title: Cluster Analysis for Large, High-Dimensional Datasets: Methodology and Applications

abstract: Cluster analysis represents one of the most versatile methods in statistical science. It is employed in empirical sciences for the summarization of datasets into groups of similar objects, with the purpose of facilitating the interpretation and further analysis of the data. Cluster analysis is of particular importance in the exploratory investigation of data of high complexity, such as that derived from molecular biology or image databases. Consequently, recent work in the field of cluster analysis has focused on designing algorithms that can provide meaningful solutions for data with high cardinality and/or dimensionality, under the natural restriction of limited resources. The present thesis aims to develop improved methods for the clustering of high-dimensional datasets, as well as further applications of such algorithms in practical settings. ::: In the first part of the thesis, a more detailed review of the representative clustering algorithms focused on the analysis of very large or high-dimensional datasets is provided. Subsequently, a newly developed method for this purpose is described and evaluated. The developed algorithm is based on the principles of projection pursuit and grid partitioning, and focuses on reducing computational requirements for large datasets without loss of performance. In the second part of the thesis, a novel method for generating synthetic datasets with variable structure and clustering difficulty, that is aimed at evaluating clustering algorithms is presented. In the third part of the thesis, the applications of cluster analysis to the field of automatic image classification are investigated. A novel system for the semi-supervised annotation of images is described and evaluated. The system is based on a vocabulary of clusters of visual features extracted from images with known classification.


title: Reduction of Speckle Noise and Image Enhancement of Images Using Filtering Technique

abstract: Reducing noise from the medical images, a satellite image etc. is a challenge for the researchers in digital image processing. Several approaches are there for noise reduction. Generally speckle noise is commonly found in synthetic aperture radar images, satellite images and medical images. This paper proposes filtering techniques for the removal of speckle noise from the digital images. Quantitative measures are done by using signal to noise ration and noise level is measured by the standard deviation.


title: Exploring social tagging for personalized community recommendations

abstract: Users of social Web sites actively create and join communities as a way to collectively share their media content and rich experience with diverse groups of people. In this study we focus on the issue of recommending social communities (or groups) to individual users. We address specifically the potential of social tagging for accentuating users’ interests and characterizing communities. We also discuss some unique methods of improving several techniques that have been adapted for use in the context of community recommendations: collaborative filtering, a random walk model, a Katz influence model, a latent semantic model, and a user-centric tag model. We effectively incorporate social tagging information in each algorithm. We present empirical evaluations using real datasets from CiteULike and Last.fm. Our experimental results demonstrate that the different algorithms incorporated with social tagging offer significant advantages in improving both the recommendation quality and coverage, and demonstrate their feasibility for community recommendations in dealing with sparsity-related limitations.


title: Evaluation Measures for Models Assessment over Imbalanced Data Sets

abstract: Imbalanced data learning is one of the challenging problems in data mining; among this matter, founding the right model assessment measures is almost a primary research issue. Skewed class distribution causes a misreading of common evaluation measures as well it lead a biased classification. This article presents a set of alternative for imbalanced data learning assessment, using a combined measures (G-means, likelihood ratios, Discriminant power, F-Measure Balanced Accuracy, Youden index, Matthews correlation coefficient), and graphical performance assessment (ROC curve, Area Under Curve, Partial AUC, Weighted AUC, Cumulative Gains Curve and lift chart, Area Under Lift AUL), that aim to provide a more credible evaluation. We analyze the applications of these measures in churn prediction models evaluation, a well known application of imbalanced data Keywords: imbalanced data, Model assessment, accuracy , G-means, likelihood ratios, F-Measure, Youden index, Matthews correlation coefficient, ROC, AUC, P-AUC,W-AUC, Lift, AUL


Physical activity recognition from sub-bandage sensors using both feature selection and extraction


title: A six-axis micro platform for in situ calibration of MEMS inertial sensors

abstract: This paper reports an advanced multi-axis micro actuation and sensing platform for in situ self-calibration of generic MEMS inertial sensors. Compared to previously reported platforms, the device provides the best performance at smallest footprint, with six-axis actuation at low power consumption, off-axis motion compensation, closed-loop capacitive sensing of applied stimulus, integrated interconnects to an inertial sensor load, and increased shock protection. The entire platform is confined to 9×9 mm2.


title: Stable Marriage with Multi-Modal Preferences

abstract: We introduce a generalized version of the famous Stable Marriage problem, now based on multi-modal preference lists. The central twist herein is to allow each agent to rank its potentially matching counterparts based on more than one"evaluation mode"(e.g., more than one criterion); thus, each agent is equipped with multiple preference lists, each ranking the counterparts in a possibly different way. We introduce and study three natural concepts of stability, investigate their mutual relations and focus on computational complexity aspects with respect to computing stable matchings in these new scenarios. Mostly encountering computational hardness (NP-hardness), we can also spot few islands of tractability and make a surprising connection to the \textsc{Graph Isomorphism} problem.


title: Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting

abstract: This paper is concerned with identifying the arm with the highest mean in a multi-armed bandit problem using as few independent samples from the arms as possible. While the so-called “best arm problem” dates back to the 1950s, only recently were two qualitatively different algorithms proposed that achieve the optimal sample complexity for the problem. This paper reviews these recent advances and shows that most best-arm algorithms can be described as variants of the two recent optimal algorithms. For each algorithm type we consider a specific instance to analyze both theoretically and empirically thereby exposing the core components of the theoretical analysis of these algorithms and intuition about how the algorithms work in practice. The derived sample complexity bounds are novel, and in certain cases improve upon previous bounds. In addition, we compare a variety of state-of-the-art algorithms empirically through simulations for the best-arm-problem.


title: An Empirical Study of Application of Data Mining Techniques in Library System

abstract: Few years ago, the information flow in library was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that libraries face is the explosive growth of library data and to use this data to improve the quality of managerial decisions. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper addresses the applications of data mining in library to extract useful information from the huge data sets and providing analytical tool to view and use this information for decision making processes by taking real life examples.


title: TWINE: A real-time system for TWeet analysis via INformation Extraction

abstract: In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and sophisticated systems for capturing and understanding the underlying information ::: enclosed in them. In this paper we present TWINE, a real-time ::: system for the big data analysis and exploration of information extracted from Twitter streams. The proposed system based on a Named Entity Recognition and Linking pipeline and a multi-dimensional spatial geo-localization is managed by a scalable and flexible architecture for an interactive visualization of micropost streams insights. The demo is available at ::: http://twine-mind.cloudapp.net/streaming


title: Compressed Sensing for Bioelectric Signals: A Review

abstract: This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.


title: Simulation-based control of building systems operation

abstract: Abstract Building performance simulation research has traditionally concentrated on the building design phase. However, computational modeling can also apply to the building operation phase. To explore this potential, we (a) describe the model-based building systems control idea, (b) illustrate this idea using a simple computational scenario pertaining to the control of the thermal environment in buildings, (c) report on a prototypical implementation of a model-based lighting control strategy.


Early sea changes in borderline personality disorder.


title: Assemblies of Carbon Nanotubes and Unencapsulated Sub-10-nm Gold Nanoparticles

abstract: The development ofassemblies consisting ofunencapsulated, sub-10-nm gold particles attached to individual carbon nanotubes (CNTs) with diameters of 2 nm is described. The assemblies are formed on the surface ofa porous anodic alumina (PAA) template on which the CNTs (singleor double-walled) are grown by plasma-enhanced chemical vapor deposition. The Au nanoparticles are formed through an indirect evaporation technique using a silicon nitride membrane mask, and diffuse along the PAA surface into the regions containing CNTs. The nanoparticles bind relatively strongly to the CNTs, as indicated by observations ofnanoparticles that are suspended over pores or that move along with the CNTs. This approach may provide a new method to functionalize CNTs for chemical or biological sensing and fundamental studies of nanoscale contacts to CNTs.


title: Conjoined twins: implications for blastogenesis.

abstract: It is difficult to draw sweeping general conclusions about the blastogenesis of CT, principally because so few thoroughly studied cases are reported. It is to be hoped that methods such as painstaking gross or electronic dissection will increase the number of well-documented cases. Nevertheless, the following conclusions can be proposed: 1. Most CT can be classified into a few main anatomic types (or paradigms), and there are also rare transitional types that show gradation between the main types. 2. Most CT have two full notochordal axes (Fig. 5); the ventral organs induced along these axes may be severely disorientated, malformed, or aplastic in the process of being arranged within one body. Reported anatomic types of CT represent those notochordal arrangements that are compatible with reasonably complete embryogenesis. New ventro-lateral axes are formed in many types of CT because of space constriction in the ventral zones. The new structures represent areas of "mutual recognition and organization" rather than "fusion" (Fig. 17). 3. Orientations of the pairs of axes in the embryonic disc can be deduced from the resulting anatomy. Except for dicephalus, the axes are not side by side. Notochords are usually "end-on" or ventro-ventral in orientation (Fig. 5). 4. A single gastrulation event or only partial duplicated gastrulation event seems to occur in dicephalics, despite a full double notochord. 5. The anatomy of diprosopus requires further clarification, particularly in cases with complete crania rather than anencephaly-equivalent. Diprosopus CT offer the best opportunity to study the effects of true forking of the notochord, if this actually occurs. 6. In cephalothoracopagus, thoracopagus, and ischiopagus, remarkably complete new body forms are constructed at right angles to the notochordal axes. The extent of expression of viscera in these types depends on the degree of noncongruity of their ventro-ventral axes (Figs. 4, 11, 15b). 7. Some organs and tissues fail to develop (interaction aplasia) because of conflicting migrational pathways or abnormal concentrations of morphogens in and around the neoaxes. 8. Where the cardiovascular system is discordantly expressed in dicephalus and thoracopagus twins, the right heart is more severely malformed, depending on the degree of interaction of the two embryonic septa transversa. 9. The septum transversum provides mesenchymal components to the heawrt and liver; the epithelial components (derived fro the foregut[s]) may vary in number from the number of mesenchymal septa transversa contributing to the liver of the CT embryo.(ABSTRACT TRUNCATED AT 400 WORDS)


title: Tracking the Orientation and Axes Lengths of an Elliptical Extended Object

abstract: Extended object tracking considers the simultaneous estimation of the kinematic state and the shape parameters of a moving object based on a varying number of noisy detections. The main challenge in extended object tracking is the nonlinearity and high dimensionality of the estimation problem. This study presents compact closed-form expressions for a recursive Kalman filter that explicitly estimates the orientation and axes lengths of an extended object based on detections that are scattered over the object surface (according to a Gaussian distribution). Existing approaches are either based on Monte Carlo approximations or do not allow for explicitly maintaining all ellipse parameters. The performance of the novel approach is demonstrated with respect to the state of the art by means of simulations.


title: A reality check for content centric networking

abstract: Content-Centric Networking (CCN) is a novel networking paradigm centered around content distribution rather than host-to-host connectivity. This change from host-centric to content-centric has several attractive advantages, such as network load reduction, low dissemination latency, and energy efficiency. However, it is unclear whether today's technology is ready for the CCN (r)evolution. The major contribution of this paper is a systematic evaluation of the suitability of existing software and hardware components in today's routers for the support of CCN. Our main conclusion is that a CCN deployment is feasible at a Content Distribution Network (CDN) and ISP scale, whereas today's technology is not yet ready to support an Internet scale deployment.


title: Penalized-Distance Volumetric Skeleton Algorithm

abstract: Introduces a refined general definition of a skeleton that is based on a penalized distance function and that cannot create any of the degenerate cases of the earlier CEASAR (Center-line Extraction Algorithm-Smooth, Accurate and Robust) and TEASAR (Tree-structure Extraction Algorithm for Skeletons-Accurate and Robust) algorithms. Additionally, we provide an algorithm that finds the skeleton accurately and rapidly. Our solution is fully automatic, which frees the user from having to engage in manual data pre-processing. We present the accurate skeletons computed on a number of test data sets. The algorithm is very efficient, as demonstrated by the running times, which were all below seven minutes.


Articulatory feature-based methods for acoustic and audio-visual speech recognition: 2006 JHU summer workshop final report


On The Architecture Scheduling Problem Of Industry 4.0


title: Learning Class-specific Word Representations for Early Detection of Hoaxes in Social Media.

abstract: As people increasingly use social media as a source for news consumption, its unmoderated nature enables the diffusion of hoaxes, which in turn jeopardises the credibility of information gathered from social media platforms. To mitigate this problem, we study the development of a hoax detection system that can distinguish true and false reports early on. We introduce a semi-automated approach that leverages the Wikidata knowledge base to build large-scale datasets for veracity classification, which enables us to create a dataset with 4,007 reports including over 13 million tweets, 15% of which are fake. We describe a method for learning class-specific word representations using word embeddings, which we call multiw2v. Our approach achieves competitive results with F1 scores over 72% within 10 minutes of the first tweet being posted, outperforming other baselines. Our dataset represents a realistic scenario with a real distribution of true and false stories, which we release for further use as a benchmark in future research.


title: Unveiling clusters of events for alert and incident management in large-scale enterprise it

abstract: Large enterprise IT (Information Technology) infrastructure components generate large volumes of alerts and incident tickets. These are manually screened, but it is otherwise difficult to extract information automatically from them to gain insights in order to improve operational efficiency. We propose a framework to cluster alerts and incident tickets based on the text in them, using unsupervised machine learning. This would be a step towards eliminating manual classification of the alerts and incidents, which is very labor intense and costly. Our framework can handle the semi-structured text in alerts generated by IT infrastructure components such as storage devices, network devices, servers etc., as well as the unstructured text in incident tickets created manually by operations support personnel. After text pre-processing and application of appropriate distance metrics, we apply different graph-theoretic approaches to cluster the alerts and incident tickets, based on their semi-structured and unstructured text respectively. For automated interpretation and read-ability on semi-structured text clusters, we propose a method to visualize clusters that preserves the structure and human-readability of the text data as compared to traditional word clouds where the text structure is not preserved; for unstructured text clusters, we find a simple way to define prototypes of clusters for easy interpretation. This framework for clustering and visualization will enable enterprises to prioritize the issues in their IT infrastructure and improve the reliability and availability of their services.


title: Learning Structured Sparsity in Deep Neural Networks

abstract: High demand for computation resources severely hinders deployment of large-scale Deep Neural Networks (DNN) in resource constrained devices. In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs. SSL can: (1) learn a compact structure from a bigger DNN to reduce computation cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently accelerate the DNNs evaluation. Experimental results show that SSL achieves on average 5.1x and 3.1x speedups of convolutional layer computation of AlexNet against CPU and GPU, respectively, with off-the-shelf libraries. These speedups are about twice speedups of non-structured sparsity; (3) regularize the DNN structure to improve classification accuracy. The results show that for CIFAR-10, regularization on layer depth can reduce 20 layers of a Deep Residual Network (ResNet) to 18 layers while improve the accuracy from 91.25% to 92.60%, which is still slightly higher than that of original ResNet with 32 layers. For AlexNet, structure regularization by SSL also reduces the error by around ~1%. Open source code is in this https URL


title: Multiagent-Based Reinforcement Learning for Optimal Reactive Power Dispatch

abstract: This paper proposes a fully distributed multiagent-based reinforcement learning method for optimal reactive power dispatch. According to the method, two agents communicate with each other only if their corresponding buses are electrically coupled. The global rewards that are required for learning are obtained with a consensus-based global information discovery algorithm, which has been demonstrated to be efficient and reliable. Based on the discovered global rewards, a distributed Q-learning algorithm is implemented to minimize the active power loss while satisfying operational constraints. The proposed method does not require accurate system model and can learn from scratch. Simulation studies with power systems of different sizes show that the method is very computationally efficient and able to provide near-optimal solutions. It can be observed that prior knowledge can significantly speed up the learning process and decrease the occurrences of undesirable disturbances. The proposed method has good potential for online implementation.


title: Induction Heating Appliance With a Mobile Double-Coil Inductor

abstract: An induction heating appliance designed to uniformly heat up metallic plates is studied in this paper. It consists of one planar inductor with two concentric coils attached to a mechanism, which allows moving the inductor under the plate while heating. This system is a possible solution for the growing concept of flexible induction cooking hobs, improving their performance in flexibility and in thermal distribution in the pans. With different combinations of motion and the selective activation of the inductor coils, any pan can be uniformly heated regardless its size or position on the hob. In this paper, we develop a thermal model to analyze the temperature distribution obtained in the pans for each diameter and strategy used. The model is solved using finite differences, and it is validated with experimental measurements. From the calculations, the best strategy for each pan diameter is obtained.


title: A dynamic traffic sharing with minimal administration on multihomed networks

abstract: Multihomed network is one of the most efficient configuration to improve the response time of network services. However, it is hard to introduce or manage because the existing configuration methods have several problems in that they require much technical skill, involve administrative over-burden for the administrator and so on. In this paper, we propose a dynamic traffic sharing technique and suitable backbone selection metrics to address some of these problems. Using the proposed technique, an appropriate backbone can be selected per connection with minimal technical skill and low administrative cost. In addition, the proposed metrics performs more efficient traffic sharing as compared to others techniques that were investigated.


Supply Chain Risk Assessment in the Fashion Retail Industry : An Analytic Network Process Approach


title: Rediscovering the later version of Maslow's hierarchy of needs: Self-transcendence and opportunities for theory, research, and unification.

abstract: The conventional description of Abraham Maslow’s (1943, 1954) hierarchy of needs is inaccurate as a description of Maslow’s later thought. Maslow (1969a) amended his model, placing self-transcendence as a motivational step beyond self-actualization. Objections to this reinterpretation are considered. Possible reasons for the persistence of the conventional account are described. Recognizing self-transcendence as part of Maslow’s hierarchy has important consequences for theory and research: (a) a more comprehensive understanding of worldviews regarding the meaning of life; (b) broader understanding of the motivational roots of altruism, social progress, and wisdom; (c) a deeper understanding of religious violence; (d) integration of the psychology of religion and spirituality into the mainstream of psychology; and (e) a more multiculturally integrated approach to psychological theory.


title: Incremental FP-Growth mining strategy for dynamic threshold value and database based on MapReduce

abstract: With the coming of the Big Data era, data mining has been confronted with new opportunities and challenges. Some limitations are exposed when traditional association rule mining algorithms are used to deal with large-scale data. In the Apriori algorithm, scanning the external storage repeatedly leads to high I/O load and brings about low performance. As for FP-Growth algorithm, the effectiveness is limited by internal memory size because mining process is on the base of large tree-form data structure. What's more, although remarkable achievements have been scored, there are still problems in dynamic scenarios. The paper presents a parallelized incremental FP-Growth mining strategy based on MapReduce, which aims to process large-scale data. The proposed incremental algorithm realizes effective data mining when threshold value and original database change at the same time. This novel algorithm is implemented on Hadoop and shows great advantages according to the experimental results.


title: Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation

abstract: To have a continuous navigation solution that does not suffer from interruption, GPS is integrated with relative positioning techniques such as odometry and inertial navigation. Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced multisensor system consisting of one microelectromechanical-system (MEMS)-based single-axis gyroscope used together with the vehicle's odometer, and the whole system is integrated with GPS. This system provides a 2-D navigation solution, which is adequate for land vehicles. The traditional technique for this multisensor integration problem is Kalman filtering (KF). Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, the KF with its linearized models has limited capabilities in providing accurate positioning. Particle filtering (PF) has recently been suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions. An enhanced version of PF is utilized in this paper and is called the Mixture PF. Since PF can accommodate nonlinear models, this paper uses total-state nonlinear system and measurement models. In addition, sophisticated models are used to model the stochastic drift of the MEMS-based gyroscope. A nonlinear system identification technique based on parallel cascade identification (PCI) is used to model this stochastic gyroscope drift. In this paper, the performance of the PCI model is compared with that of higher order autoregressive (AR) stochastic models. Such higher order models are difficult to use with KF since the size of the dynamic matrix and the error-covariance matrix becomes very large and complicates the KF operation. The performance of the proposed 2-D navigation solution using Mixture PF with both PCI and higher order AR models is examined by road-test trajectories in a land vehicle. The two proposed combinations are compared with four other 2-D solutions: a Mixture PF with the Gauss-Markov (GM) model for the gyro drift, a Mixture PF with only white Gaussian noise (WGN) for stochastic gyro errors, and two different KF solutions with GM model for the gyro drift. The experimental results show that the two proposed solutions outperform all the compared counterparts.


title: Automated Attacks on Compression-Based Classifiers

abstract: Methods of compression-based text classification have proven their usefulness for various applications. However, in some classification problems, such as spam filtering, a classifier confronts one or many adversaries willing to induce errors in the classifier's judgment on certain kinds of input. In this paper, we consider the problem of finding thrifty strategies for character-based text modification that allow an adversary to revert classifier's verdict on a given family of input texts. We propose three statistical statements of the problem that can be used by an attacker to obtain transformation models which are optimal in some sense. Evaluating these three techniques on a realistic spam corpus, we find that an adversary can transform a spam message (detectable as such by an entropy-based text classifier) into a legitimate one by generating and appending, in some cases, as few additional characters as 11% of the original length of the message.


title: The draft genome and transcriptome of Cannabis sativa

abstract: BackgroundCannabis sativa has been cultivated throughout human history as a source of fiber, oil and food, and for its medicinal and intoxicating properties. Selective breeding has produced cannabis plants for specific uses, including high-potency marijuana strains and hemp cultivars for fiber and seed production. The molecular biology underlying cannabinoid biosynthesis and other traits of interest is largely unexplored.ResultsWe sequenced genomic DNA and RNA from the marijuana strain Purple Kush using shortread approaches. We report a draft haploid genome sequence of 534 Mb and a transcriptome of 30,000 genes. Comparison of the transcriptome of Purple Kush with that of the hemp cultivar 'Finola' revealed that many genes encoding proteins involved in cannabinoid and precursor pathways are more highly expressed in Purple Kush than in 'Finola'. The exclusive occurrence of Δ9-tetrahydrocannabinolic acid synthase in the Purple Kush transcriptome, and its replacement by cannabidiolic acid synthase in 'Finola', may explain why the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC) is produced in marijuana but not in hemp. Resequencing the hemp cultivars 'Finola' and 'USO-31' showed little difference in gene copy numbers of cannabinoid pathway enzymes. However, single nucleotide variant analysis uncovered a relatively high level of variation among four cannabis types, and supported a separation of marijuana and hemp.ConclusionsThe availability of the Cannabis sativa genome enables the study of a multifunctional plant that occupies a unique role in human culture. Its availability will aid the development of therapeutic marijuana strains with tailored cannabinoid profiles and provide a basis for the breeding of hemp with improved agronomic characteristics.


title: Computing Multidimensional Persistence

abstract: The theory of multidimensional persistence captures the topology of a multifiltration -- a multiparameter family of increasing spaces. Multifiltrations arise naturally in the topological analysis of scientific data. In this paper, we give a polynomial time algorithm for computing multidimensional persistence. We recast this computation as a problem within computational algebraic geometry and utilize algorithms from this area to solve it. While the resulting problem is Expspace-complete and the standard algorithms take doubly-exponential time, we exploit the structure inherent withing multifiltrations to yield practical algorithms. We implement all algorithms in the paper and provide statistical experiments to demonstrate their feasibility.


title: Global optimization of factor models and dictionary learning using alternating minimization

abstract: Learning new representations of input observations in machine learning is often tackled using a factorization of the data. For many such problems, including sparse coding and matrix completion, learning these factorizations can be difficult, in terms of efficiency and to guarantee that the solution is a global minimum. Recently, a general class of objectives have been introduced-which we term induced dictionary learning models (DLMs)-that have an induced convex form that enables global optimization. Though attractive theoretically, this induced form is impractical, particularly for large or growing datasets. In this work, we investigate the use of practical alternating minimization algorithms for induced DLMs, that ensure convergence to global optima. We characterize the stationary points of these models, and, using these insights, highlight practical choices for the objectives. We then provide theoretical and empirical evidence that alternating minimization, from a random initialization, converges to global minima for a large subclass of induced DLMs. In particular, we take advantage of the existence of the (potentially unknown) convex induced form, to identify when stationary points are global minima for the dictionary learning objective. We then provide an empirical investigation into practical optimization choices for using alternating minimization for induced DLMs, for both batch and stochastic gradient descent.


title: The techniques for face recognition with support vector machines

abstract: The development of automatic visual control system is a very important research topic in computer vision. This face identification system must be robust to the various quality of the images such as light, face expression, glasses, beards, moustaches etc. We propose using the wavelet transformation algorithms for reduction the source data space. We have realized the method of the expansion of the values of pixels to the whole intensity range and the algorithm of the equalization of histogram to adjust image intensity values. The support vector machines (SVM) technology has been used for the face recognition in our work.


title: Viability Analysis of a Decentralized Energy Market Based on Blockchain

abstract: This paper considers the design, the development and the experimental evaluation, of a blockchain based smart contract specifying the operating rules of a real time, uniform-price double auction energy market. Producers and consumers interact with this contract sending their offers and bids accordingly and the contract clears the market based on a double auction model. We propose four different approaches for implementing, through the Ethereum platform, both the P2P network as well as the smart contract. We systematically compare the above approaches on the basis of their decentralization nature, operating costs, computational costs, effectiveness, security, privacy and beyond. This comparison is achieved through large scale, real time simulations based on the GridLAB-D platform.


title: Rethinking risk management

abstract: This paper presents a theory of corporate risk management that attempts to go beyond the "variance-minimization" model that dominates most academic discussions of the subject. It argues that the primary goal of risk management is not to dampen swings in corporate cash flows or value, but rather to provide protection against the possibility of "costly lower-tail outcomes"-situations that would cause financial distress or make a company unable to carry out its investment strategy. (In the jargon of finance specialists, risk management can be viewed as the purchase of well-out-of-the-money put options designed to limit downside risk.) 1996 Morgan Stanley.


title: Analysis of magnetic circuit on ALA rotor synchronous reluctance motor

abstract: This article provides a novel analytical method of magnetic circuit on Axially-Laminated Anisotropic (ALA) rotor synchronous reluctance motor when the motor is magnetized on the d-axis. To simplify the calculation, the reluctance of stator magnet yoke and rotor magnetic laminations and leakage magnetic flux all are ignored. With regard to the uneven air-gap brought by the teeth and slots of the stator and rotor, the method resolves the problem with the equivalent air-gap length distribution function, and clarifies the magnetic circuit when the stator teeth are saturated or unsaturated. In order to conduct exact computation, the high order harmonics of the stator magnetic potential are also taken into account.


title: Natural language Interface for Database: A Brief review

abstract: Information is playing an important role in our lives. One of the major sources of information is databases. Databases and database technology are having major impact on the growing use of computers. Almost all IT applications are storing and retrieving information from databases. Retrieving information database requires knowledge of database languages like SQL. The Structured Query Language (SQL) norms are been pursued in almost all languages for relational database systems. However, not everybody is able to write SQL queries as they may not be aware of the structure of the database. So this has led to the development of Intelligent Database System ( IDBS) . There is an overwhelming need for non-expert users to query relational databases in their natural language instead of working with the values of the attributes. As a result many intelligent natural language interfaces to databases have been developed, which provides flexible options for manipulating queries. The idea of using Natural Language instead of SQL has prompted the development of new type of processing called Natural language Interface to Database. NLIDB is a step towards the development of intelligent database systems (IDBS) to enhance the users in performing flexible querying in databases. This paper is an introduction to Intelligent Database System and Natural Language Interface to Databases. Then a brief overview of NLIDB subcomponents is given and then discussion then moves on to NLIDB architectures and various approaches for the development of NLIDB systems.


title: PREDICTING MOVIE SUCCESS AND ACADEMY AWARDS THROUGH SENTIMENT AND SOCIAL NETWORK ANALYSIS

abstract: This paper introduces a new Web mining approach that combines social network analysis and automatic sentiment analysis. We show how weighting the forum posts of the contributors according to their network position allow us to predict trends and real world events in the movie business. To test our approach we conducted two experiments analyzing online forum discussions on the Internet movie database (IMDb) by examining the correlation of the social network structure with external metrics such as box office revenue and Oscar Awards. We find that discussion patterns on IMDb predict Academy Awards nominations and box office success. Two months before the Oscars were given we were able to correctly predict nine Oscar nominations. We also found that forum contributions correlated with box office success of 20 top grossing movies of 2006.


title: Spatial Features Based No Reference Image Quality Assessment for JPEG2000

abstract: Perceptual image quality evaluation has become an important issue due to increasing transmission of multimedia contents over the Internet and 3G mobile networks. Most of the no reference perceptual image quality evaluations traditionally attempted to quantify the predefined artifacts of the coded images. Under the assumption that human visual perception is very sensitive to edge information of an image and any kinds of artifacts create pixel distortion, we propose a new philosophy for designing a no reference image quality evaluation model for JPEG2000 images, which uses pixel distortions and edge information. Subjective experiment results on the images are used to train and test the model, which achieve good quality prediction performance.


title: OptaSense: Fibre Optic Distributed Acoustic Sensing for Border Monitoring

abstract: The OptaSense® Distributed Acoustic Sensing (DAS) system is an acoustic and seismic sensing capability that uses simple fibre optic communications cables as the sensor. Using existing or new cables, it can provide low-cost and high-reliability surface crossing and tunnel construction detection, with power and communications services needed only every 80-100 km. The technology has been proven in worldwide security operations at over one hundred locations in a variety of industries including oil and gas pipelines, railways, and high-value facility perimeters -- a total of 100,000,000 kilometre-hours of linear asset protection. The system reliably detects a variety of border threats with very few nuisance alarms. It can work in concert with existing border surveillance technologies to provide security personnel a new value proposition for fighting trans-border crime. Its ability to detect, classify and locate activity over hundreds of kilometres and provide information in an accurate and actionable way has proven OptaSense to be a cost-effective solution for monitoring long borders. It has been scaled to cover 1500 km controlled by a single central monitoring station in pipeline applications.


title: End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning

abstract: This paper presents a model for end-to-end learning of task-oriented dialog systems. The main component of the model is a recurrent neural network (an LSTM), which maps from raw dialog history directly to a distribution over system actions. The LSTM automatically infers a representation of dialog history, which relieves the system developer of much of the manual feature engineering of dialog state. In addition, the developer can provide software that expresses business rules and provides access to programmatic APIs, enabling the LSTM to take actions in the real world on behalf of the user. The LSTM can be optimized using supervised learning (SL), where a domain expert provides example dialogs which the LSTM should imitate; or using reinforcement learning (RL), where the system improves by interacting directly with end users. Experiments show that SL and RL are complementary: SL alone can derive a reasonable initial policy from a small number of training dialogs; and starting RL optimization with a policy trained with SL substantially accelerates the learning rate of RL.


title: HEVC saliency map computation

abstract: This paper investigates whether the information related to the human visual saliency is still preserved at the level of the HEVC compressed stream syntax elements. In this respect, a new saliency model, matched to the peculiarities of this emerging standard is defined. It consists of four elementary maps, describing the four main saliency features: intensity, color, orientation and motion. These maps are defined based on the energies of the luma and chroma coefficients, on the variations of the intra prediction modes and on the energy of motion vectors, respectively. They are fusioned according to 48 static and static-dynamic pooling formulas. The results are compared to three state-of-the-art uncompressed (pixel) domain as well as to the MPEG-4 AVC compressed domain saliency maps. It is brought to light that the HEVC saliency model outperforms (with singular exceptions) the state-of-the-art uncompressed domain and is as good as MPEG-4 AVC saliency model. We can thus state that, as its MPEG-4 AVC ancestor, although not designed based upon visual saliency principles, the HEVC compression standard preserves this human visual property at the level of its syntax elements


title: The effects of multiview depth video compression on multiview rendering

abstract: This article investigates the interaction between different techniques for depth compression and view synthesis rendering with multiview video plus scene depth data. Two different approaches for depth coding are compared, namely H.264/MVC, using temporal and inter-view reference images for efficient prediction, and the novel platelet-based coding algorithm, characterized by being adapted to the special characteristics of depth-images. Since depth-images are a 2D representation of the 3D scene geometry, depth-image errors lead to geometry distortions. Therefore, the influence of geometry distortions resulting from coding artifacts is evaluated for both coding approaches in two different ways. First, the variation of 3D surface meshes is analyzed using the Hausdorff distance and second, the distortion is evaluated for 2D view synthesis rendering, where color and depth information are used together to render virtual intermediate camera views of the scene. The results show that-although its rate-distortion (R-D) performance is worse-platelet-based depth coding outperforms H.264, due to improved sharp edge preservation. Therefore, depth coding needs to be evaluated with respect to geometry distortions.


title: mm-Wave Silicon ICs: Challenges and Opportunities

abstract: Millimeter-waves offer promising opportunities and interesting challenges to silicon integrated circuit and system designers. These challenges go beyond standard circuit design questions and span a broader range of topics including wave propagation, antenna design, and communication channel capacity limits. It is only meaningful to evaluate the benefits and shortcoming of silicon-based mm-wave integrated circuits in this broader context. This paper reviews some of these issues and presents several solutions to them.


title: Recognising textual entailment with robust logical inference

abstract: We use logical inference techniques for recognising textual entailment, with theorem proving operating on deep semantic interpretations as the backbone of our system. However, the performance of theorem proving on its own turns out to be highly dependent on a wide range of background knowledge, which is not necessarily included in publically available knowledge sources. Therefore, we achieve robustness via two extensions. Firstly, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. Secondly, we use machine learning to combine these deep semantic analysis techniques with simple shallow word overlap. The resulting hybrid model achieves high accuracy on the RTE testset, given the state of the art. Our results also show that the various techniques that we employ perform very differently on some of the subsets of the RTE corpus and as a result, it is useful to use the nature of the dataset as a feature.


title: Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

abstract: Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction.


title: Proximal Alternating Direction Method with Relaxed Proximal Parameters for the Least Squares Covariance Adjustment Problem

abstract: We consider the problem of seeking a symmetric positive semidefinite matrix in a closed convex set to approximate a given matrix. This problem may arise in several areas of numerical linear algebra or come from finance industry or statistics and thus has many applications. For solving this class of matrix optimization problems, many methods have been proposed in the literature. The proximal alternating direction method is one of those methods which can be easily applied to solve these matrix optimization problems. Generally, the proximal parameters of the proximal alternating direction method are greater than zero. In this paper, we conclude that the restriction on the proximal parameters can be relaxed for solving this kind of matrix optimization problems. Numerical experiments also show that the proximal alternating direction method with the relaxed proximal parameters is convergent and generally has a better performance than the classical proximal alternating direction method.


title: The prognosis of common mental disorders in adolescents: a 14-year prospective cohort study

abstract: Summary Background Most adults with common mental disorders report their first symptoms before 24 years of age. Although adolescent anxiety and depression are frequent, little clarity exists about which syndromes persist into adulthood or resolve before then. In this report, we aim to describe the patterns and predictors of persistence into adulthood. Methods We recruited a stratified, random sample of 1943 adolescents from 44 secondary schools across the state of Victoria, Australia. Between August, 1992, and January, 2008, we assessed common mental disorder at five points in adolescence and three in young adulthood, commencing at a mean age of 15·5 years and ending at a mean age of 29·1 years. Adolescent disorders were defined on the Revised Clinical Interview Schedule (CIS-R) at five adolescent measurement points, with a primary cutoff score of 12 or higher representing a level at which a family doctor would be concerned. Secondary analyses addressed more severe disorders at a cutoff of 18 or higher. Findings 236 of 821 (29%; 95% CI 25–32) male participants and 498 of 929 (54%; 51–57) female participants reported high symptoms on the CIS-R (≥12) at least once during adolescence. Almost 60% (434/734) went on to report a further episode as a young adult. However, for adolescents with one episode of less than 6 months duration, just over half had no further common mental health disorder as a young adult. Longer duration of mental health disorders in adolescence was the strongest predictor of clear-cut young adult disorder (odds ratio [OR] for persistent young adult disorder vs none 3·16, 95% CI 1·86–5·37). Girls (2·12, 1·29–3·48) and adolescents with a background of parental separation or divorce (1·62, 1·03–2·53) also had a greater likelihood of having ongoing disorder into young adulthood than did those without such a background. Rates of adolescent onset disorder dropped sharply by the late 20s (0·57, 0·45–0·73), suggesting a further resolution for many patients whose symptoms had persisted into the early 20s. Interpretation Episodes of adolescent mental disorder often precede mental disorders in young adults. However, many such disorders, especially when brief in duration, are limited to the teenage years, with further symptom remission common in the late 20s. The resolution of many adolescent disorders gives reason for optimism that interventions that shorten the duration of episodes could prevent much morbidity later in life. Funding Australia's National Health and Medical Research Council.


title: 23.5 A dual 64Gbaud 10kΩ 5% THD linear differential transimpedance amplifier with automatic gain control in 0.13µm BiCMOS technology for optical fiber coherent receivers

abstract: Long-haul optical links are experiencing a transition to coherent techniques because they enable the use of modulation techniques with high spectral efficiency, such as QPSK and QAM [1], which in turn require highly linear low-noise optical-electrical front-ends. To cope with the ever-growing demand for high data rates in the consumer market, the Ethernet standard for long-haul optical links is moving soon from 100 to 400Gb/s. A possible candidate symbol rate to reach this bit rate using complex modulation formats is 64Gbaud.


title: Bimodal Content Defined Chunking for Backup Streams

abstract: Data deduplication has become a popular technology for reducing the amount of storage space necessary for backup and archival data. Content defined chunking (CDC) techniques are well established methods of separating a data stream into variable-size chunks such that duplicate content has a good chance of being discovered irrespective of its position in the data stream. Requirements for CDC include fast and scalable operation, as well as achieving good duplicate elimination. While the latter can be achieved by using chunks of small average size, this also increases the amount of metadata necessary to store the relatively more numerous chunks, and impacts negatively the system's performance. We propose a new approach that achieves comparable duplicate elimination while using chunks of larger average size. It involves using two chunk size targets, and mechanisms that dynamically switch between the two based on querying data already stored; we use small chunks in limited regions of transition from duplicate to nonduplicate data, and elsewhere we use large chunks. The algorithms rely on the block store's ability to quickly deliver a high-quality reply to existence queries for already-stored blocks. A chunking decision is made with limited lookahead and number of queries. We present results of running these algorithms on actual backup data, as well as four sets of source code archives. Our algorithms typically achieve similar duplicate elimination to standard algorithms while using chunks 2-4 times as large. Such approaches may be particularly interesting to distributed storage systems that use redundancy techniques (such as error-correcting codes) requiring multiple chunk fragments, for which metadata overheads per stored chunk are high. We find that algorithm variants with more flexibility in location and size of chunks yield better duplicate elimination, at a cost of a higher number of existence queries.


title: Accelerated Methods for Deep Reinforcement Learning

abstract: Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs. We confirm that both policy gradient and Q-value learning algorithms can be adapted to learn using many parallel simulator instances. We further find it possible to train using batch sizes considerably larger than are standard, without negatively affecting sample complexity or final performance. We leverage these facts to build a unified framework for parallelization that dramatically hastens experiments in both classes of algorithm. All neural network computations use GPUs, accelerating both data collection and training. Our results include using an entire NVIDIA DGX-1 to learn successful strategies in Atari games in single-digit minutes, using both synchronous and asynchronous algorithms.


title: Collision detection for volumetric objects

abstract: We propose a probability model for the handling of complicated interactions between volumetric objects. In our model each volume is associated with a "probability map" that assigns a "surface crossing" probability to each space point according to local volume properties. The interaction between two volumes is then described by finding the intersecting regions between the volumes, and calculating the "collision probabilities" at each intersecting point from the surface crossing probabilities. To enable fast and efficient calculations, we introduce the concept of a distance map and develop two hierarchical collision detection algorithms, taking advantage of the uniform structure of volumetric datasets.


title: IoTSense: Behavioral Fingerprinting of IoT Devices

abstract: The Internet-of-Things (IoT) has brought in new challenges in, device identification --what the device is, and, authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or scalability problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. We believe that device fingerprinting can be used to solve both these problems effectively. In this work, we present a methodology to perform device behavioral fingerprinting that can be employed to undertake device type identification. A device behavior is approximated using features extracted from the network traffic of the device. These features are used to train a machine learning model that can be used to detect similar device types. We validate our approach using five-fold cross validation; we report a identification rate of 86-99% and a mean accuracy of 99%, across all our experiments. Our approach is successful even when a device uses encrypted communication. Furthermore, we show preliminary results for fingerprinting device categories, i.e., identifying different device types having similar functionality.


title: Using Software-Defined Networking for Ransomware Mitigation: the Case of CryptoWall

abstract: Currently, different forms of ransomware are increasingly threatening Internet users. Modern ransomware encrypts important user data, and it is only possible to recover it once a ransom has been paid. In this article we show how software-defined networking can be utilized to improve ransomware mitigation. In more detail, we analyze the behavior of popular ransomware — CryptoWall — and, based on this knowledge, propose two real-time mitigation methods. Then we describe the design of an SDN-based system, implemented using OpenFlow, that facilitates a timely reaction to this threat, and is a crucial factor in the case of crypto ransomware. What is important is that such a design does not significantly affect overall network performance. Experimental results confirm that the proposed approach is feasible and efficient.


title: Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

abstract: Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitatively and quantitatively, demonstrate the significance of our method.


title: The nursing shortage: solutions for the short and long term.

abstract: With predictions that this nursing shortage will be more severe and have a longer duration than has been previously experienced, traditional strategies implemented by employers will have limited success. The aging nursing workforce, low unemployment, and the global nature of this shortage compound the usual factors that contribute to nursing shortages. For sustained change and assurance of an adequate supply of nurses, solutions must be developed in several areas: education, healthcare deliver systems, policy and regulations, and image. This shortage is not solely nursing's issue and requires a collaborative effort among nursing leaders in practice and education, health care executives, government, and the media. This paper poses several ideas of solutions, some already underway in the United States, as a catalyst for readers to initiate local programs.


title: Intracranial EEG Correlates of Expectancy and Memory Formation in the Human Hippocampus and Nucleus Accumbens

abstract: The human brain is adept at anticipating upcoming events, but in a rapidly changing world, it is essential to detect and encode events that violate these expectancies. Unexpected events are more likely to be remembered than predictable events, but the underlying neural mechanisms for these effects remain unclear. We report intracranial EEG recordings from the hippocampus of epilepsy patients, and from the nucleus accumbens of depression patients. We found that unexpected stimuli enhance an early (187 ms) and a late (482 ms) hippocampal potential, and that the late potential is associated with successful memory encoding for these stimuli. Recordings from the nucleus accumbens revealed a late potential (peak at 475 ms), which increases in magnitude during unexpected items, but no subsequent memory effect and no early component. These results are consistent with the hypothesis that activity in a loop involving the hippocampus and the nucleus accumbens promotes encoding of unexpected events.


title: FACE: Automated Digital Evidence Discovery and Correlation

abstract: Digital forensic tools are being developed at a brisk pace in response to the ever increasing variety of forensic targets. Most tools are created for specific tasks - filesystem analysis, memory analysis, network analysis, etc. - and make little effort to interoperate with one another. This makes it difficult and extremely time-consuming for an investigator to build a wider view of the state of the system under investigation. In this work, we present FACE, a framework for automatic evidence discovery and correlation from a variety of forensic targets. Our prototype implementation demonstrates the integrated analysis and correlation of a disk image, memory image, network capture, and configuration log files. The results of this analysis are presented as a coherent view of the state of a target system, allowing investigators to quickly understand it. We also present an advanced open-source memory analysis tool, ramparser, for the automated analysis of Linux systems.


title: Control of underwater vehicle/manipulator with composite dynamics

abstract: This paper is devoted to the problem of control design of an underwater vehicle/manipulator (UVM) system composed of a free navigating platform equipped with a robot manipulator. This composite system is driven by actuators and sensors of different nature having substantially different bandwidth characteristics. Such a difference allows for a setup which can naturally treated by standard singular perturbation theory. The major finding here is that the computed torque control law can be substantially simplified while preserving the closed-loop stability, in the sense of perturbation theory.


title: The Essential Role and the Continuous Evolution of Modulation Techniques for Voltage-Source Inverters in the Past, Present, and Future Power Electronics

abstract: The cost reduction of power-electronic devices, the increase in their reliability, efficiency, and power capability, and lower development times, together with more demanding application requirements, has driven the development of several new inverter topologies recently introduced in the industry, particularly medium-voltage converters. New more complex inverter topologies and new application fields come along with additional control challenges, such as voltage imbalances, power-quality issues, higher efficiency needs, and fault-tolerant operation, which necessarily requires the parallel development of modulation schemes. Therefore, recently, there have been significant advances in the field of modulation of dc/ac converters, which conceptually has been dominated during the last several decades almost exclusively by classic pulse-width modulation (PWM) methods. This paper aims to concentrate and discuss the latest developments on this exciting technology, to provide insight on where the state-of-the-art stands today, and analyze the trends and challenges driving its future.


title: An Experience in the Prevention of HPV by and for Adolescents: A Community Randomized Trial of the Effect of Peer Health Education on Primary Prevention in a 1-Year Follow Up

abstract: Background: HPV is the most common sexually transmitted disease in many regions of the world. 15% was found in women aged 15-19 years but the highest incidence of infection from HPV can be found in sexually active adolescents: between 50% and 80% of them catch the infection within two to three years from their first sexual relationship. Methods: A community randomized trial regarding HPV infection and HPV vaccination, and sexual health was carried out, in a sample of secondary school students. Peer education intervention was carried out and 2 follows-up were carried out after the educative intervention to evaluate the effective outcomes in a 1-year follow up of the effect of peer health education on primary prevention knowledge, attitude and behaviour towards HPV. Results: The sample of 900 students, with age of 16.6 ± 1.4 ds, 34.4% of which have sexual relationships. 64.6% of students in the experimental group said that they were aware of HPV, 83.4% were aware of how it is transmitted and 71.1% knew HPV vaccination, 54.7% perceived the level of danger with significant gender-related differences the percentages increased at T1. At T0, 14.1% of females were vaccinated: T1 they were 17.5% and 19.2% at T2. The main factors associated with the students’ propensity to vaccination were: having at least one sister; being in favor of vaccinations in general; knowing that the vaccine is aimed at preventing cervical cancer; and being aware that they could be infected by HPV. Conclusions: The study carried out highlights important differences between the experimental group and the control group in terms of knowledge but, most importantly, in terms of behaviour and it proves how the application of new educational methods based on the involvement of youngsters right from the initial stages of the project can help them to change their behaviour and maintain it in time.


title: Performance comparison of lung ultrasound and chest x-ray for the diagnosis of pneumonia in the ED.

abstract: OBJECTIVE ::: The aim of our study was to assess the potential of bedside lung ultrasound examination by the attending emergency physician in the diagnosis of acute pneumonia. ::: ::: ::: MATERIAL AND METHODS ::: This observational single-center study was conducted between January 2010 and June 2012 in the emergency unit of a general hospital, and analyzed 144 adult patients. The ultrasound examination was performed by one of five trained emergency physicians, and a chest radiograph interpreted by a radiologist. The primary end point was the diagnosis of hospital discharge. ::: ::: ::: RESULTS ::: We found a sensitivity of 0.95 for the ultrasound examination against 0.6 for radiography (P < .05). The negative predictive value was 0.67 against 0.25 for radiography (P < .05). ::: ::: ::: CONCLUSION ::: These results exhort to promote the use of thoracic ultrasound in the first-line diagnosis of pneumonia.


title: A modified joint trilateral filter based depth map refinement method

abstract: We present a modified joint trilateral filter based method for depth map refinement. Seeded region growing algorithm and Euclidean distance transform(EDT) are involved for error pixels extraction. With the error information wiped out, a joint trilateral filter method is employed as a hole filling strategy to fill the values. Compared to the basic bilateral filter method, structural similarity index(SSIM) is added as the third lateral, while a threshold and an aberration condition are involved for modification to protect the edges and improve the confidence of the filling values. Both qualitative analysis and quantitative analysis are shown to testify the performance. Comparisons between our method and the other popular methods are provided.


title: IoT Applications on Secure Smart Shopping

abstract: We propose to design a smart shopping system based on RFID. The smart carts used in such a system can navigate the customers to the desired products, and they can calculate the billing information as the customer shops. Items that are put in a smart shopping cart can be automatically recognized and billed. In this way, customers can avoid long queuing time at the check-out points. The grocery management also becomes much easier: The sensors on the grocery shelf can keep updating the number of items to the central server so that the server is always aware of the items' status in the store. Furthermore, the inventory management also becomes much easier, because all the items can be automatically read by a RFID reader instead of manually scanned by a laborer. In this paper, we propose a smart shopping cart system with consideration of how this system can work functionally and securely.


title: Accurate Angle Estimator for High-Frame-Rate 2-D Vector Flow Imaging

abstract: This paper presents a novel approach for estimating 2-D flow angles using a high-frame-rate ultrasound method. The angle estimator features high accuracy and low standard deviation (SD) over the full 360° range. The method is validated on Field II simulations and phantom measurements using the experimental ultrasound scanner SARUS and a flow rig before being tested in vivo. An 8-MHz linear array transducer is used with defocused beam emissions. In the simulations of a spinning disk phantom, a 360° uniform behavior on the angle estimation is observed with a median angle bias of 1.01° and a median angle SD of 1.8°. Similar results are obtained on a straight vessel for both simulations and measurements, where the obtained angle biases are below 1.5° with SDs around 1°. Estimated velocity magnitudes are also kept under 10% bias and 5% relative SD in both simulations and measurements. An in vivo measurement is performed on a carotid bifurcation of a healthy individual. A 3-s acquisition during three heart cycles is captured. A consistent and repetitive vortex is observed in the carotid bulb during systoles.


Beyond Lithium Ion Batteries


title: Simulation in manufacturing and business: A review

abstract: This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems.


title: Hybrid framework for information extraction for geographical terms in Hindi language texts

abstract: A hybrid information extraction (IE) framework based on geographical term detection approach has been developed to extract geographical information from an unrestricted Hindi text. The relationship between geographical entities extracted with the adjacent text is shown graphically so that information about these entities can be related. The system, a combination of statistically and linguistically motivated techniques, identifies single geographical names and multiple geographical names as well. The method is applied on Hindi language text, but the approach can be adapted for other languages also. The paper presents some experiments illustrating the accuracy of the method. The system being developed is in a prototype stage and will be extended to include relation mark-up as well.


title: Apply Fuzzy Decision Tree to Information Security Risk Assessment

abstract: As computer becomes popular and internet advances rapidly, information application systems are used extensively in organizations. Various information application systems such as attendance systems, accounting systems, and statistical systems have already replaced manual operations. In such a drastic change, the information security issue encountered by organizations becomes increasingly significant. Information security risk assessment is the core of information security. It focuses on the assessments of assets with confidentiality, integrity and availability. Moreover, vulnerability of information systems and threats to the outside are also included in the scope of consideration. This study adopts fuzzy decision tree to evaluate the information security risk assessment for decision-makers. There are 155 input-output data with 22 attributes used to measure the value at risk obtained from ISO/IEC 27001 information security management system standard and ISO/IEC27005: 2008 Information technology. Another zoo dataset collected from UCI repository is also used to test the performance for the proposed algorithm. From simulation results, the proposed approach outperforms other existing approaches.


title: Millimeter-wave beam multiplexing method using hybrid beamforming

abstract: A millimeter-wave beam multiplexing method using a hybrid beamforming is proposed. We theoretically show that inter-subarray coding at the subarray type interleaved configuration can reduce interference and multiple beams of the same high gain as the full connection type. The condition that a hybrid beamforming of the subarray type can create the multiple high gain beams is clarified. For the case of beam multiplexing, the interleaved configuration with inter-subarray coding is confirmed to be robust for interference increasing by errors of beam direction estimation. Therefore, we think that the interleaved configuration is suitable for millimeter-wave beam multiplexing.


title: Analysis of an IEC 61850 based Electric Substation Communication Architecture

abstract: The ever-increasing demand for power and limitations on upgrade have led to increased operating voltages and consequently, larger and number of Substations. Substation Automation systems (SAS) are responsible for the protection, monitoring and control of electric equipment within an electric substation. The big evolution for SAS came with introduction of IEC 61850 standard for power utilities that provides integrated and interoperable data communications within and around Substation. In order to realize the benefits of the standard, the SAS design engineering needs to be thoughtful of practical challenges such as implementation of communication network. The success of SAS depends largely on efficient and reliable communication architecture design. In this paper, we present an analysis of the performance of communication infrastructure using the experimental set up of typical substation architecture. This set up is then used to examine sample substation architectures to determine if they meet the communication performance requirements.


title: Design, implementation and performance of a modular power electronic transformer (PET) for railway application

abstract: Nowadays, power electronic transformers (PETs) are under consideration in various applications since PETs offer several advantages over traditional low-frequency transformers, including reduced weight and energy saving. This paper describes the practical design and implementation of a modular PET demonstrator, which is to be installed in a test locomotive and to operate in the field. The experimental results, including the performance of the developed PET demonstrator with different operating conditions, are presented.


title: MoFAP: A Multi-level Representation for Action Recognition

abstract: This paper proposes a multi-level video representation by stacking the activations of motion features, atoms, and phrases (MoFAP). Motion features refer to those low-level local descriptors, while motion atoms and phrases can be viewed as mid-level "temporal parts". Motion atom is defined as an atomic part of action, and captures the motion information of video in a short temporal scale. Motion phrase is a temporal composite of multiple motion atoms defined with an AND/OR structure. It further enhances the discriminative capacity of motion atoms by incorporating temporal structure in a longer temporal scale. Specifically, we first design a discriminative clustering method to automatically discover a set of representative motion atoms. Then, we mine effective motion phrases with high discriminative and representative capacity in a bottom-up manner. Based on these basic units of motion features, atoms, and phrases, we construct a MoFAP network by stacking them layer by layer. This MoFAP network enables us to extract the effective representation of video data from different levels and scales. The separate representations from motion features, motion atoms, and motion phrases are concatenated as a whole one, called Activation of MoFAP. The effectiveness of this representation is demonstrated on four challenging datasets: Olympic Sports, UCF50, HMDB51, and UCF101. Experimental results show that our representation achieves the state-of-the-art performance on these datasets.


title: Named entity recognition and disambiguation using linked data and graph-based centrality scoring

abstract: Named Entity Recognition (NER) is a subtask of information extraction and aims to identify atomic entities in text that fall into predefined categories such as person, location, organization, etc. Recent efforts in NER try to extract entities and link them to linked data entities. Linked data is a term used for data resources that are created using semantic web standards such as DBpedia. There are a number of online tools that try to identify named entities in text and link them to linked data resources. Although one can use these tools via their APIs and web interfaces, they use different data resources and different techniques to identify named entities and not all of them reveal this information. One of the major tasks in NER is disambiguation that is identifying the right entity among a number of entities with the same names; for example "apple" standing for both "Apple, Inc." the company and the fruit. We developed a similar tool called NERSO, short for Named Entity Recognition Using Semantic Open Data, to automatically extract named entities, disambiguating and linking them to DBpedia entities. Our disambiguation method is based on constructing a graph of linked data entities and scoring them using a graph-based centrality algorithm. We evaluate our system by comparing its performance with two publicly available NER tools. The results show that NERSO performs better.


title: Knowledge Amalgam: Generating Jokes and Quotes Together

abstract: Generating humor and quotes are very challenging problems in the field of computational linguistics and are often tackled separately. In this paper, we present a controlled Long Short-Term Memory (LSTM) architecture which is trained with categorical data like jokes and quotes together by passing category as an input along with the sequence of words. The idea is that a single neural net will learn the structure of both jokes and quotes to generate them on demand according to input category. Importantly, we believe the neural net has more knowledge as it's trained on different datasets and hence will enable it to generate more creative jokes or quotes from the mixture of information. May the network generate a funny inspirational joke!


title: Effects of Field of View and Visual Complexity on Virtual Reality Training Effectiveness for a Visual Scanning Task

abstract: Virtual reality training systems are commonly used in a variety of domains, and it is important to understand how the realism of a training simulation influences training effectiveness. We conducted a controlled experiment to test the effects of display and scenario properties on training effectiveness for a visual scanning task in a simulated urban environment. The experiment varied the levels of field of view and visual complexity during a training phase and then evaluated scanning performance with the simulator's highest levels of fidelity and scene complexity. To assess scanning performance, we measured target detection and adherence to a prescribed strategy. The results show that both field of view and visual complexity significantly affected target detection during training; higher field of view led to better performance and higher visual complexity worsened performance. Additionally, adherence to the prescribed visual scanning strategy during assessment was best when the level of visual complexity during training matched that of the assessment conditions, providing evidence that similar visual complexity was important for learning the technique. The results also demonstrate that task performance during training was not always a sufficient measure of mastery of an instructed technique. That is, if learning a prescribed strategy or skill is the goal of a training exercise, performance in a simulation may not be an appropriate indicator of effectiveness outside of training—evaluation in a more realistic setting may be necessary.


title: The Security of Cipher Block Chaining

abstract: The Cipher Block Chaining - Message Authentication Code (CBC MAC) specifies that a, message x = x1 ... xm be authenticated among parties who share a secret key a by tagging x with a prefix of fa(m)(x) def = fa(fa(... fa(fa(x1)?x2)?...?xm-1)?xm), where f is some underlying block cipher (eg. f = DES). This method is a pervasively used international and U.S. standard. We provide its first formal justification, showing the following general lemma: that cipher block chaining a pseudorandom function gives a pseudorandom function. Underlying our results is a technical lemma of independent interest, bounding the success probability of a computationally unbounded adversary in distinguishing between a random ml-bit to l-bit function and the CBC MAC of a random l-bit to l-bit function.


title: Screening in new credit markets: Can individual lenders infer borrower creditworthiness in peer-to-peer lending?” Working paper

abstract: The current banking crisis highlights the challenges faced in the traditional lending model, particularly in terms of screening smaller borrowers. The recent growth in online peer-to-peer lending marketplaces offers opportunities to examine different lending models that rely on screening by multiple peers. This paper evaluates the screening ability of lenders in such peer-to-peer markets. Our methodology takes advantage of the fact that lenders do not observe a borrowerâ€™s true credit score but only see an aggregate credit category. We find that lenders are able to use available information to infer a third of the variation in creditworthiness that is captured by a borrowerâ€™s credit score. This inference is economically significant and allows lenders to lend at a 140-basis-points lower rate for borrowers with (unobserved to lenders) better credit scores within a credit category. While lenders infer the most from standard banking â€œhardâ€ information, they also use non-standard (subjective) information. Our methodology shows, without needing to code subjective information that lenders learn even from such â€œsofterâ€ information, particularly when it is likely to provide credible signals regarding borrower creditworthiness. Our findings highlight the screening ability of peer-to-peer markets and suggest that these emerging markets may provide a viable complement to traditional lending markets, especially for smaller borrowers.


title: Toward interoperability in a web of things

abstract: In this position paper we explore the challenges and issues around interoperability in the web of things. A key concern is how to increase interoperability while maintaining a high degree of innovation and exploration in the community. To that end we propose a hub- centric approach toward interoperability consisting of four levels or stages. We are working to validate this approach in the context of a large-scale IoT ecosystem project consisting of eight IoT hubs in different domains where a key requirement is hub-to-hub and hub- application interoperability.


title: A Low-Cost Real-Time Embedded Stereo Vision System for Accurate Disparity Estimation Based on Guided Image Filtering

abstract: Stereo matching, a key element towards extracting depth information from stereo images, is widely used in several embedded consumer electronic and multimedia systems. Such systems demand high processing performance and accurate depth perception, while their deployment in embedded and mobile environments implies that cost, energy and memory overheads need to be minimized. Hardware acceleration has been demonstrated in efficient embedded stereo vision systems. To this end, this paper presents the design and implementation of a hardware-based stereo matching system able to provide high accuracy and concurrently high performance for embedded vision devices, which are associated with limited hardware and power budget. We first implemented a compact and efficient design of the guided image filter, an edge-preserving filter, which reduces the hardware complexity of the implemented stereo algorithm, while at the same time maintains high-quality results. The guided filter design is used in two parts of the stereo matching pipeline, showing that it can simplify the hardware complexity of the Adaptive Support Weight aggregation step, and efficiently enable a powerful disparity refinement unit, which improves matching accuracy, even though cost aggregation is based on simple, fixed support strategies. We implemented several variants of our design on a Kintex-7 FPGA board, which was able to process HD video (1,280 × 720) in real-time (60 fps), using ∼57.5k and ∼71k of the FPGA's logic (CLB) and register resources, respectively. Additionally, the proposed stereo matching design delivers leading accuracy when compared to state-of-the-art hardware implementations based on the Middlebury evaluation metrics (at least 1.5 percent less bad matching pixels).


title: A study of key technologies for IoT and associated security challenges

abstract: There are many hitches regarding security matters for Internet of Things (IoT), which need to be solved yet, including RFID tag security, cyber security wireless security, network transmission security and privacy protection etc. This article basically explores the existing studies on IoT security issues and the mixture of two main technologies of IoT in context of their threats, corresponding security requirements and their solutions while moving toward synthesizing a model for the security and data piracy issues from various viewpoints. This generic model for implementing security comprises of, combination of security standards and corresponding security requirements heading on the functional architecture of IoT.


title: Pushing the Limits of Global Governance: Trading Rights, Censorship and WTO Jurisprudence--A Commentary on the China-Publications Case

abstract: For decades, China has maintained state import monopoly in cultural products. The opaque state-trading operations ensure a maximum level of flexibility and efficacy in the government censorship of imports. The WTO judiciary held in the China-Publications case that this practice is inconsistent with China’s trading rights commitments under its Accession Protocol and cannot be justified by the public morals exception of the GATT. To comply with the WTO ruling, China must restructure its censorship regime, which it apparently is not prepared to do. This article analyzes the implications of the WTO decision and provides a critical assessment of the new WTO jurisprudence regarding trading rights and the China Accession Protocol.


title: Bit-Regularized Optimization of Neural Nets

abstract: We present a novel regularization strategy for training neural networks which we call ``BitNet''. The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over a real valued range. Our key idea is to control the expressive power of the network by dynamically quantizing the range and set of values that the parameters can take. We formulate this idea using a novel end-to-end approach that regularizes a typical classification loss function. Our regularizer is inspired by the Minimum Description Length (MDL) principle. For each layer of the network, our approach optimizes a translation and scaling factor along with integer-valued parameters. We empirically compare BitNet to an equivalent unregularized model on the MNIST and CIFAR-10 datasets. We show that BitNet converges faster to a superior quality solution. Additionally, the resulting model is significantly smaller in size due to the use of integer instead of floating-point parameters.


title: Altruism and Patterns of Social Interaction

abstract: Prosocial behavior implies an interaction of at least two persons: the helper (A) and the recipient of help (B). Therefore, prosocial responses have to be understood in the context of social interaction in general.


title: First-Orderized ResearchCyc: Expressivity and Efficiency in a Common-Sense Ontology

abstract: Cyc is the largest existing common-sense knowledge base. Its ontology makes heavy use of higher-order logic constructs such as a context system, first class predicates, etc. Many of these higher-order constructs are believed to be key to Cyc’s ability to represent common-sense knowledge and reason with it efficiently. In this paper, we present a translati on of a large part (around 90%) of the Cyc ontology into FirstOrder Logic. We discuss our methodology, and the tradeoffs between expressivity and efficiency in representation and reasoning. We also present the results of experiments using VAMPIRE, SPASS, and the E Theorem Prover on the firstorderized Cyc KB. Our results indicate that, while the use of higher-order logic is not essential to the representabilit y of common-sense knowledge, it greatly improves the efficiency of reasoning.


Cognitive Screening for the Safe Driving Competence of Older People with Mild Cognitive Impairment or Early Dementia


title: Accurate and dense wide-baseline stereo matching using SW-POC

abstract: This paper proposes an accurate and dense wide-baseline stereo matching method using Scaled Window Phase-Only Correlation (SW-POC). The wide-baseline setting of the stereo camera can improve the accuracy of the 3D reconstruction compared with the short-baseline setting. However, it is difficult to find accurate and dense correspondence from wide-baseline stereo images due to its large perspective distortion. Addressing this problem, we employ the SW-POC, which is a correspondence matching method using 1D POC with the concept of Scale Window Matching (SWM). The use of SW-POC makes it possible to find the accurate and dense correspondence from a wide-baseline stereo image pair with low computational cost. We also apply the proposed method to 3D reconstruction using a moving and uncalibrated consumer digital camera.


title: Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community

abstract: An online learning community enables learners to access up-to-date information via the Internet anytime-anywhere because of the ubiquity of the World Wide Web (WWW). Students can also interact with one another during the learning process. Hence, researchers want to determine whether such interaction produces learning synergy in an online learning community. In this paper, we take the Technology Acceptance Model as a foundation and extend the external variables as well as the Perceived Variables as our model and propose a number of hypotheses. A total of 436 Taiwanese senior high school students participated in this research, and the online learning community focused on learning English. The research results show that all the hypotheses are supported, which indicates that the extended variables can effectively predict whether users will adopt an online learning community. Finally, we discuss the implications of our findings for the future development of online English learning communities.


title: Design of variable gain amplifier with gain-bandwidth product up to 354 GHz implemented in InP-InGaAs DHBT technology

abstract: A high-gain and wide-band variable gain amplifier (VGA) is developed using 300-GHz InP-InGaAs double-heterojunction bipolar transistor (DHBT). Negative-R/sub E/ quad is used to enhance amplifier gain-bandwidth product. At maximum gain, the single-ended S/sub 21/ of 17 dB and the associated 3-dB bandwidth of 50 GHz are measured to produce a gain-bandwidth product of 354 GHz in a VGA including a Gilbert multiplier and an output driver. The gain-bandwidth product is twice the value measured from the VGA designed by single resistor degeneration in the same process. The circuit is designed in terms of detailed stability considerations and the experimental results show it to be unconditionally stable over 0.5-50 GHz. The linearity of the VGA is affected by nonlinear effects in DHBTs, and different design approaches are analyzed. An output interception point of the third harmonic of 16.2 dBm is measured.


title: Hyperbolic Ricci Flow and Its Application in Studying Lateral Ventricle Morphometry

abstract: Here we propose a novel method to compute surface hyperbolic parameterization for studying brain morphology with the Ricci flow method. Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmuller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. A shape index can be defined based on Teichmuller space coordinates, and this shape index is intrinsic and invariant under scaling, translation, rotation, general isometric deformation, and conformal deformation. Using the Ricci flow method, we can conformally map a surface with a negative Euler number to the Poincare disk and the Teichmuller space coordinates can be computed by geodesic lengths under hyperbolic metric. For lateral ventricular surface registration, we further convert the parameterization to the Klein model where a convex polygon is guaranteed for a multiply connected surface. With the Klein model, diffeomorphisms between lateral ventricular surfaces can be computed with some well known surface registration methods. Compared with prior work, the parameterization does not have any singularities and the intrinsic parameterizations help shape indexing and surface registration. Our preliminary experimental results showed its great promise for analyzing anatomical surface morphology.


RADIX-10 PARALLEL DECIMAL MULTIPLIER 1


title: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

abstract: Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.