and game engine

abstract: Augmented Reality (AR) refers to the science and technology which enables 3D/2D computer graphics to overlay video frames in real time so as to add contextual information to deepen people's understanding of the real scene. In particular, outdoor AR as an emerging technology opens up new possibilities and applications for Location Based Services (LBS). As with the increasing amount of 3D city models and development of 3D computer graphics technology, insufficient research attention has been put into 3D city model based AR navigation, this paper aims to contribute to outdoor AR tracking research by using 3D city models and a 3D game engine. The camera pose and position are calculated through 2D/3D correspondence between the live video frame and 3D city models. The augmented live video feed is rendered in a 3D game engine while the 3D model runs in the background to facilitate the real time registration of virtual objects into the real scene as well as position determination of the moving camera.


title: Protective Effect of Calendula officinalis L. Flowers Against Monosodium Glutamate Induced Oxidative Stress and Excitotoxic Brain Damage in Rats

abstract: Monosodium glutamate (MSG) is a popular flavour enhancer used in food industries; however, excess MSG is neurotoxic. Oxidative stress is well documented in MSG induced neurotoxicity. The compounds having antioxidant and anti-inflammatory properties reportedly possess beneficial effects against various neurotoxic insults. Calendula officinalis Linn. flower extract (COE) is known for its potent antioxidant and anti-inflammatory activities. Hence, this present study has been designed to evaluate the neuroprotective effect of COE on MSG-induced neurotoxicity in rats. Adult Wistar rats were administered systemically for 7 days with MSG and after one h of MSG injection, rats were treated with COE (100 and 200 mg/kg) orally. At the end the treatment period, animals were assessed for locomotor activity and were sacrificed; brains were isolated for estimation of LPO, GSH, CAT, TT, GST, Nitrite and histopathological studies. MSG caused a significant alteration in animal behavior, oxidative defense (raised levels of LPO, nitrite concentration, depletion of antioxidant levels) and hippocampal neuronal histology. Treatment with COE significantly attenuated behavioral alterations, oxidative stress, and hippocampal damage in MSG-treated animals. Hence, this study demonstrates that COE protects against MSG-induced neurotoxicity in rats. The antioxidant and anti-inflammatory properties of COE may be responsible for its observed neuroprotective action.


title: A Novel Resource Scheduling Approach in Container Based Clouds

abstract: Cloud computing is a new paradigm to deliver computing resources to customers in a pay-as-you-go model. In this paper, the recently emerged container-based virtualization technology is adopted for building the infrastructure of a cloud data center. Cloud providers are concerned with the resource usage in a multi-type resource sharing environment while cloud customers desire higher quality of services. According to this, we propose a resource scheduling approach for the container virtualized cloud environments to reduce response time of customers' jobs and improve providers' resource utilization rate. The stable matching theory is applied to generate an optimal mapping from containers to physical servers. Simulations are implemented to evaluate our resource scheduling approach. The results show that our approach achieves a better performance for customers and maximal profits for cloud providers by improving the resource utilization.


title: Security assessment framework for IoT service

abstract: What are the critical requirements to be considered for the security measures in Internet of Things (IoT) services? Further, how should those security resources be allocated? To provide valuable insight into these questions, this paper introduces a security assessment framework for the IoT service environment from an architectural perspective. Our proposed framework integrates fuzzy DEMATEL and fuzzy ANP to reflect dependence and feedback interrelations among security criteria and, ultimately, to weigh and prioritize them. The results, gleaned from the judgments of 38 security experts, revealed that security design should put more importance on the service layer, especially to ensure availability and trust. We believe that these results will contribute to the provision of more secure and reliable IoT services.


title: A novel approach to data deduplication over the engineering-oriented cloud systems

abstract: This paper presents a duplication-less storage system over the engineering-oriented cloud computing platforms. Our deduplication storage system, which manages data and duplication over the cloud system, consists of two major components, a front-end deduplication application and a mass storage system as back-end. Hadoop distributed file system HDFS is a common distribution file system on the cloud, which is used with Hadoop database HBase. We use HDFS to build up a mass storage system and employ HBase to build up a fast indexing system. With a deduplication application, a scalable and parallel deduplicated cloud storage system can be effectively built up. We further use VMware to generate a simulated cloud environment. The simulation results demonstrate that our deduplication storage system is sufficiently accurate and efficient for distributed and cooperative data intensive engineering applications.


title: Union simulation on lower limbs rehabilitation robot based on MATLAB and ADAMS

abstract: For people who suffers the lower limbs motor dysfunction, using lower limbs exoskeleton rehabilitation devices is a very effective method to restore the motor ability of lower limbs. However, it works only if the device has a suitable mechanical structure and appropriate design parameters for the specific individual. To get this work completed, we carry out the modeling and simulation of a new proposed lower limbs rehabilitation robot with joint help of two kinds of software, i.e. the MATLAB and the ADAMS. Firstly, the three-dimensional model of the robot is created with the SolidWorks software. After importing the model into ADAMS, some essential kinematic pairs are added into the movable joints. Meanwhile, the control system model is established using the MATLAB/SIMULINK software. Then, the union simulation of the lower limbs rehabilitation robot is carried out by using the interface modular ADAMS/CONTROL. The results show that the proposed rehabilitation robot has a good kinematic performance, which means that the device can be used in the actual rehabilitation process. Besides, the results also indicate that the control method is of good trajectory tracking ability. These simulated results will be significant for the development of physical prototype of lower limbs rehabilitation robot.


title: Real Time DC Motor Speed Control using PIDController in LabVIEW

abstract: The proportional integral derivative (PID) controller is the most common form of feedback used in the control systems. It can be used for various Industrial applications. One of the applications used here is to control the speed of the DC motor. Controlling the speed of a DC motors is very important as any small change can lead to instability of the closed loop system. The aim of this paper is to show how DC motor can be controlled by using a PID controller in LabVIEW. DC Motor will be interfaced with LabVIEW using an Arduino Uno board. Arduino Uno board plays the role of low cost data acquisition board. The speed of the DC motor will be set by creating a Graphic User Interface (GUI) for PID Controller in LabVIEW. LabVIEW will in turn pass this speed to the DC motor using a PWM pins on the Arduino Uno board. DC motor will move with the speed set by the user in LabVIEW. The speed of the dc motor will be sensed by using the tachometer. Tachometer is a sensor which measures the revolutions. From tachometer, the output is sent back to the PID Controller in LabVIEW via Arduino board. PID Controller compares the actual speed of the DC motor with the set speed. If its speed is not same, PID Controller will try to minimize the error and bring the motor to the set point value.


title: Steady-state acoustic scattering by cracked oil pipeline

abstract: The exact solution of problem for plane stationary sound wave scattering from a cylindrical shell weakened by long linear slit is found. The shell is submerged in ideal fluid and filled by a viscous fluid. The vibration of elastic thin shell is described by the Kirchhoff-Love theory. The numerical analysis of form-function versus the dimensionless frequency or slit location is carried out. The influence of shell geometrical defect on diffraction process is studied.


title: Interactive evolution for the procedural generation of tracks in a high-end racing game

abstract: We present a framework for the procedural generation of tracks for a high-end car racing game (TORCS) using interactive evolution. The framework maintains multiple populations and allow users to work both on their own population (in single-user mode) or to collaborate with other users on a shared population. Our architecture comprises a web frontend and an evolutionary backend. The former manages the interaction with users (e.g., logs registered and anonymous users, collects evaluations, provides access to all the evolved populations) and maintains the database server that stores all the present/past populations. The latter runs all the tasks related to evolution (selection, recombination and mutation) and all the tasks related to the target racing game (e.g., the track generation). We performed two sets of experiments involving five human subjects to evolve racing tracks alone (in a single-user mode) or cooperatively. Our preliminary results on five human subjects show that, in all the experiments, there is an increase of users' satisfaction as the evolution proceeds. Users stated that they perceived improvements in the quality of the individuals between subsequent populations and that, at the end, the process produced interesting tracks.


title: What should we do? A structured review of SCADA system cyber security standards

abstract: SCADA (Supervisory Control and Data Acquisition) system is the core component of industrial and critical infrastructure, and cyber security of SCADA system has become the key consideration of system managers and engineers. Therefore, a great many of standards, guidelines and best practices have been developed to give reference of SCADA system cyber security, hoping to provide some instructions for system managers. Unfortunately, there is little consensus on what to do. Whats worse, it is difficult to choose the right one for a particular industrial scene. These standards are usually long and complex texts, whose reading and understanding often takes much time and effort. We provide a comprehensive and structured review of SCADA cyber security standards, guidelines and best practices with three dimensions: release time, geographic location and intended audience. Finally, we use the theory of defense-in-depth as a reference to evaluate these standards. It is concluded that no standard performs better than others on all the criteria and that we should integrate different standards to apply them to a specific industrial scene.


title: Heart monitoring systems—A review

abstract: Cardiography Electrocardiography Phonocardiography Photoplethysmography Seismocardiography abstract To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.


External-memory graph algorithms


A Review of Online Rogue Access Point Detection


title: A current-source inverter fed induction motor drive system with reduced losses

abstract: Standard low- and medium-power induction motor drives are based on the pulse width modulation (PWM) voltage-source inverter (VSI) fed from a diode rectifier. The dual topology, based on the current-source inverter/rectifier structure is used in medium- and high-power applications. This paper analyzes the existing motor drives based on current-source topologies and proposes a control strategy that addresses some of the drawbacks of this approach compared to the voltage-source approach. The proposed strategy features the following: (1) an online operated PWM CSI, using instantaneous output capacitor voltage control based on space-vector modulation; and (2) an additional inverter modulation index control loop, ensuring constant inverter modulation index and minimum DC-link current operation. The resulting additional advantages include the following: (1) fixed and reduced motor voltage distortion; (2) minimized DC-bus inductor losses; (3) minimized switch conduction losses; and (4) elimination of motor circuit resonances. Experimental results based on a DSP implementation are given.


title: Patterns of information seeking on the Web: A qualitative Study of domain expertise and Web expertise." IT& Society 1(3): 64

abstract: This research examines the pattern of Web information seeking in four groups of nurses with different combinations of domain expertise and Web expertise. Protocols were gathered as the nurses carried out information-seeking tasks in the domain of osteoporosis. Domain and Web novices searched breadth-first and did little or no evaluation of the results. Domain expert/Web novices also searched breadth-first but evaluated information more thoroughly using osteoporosis knowledge. Domain novice/Web experts searched in a mixed, breadth-first/depth-first pattern and attempted to evaluate information using general criteria. Domain expert/Web experts carried out depth-first searches, following deep trails of information and evaluated information based on the most varied and sophisticated criteria. The results suggest that there are distinct differences in searching patterns related to expertise. Implications of these findings and suggestions for future research are provided.


title: RoBA Multiplier: A Rounding-Based Approximate Multiplier for High-Speed yet Energy-Efficient Digital Signal Processing

abstract: In this paper, we propose an approximate multiplier that is high speed yet energy efficient. The approach is to round the operands to the nearest exponent of two. This way the computational intensive part of the multiplication is omitted improving speed and energy consumption at the price of a small error. The proposed approach is applicable to both signed and unsigned multiplications. We propose three hardware implementations of the approximate multiplier that includes one for the unsigned and two for the signed operations. The efficiency of the proposed multiplier is evaluated by comparing its performance with those of some approximate and accurate multipliers using different design parameters. In addition, the efficacy of the proposed approximate multiplier is studied in two image processing applications, i.e., image sharpening and smoothing.


title: Behavioral Fingerprinting of IoT Devices

abstract: The Internet-of-Things (IoT) has brought in new challenges in device identification --what the device is, and authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. Almost always an artificially created identity is softly associated with the device. We believe that device fingerprinting can be used to solve both these problems effectively. In this work, we present a methodology to perform IoT device behavioral fingerprinting that can be employed to undertake strong device identification. A device behavior is approximated using features extracted from the network traffic of the device. These features are used to train a machine learning model that can be used to detect similar device-types. We validate our approach using five-fold cross validation; we report a identification rate of 93-100 and a mean accuracy of 99%, across all our experiments. Furthermore, we show preliminary results for fingerprinting device categories, i.e., identifying different devices having similar functionality.


title: Face Presentation Attack Detection in Learned Color-liked Space

abstract: Face presentation attack detection (PAD) has become a thorny problem for biometric systems and numerous countermeasures have been proposed to address it. However, majority of them directly extract feature descriptors and distinguish fake faces from the real ones in existing color spaces (e.g. RGB, HSV and YCbCr). Unfortunately, it is unknown for us which color space is the best or how to combine different spaces together. To make matters worse, the real and fake faces are overlapped in existing color spaces. So, in this paper, a learned distinguishable color-liked space is generated to deal with the problem of face PAD. More specifically, we present an end-to-end deep learning network that can map existing color spaces to a new learned color-liked space. Inspired by the generator of generative adversarial network (GAN), the proposed network consists of a space generator and a feature extractor. When training the color-liked space, a new triplet combination mechanism of points-to-center is explored to maximize interclass distance and minimize intraclass distance, and also keep a safe margin between the real and presented fake faces. Extensive experiments on two standard face PAD databases, i.e., Relay-Attack and OULU-NPU, indicate that our proposed color-liked space analysis based countermeasure significantly outperforms the state-of-the-art methods and show excellent generalization capability.


title: Understanding dynamic behavior of mm-wave CML divider with injection-locking concept

abstract: An analytical framework has been developed to describe the locking behavior of millimeter (mm) wave current-mode logic (CML) frequency divider. Unlike traditional analysis based on RC delay, the proposed model is established with injection-locking concept from analog perspective. Both analytical formulas and graphic interpretation are provided for design insights. The model has been validated by exhaustive simulations and important guidelines have been concluded for circuit design.


title: Issues in Implementing IT Governance in Small and Medium Enterprises

abstract: Nowadays, almost all the enterprises in the world fall in Small Medium Enterprises (SMEs) category even though SMEs has different definitions in different countries. At the same time, there is no exemplar standard and /or framework for information technology (IT) governance for SMEs. This paper presents the main issues in implementing IT governance in SMEs. Firstly, it explains the definition of SMEs and their characteristics, secondly it discusses IT governance definition and its framework. Finally, some issues and approaches for ITG implementation in SMEs are described, and end with conclusion and future work.


title: Sentiment Analysis of Twitter Data: A Survey of Techniques

abstract: With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide a research on twitter data streams.General challenges and applications of Sentiment Analysis on Twitter are also discussed in this paper.


What is Sexual Orientation and Do Women Have One


title: Pose normalization for robust face recognition based on statistical affine transformation

abstract: A framework for pose-invariant face recognition using the pose alignment method is described in this paper. The main idea is to normalize the face view in depth to frontal view as the input of face recognition framework. Concretely, an inputted face image is first normalized using the irises information, and then the pose subspace algorithm is employed to perform the pose estimation. To model the pose-invariance, the face region is divided into three rectangles with different mapping parameters in this pose alignment algorithm. So the affine transformation parameters associated with the different poses can be used to align the input pose image to frontal view. To evaluate this algorithm objectively, the views after the pose alignment are incorporated into the frontal face recognition system. Experimental results show that it has the better performance and it increases the recognition rate statistically by 17.75% under the pose that rotated within 30 degree.


title: Heavy metal removal mechanism of acid mine drainage in wetlands: A critical review

abstract: Acid mine drainage (AMD) is one of the most significant environmental challenges facing the mining industry worldwide. Water infiltrating through the metal sulphide minerals, effluents of mineral processing plants and seepage from tailing dams becomes acidic and this acidic nature of the solution allows the metals to be transported in their most soluble form. The conventional treatment technologies used in the treatment of acid mine drainage are expensive both in terms of operating and capital costs. One of the methods of achieving compliance using passive treatment systems at low cost, producing treated water pollution free, and fostering a community responsibility for acid mine water treatment involves the use of wetland treatment system. These wetlands absorb and bind heavy metals and make them slowly concentrated in the sedimentary deposits to become part of the geological cycle. In this paper a critical review of the heavy metal removal mechanism involving various physical, chemical and biological processes, which govern wetland performance, have been made. This information is important for the siting and use of wetlands for remediation of heavy metals.


title: A Theoretical Integration of User Satisfaction and Technology Acceptance

abstract: In general, perceptions of information systems (IS) success have been investigated within two primary research streams--the user satisfaction literature and the technology acceptance literature. These two approaches have been developed in parallel and have not been reconciled or integrated. This paper develops an integrated research model that distinguishes beliefs and attitudes about the system (i.e., object-based beliefs and attitudes) from beliefs and attitudes about using the system (i.e., behavioral beliefs and attitudes) to build the theoretical logic that links the user satisfaction and technology acceptance literature. The model is then tested using a sample of 465 users from seven different organizations who completed a survey regarding their use of data warehousing software. The proposed model was supported, providing preliminary evidence that the two perspectives can and should be integrated. The integrated model helps build the bridge from design and implementation decisions to system characteristics (a core strength of the user satisfaction literature) to the prediction of usage (a core strength of the technology acceptance literature).


title: Implementation Quality and Positive Experiences in After-School Programs

abstract: Data collected during an evaluation of a multi-site trial of an enhanced after-school program were used to relate quality of program implementation to student experiences after school. The enhanced after-school program incorporated a drug use and violence prevention component that was shown to be effective in previous research. Building on Durlak and Dupre’s (Am J Community Psychol 41:327–350, 2008) dimensions of implementation, we assessed the level of dosage, quality of management and climate, participant responsiveness, and staffing quality achieved at the five program sites. We evaluated how these characteristics co-varied with self-reported positive experiences after-school. The study illustrates how multiple dimensions of program implementation can be measured, and shows that some but not all dimensions of implementation are related to the quality of student after-school experiences. Measures of quality of management and climate, participant responsiveness, and staffing stability were most clearly associated with youth experiences. The importance of measuring multiple dimensions of program implementation in intervention research is discussed.


title: Experimental evaluation of four-phase floating interleaved boost converter design and control for fuel cell applications

abstract: Power electronic converters are essential part of hybrid fuel cell automotive application systems. The converter needs to provide high-voltage ratio for a wide range of input voltage. In addition, the converter should have high efficiency for a wide range of duty cycle control range. A four-phase floating interleaved boost converter (FIBC) is analysed and a small-signal AC model using an averaged pulse-width modulation (PWM) switch technique is used for supporting the feedback controller and aiding a frequency response design. The small-signal AC model as well as the current controller are validated by simulation and evaluated by experimental results. The proposed converter has competitive device ratings, the total inductance volume and weight is decreased, current ripple is minimised and converter efficiency and reliability are improved. Proof of concept of the proposed topology is demonstrated through an experimental prototype.


title: Light-Duty Alternative Fuel Vehicles: Federal Test Procedure Emissions Results

abstract: In support of the U.S. Department of Energy's development and deployment of alternative fuels for environmental and national security reasons, NREL has managed a series of light-duty vehicle emissions tests on alternative fuel vehicles (AFVs). The purpose of this report is to give a detailed evaluation of the final emissions test results on vehicles tested on methanol, ethanol, and compressed natural gas.


title: Structural and degradation characteristics of an innovative porous PLGA/TCP scaffold incorporated with bioactive molecular icaritin

abstract: Phytomolecules may chemically bind to scaffold materials for medical applications. The present study used an osteoconductive porous poly(l-lactide-co-glycolide)/tricalcium phosphate (PLGA/TCP) to incorporate an exogenous phytoestrogenic molecule icaritin to form a PLGA/TCP/icaritin composite scaffold material with potential slow release of icaritin during scaffold degradation. Accordingly, the present study was designed to investigate its in vitro degradation characteristics and the release pattern of icaritin at three different doses (74 mg, 7.4 mg and 0.74 mg per 100 g PLGA/TCP, i.e. in the PLGA/TCP/icaritin-H, -M and -L groups, respectively). A PLGA/TCP/icaritin porous composite scaffold was fabricated using a computer-controlled printing machine. The PLGA/TCP/icaritin scaffolds were incubated in saline at 37 °C for 12 weeks and the pure PLGA/TCP scaffold served as a control. During the 12 weeks in vitro degradation, the scaffolds in all four groups showed changes, including a decrease in weight, volume and pore size of the composite scaffold, while there was a decrease in acidity and an increase in Ca and lactic acid concentrations in the degradation medium, especially after 7 weeks. The rate of degradation was explained by the relationship with the content of icaritin incorporated into the scaffolds. The higher the icaritin content in the scaffolds, the slower the degradation could be observed during 12 weeks. After 12 weeks, the SEM showed that the surface of the PLGA/TCP and PLGA/TCP/icaritin-L groups was relatively smooth with a gradual decrease in number and size of the micropores, while the porous morphology on the surface of the PLGA/TCP/icaritin-M and PLGA/TCP/icaritin-H groups was partly maintained, accompanied by a decrease in phosphate (P) and calcium (Ca) contents at the surface. Though the mechanical property of the PLGA/TCP/icaritin scaffold decreased after degradation, its porous structure was maintained, which was essential for cell migration and ingrowth of newly regenerated tissues in vivo. The controlled release of icaritin from the composite scaffold reached about 70% of the incorporated icaritin into the degradation medium after 12 weeks. The above findings suggested that the structural and degradation properties of the porous composite PLGA/TCP/icaritin scaffold were dependent on icaritin concentrations. This innovative composite porous scaffold material developed in the present study may be used as a good scaffold material for enhancing bone repair, especially at high concentrations of icaritin. In vivo confirmation is, however, needed to substantiate our in vitro findings.


title: ISCEV standard for clinical visual evoked potentials: (2016 update)

abstract: Visual evoked potentials (VEPs) can provide important diagnostic information regarding the functional integrity of the visual system. This document updates the ISCEV standard for clinical VEP testing and supersedes the 2009 standard. The main changes in this revision are the acknowledgment that pattern stimuli can be produced using a variety of technologies with an emphasis on the need for manufacturers to ensure that there is no luminance change during pattern reversal or pattern onset/offset. The document is also edited to bring the VEP standard into closer harmony with other ISCEV standards. The ISCEV standard VEP is based on a subset of stimulus and recording conditions that provide core clinical information and can be performed by most clinical electrophysiology laboratories throughout the world. These are: (1) Pattern-reversal VEPs elicited by checkerboard stimuli with large 1 degree (°) and small 0.25° checks. (2) Pattern onset/offset VEPs elicited by checkerboard stimuli with large 1° and small 0.25° checks. (3) Flash VEPs elicited by a flash (brief luminance increment) which subtends a visual field of at least 20°. The ISCEV standard VEP protocols are defined for a single recording channel with a midline occipital active electrode. These protocols are intended for assessment of the eye and/or optic nerves anterior to the optic chiasm. Extended, multi-channel protocols are required to evaluate postchiasmal lesions.


title: Practical Bayesian Optimization of Machine Learning Algorithms

abstract: The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience, rules of thumb, or sometimes brute-force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.


The Effect of Ground Colour on Memory Performance


title: Decoupled Greedy Learning of CNNs

abstract: A commonly cited inefficiency of neural network training by back-propagation is the update locking problem: each layer must wait for the signal to propagate through the network before updating. We consider and analyze a training procedure, Decoupled Greedy Learning (DGL), that addresses this problem more effectively and at scales beyond those of previous solutions. It is based on a greedy relaxation of the joint training objective, recently shown to be effective in the context of Convolutional Neural Networks (CNNs) on large-scale image classification. We consider an optimization of this objective that permits us to decouple the layer training, allowing for layers or modules in networks to be trained with a potentially linear parallelization in layers. We show theoretically and empirically that this approach converges. In addition, we empirically find that it can lead to better generalization than sequential greedy optimization and even standard end-to-end back-propagation. We show that an extension of this approach to asynchronous settings, where modules can operate with large communication delays, is possible with the use of a replay buffer. We demonstrate the effectiveness of DGL on the CIFAR-10 datasets against alternatives and on the large-scale ImageNet dataset, where we are able to effectively train VGG and ResNet-152 models.


title: Value-added food: single cell protein.

abstract: The alarming rate of population growth has increased the demand for food production in third-world countries leading to a yawning gap in demand and supply. This has led to an increase in the number of hungry and chronically malnourished people. This situation has created a demand for the formulation of innovative and alternative proteinaceous food sources. Single cell protein (SCP) production is a major step in this direction. SCP is the protein extracted from cultivated microbial biomass. It can be used for protein supplementation of a staple diet by replacing costly conventional sources like soymeal and fishmeal to alleviate the problem of protein scarcity. Moreover, bioconversion of agricultural and industrial wastes to protein-rich food and fodder stocks has an additional benefit of making the final product cheaper. This would also offset the negative cost value of wastes used as substrate to yield SCP. Further, it would make food production less dependent upon land and relieve the pressure on agriculture. This article reviews diversified aspects of SCP as an alternative protein-supplementing source. Various potential strains and substrates that could be utilized for SCP production are described. Nutritive value and removal of nucleic acids and toxins from SCP as a protein-supplementing source are discussed. New processes need to be exploited to improve yield. In that direction the solid state fermentation (SSF) method and its advantages for SCP production are highlighted.


title: Using Program Analysis to Improve Database Applications

abstract: Applications that interact with database management systems (DBMSs) are ubiquitous. Such database applications are usually hosted on an application server and perform many small accesses over the network to a DBMS hosted on the database server to retrieve data for processing. For decades, the database and programming systems research communities have worked on optimizing such applications from different perspectives: database researchers have built highly efficient DBMSs, and programming systems researchers have developed specialized compilers and runtime systems for hosting applications. However, there has been relatively little work that optimizes database applications by considering these specialized systems in combination and looking for optimization opportunities that span across them. In this article, we highlight three projects that optimize database applications by looking at both the programming system and the DBMS in a holistic manner. By carefully revisiting the interface between the DBMS and the application, and by applying a mix of declarative database optimization and modern program analysis techniques, we show that a speedup of multiple orders of magnitude is possible in real-world applications.


title: Silesian Deception Database: Presentation and Analysis

abstract: Numerous studies have examined behavioral cues to deception with low temporal video resolution, which does not enable to track the facial movements dynamics. Another problem is the lack of publicly available video databases that allow to develop computer vision algorithms dedicated to the automatic deception recognition. In this paper, we describe a novel publicly available database that consists of 101 video recordings acquired with the use of a high speed camera at 100 fps in a well-controlled laboratory environment and proper illumination. Within this database, over 1.1 million frames were coded providing the ground truth for the potential cues of deception displayed on the subject's face during telling the truth and lying. Some preliminary psychological implications are also presented.


title: Learning the easy things first: Self-paced visual category discovery

abstract: Objects vary in their visual complexity, yet existing discovery methods perform “batch” clustering, paying equal attention to all instances simultaneously — regardless of the strength of their appearance or context cues. We propose a self-paced approach that instead focuses on the easiest instances first, and progressively expands its repertoire to include more complex objects. Easier regions are defined as those with both high likelihood of generic objectness and high familiarity of surrounding objects. At each cycle of the discovery process, we re-estimate the easiness of each subwindow in the pool of unlabeled images, and then retrieve a single prominent cluster from among the easiest instances. Critically, as the system gradually accumulates models, each new (more difficult) discovery benefits from the context provided by earlier discoveries. Our experiments demonstrate the clear advantages of self-paced discovery relative to conventional batch approaches, including both more accurate summarization as well as stronger predictive models for novel data.


title: Sensing-Fi: Wi-Fi CSI and accelerometer fusion system for fall detection

abstract: Falling is a major cause of death among elders. To detect falls, numerous approaches have been proposed in the past decade, including computer-vision based image processing, wearable sensors, and acoustic signal processing, etc. Though the recent advancement in infra-red LED, depth camera, MEMS (Micro-Electro-Mechanical Systems) sensors and machine learning algorithms may have enlarged the application scope, the privacy intrusion and lack of convenience still remain to be open issues which prevent these systems from large deployment. A novel Sensing-Fi system described in this paper aims to overcome such deficiencies. The system design is comprised of harnessing Wi-Fi Channel State Information (CSI) coupled with ground-mounted accelerometer to detect floor vibration, hence the system is completely passive and noninvasive, i.e., the user is not required to wear the technology contrary to wearable accelerometers. In addition, contrary to the existing standalone Wi-Fi CSI fall detection systems, it overcomes the constraint by which only one user can be present in the room. The system has shown promising results with 95% accuracy.