title: Continuous monitoring of stress on smartphone using heart rate variability

abstract: Continuous monitoring of an individual's stress levels is essential to manage stress and mental state in an effective way. With increasing ubiquity of wearable heart rate monitors and their unobtrusiveness, HRV (Heart rate variability) derived from heart rate signals has emerged as one of the most relevant parameters for continuous monitoring of stress. In the present work, we have made an attempt to address the challenges about distinguishing between stressed and non-stressed state of a person based on just one minute of IBI (Inter Beat Interval) records with good accuracy. Such ultra-short term analysis of HRV is particularly advantageous towards capturing very short term fluctuations in mental stress levels and enhanced scope for frequent monitoring. We have analyzed various time domain, frequency domain and nonlinear HRV features to narrow down to a most influential set of features for accurate classification between stressed and non-stressed state. We have identified RMSSD (root mean square of successive differences) of IBI series to be the most direct indicator of stressed state. We also provide a continuous stress score which, when used in continuous monitoring scenario, provides the user with adequate details about his/her stress levels. This helps the user to understand stress patterns across a day in a better way and to take appropriate measures to manage stressful situations. We have developed and deployed a system, based on above concept, on smartphone as an android application for real-time stress monitoring.


title: Generalized Feedback Shift Register Pseudorandom Number Algorithm

abstract: The generalized feedback shift register pseudorandom number algorithm has several advantages over all other pseudorandom number generators. These advantages are: (1) it produces multidimensional pseudorandom numbers; (2) it has an arbitrarily long period independent of the word size of the computer on which it is implemented; (3) it is faster than other pseudorandom number generators; (4) the “same” floating-point pseudorandom number sequence is obtained on any machine, that is, the high order mantissa bits of each pseudorandom number agree on all machines— examples are given for IBM 360, Sperry-Rand-Univac 1108, Control Data 6000, and Hewlett-Packard 2100 series computers; (5) it can be coded in compiler languages (it is portable); (6) the algorithm is easily implemented in microcode and has been programmed for an Interdata computer.


title: Dynamic control and dead-time compensation method of an isolated dual-active-bridge DC-DC converter

abstract: This paper presents dynamic control and a dead-time compensation method of an isolated dual-active-bridge (DAB) dc-dc converter. Conventional phase-shift control methods for the DAB converter may cause dc offsets in both inductor current and transformer magnetic flux density in transient states. The dynamic control method in this paper independently controls the diagonal switches in each H-bridge converter to modify the duty ratios in transient states. This method enables both inductor current and transformer magnetic flux density to be settled within a half switching period without any dc offsets. Moreover, the phase-shift error caused by the dead time is analyzed in terms of the switching angle. This analysis reveals that the dead time causes the switching-angle error in only one of the two H-bridge converters and may cause dc offsets. A new dead-time compensation method based on this analysis is proposed to eliminate the dc offsets and to achieve a good dynamic response. A 5-kW experimental system verifies the validity of the proposed control method.


Low-Cost IMU Implementation via Sensor Fusion Algorithms in the Arduino Environment


title: Towards Integrating TOSCA and ITIL

abstract: The integration of low level management functionalities provided by TOSCA and high level processes as defined by ITIL may provide significant improvement opportunities to the application provider as on both levels workflow technology can be employed. In this paper, we present Key Performance Indicator Analysis Plans as first idea how both approaches can be integrated.


title: Image Segmentation Techniques: A Survey

abstract: Image segmentation is a mechanism used to divide an image into multiple segments. It will make image smooth and easy to evaluate. Segmentation process also helps to find region of interest in a particular image. The main goal is to make image more simple and meaningful. Existing segmentation techniques can't satisfy all type of images. This survey addressed various image segmentation techniques, evaluates them and presents the issues related to those techniques. 


title: Persuasive Systems Design: Key Issues, Process Model, and System Features

abstract: A growing number of information technology systems and services are being developed to change users’ attitudes or behavior or both. Despite the fact that attitudinal theories from social psychology have been quite extensively applied to the study of user intentions and behavior, these theories have been developed for predicting user acceptance of the information technology rather than for providing systematic analysis and design methods for developing persuasive software solutions. This article is conceptual and theory-creating by its nature, suggesting a framework for Persuasive Systems Design (PSD). It discusses the process of designing and evaluating persuasive systems and describes what kind of content and software functionality may be found in the final product. It also highlights seven underlying postulates behind persuasive systems and ways to analyze the persuasion context (the intent, the event, and the strategy). The article further lists 28 design principles for persuasive system content and functionality, describing example software requirements and implementations. Some of the design principles are novel. Moreover, a new categorization of these principles is proposed, consisting of the primary task, dialogue, system credibility, and social support categories.


Knowledge Representation in pAninI Framework Using Neural Network Model


title: The Power of Good: A Leader's Personal Power as a Mediator of the Ethical Leadership-Follower Outcomes Link

abstract: The study’s goal was to examine the socially responsible power use in the context of ethical leadership as an explanatory mechanism of the ethical leadership-follower outcomes -link. Drawing on the attachment theory (Bowlby, 1969/1982), we explored a power-based process model, which assumes that a leader’s personal power is an intervening variable in the relationship between ethical leadership and follower outcomes, while incorporating the moderating role of followers’ moral identity in this transformation process. The results of a two-wave field study (N = 235) that surveyed employees and a scenario experiment (N = 169) fully supported the proposed (moderated) mediation models, as personal power mediated the positive relationship between ethical leadership and a broad range of tested follower outcomes (i.e., leader effectiveness, follower extra effort, organizational commitment, job satisfaction, and work engagement), as well as the interactive effects of ethical leadership and follower moral identity on these follower outcomes. Theoretical and practical implications are discussed.


CNN Based Query by Example Spoken Term Detection


title: Gradient Reversal Against Discrimination

abstract: No methods currently exist for making arbitrary neural networks fair. In this work we introduce GRAD, a new and simplified method to producing fair neural networks that can be used for auto-encoding fair representations or directly with predictive networks. It is easy to implement and add to existing architectures, has only one (insensitive) hyper-parameter, and provides improved individual and group fairness. We use the flexibility of GRAD to demonstrate multi-attribute protection.


title: Learning Multiple Tasks with Multilinear Relationship Networks

abstract: Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and improve feature transferability in the multiple task-specific layers. This paper presents Multilinear Relationship Networks (MRN) that discover the task relationships based on novel tensor normal priors over parameter tensors of multiple task-specific layers in deep convolutional networks. By jointly learning transferable features and multilinear relationships of tasks and features, MRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Experiments show that MRN yields state-of-the-art results on three multi-task learning datasets.


title: Global-, Regional-, and Country-Level Economic Impacts of Dental Diseases in 2015

abstract: Up-to-date information about the economic impact of dental diseases is essential for health care decision makers when seeking to make rational use of available resources. The purpose of this study was to provide up-to-date estimates for dental expenditures (direct costs) and productivity losses (indirect costs) due to dental diseases on the global, regional, and country level. Direct costs of dental diseases were estimated using a previously established systematic approach; indirect costs were estimated using an approach developed by the World Health Organization Commission on Macroeconomics and Health and factoring in 2015 values for gross domestic product and disability-adjusted life years from the Global Burden of Disease Study. The estimated direct costs of dental diseases amounted to $356.80 billion and indirect costs were estimated at $187.61 billion, totaling worldwide costs due to dental diseases of $544.41 billion in 2015. After adjustment for purchasing power parity, the highest levels of per capita dental expenditures were found for High-Income North America, Australasia, Western Europe, High-Income Asia Pacific, and East Asia; the highest levels of per capita productivity losses were found for Western Europe, Australasia, High-Income North America, High-Income Asia Pacific, and Central Europe. Severe tooth loss was found to imply 67% of global productivity losses due to dental diseases, followed by severe periodontitis (21%) and untreated caries (12%). From an economic perspective, improvements in population oral health may be highly beneficial and could contribute to further increases in people's well-being given available resources.


title: Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

abstract: Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.


title: Microstructure and magnetic properties of oxidized titanium nitride thin films in situ grown by pulsed laser deposition

abstract: Different oxidation states of titanium nitride thin films, including pure TiN(h 0 0), TiN1−xOx(h 0 0), Ti2O3(0 0 l) and pure anatase TiO2(0 0 l), were prepared by pulsed laser deposition with various oxygen pressures using a TiN target. Elaborative evolutions of the crystal and electronic structures of the obtained films were examined systematically by x-ray diffraction and x-ray absorption spectroscopy. We found that the Ti2O3(0 0 l) film, which was prepared at oxygen pressures , exhibited the maximum room temperature ferromagnetism (RTFM) behaviour. The bound magnetic polaron model is used to clarify the origin of RTFM in these films.


title: Riemannian metrics for neural networks II: recurrent networks and learning symbolic data sequences

abstract: Recurrent neural networks are powerful models for sequential data, able ::: to represent complex dependencies in the sequence that simpler models such as hidden ::: Markov models cannot handle. Yet they are notoriously hard to train. ::: ::: Here we introduce a training procedure using a gradient ascent in a ::: Riemannian metric: this produces an ::: algorithm independent from design choices such as the ::: encoding of parameters and unit activities. This metric gradient ascent is ::: designed to have an algorithmic cost close to backpropagation through ::: time for sparsely connected networks. ::: ::: We use this procedure on \emph{gated leaky neural networks} (GLNNs), ::: a variant of recurrent neural networks with an architecture ::: inspired by finite automata and an evolution equation inspired by ::: continuous-time networks. ::: ::: GLNNs trained with a Riemannian gradient are demonstrated to effectively capture a variety of ::: structures in synthetic problems: basic block nesting ::: as in context-free grammars (an important feature of natural languages, ::: but difficult to learn), intersections of multiple independent ::: Markov-type relations, or long-distance relationships such as the ::: distant-XOR problem. ::: ::: This method does not require adjusting the network structure or initial ::: parameters: the network used is a sparse random graph and the ::: initialization is identical for all problems considered.


title: Development of an intelligent agent-based AGV controller for a flexible manufacturing system

abstract: Automated guided vehicles (AGVs) are the most flexible means to transport materials among workstations of a flexible manufacturing system. Complex issues associated with the design of AGV control of these systems are conflict-free shortest path, minimum time motion planning and deadlock avoidance. This research presents an intelligent agent-based framework to overcome the inefficacies associated with the aforementioned issues. Proposed approach describes the operational control of AGVs by integrating different activities such as path generation, journey time enumeration, collision and deadlock identification, waiting node location and its time estimation, and decision making on the selection of the conflict-free shortest feasible path. It represents efficient algorithms and rules associated with each agent for finding the conflict-free minimum time motion planning of AGVs, which are needed to navigate unidirectional and bidirectional flow path network. A collaborative architecture of AGV agent and its different modules are also presented. Three complex experimental scenarios are simulated to test the robustness of the proposed approach. It is shown that the proposed agent-based controller is capable of generating optimal, collision- and deadlock-free path with less computational efforts.


title: INFORMATION SECURITY CHALLENGES OF SOCIAL MEDIA FOR COMPANIES

abstract: For companies and their employees, social media allows new ways to communicate with customers and colleagues. Vast amounts of information are being exchanged in social media. Information is a highly valuable asset, and therefore questions concerning information security become more and more important. Companies are becoming increasingly worried about information security in social media, but so far, this issue has not been studied. The present research closes this gap by studying the information security challenges social media represents for organizations. The research was conducted as a qualitative multiple case study for which information security managers from eleven public and private companies in one European country were interviewed. The study has three main findings. First, challenges arising from employees’ actions or unawareness in social media (especially reputation damage) seem to represent bigger threats to information security than threats caused by outside attacks. Second, the confusion of private and professional roles in social media represents an information security risk, and distinguishing between these roles becomes more difficult the higher an employee’s position in the company. Third, communication with employees and colleagues represents an information security challenge especially when communication is not steered by the company. Implications for research and practice are discussed.


title: The Semantic Web needs more cognition

abstract: One of the key deficiencies of the Semantic Web is its lack of cognitive plausibility. We argue that by accounting for people's reasoning mechanisms and cognitive representations, the usefulness of information coming from the Semantic Web will be enhanced. More specifically, the utilization and integration of conceptual spaces is proposed as a knowledge representation that affords two important human cognitive mechanisms, i.e., semantic similarity and concept combination. Formal conceptual space algebra serves as the basis for the Conceptual Space Markup Language (CSML), which facilitates the engineering of ontologies using a geometric framework. We demonstrate the usefulness of the approach through a concrete example and suggest directions for future work, especially the need for combining geometric representations and reasoning mechanisms with existing Semantic Web structures.


title: High sensitivity and high S/N microphone achieved by PZT film with central-circle electrode design

abstract: This study based on piezo-electric sensing principle to develop a high S/N ratio MEMS microphone. The patterned PZT and electrodes designed implemented above the clamped diaphragm to generate high stress concentration on PZT and Si bimorph diaphragm. The proposed central-circle electrode design can induce more charge than the edge-surrounded electrode design (served as the reference design). The proposed design also provides a smooth stress distribution across electrode to tolerate more process variation such as offset from double-side alignment and boundary change by DRIE undercut. The measured results demonstrate the S/N is about 64dB (reference design: 53dB) for the piezo-electric microphone.


title: Automating exploratory data analysis for efficient data mining

abstract: Having access to large data sets for the purpose of predictive data mining does not guarantee good models, even when the size of the training data is virtually unlimited. Instead, careful data preprocessing is required, including data cleansing, handling missing values, attribute representation and encoding, and generating derived attributes. In particular, the selection of the most appropriate subset of attributes to include is a critical step in building an accurate and efficient model. We describe an automated approach to the exploration, preprocessing, and selection of the optimal attribute subset whose goal is to simplify the KDD process and dramatically shorten the time to build a model. Our implementation finds inappropriate and suspicious attributes, performs target dependency analysis, determining optimal attribute encoding, generates new derived attributes, and provides a flexible approach to attribute selection. We present results generated by an industrial KDD environment called the Accrue Decision Series on several real world Web data sets.


Estimating and Exploring the Product Form Design Space Using Deep Generative Models


title: Epigastric hernia and its etiology

abstract: BACKGROUND ::: Controversies still prevail as to how exactly epigastric hernia occurs. Both the vascular lacunae hypothesis and the tendinous fibre decussation hypothesis have proved to be widely accepted as possible explanations for the etiology. ::: ::: ::: PATIENT ::: We present a patient who suffered from early-onset epigastric hernia. ::: ::: ::: CONCLUSIONS ::: We believe the identification of the ligamentum teres and its accompanying vessel at its fascial defect supports the vascular lacunae hypothesis. However, to further our understanding, biopsy of the linea alba in patients with epigastric hernias is indicated.


Relative clustering validity criteria: A comparative overview


POLYVINYL ALCOHOL FIBER REINFORCED ENGINEERED CEMENTITIOUS COMPOSITES: MATERIAL DESIGN AND PERFORMANCES


title: On Low Overlap Among Search Results of Academic Search Engines

abstract: Number of published scholarly articles is growing exponentially. To tackle this information overload, researchers are increasingly depending on niche academic search engines. Recent works have shown that two major general web search engines: Google and Bing, have high level of agreement in their top search results. In contrast, we show that various academic search engines have low degree of agreement among themselves. We performed experiments using 2500 queries over four academic search engines. We observe that overlap in search result sets of any combination of academic search engines is significantly low and in most of the cases the search result sets are mutually exclusive. We also discuss implications of this low overlap.


title: Obstacle Avoidance for Kinematically Redundant Manipulators in Dynamically Varying Environments

abstract: The vast majority of work to date concerned with obstacle avoidance for manipulators has dealt with task descriptions in the form ofpick-and-place movements. The added flexibil ity in motion control for manipulators possessing redundant degrees offreedom permits the consideration of obstacle avoidance in the context of a specified end-effector trajectory as the task description. Such a task definition is a more accurate model for such tasks as spray painting or arc weld ing. The approach presented here is to determine the re quired joint angle rates for the manipulator under the con straints of multiple goals, the primary goal described by the specified end-effector trajectory and secondary goals describ ing the obstacle avoidance criteria. The decomposition of the solution into a particular and a homogeneous component effectively illustrates the priority of the multiple goals that is exact end-effector control with redundant degrees of freedom maximizing the distance to obstacles. An efficient numerical ...


Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models


title: High-Voltage Spark Gap in a Regime of Subnanosecond Switching

abstract: This paper describes an investigation of a high-voltage spark gap in the conditions of subnanosecond switching. The high-voltage pulsed generator “Sinus” is used to charge a coaxial line loaded to a high-pressure spark gap. Typical charging time for the coaxial line is in a range from 1 to 2 ns, maximum charging voltage is up to 250 kV, and a range of pressures for the gap is from 2 to 9 MPa. The theoretical models of the switching process on subnanosecond time scale are examined. A general equation for temporal behavior of the gap voltage, which is applicable for the avalanche model and the Rompe-Weitzel model has been obtained. It is revealed that the approach based on the avalanche model offers a possibility to describe the switching process only at extremely high overvoltages. The Rompe-Weitzel model demonstrates a good agreement with the experimental data both for the conditions of static breakdown and for the regime of a high overvolted gap. Comparison of experimental and calculated voltage waveforms has made it possible to estimate an empirical constant in the Rompe-Weitzel model (the price of ionization). This constant is varied from 420 to 920 eV, depending on the initial electric field in the gap.


title: Robust kernel isomap

abstract: Isomap is one of widely used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional scaling). In this paper we pay our attention to two critical issues that were not considered in Isomap, such as: (1) generalization property (projection property); (2) topological stability. Then we present a robust kernel Isomap method, armed with such two properties. We present a method which relates the Isomap to Mercer kernel machines, so that the generalization property naturally emerges, through kernel principal component analysis. For topological stability, we investigate the network flow in a graph, providing a method for eliminating critical outliers. The useful behavior of the robust kernel Isomap is confirmed through numerical experiments with several data sets.


title: How can neuroscience contribute to moral philosophy, psychology and education based on Aristotelian virtue ethics?

abstract: The present essay discusses the relationship between moral philosophy, psychology and education based on virtue ethics, contemporary neuroscience, and how neuroscientific methods can contribute to studies of moral virtue and character. First, the present essay considers whether the mechanism of moral motivation and developmental model of virtue and character are well supported by neuroscientific evidence. Particularly, it examines whether the evidence provided by neuroscientific studies can support the core argument of virtue ethics, that is, motivational externalism. Second, it discusses how experimental methods of neuroscience can be applied to studies in human morality. Particularly, the present essay examines how functional and structural neuroimaging methods can contribute to the development of the fields by reviewing the findings of recent social and developmental neuroimaging experiments. Meanwhile, the present essay also considers some limitations embedded in such discussions regarding the relationship between the fields and suggests directions for future studies to address these limitations.


title: Image Quality Metrics: PSNR vs. SSIM

abstract: In this paper, we analyse two well-known objective image quality metrics, the peak-signal-to-noise ratio (PSNR) as well as the structural similarity index measure (SSIM), and we derive a simple mathematical relationship between them which works for various kinds of image degradations such as Gaussian blur, additive Gaussian white noise, jpeg and jpeg2000 compression. A series of tests realized on images extracted from the Kodak database gives a better understanding of the similarity and difference between the SSIM and the PSNR.


title: High-resolution image segmentation using fully parallel mean shift

abstract: In this paper, we present a fast and effective method of image segmentation. Our design follows the bottom-up approach: first, the image is decomposed by nonparametric clustering; then, similar classes are joined by a merging algorithm that uses color, and adjacency information to obtain consistent image content. The core of the segmenter is a parallel version of the mean shift algorithm that works simultaneously on multiple feature space kernels. Our system was implemented on a many-core GPGPU platform in order to observe the performance gain of the data parallel construction. Segmentation accuracy has been evaluated on a public benchmark and has proven to perform well among other data-driven algorithms. Numerical analysis confirmed that the segmentation speed of the parallel algorithm improves as the number of utilized processors is increased, which indicates the scalability of the scheme. This improvement was also observed on real life, high-resolution images.


title: Using convolutional neural networks for classification of malware represented as images

abstract: The number of malicious files detected every year are counted by millions. One of the main reasons for these high volumes of different files is the fact that, in order to evade detection, malware authors add mutation. This means that malicious files belonging to the same family, with the same malicious behavior, are constantly modified or obfuscated using several techniques, in such a way that they look like different files. In order to be effective in analyzing and classifying such large amounts of files, we need to be able to categorize them into groups and identify their respective families on the basis of their behavior. In this paper, malicious software is visualized as gray scale images since its ability to capture minor changes while retaining the global structure helps to detect variations. Motivated by the visual similarity between malware samples of the same family, we propose a file agnostic deep learning approach for malware categorization to efficiently group malicious software into families based on a set of discriminant patterns extracted from their visualization as images. The suitability of our approach is evaluated against two benchmarks: the MalImg dataset and the Microsoft Malware Classification Challenge dataset. Experimental comparison demonstrates its superior performance with respect to state-of-the-art techniques.


title: Doing More with the Dew: A New Approach to Cloud-Dew Architecture

abstract: While the popularity of cloud computing is exploding, a new network computing paradigm is just beginning. In this paper, we examine this exciting area of research known as dew computing and propose a new design of cloud-dew architecture. Instead of hosting only one dew server on a user's PC - as adopted in the current dewsite application - our design promotes the hosting of multiple dew servers instead, one for each installed domain. Our design intends to improve upon existing cloud-dew architecture by providing significantly increased freedom in dewsite development, while also automating the chore of managing dewsite content based on the user's interests and browsing habits. Other noteworthy benefits, all at no added cost to dewsite users, are briefly explored as well.


title: Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory

abstract: We propose a new technique to simulate dynamic patterns of crowd behaviors using stress modeling. Our model accounts for permanent, stable disposition and the dynamic nature of human behaviors that change in response to the situation. The resulting approach accounts for changes in behavior in response to external stressors based on well-known theories in psychology. We combine this model with recent techniques on personality modeling for multi-agent simulations to capture a wide variety of behavioral changes and stressors. The overall formulation allows different stressors, expressed as functions of space and time, including time pressure, positional stressors, area stressors and inter-personal stressors. This model can be used to simulate dynamic crowd behaviors at interactive rates, including walking at variable speeds, breaking lane-formation over time, and cutting through a normal flow. We also perform qualitative and quantitative comparisons between our simulation results and real-world observations.


title: Ridge Regression Learning Algorithm in Dual Variables

abstract: In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space. The feature space representation can result in a large increase in the number of parameters used by the algorithm. In order to combat this “curse of dimensionality”, the algorithm allows the use of kernel functions, as used in Support Vector methods. We also discuss a powerful family of kernel functions which is constructed using the ANOVA decomposition method from the kernel corresponding to splines with an infinite number of nodes. This paper introduces a regression estimation algorithm which is a combination of these two elements: the dual version of Ridge Regression is applied to the ANOVA enhancement of the infinitenode splines. Experimental results are then presented (based on the Boston Housing data set) which indicate the performance of this algorithm relative to other algorithms.


title: Mutational heterogeneity in cancer and the search for new cancer-associated genes

abstract: As the sample size in cancer genome studies increases, the list of genes identified as significantly mutated is likely to include more false positives; here, this problem is identified as stemming largely from mutation heterogeneity, and a new analytical methodology designed to overcome this problem is described.


title: Interactive proofs and the hardness of approximating cliques

abstract: The contribution of this paper is two-fold. First, a connection is established between approximating the size of the largest clique in a graph and multi-prover interactive proofs. Second, an efficient multi-prover interactive proof for NP languages is constructed, where the verifier uses very few random bits and communication bits. Last, the connection between cliques and efficient multi-prover interaction proofs, is shown to yield hardness results on the complexity of approximating the size of the largest clique in a graph. Of independent interest is our proof of correctness for the multilinearity test of functions.


title: A Mobile Automated Skin Lesion Classification System

abstract: Melanoma skin cancer accounts for less than 5% of skin cancer cases but causes the most deaths due to skin cancer. Convenient automated diagnosis of skin lesions and melanoma recognition can greatly improve early detection of melanomas. This paper presents a prototype of an image-based automated melanoma recognition system on Android smart phones. The system consists of three major components: image segmentation, feature calculation, and classification. It is designed to run on a mobile device with a camera, such as a smart phone or a tablet PC. A skin lesion image is converted to a monochrome image for outline contour detection. Color and shape features of the lesion are extracted and used as input to a kNN classifier. Initial experimental result shows that the system is efficient and works well on well-lighted test images, achieving an average accuracy of 66.7%, with average malignant class recall/sensitivity of 60.7% and specificity of 80.5%.


title: Illusion of Transition: Russia's Muscovite Future

abstract: The Russian economy, according to statistics that should be taken with buckets of salt [Economist, 2003] , has been in a sharp ascent since the infamous financial crisis of 1998. Although official data indicate that Russia's post-Soviet depression is still nearly as deep as the nadir of America's Great Depression, economic activity has partially rebounded, sparking a heated and sometimes acrimonious debate about past failures and future prospects. Critics of the Washington consensus have tended to focus their attention on missed policy opportunities1 and path dependence, arguing not only that Russia's hyper depression was needlessly severe, but that its future is blighted by the institutionalization of the super-rich "oligarchs."2 Until recently, defenders of the International Monetary Fund (IMF)/World Bank position counter-argued that the painful medicine of yesterday was the elixir of tomorrow's prosperity because the property rights created have made oligarchy a flash in the pan. The IMF reversed field in the spring of 2004, though, questioning oligarchic efficiency [World Bank, 2004] and raising the possibility that Russia's transition may have been "pirated" [Goldman, 2003; Havrylyshyn, 2004]. A discordant note sounded by Andrei Shleifer and Daniel Treisman [2004] claiming that Russia has already become a "normal country" challenges the IMF's new orthodoxy,3 but cannot withstand serious scrutiny [Rosefielde, 2005a; Shlapentokh, 2004; Yuriev, 2004]. Consequently, the main question that needs to be explored is whether Russia can become a normal country, understood as democratic free enterprise, or EU social democracy, in the foreseeable future.


title: The strategic planning of e-commerce business to deployment with TOWS matrix by using K-mean and linear regression

abstract: The expansion and growth of e-commerce business has increased steadily every year. Both original and new operators who are interested in this business because the returns are worth it. But it's not always true that the success of this business. Due to the strategic planning and implementation of business strategy mistakes. Therefore, strategic planning and business strategy, it is necessary and very important. In this paper present the method of selection business strategies planning for the business's e-commerce by using SWOT analysis to help plan and implement strategies in alignment with K-mean method, selection using linear regression for selecting the most appropriate strategy, modeling by using ensemble classifier with Random forest method. Performance measurement this model accuracy is rated over 80 percent through experiments.


title: Game Literacy in Theory and Practice

abstract: A waferizing apparatus having a rotatable disc. There are knife carriers mounted on the disc with spaced openings between the carriers. Openings in the disc correspond to the openings in the carrier and there is a knife associated with each opening. A knife clamp is bolted to the carrier to clamp each knife in position. Each knife clamp is urged resiliently inwardly against the carrier to clamp each knife in position in the apparatus. An expandable device can expand to separate the clamp and the carrier to permit removal of each knife.


K.V.Rukmani , “Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms,” Int


title: Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

abstract: Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.


title: DC bus voltage control for a distributed power system

abstract: This paper addresses voltage control of distributed DC power systems. DC power systems have been discussed as a result of the introduction of renewable, small-scale power generation units. Also, telecommunication power systems featuring UPS properties might benefit from a broader introduction of DC power systems. Droop control is utilized to distribute the load between the source converters. In order to make the loading of the source converters equal, in per unit, the voltage control algorithm for each converter has to be designed to act similar. The DC side capacitor of each converter, needed for filtering, is also determined as a consequence. The root locus is investigated for varying DC bus impedance. It is found that the risk of entering converter over-modulation is a stronger limitation than stability, at least for reasonable DC bus cable parameters. The stationary and dynamic properties during load variations are also investigated.


title: Synthetic Aperture Radar Image Synthesis by Using Generative Adversarial Nets

abstract: Synthetic aperture radar (SAR) image simulators based on computer-aided drawing models play an important role in SAR applications, such as automatic target recognition and image interpretation. However, the accuracy of such simulators is due to geometric error and simplification in the electromagnetic calculation. In this letter, an end-to-end model was developed that could directly synthesize the desired images from the known image database. The model was based on generative adversarial nets (GANs), and its feasibility was validated by comparisons with real images and ray-tracing results. As a further step, the samples were synthesized at angles outside of the data set. However, the training process of GAN models was difficult, especially for SAR images which are usually affected by noise interference. The major failure modes were analyzed in experiments, and a clutter normalization method was proposed to ameliorate them. The results showed that the method improved the speed of convergence up to 10 times. The quality of the synthesized images was also improved.


title: Automated Diagnosis of Glaucoma Using Digital Fundus Images

abstract: Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity of 100% and 80% respectively.


title: Modeling and control of FAST-Hex: A fully-actuated by synchronized-tilting hexarotor

abstract: We present FAST-Hex, a novel UAV concept which is able to smoothly change its configuration from underactuated to fully actuated by using only one additional motor that tilts all propellers at the same time. FAST-Hex can adapt to the task at hand by finely tuning its configuration from the efficient (but underactuated) flight (typical of coplanar multi-rotor platforms) to the full-pose-tracking (but less efficient) flight, which is attainable by non-coplanar multi-rotors. We also introduce a novel full-pose geometric controller for generic multi-rotors (not only the FAST-Hex) that outperforms classical inverse dynamics approaches. The controller receives as input any reference pose in ℝ3×SO(3) (3D position + 3D orientation). Exact tracking is achieved if the reference pose is feasible with respect to the propeller spinning rate saturations. In case of unfeasibility a new feasible desired trajectory is generated online giving priority to the positional part. The new controller is tested with the FAST-Hex but can be used for many other multi-rotor platforms: underactuated, slightly fully-actuated and completely fully-actuated.


title: Introduction to the special section on educational data mining

abstract: Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.


title: Efficient algorithms for updating betweenness centrality in fully dynamic graphs

abstract: Devise an algorithm for updating betweenness centrality in fully dynamic graphs.Recalculate centrality without computing all pairs shortest paths in the entire graph.Adapt a highest centrality edge finding algorithm based on the proposed algorithm.Adapt a community detection algorithm using the proposed algorithms.Experimental results show the proposed algorithm outperforms existing algorithms. Betweenness centrality of a vertex (edge) in a graph is a measure for the relative participation of the vertex (edge) in the shortest paths in the graph. Betweenness centrality is widely used in various areas such as biology, transportation, and social networks. In this paper, we study the update problem of betweenness centrality in fully dynamic graphs. The proposed update algorithm substantially reduces the number of shortest paths which should be re-computed when a graph is changed. In addition, we adapt a community detection algorithm using the proposed algorithm to show how much benefit can be obtained from the proposed algorithm in a practical application. Experimental results on real graphs show that the proposed algorithm efficiently update betweenness centrality and detect communities in a graph.


Supervised Attention for Sequence-to-Sequence Constituency Parsing


title: Clinical human factors: the need to speak up to improve patient safety

abstract: This article aims to inspire nurses to recognise how human factors affect individual and team performance. Use of a case study and learning derived from a subsequent independent inquiry exposes the dynamics that can affect teamwork and inhibit effective communication with devastating consequences. The contribution of situational awareness and the importance of nurses speaking up when they have concerns are demonstrated as vital components in the delivery of safe patient care.


title: Smatch: an Evaluation Metric for Semantic Feature Structures

abstract: The evaluation of whole-sentence semantic structures plays an important role in semantic parsing and large-scale semantic structure annotation. However, there is no widely-used metric to evaluate wholesentence semantic structures. In this paper, we present smatch, a metric that calculates the degree of overlap between two semantic feature structures. We give an efficient algorithm to compute the metric and show the results of an inter-annotator agreement study.


title: Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning

abstract: Modern neural image captioning systems typically adopt the encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for caption generation. Inspired by the robustness analysis of CNN-based image classifiers to adversarial perturbations, we propose \textbf{Show-and-Fool}, a novel algorithm for crafting adversarial examples in neural image captioning. Unlike image classification tasks with a finite set of class labels, finding visually-similar adversarial examples in an image captioning system is much more challenging since the space of possible captions in a captioning system is almost infinite. In this paper, we design three approaches for crafting adversarial examples in image captioning: (i) targeted caption method; (ii) targeted keyword method; and (iii) untargeted method. We formulate the process of finding adversarial perturbations as optimization problems and design novel loss functions for efficient search. Experimental results on the Show-and-Tell model and MSCOCO data set show that Show-and-Fool can successfully craft visually-similar adversarial examples with randomly targeted captions, and the adversarial examples can be made highly transferable to the Show-Attend-and-Tell model. Consequently, the presence of adversarial examples leads to new robustness implications of neural image captioning. To the best of our knowledge, this is the first work on crafting effective adversarial examples for image captioning tasks.


title: Federated Byzantine Agreement to Ensure Trustworthiness of Digital Manufacturing Platforms

abstract: In this paper, we explore the use of the Stellar Consensus Protocol (SCP) and its Federated Byzantine Agreement (FBA) algorithm for ensuring trust and reputation between federated, cloud-based platform instances (nodes) and their participants. Our approach is grounded on federated consensus mechanisms, which promise data quality managed through computational trust and data replication, without a centralized authority. We perform our experimentation on the ground of the NIMBLE cloud manufacturing platform, which is designed to support growth of B2B digital manufacturing communities and their businesses through federated platform services, managed by peer-to-peer networks. We discuss the message exchange flow between the NIMBLE application logic and Stellar consensus logic.


title: The Grammar of Carnatic Music

abstract: This book argues that Carnatic music as it is practiced today can be traced to the musical practices of early/mid eighteenth century. Earlier varieties or 'incarnations' of Indian music elaborately described in many musical treatises are only of historical relevance today as the music described is quite different from current practices. It is argued that earlier varieties may not have survived because they failed to meet the three crucial requirements for a language-like organism to survive i.e., a robust community of practitioners/listeners which the author calls the Carnatic Music Fraternity, a sizeable body of musical texts and a felt communicative need. In fact, the central thesis of the book is that Carnatic music, like language, survived and evolved from early/mid eighteenth century when these three requirements were met for the first time in the history of Indian music. The volume includes a foreword by Paul Kiparsky.


title: Coherent Assembly Dynamics in the Cortex: Multi-Neuron Recordings, Network Simulations and Anatomical Considerations

abstract: The anatomical structure of the neo-cortex and, particularly, its massive connectivity strongly suggest that the functional organization of this part of the brain is based upon interactions within and among groups of cells (Braitenberg and Schuz, 1991). This observation has prompted neurobiologists as early as Sherrington (1941) and Hebb (1949) (see also James, 1890) to speculate that cortical neurons do not act in isolation, but rather that they organize into cell assemblies for various computational tasks (see Gerstein et al., 1989, for a review of different definitions of the concept of cell assembly). One operational definition for the cell assembly has been particularly influential: near-simultaneity or some other specific timing relation in the firing of the participating neurons. As, for instance, elaborated in the concept of the ‘synfire chain’ (Abeles, 1982, 1991), the synaptic influence of multiple neurons converging onto others in the cortical network is much stronger if they fire in (near-) coincidence. Thus, temporal coherence or synchronous firing, postulated as a mechanism for perceptual integration (Hebb, 1949), would in fact be directly available to the brain as a potential neural code (Perkel and Bullock, 1968; Gerstein and Michalski, 1981; Johannesma et al., 1986).


title: Events Detection for an Audio-Based Surveillance System

abstract: The present research deals with audio events detection in noisy environments for a multimedia surveillance application. In surveillance or homeland security most of the systems aiming to automatically detect abnormal situations are only based on visual clues while, in some situations, it may be easier to detect a given event using the audio information. This is in particular the case for the class of sounds considered in this paper, sounds produced by gun shots. The automatic shot detection system presented is based on a novelty detection approach which offers a solution to detect abnormality (abnormal audio events) in continuous audio recordings of public places. We specifically focus on the robustness of the detection against variable and adverse conditions and the reduction of the false rejection rate which is particularly important in surveillance applications. In particular, we take advantage of potential similarity between the acoustic signatures of the different types of weapons by building a hierarchical classification system


title: A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

abstract: This paper presents a novel online-capable interaction-aware intention and maneuver prediction framework for dynamic environments. The main contribution is the combination of model-based interaction-aware intention estimation with maneuver-based motion prediction based on supervised learning. The advantages of this framework are twofold. On one hand, expert knowledge in the form of heuristics is integrated, which simplifies the modeling of the interaction. On the other hand, the difficulties associated with the scalability and data sparsity of the algorithm due to the so-called curse of dimensionality can be reduced, as a reduced feature space is sufficient for supervised learning. The proposed algorithm can be used for highly automated driving or as a prediction module for advanced driver assistance systems without the need of intervehicle communication. At the start of the algorithm, the motion intention of each driver in a traffic scene is predicted in an iterative manner using the game-theoretic idea of stochastic multiagent simulation. This approach provides an interpretation of what other drivers intend to do and how they interact with surrounding traffic. By incorporating this information into a Bayesian network classifier, the developed framework achieves a significant improvement in terms of reliable prediction time and precision compared with other state-of-the-art approaches. By means of experimental results in real traffic on highways, the validity of the proposed concept and its online capability is demonstrated. Furthermore, its performance is quantitatively evaluated using appropriate statistical measures.


title: An Inoperability Input-Output Model (IIM) for disruption propagation analysis

abstract: Today's Supply Chains (SCs) are global and getting more complex, due to the exigencies of the world economy, making them hard to manage. This is exacerbated by the increasingly frequent occurrence of natural disasters and other high impact disruptions. To stay competitive, companies are therefore seeking ways to better understand the impact of such disruptions to their SCs. In addressing this need, this paper proposes an approach for disruption propagation analysis. We develop a method based on a variation of an Inoperability Input-Output Model (adapted from a Leontief I-O model) to quantify the impact of disruptions across the entire SC. We then analyse the factors that have the most influential impacts on the SCs during disruptions. Initial results show that trading volume handled by a company/ node is an important factor in determining the disruption impact to SCs, besides the number of SC partners (connections) as implied from previous work.


title: Rough sets theory for multicriteria decision analysis

abstract: The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation. It operates on a data table composed of a set U of objects (actions) described by a set Q of attributes. Its basic notions are: indiscernibility relation on U, lower and upper approximation of either a subset or a partition of U, dependence and reduction of attributes from Q, and decision rules derived from lower approximations and boundaries of subsets identified with decision classes. The original rough set idea is failing, however, when preference-orders of attribute domains (criteria) are to be taken into account. Precisely, it cannot handle inconsistencies following from violation of the dominance principle. This inconsistency is characteristic for preferential information used in multicriteria decision analysis (MCDA) problems, like sorting, choice or ranking. In order to deal with this kind of inconsistency a number of methodological changes to the original rough sets theory is necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets in multicriteria sorting. To approximate preference relations in multicriteria choice and ranking problems, another change is necessary: substitution of the data table by a pairwise comparison table, where each row corresponds to a pair of objects described by binary relations on particular criteria. In all those MCDA problems, the new rough set approach ends with a set of decision rules playing the role of a comprehensive preference model. It is more general than the classical functional or relational model and it is more understandable for the users because of its natural syntax. In order to workout a recommendation in one of the MCDA problems, we propose exploitation procedures of the set of decision rules. Finally, some other recently obtained results are given: rough approximations by means of similarity relations, rough set handling of missing data, comparison of the rough set model with Sugeno and Choquet integrals, and results on equivalence of a decision rule preference model and a conjoint measurement model which is neither additive nor transitive.


title: Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation

abstract: In this paper, we address the issue of deciding when to stop active learning for building a labeled training corpus. Firstly, this paper presents a new stopping criterion, classification-change, which considers the potential ability of each unlabeled example on changing decision boundaries. Secondly, a multi-criteria-based combination strategy is proposed to solve the problem of predefining an appropriate threshold for each confidence-based stopping criterion, such as max-confidence, min-error, and overall-uncertainty. Finally, we examine the effectiveness of these stopping criteria on uncertainty sampling and heterogeneous uncertainty sampling for active learning. Experimental results show that these stopping criteria work well on evaluation data sets, and the combination strategies outperform individual criteria.


title: Using Real-Time Road Traffic Data to Evaluate Congestion

abstract: Providing citizens with accurate information on traffic conditions can encourage journeys at times of low congestion, and uptake of public transport. The TIME project (Transport Information Monitoring Environment) has focussed on urban traffic, using the city of Cambridge as an example. We have investigated sensor and network technology for gathering traffic data, and have designed and built reusable software components to distribute, process and store sensor data in real time. Instrumenting a city to provide this information is expensive and potentially invades privacy. Increasingly, public transport vehicles are equipped with sensors to provide arrival time estimates at bus stop displays in real-time. We have shown that these data can be used for a number of purposes. Firstly, archived data can be analysed statistically to understand the behaviour of traffic under a range of "normal" conditions at different times, for example in and out of school term. Secondly, periods of extreme congestion resulting from known incidents can be analysed to show the behaviour of traffic over time. Thirdly, with such analyses providing background information, real-time data can be interpreted in context to provide more reliable and accurate information to citizens. In this paper we present some of the findings of the TIME project.


title: zPatch: Hybrid Resistive/Capacitive eTextile Input

abstract: We present zPatch: an eTextile patch for hover, touch, and pressure input, using both resistive and capacitive sensing. zPatches are made by layering a piezo-resistive material between silver-plated ripstop, and embedding it in non-conductive fabric to form a patch. zPatches can be easily ironed onto most fabrics, in any location, enabling easy prototyping or ad hoc modifications of existing garments. We provide open-source resources for building and programming zPatches and present measures of the achieva-ble sensing resolution of a zPatch. A pressure based targeting task demonstrated users could reliably hit pressure tar-gets at up to 13 levels, given appropriate feedback. We demonstrate that the hybrid sensing approach reduces false activations and helps distinguish between gestures. Finally, we present example applications in which we use zPatches for controlling a music player, text entry and gaming input.


title: DRAMsim: a memory system simulator

abstract: As memory accesses become slower with respect to the processor and consume more power with increasing memory size, the focus of memory performance and power consumption has become increasingly important. With the trend to develop multi-threaded, multi-core processors, the demands on the memory system will continue to scale. However, determining the optimal memory system configuration is non-trivial. The memory system performance is sensitive to a large number of parameters. Each of these parameters take on a number of values and interact in fashions that make overall trends difficult to discern. A comparison of the memory system architectures becomes even harder when we add the dimensions of power consumption and manufacturing cost. Unfortunately, there is a lack of tools in the public-domain that support such studies. Therefore, we introduce DRAMsim, a detailed and highly-configurable C-based memory system simulator to fill this gap. DRAMsim implements detailed timing models for a variety of existing memories, including SDRAM, DDR, DDR2, DRDRAM and FB-DIMM, with the capability to easily vary their parameters. It also models the power consumption of SDRAM and its derivatives. It can be used as a standalone simulator or as part of a more comprehensive system-level model. We have successfully integrated DRAMsim into a variety of simulators including MASE [15], Sim-alpha [14], BOCHS[2] and GEMS[13]. The simulator can be downloaded from www.ece.umd.edu/dramsim.


title: LivDet 2017 Fingerprint Liveness Detection Competition 2017

abstract: Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing images coming from artificial replicas of the fingerprint characteristic, made up of materials like silicone, gelatine or latex, and images coming from alive fingerprints. Images are captured by modern scanners, typically relying on solid-state or optical technologies. Since from 2009, the Fingerprint Liveness Detection Competition (LivDet) aims to assess the performance of the state-of-the-art algorithms according to a rigorous experimental protocol and, at the same time, a simple overview of the basic achievements. The competition is open to all academics research centers and all companies that work in this field. The positive, increasing trend of the participants number, which supports the success of this initiative, is confirmed even this year: 17 algorithms were submitted to the competition, with a larger involvement of companies and academies. This means that the topic is relevant for both sides, and points out that a lot of work must be done in terms of fundamental and applied research.


title: Image Segmentation in Video Sequences: A Probabilistic Approach

abstract: "Background subtraction" is an old technique for finding moving objects in a video sequence--for example, cars driving on a freeway. The idea is that subtracting the current image from a time-averaged background image will leave only nonstationary objects. It is, however, a crude approximation to the task of classifying each pixel of the current image; it fails with slow-moving objects and does not distinguish shadows from moving objects. The basic idea of this paper is that we can classify each pixel using a model of how that pixel looks when it is part of different classes. We learn a mixture-of-Gaussians classification model for each pixel using an unsupervised technique--an efficient, incremental version of EM. Unlike the standard image-averaging approach, this automatically updates the mixture component for each class according to likelihood of membership; hence slow-moving objects are handled perfectly. Our approach also identifies and eliminates shadows much more effectively than other techniques such as thresholding. Application of this method as part of the Roadwatch traffic surveillance project is expected to result in significant improvements in vehicle identification and tracking.


title: Automatic Optimization for MapReduce Programs

abstract: The MapReduce distributed programming framework has become popular, despite evidence that current implementations are inefficient, requiring far more hardware than a traditional relational databases to complete similar tasks. MapReduce jobs are amenable to many traditional database query optimizations (B+Trees for selections, column-store-style techniques for projections, etc), but existing systems do not apply them, substantially because free-form user code obscures the true data operation being performed. For example, a selection in SQL is easily detected, but a selection in a MapReduce program is embedded in Java code along with lots of other program logic. We could ask the programmer to provide explicit hints about the program's data semantics, but one of MapReduce's attractions is precisely that it does not ask the user for such information. ::: ::: This paper covers Manimal, which automatically analyzes MapReduce programs and applies appropriate data-aware optimizations, thereby requiring no additional help at all from the programmer. We show that Manimal successfully detects optimization opportunities across a range of data operations, and that it yields speedups of up to 1,121% on previously-written MapReduce programs.


title: Energy optimisation of single train operation based on tabu search

abstract: Amid concerns about rising energy prices and environmental issues, the optimisation of automatic train operation (ATO) is significant for energy-efficient operation of trains. Different from the previous researches, this paper presents a more accurate model of the train energy consumption by considering the control strategy of ATO. It then proposes two modifications of tabu search (TS) algorithm, which are named as up-down modification (UDM) and left-right modification (LRM), to optimise train recommended speed curve based on the presented model. The simulation results based on the Beijing Subway illustrate that the proposed approaches can achieve a good performance on energy reduction. Sensitivities of the parameters are analysed to illustrate that the proposed approach is practical and meaningful. In addition, the computation time is within 2 s, which is short enough to be applied in the real-time control of trains.


title: Distance-based outliers: algorithms and applications

abstract: This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. Existing methods that we have seen for finding outliers can only deal efficiently with two dimensions/attributes of a dataset. In this paper, we study the notion of DB (distance-based) outliers. Specifically, we show that (i) outlier detection can be done efficiently for large datasets, and for k-dimensional datasets with large values of k (e.g., $k \ge 5$); and (ii), outlier detection is a meaningful and important knowledge discovery task.First, we present two simple algorithms, both having a complexity of $O(k \: N^2)$, k being the dimensionality and N being the number of objects in the dataset. These algorithms readily support datasets with many more than two attributes. Second, we present an optimized cell-based algorithm that has a complexity that is linear with respect to N, but exponential with respect to k. We provide experimental results indicating that this algorithm significantly outperforms the two simple algorithms for $k \leq 4$. Third, for datasets that are mainly disk-resident, we present another version of the cell-based algorithm that guarantees at most three passes over a dataset. Again, experimental results show that this algorithm is by far the best for $k \leq 4$. Finally, we discuss our work on three real-life applications, including one on spatio-temporal data (e.g., a video surveillance application), in order to confirm the relevance and broad applicability of DB outliers.


title: Support of Decision Making by Business Intelligence Tools

abstract: Issues of business intelligence applications are discussed. Main parts of business intelligence tools are briefly described. The attention is paid to the data warehouses and to a new tool in this field, to the operational data stores. These technologies enable to get the data of first-rate quality. Some new features of operational data stores' applications are proposed. The role of users in the design of metadata is explained and its influence on the efficiency of information systems is emphasized. Business intelligence tools are the necessary conditions for applications of data mining, an efficient tool for getting new information from large datasets.


title: A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

abstract: Build a large-scale 3D shape retrieval benchmark that supports multi-modal queries.Evaluate the 26 3D shape retrieval methods using 3 types of metrics.Solicit and identify state-of-the-art methods and promising related techniques.Perform detailed analysis on diverse methods w.r.t accuracy and efficiency.Make benchmark and evaluation tools freely available to the community. Large-scale 3D shape retrieval has become an important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale comprehensive and sketch-based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large-scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain-specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query-by-Model or Query-by-Sketch 3D shape retrieval methods and to solicit state-of-the-art approaches, we perform a more comprehensive comparison of twenty-six (eighteen originally participating algorithms and eight additional state-of-the-art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites (http://www.itl.nist.gov/iad/vug/sharp/contest/2014/Generic3D/, 2014, http://www.itl.nist.gov/iad/vug/sharp/contest/2014/SBR/, 2014).


title: Universal-input single-stage PFC flyback with variable boost inductance for high-brightness LED applications

abstract: This paper presents a single-stage flyback power-factor-correction circuit with a variable boost inductance for high-brightness light-emitting-diode applications for the universal input voltage (90–270 Vrms). The proposed circuit overcomes the limitations of the conventional single-stage PFC flyback with a constant boost inductance, which cannot be designed to achieve a practical bulk-capacitor voltage level (i.e., less than 450 V) at high line while meeting the IEC 61000–3–2 Class C line current harmonic limits at low line. According to the proposed variable boost inductance method, the boost inductance is constant in the high-voltage range and it is reduced in the low-voltage range, resulting in discontinuous-conduction-mode operation and a low total harmonic distortion (THD) in both the high-voltage and low-voltage ranges. Measurements obtained on a 24-V/91-W experimental prototype are as follows: PF = 0.9873, THD = 12%, and efficiency = 88% at nominal low line (120 V rms ); and PF = 0.9474, THD = 10.39%, and efficiency = 91% at nominal high line (230 V rms ). The line current harmonics satisfy the IEC 61000–3–2 Class C limits with enough margin.


title: Communication Problems In Requirements Engineering: A Field Study

abstract: The requirements engineering phase of software development projects is characterized by the intensity and importance of communication activities. During this phase, the various stakeholders must be able to communicate their requirements to the analysts, and the analysts need to be able to communicate the specifications they generate back to the stakeholders for validation. This paper describes a field investigation into the problems of communication between disparate communities involved in the requirements specification activities. The results of this study are discussed in terms of their relation to three major communication barriers: (1) ineffectiveness of the current communication channels; (2) restrictions on expressiveness imposed by notations; and (3) social and organizational barriers. The results confirm that organizational and social issues have great influence on the effectiveness of communication. They also show that in general, end-users find the notations used by software practitioners to model their requirements difficult to understand and validate.


title: Satellite Communications Supporting Internet of Remote Things

abstract: This paper focuses on the use of satellite communication systems for the support of Internet of Things (IoT). We refer to the IoT paradigm as the means to collect data from sensors or RFID and to send control messages to actuators. In many application scenarios, sensors and actuators are distributed over a very wide area; in some cases, they are located in remote areas where they are not served by terrestrial access networks and, as a consequence, the use of satellite communication systems becomes of paramount importance for the Internet of Remote Things (IoRT). The enabling factors of IoRT through satellite are: 1) the interoperability between satellite systems and sensors/actuators and 2) the support of IPv6 over satellite. Furthermore, radio resource management algorithms are required to enhance the efficiency of IoT over satellite. In this work, we provide an integrated view of satellite-based IoT, handling this topic as a jigsaw puzzle where the pieces to be assembled are represented by the following topics: MAC protocols for satellite routed sensor networks, efficient IPv6 support, heterogeneous networks interoperability, quality of service (QoS) management, and group-based communications.


title: What's in a Domain? Analyzing Genre and Topic Differences in Statistical Machine Translation

abstract: Domain adaptation is an active field of research in statistical machine translation (SMT), but so far most work has ignored the distinction between the topic and genre of documents. In this paper we quantify and disentangle the impact of genre and topic differences on translation quality by introducing a new data set that has controlled topic and genre distributions. In addition, we perform a detailed analysis showing that differences across topics only explain to a limited degree translation performance differences across genres, and that genre-specific errors are more attributable to model coverage than to suboptimal scoring of translation candidates.


title: Sleep quality and academic performance of nursing students

abstract: Quality of sleep is one of the important elements of the learning process and memory. However, sleep problems are prominent among college students, which could affect their academic performance. The aim of this study wasto identify the association between quality of sleep and its impact towards academic performance among nursing students at the Faculty of Nursing in International Islamic University, Malaysia (IIUM). A quantitative, cross-sectional study was conducted in the Facultyof Nursing at Jalan Hospital Campus. There were 105 nursing students involved as participants and they were selected by using the convenience sampling method. The questionnaires consisted of three parts; Socio-demographic (age, gender, level of study and participation in extracurricular activities), Pittsburgh Sleep Quality Index (PSQI) and Academic performance (GPA). About 48.6% out ofa total of 105 nursing students had a PSQI score of five and more which means ‘good sleep quality’ and 51.4% had a score of less than 5 which means ‘poor sleep quality’. The result showed that there was a significant association between sleep quality and academic performance. The findings provide information regarding sleep quality of students in the Faculty of Nursing and its impact towards academic performance. Thus, it is recommended for responsible parties to design and implement appropriate interventions to improve sleep quality for academic performance enhancement.


title: Text recognition and face detection aid for visually impaired person using Raspberry PI

abstract: Speech and text is the main medium for human communication. A person needs vision to access the information in a text. However those who have poor vision can gather information from voice. This paper proposes a camera based assistive text reading to help visually impaired person in reading the text present on the captured image. The faces can also be detected when a person enter into the frame by the mode control. The proposed idea involves text extraction from scanned image using Tesseract Optical Character Recognition (OCR) and converting the text to speech by e-Speak tool, a process which makes visually impaired persons to read the text. This is a prototype for blind people to recognize the products in real world by extracting the text on image and converting it into speech. Proposed method is carried out by using Raspberry pi and portability is achieved by using a battery backup. Thus the user can carry the device anywhere and able to use at any time. Upon entering the camera view previously stored faces are identified and informed which can be implemented as a future technology. This technology helps millions of people in the world who experience a significant loss of vision.


title: A reconfigurable three- and single-phase AC/DC non-isolated bi-directional converter for multiple worldwide voltages

abstract: AC/DC converters for worldwide deployment or multiple applications are often required to operate with single-or three-phase AC connections at a variety of voltages. Electric vehicle charging is an excellent example: a typical AC/DC converter optimized for a 400 V three-phase European connection will only produce about 35% of rated power when operating on a 240 V single-phase connection in North America. This paper proposes a reconfigurable AC-DC converter with integral DC-DC stage that allows the single-phase AC power to roughly double compared to a standard configuration, up to 2/3 of the DC/DC stage rating. The system is constructed with two conventional back-to-back three-phase two-level bridges. When operating in single-phase mode, rather than leaving the DC side under-utilized, our proposed design repurposes one of the DC phase legs and the unused AC phase to create two parallel single-phase connections. This roughly doubles the AC output current and hence doubles the single phase AC power.


title: A Low-Profile Dual-Band Dual-Polarized Antenna With an AMC Surface for WLAN Applications

abstract: In this letter, a novel low-profile dual-band dual-polarized antenna with an artificial magnetic conductor (AMC) surface for wireless local area network (WLAN) applications is presented. Two sets of bowtie patches are introduced to form two dipoles in ±45° polarizations. Two integrated baluns are used to excite the dipoles. By inserting an AMC surface, the antenna can achieve unidirectional radiation pattern and low-profile characteristics. The height of the proposed antenna is 0.088λ0 at 2.4 GHz. It can provide measured 15.6% (2.36-2.76 GHz) and 9.3% (5.12-5.62 GHz) relative bandwidth, respectively. Port-to-port isolation higher than 22 dB can be realized. Stable radiation patterns with peak gain of 7.2 dBi in lower band and 7.3 dBi in upper band are also obtained. The proposed antenna can be used for multiband base stations for WLAN applications.


title: Analysis and design of a current-fed push-pull parallel-resonant converter for cooker magnetrons

abstract: This paper presents the analysis and design of a current-fed push-pull parallel-resonant (CFPP-PR) converter for cooker magnetrons. The CFPP-PR converter is fabricated with a buck converter, a push-pull parallel-resonant inverter and a full-wave voltage doubler. The front-end buck converter synchronously operated with the following push-pull resonant circuit is a controllable dc current source. The CFPP-PR converter operated at resonant frequency exhibits zero-voltage switching and the push-pull switches only conduct buck inductor current. The magnetizing inductance of transformer resonates with equivalent parallel capacitance. The resultant resonant voltage is boosted with the turn ratio of transformer. The voltage doubler reduces the voltage stress of transformer windings. To demonstrate the analysis and design, a CFPP-PR converter with integrated circuit UC3872 is built and tested. Simulation and experiment results meet the predictions.


title: Adaptive LL(*) parsing: the power of dynamic analysis

abstract: Despite the advances made by modern parsing strategies such as PEG, LL(*), GLR, and GLL, parsing is not a solved problem. Existing approaches suffer from a number of weaknesses, including difficulties supporting side-effecting embedded actions, slow and/or unpredictable performance, and counter-intuitive matching strategies. This paper introduces the ALL(*) parsing strategy that combines the simplicity, efficiency, and predictability of conventional top-down LL(k) parsers with the power of a GLR-like mechanism to make parsing decisions. The critical innovation is to move grammar analysis to parse-time, which lets ALL(*) handle any non-left-recursive context-free grammar. ALL(*) is O(n4) in theory but consistently performs linearly on grammars used in practice, outperforming general strategies such as GLL and GLR by orders of magnitude. ANTLR 4 generates ALL(*) parsers and supports direct left-recursion through grammar rewriting. Widespread ANTLR 4 use (5000 downloads/month in 2013) provides evidence that ALL(*) is effective for a wide variety of applications.


title: Atlantic Salmon Type I IFN Subtypes Show Differences in Antiviral Activity and Cell-Dependent Expression: Evidence for High IFNb/IFNc-Producing Cells in Fish Lymphoid Tissues

abstract: This work reveals distinct roles of the two-cysteine-containing type I IFNs, IFNa and IFNd, and the four-cysteine-containing IFNb and IFNc in antiviral immunity of Atlantic salmon. IFNa and IFNc showed similar antiviral activities and ability to induce antiviral genes, IFNb was less active, and IFNd showed no activity. Expression of IFNs was compared by treatment of cells or fish with the dsRNA polyinosinic-polycytidylic acid [poly(I:C)], which induces IFNs via the viral RNA receptors MDA5 and TLR3/TLR22 and with the imidazoquinoline R848, which induces IFNs via TLR7. Poly(I:C) strongly induced IFNa in cell lines, whereas the other IFNs showed little response, indicating that IFNa is the main IFN subtype induced through the RIG-I/MDA5 pathway. In contrast, IFNb and IFNc are the main IFNs induced through the TLR7 pathway because R848 induced high transcript levels of IFNb and IFNc and low transcript levels of IFNa in the head kidney and spleen. IFNd was constitutively expressed in cells and organs but showed no response to poly(I:C) or R848. Fluorescence in situ hybridization studies showed that poly(I:C) induced IFNa and IFNc in a variety of cells in the head kidney, spleen, gills, liver, and heart, whereas R848 induced coexpression of IFNb and IFNc in distinct cells in head kidney and spleen. These cells are likely to be specialized high IFN producers because they were few in numbers despite high IFNb/IFNc transcript levels in the same organs. High IFN expression in response to TLR7 ligation is a feature shared by mammalian plasmacytoid dendritic cells.


title: Frame rate analysis of video synthetic aperture radar (ViSAR)

abstract: Video Synthetic Aperture Radar (ViSAR) is the new application of SAR technique. Through reasonable system parameter design, ViSAR system can obtain the SAR image at a faster image rates (also referred to herein as frame rate) which permit the detection of dynamic changes that are occurring in the target scene. Therefore, frame rate as one of the core parameters of ViSAR system should be carefully studied.


abstract: The problem under study consists of improvement in health care system using cloud. We passionately dedicated to help medical fraternity to find health status of vital organs of the patient's body at early stage that support effective treatment by introducing innovative and high quality hand carried non invasive health care systems and devices. Cloud based Health Care is the integration of cloud computing and health monitoring. The computing device enables the delivery of accurate medical information anytime anywhere by means of internet. Cloud based healthcare system consists of a computing device and number of sensors mounted on patient's body. In this paper we present a Cloud based Intelligent Healthcare Monitoring System (CIHMS), which can provide medical feedback/assistance to the patient through cloud (if data already available) or hospital. The suitable sensor is/are to be used to obtain adequate data related to patient's disease.


title: An Efficiency-Enhanced Hybrid Supply Modulator With Single-Capacitor Current-Integration Control

abstract: This paper presents a high-efficiency wideband hybrid supply modulator (HSM). We show that proper control of the average current from the class-AB linear amplifier (LA) and proper selection of the inductor value are essential to efficiency optimization. In line with the above criteria, a single-capacitor current-integration (SCCI) control method is proposed. Current integration is achieved by using a single capacitor, circumventing the need of the high-speed full-range current sensor required for conventional HSMs and reducing the design complexity greatly. The ripple current of the LA is controlled indirectly, and enhanced efficiency is achieved by enforcing the average output current from the LA to be around zero. A wideband LA is proposed to suppress the output voltage ripple. A proof-of-concept design using an inductor with an optimized value of 100 nH is fabricated in 130 nm CMOS technology. It switches at a peak frequency of 50 MHz, achieves up to 8% efficiency improvement when compared to recent works, and is able to track a $0.8\;{\text{V}}_\text{pp}$ sinusoidal signal with high fidelity up to 10 MHz. The measured output voltage ripple is reduced to below 8 mV. The peak conversion efficiency is 88.3% at the maximum output power of 23 dBm.


title: A Sub-2 W 39.8–44.6 Gb/s Transmitter and Receiver Chipset With SFI-5.2 Interface in 40 nm CMOS

abstract: A 39.8-44.6 Gb/s transmitter and receiver chipset designed in 40 nm CMOS is presented. The line-side TX implements a 2-tap FIR filter with delay-based pre-emphasis. The line-side RX uses a quarter-rate CDR architecture. The TX output shows 0.9 pspp ISI and 0.2 psrms RJ at 0.87 W. The RX achieves a jitter tolerance of 0.6 UIpp at 100 MHz and an input sensitivity of 20 mV pp\mathchar"702D diff at 1.05 W. The combined transmitter/receiver equalization enables 44.6 Gb/s data transmission using 231-1 PRBS at BER 10-12 over a channel with >21 dB loss at Nyquist frequency.


title: An implicit segmentation-based method for recognition of handwritten strings of characters

abstract: This paper describes an implicit segmentation-based method for recognition of strings of characters (words or numerals). In a two-stage HMM-based method, an implicit segmentation is applied to segment either words or numeral strings, and in the verification stage, foreground and background features are combined to compensate the loss in terms of recognition rate when segmentation and recognition are performed in the same process. A rigorous experimental protocol shows the performance of the proposed method for isolated characters, numeral strings, and words.


title: What to Do Next: Modeling User Behaviors by Time-LSTM.

abstract: Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users' actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users' actions are of significant importance in capturing the relations of users' actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users' sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users' short-term and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.


title: Robot Skill Learning: From Reinforcement Learning to Evolution Strategies

abstract: Policy improvement methods seek to optimize the parameters of a policy with respect to a utility function. Owing to current trends involving searching in parameter space (rather than action space) and using reward-weighted averaging (rather than gradient estimation), reinforcement learning algorithms for policy improvement, e.g. PoWER and PI2, are now able to learn sophisticated high-dimensional robot skills. A side-effect of these trends has been that, over the last 15 years, reinforcement learning (RL) algorithms have become more and more similar to evolution strategies such as (μW , λ)-ES and CMA-ES. Evolution strategies treat policy improvement as a black-box optimization problem, and thus do not leverage the problem structure, whereas RL algorithms do. In this paper, we demonstrate how two straightforward simplifications to the state-of-the-art RL algorithm PI2 suffice to convert it into the black-box optimization algorithm (μW, λ)-ES. Furthermore, we show that (μW , λ)-ES empirically outperforms PI2 on the tasks in [36]. It is striking that PI2 and (μW , λ)-ES share a common core, and that the simpler algorithm converges faster and leads to similar or lower final costs. We argue that this difference is due to a third trend in robot skill learning: the predominant use of dynamic movement primitives (DMPs). We show how DMPs dramatically simplify the learning problem, and discuss the implications of this for past and future work on policy improvement for robot skill learning


title: Power-Delay Tradeoff in Multi-User Mobile-Edge Computing Systems

abstract: Mobile-edge computing (MEC) has recently emerged as a promising paradigm to liberate mobile devices from increasingly intensive computation workloads, as well as to improve the quality of computation experience. In this paper, we investigate the tradeoff between two critical but conflicting objectives in multi-user MEC systems, namely, the power consumption of mobile devices and the execution delay of computation tasks. A power consumption minimization problem with task buffer stability constraints is formulated to investigate the tradeoff, and an online algorithm that decides the local execution and computation offloading policy is developed based on Lyapunov optimization. Specifically, at each time slot, the optimal frequencies of the local CPUs are obtained in closed forms, while the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method. Performance analysis is conducted for the proposed algorithm, which indicates that the power consumption and execution delay obeys an [O (1/V); O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters to the system performance.


title: Towards a theory of supply chain management: the constructs and measurements

abstract: Rising international cooperation, vertical disintegration, along with a focus on core activities have led to the notion that firms are links in a networked supply chain. This novel perspective has created the challenge of designing and managing a network of interdependent relationships developed and fostered through strategic collaboration. Although research interests in supply chain management (SCM) are growing, no research has been directed towards a systematic development of SCM instruments. This study identifies and consolidates various supply chain initiatives and factors to develop key SCM constructs conducive to advancing the field. To this end, we analyzed over 400 articles and synthesized the large, fragmented body of work dispersed across many disciplines. The result of this study, through successive stages of measurement analysis and refinement, is a set of reliable, valid, and unidimensional measurements that can be subsequently used in different contexts to refine or extend conceptualization and measurements or to test various theoretical models, paving the way for theory building in SCM. © 2004 Elsevier B.V. All rights reserved.


title: A Computational Model for Causal and Diagnostic Reasoning in Inference Systems

abstract: This paper introduces a representation of evidential relationships which permits updating of belief in two simultaneous modes: causal (i. e. top-down) and diagnostic (i.e. bottom-up). It extends the hierarchical tree representation by allowing multiple causes to a given manifestation. We develop an updating scheme that obeys the axioms of probability, is computationally efficient, and is compatible with experts reasoning. The belief parameters of each variable are defined and updated by those of its neighbors in such a way that the impact of each new evidence propagates and settles through the network in a single pass.


title: Data integration in machine learning

abstract: Modern data generated in many fields are in a strong need of integrative machine learning models in order to better make use of heterogeneous information in decision making and knowledge discovery. How data from multiple sources are incorporated in a learning system is key step for a successful analysis. In this paper, we provide a comprehensive review on data integration techniques from a machine learning perspective.


title: Evaluating Machine Learning for Improving Power Grid Reliability

abstract: Ensuring reliability as the electrical grid morphs into the “smart grid” will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance – in the future, we will not only react to failures, but also try to anticipate and avoid them using predictive modeling (machine learning) techniques. To help in meeting this challenge, we present the Neutral Online Visualization-aided Autonomic evaluation framework (NOVA) for evaluating machine learning algorithms for preventive maintenance on the electrical grid. NOVA has three stages provided through a unified user interface: evaluation of input data quality, evaluation of machine learning results, and evaluation of the reliability improvement of the power grid. A prototype version of NOVA has been deployed for the power grid in New York City, and it is able to evaluate machine learning systems effectively and efficiently. Appearing in the ICML 2011 Workshop on Machine Learning for Global Challenges, Bellevue, WA, USA, 2011. Copyright 2011 by the author(s)/owner(s).


title: Improving penetration testing through static and dynamic analysis

abstract: Penetration testing is widely used to help ensure the security of web applications. Using penetration testing, testers discover vulnerabilities by simulating attacks on a target web application. To do this efficiently, testers rely on automated techniques that gather input vector information about the target web application and analyze the application's responses to determine whether an attack was successful. Techniques for performing these steps are often incomplete, which can leave parts of the web application untested and vulnerabilities undiscovered. This paper proposes a new approach to penetration testing that addresses the limitations of current techniques. The approach incorporates two recently developed analysis techniques to improve input vector identification and detect when attacks have been successful against a web application. This paper compares the proposed approach against two popular penetration testing tools for a suite of web applications with known and unknown vulnerabilities. The evaluation results show that the proposed approach performs a more thorough penetration testing and leads to the discovery of more vulnerabilities than both the tools. Copyright © 2011 John Wiley & Sons, Ltd.