title: Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis

abstract: Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD). In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset. Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models. Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD.


title: Wavelet denoising based on local regularity information

abstract: We present a denoising method that is well fitted to the processing of extremely irregular signals such as (multi)fractal ones. Such signals are often encountered in practice, e.g., in biomedical applications. The basic idea is to estimate the regularity of the original data from the observed noisy ones using the large scale information, and then to extrapolate this information to the small scales. We present theoretical results describing the precise properties of the method. Numerical experiments show that this denoising scheme indeed performs well on irregular signals.


title: Artex is AnotheR TEXt summarizer

abstract: This paper describes Artex, another algorithm for Automatic Text Summarization. In order to rank sentences, a simple inner product is calculated between each sentence, a document vector (text topic) and a lexical vector (vocabulary used by a sentence). Summaries are then generated by assembling the highest ranked sentences. No ruled-based linguistic post-processing is necessary in order to obtain summaries. Tests over several datasets (coming from Document Understanding Conferences (DUC), Text Analysis Conferences (TAC), evaluation campaigns, etc.) in French, English and Spanish have shown that summarizer achieves interesting results.


title: The Rational Analysis Of Mind And Behavior

abstract: Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning leads to a radicalreinterpretation of empirical results which are typically viewed asdemonstrating human irrationality.


title: Swinging up a pendulum by energy control

abstract: Properties of simple strategies for swinging up an inverted pendulum are discussed. It is shown that the behavior critically depends on the ratio of the maximum acceleration of the pivot to the acceleration of gravity. A comparison of energy-based strategies with minimum time strategy gives interesting insights into the robustness of minimum time solutions.


title: Magnetic coupling resonant wireless energy transmission coil quantitative relation modeling and simulation research

abstract: This paper mainly studies the mathematical model of wireless power transmission based on magnetic coupling and resonance. Our works contain using the circuitry theory to modeling the system, analyzing how other variables influence on transmission efficiency and conducting simulation in MATLAB. In the experiments, two-coil model, three-coil model, four-coil model and other typical models are studied to find out and compare the characteristics of each model. The results of what we study and conduct simulations show that each model has its characteristics, which this paper will detail.


CONTROL OF TWIN ROTOR MIMO SYSTEM (TRMS) USING PID CONTROLLER


title: A Theme-Rewriting Approach for Generating Algebra Word Problems

abstract: Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called {\it rewriting} that edits existing human-authored narratives to change their theme without changing the underlying story. We apply the approach to math word problems, where it might help students stay more engaged by quickly transforming all of their homework assignments to the theme of their favorite movie without changing the math concepts that are being taught. Our rewriting method uses a two-stage decoding process, which proposes new words from the target theme and scores the resulting stories according to a number of factors defining aspects of syntactic, semantic, and thematic coherence. Experiments demonstrate that the final stories typically represent the new theme well while still testing the original math concepts, outperforming a number of baselines. We also release a new dataset of human-authored rewrites of math word problems in several themes.


title: Studying designers using a tabletop system for 3D design with a focus on the impact on spatial cognition

abstract: Tabletop systems provide a platform for developing novel interaction systems, including tangible user interfaces (TUIs). This paper presents a study of the effects of a tabletop system with tangible user interfaces on designers' spatial cognition and design communication in collaborative design. In devising an experiment that can highlight the impact on spatial cognition while using TUIs, we compared designers using a tangible user interface (TUI) on a tabletop system to designers using a graphical user interface (GUI) on a typical desktop computer with mouse and keyboard. The designers were given a configuration design task in which they manipulated 3D objects to meet design specifications. Our preliminary findings are that designers using the tabletop system with TUIs reasoned about spatial relationships among 3D objects and discovered unexpected spatial relationships, while the designers using the traditional keyboard and mouse interfaces reasoned about individual 3D objects.


title: A Novel Dual-Wideband Monopole-Like Microstrip Antenna With Controllable Frequency Response

abstract: A new modified microstrip monopole antenna with dual-band operation is presented for mobile communication applications. For this purpose, radiation patch and ground plane of the antenna are considerably modified. By using these modifications, including of a pair of modified U-shaped slots in the ground plane and a complex form of the slot, etched on the patch, two frequency wide bands of 1.38-3.98 GHz (97%) and 5.15-6.2 GHz (18%) are achieved. In the second design of the antenna, a very small slot as a switch is employed in the special positions on the ground plane to change and control the frequency response. Different cases such as multiple and single bands as well as narrow and wide bands between 1.5 and 6 GHz can be covered. The total size of the antennas is only 20 × 34 mm2. Full parametric simulation of the proposed modifications and measurement results of two manufactured prototypes are presented and discussed.


title: On the inter chip interference in DS-CDMA systems

abstract: An efficient performance analysis for DS-CDMA systems with inter chip interference due to the general assumption of spreading and despreading waveforms, is developed through the Gaussian approximation for an asynchronous environment. A study of not perfect chip synchronization and carrier recovery is included. Through the identification of three merit factor parameters, the systems performance is evaluated and compared, in the numerical results, for the choice of some waveforms in wrong synchronization conditions.


title: Abstract Spamscatter: Characterizing Internet Scam Hosting Infrastructure

abstract: Unsolicited bulk e-mail, or SPAM, is a means to an end. For virtually all such messages, the intent is to attract the recipient into entering a commercial transaction -- typically via a linked Web site. While the prodigious infrastructure used to pump out billions of such solicitations is essential, the engine driving this process is ultimately the "point-of-sale" -- the various money-making "scams" that extract value from Internet users. In the hopes of better understanding the business pressures exerted on spammers, this paper focuses squarely on the Internet infrastructure used to host and support such scams. We describe an opportunistic measurement technique called spamscatter that mines emails in real-time, follows the embedded link structure, and automatically clusters the destination Web sites using image shingling to capture graphical similarity between rendered sites. We have implemented this approach on a large real-time spam feed (over 1M messages per week) and have identified and analyzed over 2,000 distinct scams on 7,000 distinct servers.


title: Password Strength: An Empirical Analysis

abstract: It is a well known fact that user-chosen passwords are somewhat predictable: by using tools such as dictionaries or probabilistic models, attackers and password recovery tools can drastically reduce the number of attempts needed to guess a password. Quite surprisingly, however, existing literature does not provide a satisfying answer to the following question: given a number of guesses, what is the probability that a state-of-the-art attacker will be able to break a password? To answer the former question, we compare and evaluate the effectiveness of currently known attacks using various datasets of known passwords. We find that a "diminishing returns" principle applies: in the absence of an enforced password strength policy, weak passwords are common; on the other hand, as the attack goes on, the probability that a guess will succeed decreases by orders of magnitude. Even extremely powerful attackers won't be able to guess a substantial percentage of the passwords. The result of this work will help in evaluating the security of authentication means based on user- chosen passwords, and our methodology for estimating password strength can be used as a basis for creating more effective proactive password checkers for users and security auditing tools for administrators.


title: Adaptive Adversarial Attack on Scene Text Recognition

abstract: Recent studies have shown that state-of-the-art deep learning models are vulnerable to the inputs with small perturbations (adversarial examples). We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e.g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks. In this work, we speed up adversarial attacks, especially on sequential learning tasks. By leveraging the uncertainty of each task, we directly learn the adaptive multi-task weightings, without manually searching hyper-parameters. A unified architecture is developed and evaluated for both non-sequential tasks and sequential ones. To validate the effectiveness, we take the scene text recognition task as a case study. To our best knowledge, our proposed method is the first attempt to adversarial attack for scene text recognition. Adaptive Attack achieves over 99.9\% success rate with 3-6X speedup compared to state-of-the-art adversarial attacks.


title: On the Gains and Losses of Speculation in Equilibrium Markets

abstract: In computational markets utilizing algorithms that establish a market equilibrium (general equilibrium), competitive behavior is usually assumed: each agent makes its demand (supply) decisions so a ...


title: Shared Services in Irish Local Government

abstract: This paper investigates the demand for ICT shared services in Irish local government, why has the take-up to date been so limited and what the barriers to adoption are historically and are today. The research shows that there is an apparent large demand for ICT shared services from all local authorities, but that in practice take-up is low and there is a strong preference for local solutions over national offerings. A number of barriers to adoption are identified and discussed.


title: Detecting cyber threats through social network analysis: short survey

abstract: This article considers a short survey of basic methods of social networks analysis, which are used for detecting cyber threats. The main types of social network threats are presented. Basic methods of graph theory and data mining, that deals with social networks analysis are described. Typical security tasks of social network analysis, such as community detection in network, detection of leaders in communities, detection experts in networks, clustering text information and others are considered.


Scalable Visualisation of Sentiment and Stance.


title: A sub-pW timer using gate leakage for ultra low-power sub-Hz monitoring systems

abstract: In this work, we present a novel ultra-low power timer designed using the gate leakage of MOS capacitors. The test chip was fabricated in a 0.13 mum CMOS technology and the total circuit area is 480 mum2. Measurement results show that the circuit functions correctly at a wide range of supply voltages from 300 mV to 1.2 V, making it particularly suitable for subthreshold systems. The temperature sensitivity is 0.16%/degC at 600 mV and 0.6%/degC at 300 mV. The power dissipation is less than 1pW running at 20degC and 300 mV.


title: Modularity, flexibility, and knowledge management in product and organization design

abstract: This pczper investigcztes irzterrelationslzi~~s of product design, orgcznization design, processes for leartlirzg and managing knowledge, arzd competitive strategy. This paper uses the principles of nearly decotnposable systems to investigate the ability of strind(irdized interfcices between components in cz product design to embed coordination of product development processes. Embedded coordination creates 'hierczrchical coordination' without the need to continually exercise authority-erzcrblirlg effective coordination of processes without the tight coupling of orgcznizationczl structures. We develop concepts of modularity in product and organization designs based orz smndcirdized component and organization interjczces. Modular product architec- tures create information structures that provide the 'glue' that holds together the loosely coupled parts of a modular orgatzizatiorz design. By fczcilitriting loose coupling, modularity can czlso reduce the cost and dyficulty of adaptive coordination, thereby incrensing the strategic flexibility of firms to respond to erzvironmentnl change. Modularity in product and organizcztion designs therefore etznbles cz new strcitegic approach to the management of knowledge based on cztz irztentionczl, carefully mcznaged loose coupling of (I firm's learning processes czt architec- tural cznd comporzent levels of product crecztiorz processes.


title: Future of Payments - ePayments

abstract: In the modern society, no economic activity is possible without payments and settlements. In this sense, it could probably be said that payment systems are one of the most imperative and significant social infrastructures that we have. ePayments are an increasingly important part of payment systems. They allow for quick international connectivity in the payments world. ePayment can be looked as a panacea for most ills in payment world, if not all - financial inclusivity for unbanked, fast across the world transactions, safety and security of payments and cost savings over traditional payment systems. In this paper, we look at ePayments, what they entail and basic payments infrastructure. We also look at the future of ePayments as well as challenges and recommendations for ePayment systems of tomorrow. In the next paper we will deep dive into the recommendations for ePayments.


title: Extracting implicit features in online customer reviews for opinion mining

abstract: As the number of customer reviews grows very rapidly, it is essential to summarize useful opinions for buyers, sellers and producers. One key step of opinion mining is feature extraction. Most existing research focus on finding explicit features, only a few attempts have been made to extract implicit features. Nearly all existing research only concentrate on product features, few has paid attention to other features that relates to sellers, services and logistics. Therefore in this paper, we propose a novel co-occurrence association-based method, which aims to extract implicit features in customer reviews and provide more comprehensive and fine-grained mining results.


Design Network Intrusion Detection System using hybrid FuzzyNeural Network International


title: Replay-based strategy prediction and build order adaptation for StarCraft AI bots

abstract: StarCraft is a real-time strategy (RTS) game and the choice of strategy has big impact on the final results of the game. For human players, the most important thing in the game is to select the strategy in the early stage of the game. Also, it is important to recognize the opponent's strategy as quickly as possible. Because of the “fog-of-war” in the game, the player should send a scouting unit to opponent's hidden territory and the player predicts the types of strategy from the partially observed information. Usually, expert players are familiar with the relationships between two build orders and they can change the current build order if his choice is not strong to the opponent's strategy. However, players in AI competitions show quite different behaviors compared to the human leagues. For example, they usually have a pre-selected build order and rarely change their order during the game. In fact, the computer players have little interest in recognizing opponent's strategy and scouting units are used in a limited manner. The reason is that the implementation of scouting behavior and the change of build order from the scouting vision is not a trivial problem. In this paper, we propose to use replays to predict the strategy of players and make decision on the change of build orders. Experimental results on the public replay files show that the proposed method predicts opponent's strategy accurately and increases the chance of winning in the game.


Matching with Shape Contexts


title: How Much Are You Willing to Share? A"Poker-Styled"Selective Privacy Preserving Framework for Recommender Systems

abstract: Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user privacy, because users need to share their preferences with others in order to be grouped with like-minded people and receive accurate recommendations. While previous privacy preserving approaches have been successful inasmuch as they concealed user preference information to some extent from a centralized recommender system, they have also, nevertheless, incurred significant trade-offs in terms of privacy, scalability, and accuracy. They are also vulnerable to privacy breaches by malicious actors. In light of these observations, we propose a novel selective privacy preserving (SP2) paradigm that allows users to custom define the scope and extent of their individual privacies, by marking their personal ratings as either public (which can be shared) or private (which are never shared and stored only on the user device). Our SP2 framework works in two steps: (i) First, it builds an initial recommendation model based on the sum of all public ratings that have been shared by users and (ii) then, this public model is fine-tuned on each user's device based on the user private ratings, thus eventually learning a more accurate model. Furthermore, in this work, we introduce three different algorithms for implementing an end-to-end SP2 framework that can scale effectively from thousands to hundreds of millions of items. Our user survey shows that an overwhelming fraction of users are likely to rate much more items to improve the overall recommendations when they can control what ratings will be publicly shared with others.


title: Evaluation of non-geometric methods for visual odometry

abstract: Visual Odometry (VO) is one of the fundamental building blocks of modern autonomous robot navigation and mapping. While most state-of-the-art techniques use geometrical methods for camera ego-motion estimation from optical flow vectors, in the last few years learning approaches have been proposed to solve this problem. These approaches are emerging and there is still much to explore. This work follows this track applying Kernel Machines to monocular visual ego-motion estimation. Unlike geometrical methods, learning-based approaches to monocular visual odometry allow issues like scale estimation and camera calibration to be overcome, assuming the availability of training data. While some previous works have proposed learning paradigms to VO, to our knowledge no extensive evaluation of applying kernel-based methods to Visual Odometry has been conducted. To fill this gap, in this work we consider publicly available datasets and perform several experiments in order to set a comparison baseline with traditional techniques. Experimental results show good performances of learning algorithms and set them as a solid alternative to the computationally intensive and complex to implement geometrical techniques. We stress the advantages of non-geometric (learned) VO as an alternative or an addition to standard geometric methods.Ego-motion is computed with state-of-the art regression techniques, namely Support Vector Machines (SVM) and Gaussian Processes (GP).To our knowledge this is the first time SVM have been applied to VO problem.We conduct extensive evaluation on three publicly available datasets, spanning both indoor and outdoor environments.The experiments show that non-geometric VO is a good alternative, or addition, to standard VO systems.


title: Mind the Gap: Why Do People Act Environmentally and What Are the Barriers to Pro-Environmental Behavior?.

abstract: Numerous theoretical frameworks have been developed to explain the gap between the possession of environmental knowledge and environmental awareness, and displaying pro-environmental behavior. Although many hundreds of studies have been undertaken, no definitive explanation has yet been found. Our article describes a few of the most influential and commonly used analytical frameworks: early US linear progression models; altruism, empathy and prosocial behavior models; and finally, sociological models. All of the models we discuss (and many of the ones we do not such as economic models, psychological models that look at behavior in general, social marketing models and that have become known as deliberative and inclusionary processes or procedures (DIPS)) have some validity in certain circumstances. This indicates that the question of what shapes pro-environmental behavior is such a complex one that it cannot be visualized through one single framework or diagram. We then analyze the factors that have been f...


title: Design and Evaluation of Network Topology-/Speed- Aware Broadcast Algorithms for InfiniBand Clusters

abstract: It is an established fact that the network topology can have an impact on the performance of scientific parallel applications. However, little work has been done to design an easy to use solution inside a communication library supporting a parallel programming model where the complexities of making the application performance network topology agnostic is hidden from the end user. Similarly, the rapid improvements in networking technology and speed are resulting in many commodity clusters becoming heterogeneous, with respect to networking speed. For example, switches and adapters belonging to different generations (SDR - 8 Gbps, DDR - 16 Gbps and QDR - 36 Gbps speeds in InfiniBand) are integrated into a single system. This leads to an additional challenge to make the communication library aware of the performance implications of heterogeneous link speeds. Accordingly, the communication library can perform optimizations taking link speed into account. In this paper, we propose a framework to automatically detect the topology and speed of an InfiniBand network and make it available to users through an easy to use interface. We also make design changes inside the MPI library to dynamically query this topology detection service and to form a topology model of the underlying network. We have redesigned the broadcast algorithm to take into account this network topology information and dynamically adapt the communication pattern to best fit the characteristics of the underlying network. To the best of our knowledge, this is the first such work for InfiniBand clusters. Our experimental results show that, for large homogeneous systems and large message sizes, we get up to 14% improvement in the latency of the broadcast operation using our proposed network topology-aware scheme over the default scheme at the micro-benchmark level. At the application level, the proposed framework delivers up to 8% improvement in total application run-time especially as job size scales up. The proposed network speed-aware algorithms are able to attain micro-benchmark performance on the heterogeneous SDR-DDR InfiniBand cluster to perform on par with runs on the DDR only portion of the cluster for small to medium sized messages. We also demonstrate that the network speed aware algorithms perform 70% to 100% better than the naive algorithms when both are run on the heterogeneous SDR-DDR InfiniBand cluster.


title: Clinical efficacy of a new software developed for dental digital subtraction radiography.

abstract: OBJECTIVES ::: The aim of this study was to test and compare the efficacy of software developed recently for digital subtraction radiography (DSR) in vivo. ::: ::: ::: METHODS ::: An algorithm performing both manual and automated image reconstructions and contrast correction was developed for the manipulation of radiographic images. Pre- and post-operative radiographic images of ten patients were obtained and the automated subtraction analyses were performed using four different softwares (new software, Emago, Photoshop 8.0 and Paintshop Pro 9). Ten experienced dental specialists evaluated the clinical efficacy of each program and scored the softwares by using visual analogue scales (VAS). The results were statistically analysed and alpha was set as 0.05. ::: ::: ::: RESULTS ::: The newly developed algorithm received higher scores than the others (new software =67.89, Emago = 64.26, Paintshop Pro 9 = 33.41 and Photoshop 8.0 = 27.24, respectively). The clinical efficacies of the new software and Emago were not significantly different (P = 0.720); likewise, Photoshop 8.0 and Paintshop Pro 9 performed comparably (P = 0.295). ::: ::: ::: CONCLUSIONS ::: Considering this study, the new software and Emago would be suggested for DSR in dental practice.


title: Predicting Stock Prices Using Technical Analysis and Machine Learning

abstract: Historical stock prices are used to predict the direction of future stock prices. The developed stock price prediction model uses a novel two-layer reasoning approach that employs domain knowledge from technical analysis in the first layer of reasoning to guide a second layer of reasoning based on machine learning. The model is supplemented by a money management strategy that use the historical success of predictions made by the model to determine the amount of capital to invest on future predictions. Based on a number of portfolio simulations with trade signals generated by the model, we conclude that the prediction model successfully outperforms the Oslo Benchmark Index (OSEBX).


title: Micromachined Graphite Rotor Based on Diamagnetic Levitation

abstract: This letter reports the first micromachined rotor of highly oriented pyrolytic graphite (HOPG), a strong diamagnetic material. The HOPG rotor is levitated above a permanent magnet structure to allow for its friction-free rotation. The 600- $\mu $ m-thick HOPG rotor is micropatterned to have four blades with 1-mm inner diameter disc and 2-mm outer diameter including the blades. The levitation height of 132 $\mu $ m measured for the rotor matches well with a simulation result. The revolution of the rotor is demonstrated by applying gas flow, showing stable continuous actuation with the maximum rate of 500 r/min. These promising results suggest potential applications of the mechanism toward frictionless microsensors and microactuators.


title: Emoticon Analysis with Dynamic Text based Opinion Mining

abstract: Social networking is a very popular communication tool where opinions are shared by millions of users thereby generating tremendous data every day. Adjectives in this data represent emotions such as good, amazing etc. for positive nature and poor, bad, inferior for negative nature. Also emoticons such as  for happy feeling and  for sad feeling etc are used which are necessary to be treated along with adjectives to know blogger‟s expression. Opinion mining is generally performed by matching words to a dictionary or a corpus that contains static set of words. However if the user uses new words which are not present in the existing dictionary, those words may be neglected. To overcome this shortcoming, this paper proposes a novel algorithm concerned with auto addition of new words at runtime thus making the dictionary dynamic. The algorithm not only gives ratings for adjectives but also for emoticons. The polarity of these newly added words will be defined by considering the polarity of its neighboring words. The analysis shows that novel algorithm gives more accurate ratings than the previous findings. These accurate ratings can help users to choose the products wisely before buying thereby benefiting the vendor as well as users.


title: Hybrid concepts of analytic modeling of the MPM 20 engine

abstract: Mathematical modeling represents an efficient method allowing us to examine properties of a researched object [1]. Realization of mathematic models has become a fundamental part of design of many complex systems. By use of simulation, we can experiment with mathematical model similarly as with the real object without the risk of damage and with much lower expenses. Modeling represents experimental process that asserts the examined original (the modeled system) unambiguously another physical or abstract system. The article deals with approach to virtual particular analytic modeling of a compressor and explores some advanced hybrid concepts in modeling of such engine using methods of artificial intelligence.


title: Visual Place Categorization: Problem, dataset, and algorithm

abstract: In this paper we describe the problem of Visual Place Categorization (VPC) for mobile robotics, which involves predicting the semantic category of a place from image measurements acquired from an autonomous platform. For example, a robot in an unfamiliar home environment should be able to recognize the functionality of the rooms it visits, such as kitchen, living room, etc. We describe an approach to VPC based on sequential processing of images acquired with a conventional video camera. We identify two key challenges: Dealing with non-characteristic views and integrating restricted-FOV imagery into a holistic prediction. We present a solution to VPC based upon a recently-developed visual feature known as CENTRIST (CENsus TRansform hISTogram). We describe a new dataset for VPC which we have recently collected and are making publicly available. We believe this is the first significant, realistic dataset for the VPC problem. It contains the interiors of six different homes with ground truth labels. We use this dataset to validate our solution approach, achieving promising results.


title: Multi-Task Zipping via Layer-wise Neuron Sharing

abstract: Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks on-device. Yet the complexity of these neural networks needs to be trimmed down both within-model and cross-model to fit in mobile storage and memory. Previous studies focus on squeezing the redundancy within a single neural network. In this work, we aim to reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to automatically merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme that induces a minimal change in the error function. MTZ inherits information from each model and demands light retraining to re-boost the accuracy of individual tasks. Evaluations show that MTZ is able to fully merge the hidden layers of two VGG-16 networks with a 3.18% increase in the test error averaged on ImageNet and CelebA, or share 39.61% parameters between the two networks with <0.5% increase in the test errors for both tasks. The number of iterations to retrain the combined network is at least 17.8 times lower than that of training a single VGG-16 network.


title: Multibeam antennas based on phased arrays: An overview on recent ESA developments

abstract: The aim of the paper is to provide an overview on the activities recently promoted by the European Space Agency (ESA) on satellite multibeam antennas based on phased arrays. The attention is focused on antennas generating multiple spot beams coverages with a single main aperture. In particular, solutions based on direct radiating arrays, active discrete lenses, and confocal reflectors will be discussed. These solutions present some challenges but also some advantages as compared to conventional architectures based on multi reflectors with a single feed per beam. ESA is supporting since several years activities in this challenging domain.


title: Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline

abstract: Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14−CD19− PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.


title: Prediction of long-term clinical outcomes using simple functional exercise performance tests in patients with COPD: a 5-year prospective cohort study

abstract: The 1-min sit-to-stand (1-min STS) test and handgrip strength test have been proposed as simple tests of functional exercise performance in chronic obstructive pulmonary disease (COPD) patients. We assessed the long-term (5-year) predictive performance of the 1-min sit-to-stand and handgrip strength tests for mortality, health-related quality of life (HRQoL) and exacerbations in COPD patients. In 409 primary care patients, we found the 1-min STS test to be strongly associated with long-term mortality (hazard ratio per 3 more repetitions: 0.81, 95% CI 0.65 to 0.86) and moderately associated with long-term HRQoL. Neither test was associated with exacerbations. Our results suggest that the 1-min STS test may be useful for assessing the health status and long-term prognosis of COPD patients. This study was registered at http://www.clinicaltrials.gov/ (NCT00706602, 25 June 2008).


title: Evaluating Reputation of Web Services under Rating Scarcity

abstract: With the proliferation of Web services, more and more functionally equivalent services are being published by service providers on the Web. Although more services mean more flexibility for consumers, it also increases the burden of choosing as consumers may have little or no past experience with the service they will interact with. Therefore, reputation systems have been proposed and are playing a crucial role in the service-oriented environment. Current reputation systems are mainly built upon the explicit feedback or rating given by consumers after experiencing the service. Unfortunately, services at the cold-start stage, prior to being rated, face the rating scarcity problem. In this paper, we focus on this problem and address it through a novel reputation model that uses the Elo algorithm to consider consumer-implicit information in a graph analysis approach. A theoretical analysis is conducted to identify the sufficient and necessary condition for the model to converge to a stable state. Furthermore, experiments confirm our model outperforms the widely adopted reputation algorithm in both accuracy and convergence in the situation of rating scarcity.


Bioindicator Species and Their Use in Biomonitoring


title: Design and Control for LCL-Based Inverters with Both Grid-Tie and Standalone Parallel Operations

abstract: The inductor-capacitor-inductor (LCL) filter allows higher noise attenuation and universal output in which a power conditioning system or an inverter can operate in both grid-tie and standalone modes. In this paper, the LCL filter design considerations including sensor position selection and component selections are discussed for single-phase paralleled inverters operating in both grid-tie and standalone modes. For grid-tie mode operation, each inverter is operating under a single current loop with proportional-resonant controller and admittance path compensation to reduce the steady-state error by providing a high gain at the fundamental frequency. For standalone mode operation, one of the inverters is implemented with a dual-loop controller to regulate the output voltage while the rest inverters operate in single current-loop controller with communication channels in between to ensure the uniformity of current sharing. Both the simulation and experimental results verify that the designed controllers are capable of paralleling inverter operation in grid-tie and standalone modes by adapting to different controller settings while keeping the same hardware setup.


The effect of folder structure on personal file navigation


title: ClusterJoin: A Similarity Joins Framework using Map-Reduce

abstract: Similarity join is the problem of finding pairs of records with similarity score greater than some threshold. In this paper we study the problem of scaling up similarity join for different metric distance functions using MapReduce. We propose a ClusterJoin framework that partitions the data space based on the underlying data distribution, and distributes each record to partitions in which they may produce join results based on the distance threshold. We design a set of strong candidate filters specific to different distance functions using a novel bisector-based framework, so that each record only needs to be distributed to a small number of partitions while still guaranteeing correctness. To address data skewness, which is common for high dimensional data, we further develop a dynamic load balancing scheme using sampling, which provides strong probabilistic guarantees on the size of partitions, and greatly improves scalability. Experimental evaluation using real data sets shows that our approach is considerably more scalable compared to state-of-the-art algorithms, especially for high dimensional data with low distance thresholds.


title: On deep learning-based communication over the air

abstract: In this work, we demonstrate an over-the-air communications system which is solely based on deep neural networks and has, thus far, only been validated by computer simulations for block-based transmissions. Transmitter and receiver can be jointly trained end-to-end for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). We demonstrate that it is possible to build and train such a system using off-the-shelf software-defined radios (SDRs) and open-source deep learning software libraries. A comparison of the BLER performance of the "learned" system against that of a practical baseline shows competitive performance. We identify several practical challenges of training such a system over-the-air, in particular the missing channel gradient, and propose a learning procedure that circumvents this issue.


title: Occlusion Coherence: Detecting and Localizing Occluded Faces

abstract: The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that explicitly models part occlusion. The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances. This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model. We test the model on several benchmarks for landmark localization and detection including challenging new data sets featuring significant occlusion. We find that the addition of an explicit occlusion model yields a detection system that outperforms existing approaches for occluded instances while maintaining competitive accuracy in detection and landmark localization for unoccluded instances.


title: Structured Prediction Energy Networks

abstract: We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep architecture captures dependencies between labels that would lead to intractable graphical models, and performs structure learning by automatically learning discriminative features of the structured output. One natural application of our technique is multi-label classification, which traditionally has required strict prior assumptions about the interactions between labels to ensure tractable learning and prediction. We are able to apply SPENs to multi-label problems with substantially larger label sets than previous applications of structured prediction, while modeling high-order interactions using minimal structural assumptions. Overall, deep learning provides remarkable tools for learning features of the inputs to a prediction problem, and this work extends these techniques to learning features of structured outputs. Our experiments provide impressive performance on a variety of benchmark multi-label classification tasks, demonstrate that our technique can be used to provide interpretable structure learning, and illuminate fundamental trade-offs between feed-forward and iterative structured prediction.


title: Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs

abstract: The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm for analysing individual predictions made by standard LSTMs, without any changes to the underlying model. By decomposing the output of a LSTM, CD captures the contributions of combinations of words or variables to the final prediction of an LSTM. On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM's final prediction. Using the phrase-level labels in SST, we also demonstrate that CD is able to successfully extract positive and negative negations from an LSTM, something which has not previously been done.


title: An efficient mesh-based face beautifier on mobile devices

abstract: The pursuit of beautiful appearance for human greatly inspires the studies on digital beautifiers especially for the popularity of social networking services (SNS) in this booming era of mobile phones. Unfortunately, existing beautification techniques are not efficient enough for mobile devices that have limited computation and storage resources. In this paper, we propose a beautifier that is efficient on both space and time, and develop a mobile APP available online. We focus on the local beautification of facial chin that has a great effect on facial attractiveness, and simply use an averaged chin shape as the template. We leverage Laplacian as the constraint to remap the original shape to the target, and recursively apply the Laplacian constraint to the contour points from the coarse to fine scales. Thus, we only need to inverse a small matrix for the remap, which requires quite low expenses on space and time. Finally, we apply an efficient warping algorithm based on triangular meshes to generate a beautified face image. The performance of the algorithm is validated by a survey on 70 facial images since no objective metric for beauty exists. Twenty volunteers are invited to grade the results labeled as 'worse', 'unchanged' and 'better'. The results show that 81.50% of inputs are believed becoming more beautiful, and another 10.07% of them are considered unchanged. We also provide the comparisons on the beautification quality and time expenses with the algorithm based on radial basis functions (RBF).


title: Basis of theoretical formulations for new approach for modelling darrieus-type vertical axis wind turbine rotors using electrical equivalent circuit analogy

abstract: Models are crucial in the engineering design process because they can be used for both the optimisation of design parameters and the prediction of performance. Thus, models can significantly reduce design, development and optimisation costs. This paper establishes bases that can underlie the development of a novel equivalent electrical model for Darrieus-type vertical axis wind turbines (DTVAWTs). A brief review of existing models underscores the necessity of a new approach in modelling of DTVAWT. This work also addresses the physical concepts and theoretical formulations that can underpin the development of the model. After highlighting the working principle of the DTVAWT, the mechanic-electric analogy approach for the new model is presented. The paper ends with presentation of key lines of the theoretical formulation for the development of the new model.


title: Use of Analogy in Learning Physics: The Role of Representations.

abstract: Previous studies have demonstrated that analogies can promote student learning in physics and can be productively taught to students to support their learning, under certain conditions. We build on these studies to explore the use of analogy by students in a large introductory college physics course. In the first large-scale study of its kind, we demonstrate that different analogies can lead to varied student reasoning. When different analogies were used to teach electromagnetic EM waves, we found that students explicitly mapped characteristics either of waves on strings or sound waves to EM waves, depending upon which analogy students were taught. We extend these results by investigating how students use analogies. Our findings suggest that representational format plays a key role in the use of analogy.


The Hacker Ethic, Openness, and Sustainability


title: Stochastic blockmodels: First steps

abstract: Abstract A stochastic model is proposed for social networks in which the actors in a network are partitioned into subgroups called blocks. The model provides a stochastic generalization of the blockmodel. Estimation techniques are developed for the special case of a single relation social network, with blocks specified a priori. An extension of the model allows for tendencies toward reciprocation of ties beyond those explained by the partition. The extended model provides a one degree-of-freedom test of the model. A numerical example from the social network literature is used to illustrate the methods.


title: Text as data: The promise and pitfalls of automatic content analysis methods for political texts

abstract: Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation. Language is the medium for politics and political conflict. Candidates debate and state policy positions during a campaign. Once elected, representatives write and debate legislation. After laws are passed, bureaucrats solicit comments before they issue regulations. Nations regularly negotiate and then sign peace treaties, with language that signals the motivations and relative power of the countries involved. News reports document the day-to-day affairs of international relations that provide a detailed picture of conflict and cooperation. Individual candidates and political parties articulate their views through party platforms and manifestos. Terrorist groups even reveal their preferences and goals through recruiting materials, magazines, and public statements. These examples, and many others throughout political science, show that to understand what politics is about we need to know what political actors are saying and writing. Recognizing that language is central to the study of politics is not new. To the contrary, scholars of politics have long recognized that much of politics is expressed in words. But scholars have struggled when using texts to make inferences about politics. The primary problem is volume: there are simply too many political texts. Rarely are scholars able to manually read all the texts in even moderately sized corpora. And hiring coders to manually read all documents is still very expensive. The result is that


title: Augmented Reality Browsers: A Proposal for Architectural Standardization

abstract: The technology evolution of smartphones, systems and the growing of telecommunications, allow the use of Augmented Reality in the exploration of geo-referenced information, complementing the real environment of the users with various types of content displayed on robust mobile cameras through applications called augmented reality browsers. Although this type of application is being in development and growing use by society, the technology, and especially its software architecture do not have any kind of standardization. This work presents concepts about augmented reality browsers for mobile devices. It shows the main aspects and applications of those types of reality. Besides that, the specific features of those types of architecture are discussed and compared, and new architecture, whose most relevant feature is the interoperability of applications in various platforms for mobile devices, are presented. This work has the objective to develop an architectural framework for the development of these browsers.


title: SDCCN: A Novel Software Defined Content-Centric Networking Approach

abstract: Content Centric Networking (CCN) represents an important change in the current operation of the Internet, prioritizing content over the communication between end nodes. Routers play an essential role in CCN, since they receive the requests for a given content and provide content caching for the most popular ones. They have their own forwarding strategies and caching policies for the most popular contents. Despite the number of works on this field, experimental evaluation of different forwarding algorithms and caching policies yet demands a huge effort in routers programming. In this paper we propose SDCCN, a SDN approach to CCN that provides programmable forwarding strategy and caching policies. SDCCN allows fast prototyping and experimentation in CCN. Proofs of concept were performed to demonstrate the programmability of the cache replacement algorithms and the Strategy Layer. Experimental results, obtained through implementation in the Mininet environment, are presented and evaluated.


title: Implementation of a Predictive controller on the STM32 board

abstract: In the literature, there is few practical works about the implementation on miniaturized devices like FPGA (Field Programmable Gate Arrays) and microcontrollers of the Generalized Predictive Control (GPC) technique. The reason can be attributed to its programming complexity. In this work, we took advantage of the advancement in the field of embedded systems and we realized an application based on a GPC implemented on an STM32 microcontroller to control a basic second order system. Also, a PID (Proportional-IntegralDerivative) controller has been implemented in the STM32 to make a performance comparison with the GPC.


title: A pattern language for developing privacy enhancing technologies

abstract: SUMMARY ::: A privacy enhancing technology (PET) is an application or a mechanism that allows users to protect the privacy of their personally identifiable information. Early PETs were about enabling anonymous mailing and anonymous browsing, but lately, there have been active research and development efforts in many other problem domains. This paper describes the first pattern language for developing PETs. Currently, it contains 12 patterns. These privacy patterns are not limited to a specific problem domain; they can be applied to design anonymity systems for various types of online communication, online data sharing, location monitoring, voting, and electronic cash management. The pattern language guides a developer when he or she is designing a PET for an existing problem or innovating a solution for a new problem. Copyright © 2011 John Wiley & Sons, Ltd.


title: Design of a hybrid FES-mechanical intelligent haptic robotic glove

abstract: This paper relays to a research project regarding the design and development of a Hybrid FES-mechanical Intelligent Haptic Robot-Glove (IHRG) for the rehabilitation of the patients who suffered a cerebrovascular accident (CVA). The IHRG is an exoskeleton that supports the human hand and hand activities by using control architectures for dexterous grasping and manipulation. The main aim is to develop a mechanical architecture consisting of a cascade of articulated elements, whose design covers as much as possible the anatomic and functional finger phalanges, providing support for the actuation system.


title: Modeling by example

abstract: In this paper, we investigate a data-driven synthesis approach to constructing 3D geometric surface models. We provide methods with which a user can search a large database of 3D meshes to find parts of interest, cut the desired parts out of the meshes with intelligent scissoring, and composite them together in different ways to form new objects. The main benefit of this approach is that it is both easy to learn and able to produce highly detailed geometric models -- the conceptual design for new models comes from the user, while the geometric details come from examples in the database. The focus of the paper is on the main research issues motivated by the proposed approach: (1) interactive segmentation of 3D surfaces, (2) shape-based search to find 3D models with parts matching a query, and (3) composition of parts to form new models. We provide new research contributions on all three topics and incorporate them into a prototype modeling system. Experience with our prototype system indicates that it allows untrained users to create interesting and detailed 3D models.


title: Account classification in online social networks with LBCA and wavelets

abstract: We developed a wavelet-based approach for account classification that detects textual dissemination by bots on an Online Social Network (OSN). Its main objective is to match account patterns with humans, cyborgs or robots, improving the existing algorithms that automatically detect frauds. With a computational cost suitable for OSNs, the proposed approach analyses the distribution of key terms. The descriptors, a wavelet-based feature vector for each user's account, work in conjunction with a new weighting scheme, called Lexicon Based Coefficient Attenuation (LBCA) and serve as inputs to one of the classifiers tested: Random Forests and Multilayer Perceptrons. Experiments were performed using a set of posts crawled during the 2014 FIFA World Cup, obtaining accuracies within the range from 94 to 100%.


Retrieval-Based Learning : A Decade of Progress 3


title: Learning how to match fresco fragments

abstract: One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this article, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one dataset and then use it to rank predicted matches in another dataset effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.


title: Automatic number plate recognition system by character position method

abstract: Automatic number plate recognition ANPR is an important image processing technology used to recognise number plates of vehicles. Plate localisation and character recognition are two stages of ANPR. In this paper, a methodology has been proposed to develop robust ANPR system. A new algorithm has been proposed for number plate localisation which is based on character positioning method. Character recognition is done with support vector machine in which feature vector is calculated by recursive sub-divisions of character image. The problem of similar shape characters has been solved by syntactic analysis of number plate format for a particular geographical region. The system has been tested on 419 sample images from various countries with various variations in viewing angles, illuminations and distances. Experimental results show that the proposed system detects number plates and recognise characters successfully. The overall success rate of plate localisation is 97.21% and recognition of number is 95.06%.


title: Consumer acceptance of prepaid metering and consumer preferences

abstract: Prepaid metering can be an important technology for effective demand side management. However, successful adoption of this technology is hinged on a positive user experience. Hence, consumer perceptions have to be taken into account even at the early stages of development and prior to the deployment of this technology. Two consumer studies on electricity prepaid metering were conducted in the province of Palawan, Philippines. In the first study, it was shown, by applying the Technology Acceptance Model (TAM) that consumer perceptions on ease of use, usefulness, and affordability were associated with their overall attitude towards prepaid metering, which in turn was related to their intention to use prepaid metering. The result of the second study highlights consumer preferences on features of a basic prepaid metering system. These results are useful in the development and deployment of prepaid metering particularly for developing countries.


title: Bandit Based Monte-Carlo Planning

abstract: For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs the algorithm is shown to be consistent and finite sample bounds are derived on the estimation error due to sampling. Experimental results show that in several domains, UCT is significantly more efficient than its alternatives.


PAT-tree-based keyword extraction for Chinese information retrieval


title: Software quality: the elusive target [special issues section]

abstract: If you are a software developer, manager, or maintainer, quality is often on your mind. But what do you really mean by software quality? Is your definition adequate? Is the software you produce better or worse than you would like it to be? We put software quality on trial, examining both the definition and evaluation of our software products and processes.


title: Repeatability of quantitative 18F-FLT uptake measurements in solid tumors: an individual patient data multi-center meta-analysis

abstract: Introduction ::: 3′-deoxy-3′-[18F]fluorothymidine (18F–FLT) positron emission tomography (PET) provides a non-invasive method to assess cellular proliferation and response to antitumor therapy. Quantitative 18F–FLT uptake metrics are being used for evaluation of proliferative response in investigational setting, however multi-center repeatability needs to be established. The aim of this study was to determine the repeatability of 18F–FLT tumor uptake metrics by re-analyzing individual patient data from previously published reports using the same tumor segmentation method and repeatability metrics across cohorts.


Human-Machine-Learner Interaction : The Best of Both Worlds


title: Applying graph-based data mining concepts to the educational sphere

abstract: In this study, we discuss the possible application of the ubiquitous complex network approach for information extraction from educational data. Since a huge amount of data (which is detailed as well) is produced by the complex administration systems of educational institutes, instead of the classical statistical methods, new types of data processing techniques are required to handle it. We define several suitable network representations of students, teachers and subjects in public education and present some possible ways of how graph mining techniques can be used to get detailed information about them. Depending on the construction of the underlying graph, we examine several network models and discuss which are the most appropriate graph mining tools (like community detection and ranking and centrality measures) that can be applied on them. Lastly, we attempt to highlight the many advantages of using graph-based data mining in educational data against the classical evaluation techniques.


title: Experimental Verification and Finite Element Analysis of Short-Circuit Electromagnetic Force for Dry-Type Transformer

abstract: The short-circuit force induces critical mechanical stress on a transformer. This paper deals with experimental verification and finite element analysis (FEA) for short-circuit force prediction of a 50 kVA dry-type transformer. We modeled high voltage (HV) winding into 20 sections and low voltage (LV) winding into 22 sections as similar as those windings of a model transformer. With this modeling technique, we could calculate electromagnetic forces acting on each section of the windings of a dry-type transformer under short-circuit condition. The magnetic vector potentials, magnetic flux densities, and electromagnetic forces due to short-circuit current are solved by FEA. The electromagnetic forces consisting of radial and axial directions depend both on short-circuit current and leakage flux density. These results were used as input source of sequential finite element method (FEM) to predict the resultant mechanical forces considering the structural characteristics such as stress distributions or deformations of windings, accurately. The obtained resultant mechanical forces in HV winding are compared with those of the experimental ones.


title: A survey of the effects of aging on biometric identity verification

abstract: The performance of biometric authentication systems is affected by discrepancies between data stored in biometric templates and corresponding data derived from the actual owners of biometric templates. Such discrepancies are mainly attributed to within-person variations of biometric features. Among all types of within-person variations, aging-related variation displays unique characteristics that make the process of dealing with aging a challenging task. In this paper we discuss how aging affects different types of biometric features and discuss possible approaches that aim to eliminate the effects of aging so that deterioration in the long-term performance of biometric authentication systems is minimised.


Learning Content-rich Diffusion Network Embedding 1


title: Comparative controller design of an aerial robot

abstract: Abstract Flying capability opens novel opportunities in robotic applications, such as search and rescue, surveillance navigations and mapping operations. In this article, considering an aerial robot, i.e. an unmanned aerial vehicle (UAV), a few comparable controllers are designed to manage the system performance during various maneuvers. After introducing a nonlinear dynamics model of the system, an adaptive controller is proposed based on feedback linearization approach and using Lyapunov design method. Next, an optimal controller is designed to compare its performance with the designed adaptive controller. Stability analysis for the designed adaptation law is also studied and discussed. To evaluate the performance of designed controllers for a given aerial robot, a comprehensive simulation program is developed. It is shown that tracking errors for the state variables exponentially converge to zero, even in the presence of parameters uncertainty. In particular, it is shown that the proposed adaptive controller, based on its feedback linearization approach and Lyapunov stabilized characteristics, is able to perform perfect path tracking maneuvers, compared to the optimal controller that contains minor errors due to its feed-forward nature.


title: Poster: TRIM: A Truthful Incentive Mechanism for Dynamic and Heterogeneous Tasks in Mobile Crowdsensing

abstract: Stimulating user participation is of paramount importance for mobile crowdsensing applications to obtain high-quality data. Although many incentive mechanisms have been designed, most of them ignore the dynamic arrivals and different sensing requirements of tasks. Thus, the existing mechanisms will fail when being applied to the realistic scenario where tasks are publicized dynamically and heterogeneous with different sensing requirements of locations, time durations and sensing times. In this work, we propose an auction-based truthful mechanism for realistic mobile crowdsensing. Through extensive simulations, we demonstrate that our mechanism can satisfy the desired properties of truthfulness, individual rationality, computational efficiency with both low social cost and low total payment.


title: Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010

abstract: Summary Background Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex. Methods We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions. Findings In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45–54% since 1990; ischaemic heart disease and stroke YLLs increased by 17–28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted. Interpretation Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis. Funding Bill & Melinda Gates Foundation.


title: Prefrontal Phase Locking to Hippocampal Theta Oscillations

abstract: The interactions between cortical and hippocampal circuits are critical for memory formation, yet their basic organization at the neuronal network level is not well understood. Here, we demonstrate that a significant portion of neurons in the medial prefrontal cortex of freely behaving rats are phase locked to the hippocampal theta rhythm. In addition, we show that prefrontal neurons phase lock best to theta oscillations delayed by approximately 50 ms and confirm this hippocampo-prefrontal directionality and timing at the level of correlations between single cells. Finally, we find that phase locking of prefrontal cells is predicted by the presence of significant correlations with hippocampal cells at positive delays up to 150 ms. The theta-entrained activity across cortico-hippocampal circuits described here may be important for gating information flow and guiding the plastic changes that are believed to underlie the storage of information across these networks.


title: Regime Changes and Financial Markets

abstract: Regime switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. While the regimes captured by regime switching models are identified by an econometric procedure, they often correspond to different periods in regulation, policy, and other secular changes. In empirical estimates, the regime switching means, volatilities, autocorrelations, and cross-covariances of asset returns often differ across regimes, which allow regime switching models to capture the stylized behavior of many financial series including fat tails, heteroskedasticity, skewness, and time-varying correlations. In equilibrium models, regimes in fundamental processes, like consumption or dividend growth, strongly affect the dynamic properties of equilibrium asset prices and can induce non-linear risk-return trade-offs. Regime switches also lead to potentially large consequences for investors' optimal portfolio choice.


BIM-based Sustainability Analysis : An Evaluation of Building Performance Analysis Software


title: Efficient 3D reflection symmetry detection: A view-based approach

abstract: Propose a novel and efficient view-based symmetry detection algorithm.Demonstrate better accuracy and efficiency compared with two latest approaches.Dataset-level evaluation and two related applications validate good robustness and flexibility. Display Omitted Symmetries exist in many 3D models while efficiently finding their symmetry planes is important and useful for many related applications. This paper presents a simple and efficient view-based reflection symmetry detection method based on the viewpoint entropy features of a set of sample views of a 3D model. Before symmetry detection, we align the 3D model based on the Continuous Principal Component Analysis (CPCA) method. To avoid the high computational load resulting from a directly combinatorial matching among the sample views, we develop a fast symmetry plane detection method by first generating a candidate symmetry plane based on a matching pair of sample views and then verifying whether the number of remaining matching pairs is within a minimum number. Experimental results and two related applications demonstrate better accuracy, efficiency, robustness and versatility of our algorithm than state-of-the-art approaches.


title: Gradient-based boosting for statistical relational learning: The relational dependency network case

abstract: Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, comes at the expense of a more complex model-selection problem: an unbounded number of relational abstraction levels might need to be explored. Whereas current learning approaches for RDNs learn a single probability tree per random variable, we propose to turn the problem into a series of relational function-approximation problems using gradient-based boosting. In doing so, one can easily induce highly complex features over several iterations and in turn estimate quickly a very expressive model. Our experimental results in several different data sets show that this boosting method results in efficient learning of RDNs when compared to state-of-the-art statistical relational learning approaches.


title: Perspectives on domestic violence: case study from Karachi, Pakistan.

abstract: There is no adequate profile of domestic violence in Pakistan although this issue is frequently highlighted by the media. This case study used qualitative and quantitative methods to explore the nature and forms of domestic violence, circumstances, impact and coping mechanisms amongst selected women victims in Karachi. Violence was a continuum: all the women reported verbal abuse, often escalating into physical, emotional, sexual and economic abuse. The husband was the most common perpetrator. Women suffered in silence due to sociocultural norms, misinterpretation of religious beliefs, subordinate status, economic dependence and lack of legal redress. Besides short-term local measures, public policy informed by correct interpretation of religion can bring about a change in prevailing societal norms.


title: On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks

abstract: Smartphones with Near-Field Communication (NFC) may emulate contactless smart cards, which has resulted in the deployment of various access control, transportation and payment services, such as Google Pay and Apple Pay. Like contactless cards, however, NFC-based smartphone transactions are susceptible to relay attacks, and ambient sensing has been suggested as a potential countermeasure. In this study, we empirically evaluate the suitability of ambient sensors as a proximity detection mechanism for smartphone-based transactions under EMV constraints. We underpin our study using sensing data collected from 17 sensors from an emulated relay attack test-bed to assess whether they can thwart such attacks effectively. Each sensor, where feasible, was used to record 350-400 legitimate and relay (illegitimate) contactless transactions at two different physical locations. Our analysis provides an empirical foundation upon which to determine the efficacy of ambient sensing for providing a strong anti-relay mechanism in security-sensitive applications. We demonstrate that no single, evaluated mobile ambient sensor is suitable for such critical applications under realistic deployment constraints.


title: Providing a Window of Opportunity for Converting eStore Visitors

abstract: A consumer typically visits an online store a few times before making a purchase decision, and on each visit spends some time browsing the store. The durations of these visits vary not only across consumers but also for a given consumer across multiple visits. We argue that the amount of time that a consumer spends on the first visit to a website depends on how she is drawn to the website. We find that the duration of the first visit is influenced by the advertising tool—banner ad or search engine—used to attract consumers to the website. The durations of subsequent visits are influenced by the durations of earlier visits. The search durations are also influenced by the visit day of the week and time of day. In this paper, we develop a multiple-spell competing risk model to capture the underlying stochastic process, chief elements of which are two interrelated processes: a duration process and a transition process. The multistate, multiple-spell model allows us to identify a window of opportunity, within ...


LEARNING FROM DELAYED REWARDS USING INFLUENCE VALUES APPLIED TO COORDINATION IN MULTI-AGENT SYSTEMS


title: Query-Dependent Contextualization of Streaming Data

abstract: We propose a method for linking entities in a stream of short textual documents that takes into account context both inside a document and inside the history of documents seen so far. Our method uses a generic optimization framework for combining several entity ranking functions, and we introduce a global control function to control optimization. Our results demonstrate the effectiveness of combining entity ranking functions that take into account context, which is further boosted by 6% when we use an informed global control function.


Eddy Current Losses in Transformer Windings and Circuit Wiring


title: A low-power CMOS thyristor based delay element with programmability extensions

abstract: This paper presents a low-power CMOS thyristor based delay element for inclusion in standard cell ASIC libraries, and a reconfigurable delay element designed for reconfigurable devices. Our design is based on a basic delay element, which serves as a buffer which has been specially designed to have a fixed propagation delay. We present leakage power optimizations, which when applied to the circuit reduces the on-state leakage power consumption by more than 99%, while reducing the off-state leakage power by roughly 96%. We have created delay elements with delay lengths of 4, 5, 7, 9, 11, and 17 ns for inclusion in a standard cell library targeting the IBM 0.13 μm technology. The delay element is then further extended to introduce a programmability feature which allows the delay to be varied. Two reconfigurable delay elements are then added to the delay element standard cell library. The first can be configured for delays of 4, 5, or 7 ns, while the second can be programmed for delays of 9, 11, or 17 ns. Finally, potential uses of the circuits in application specific, as well as reconfigurable systems are explored.


title: PiCasso: A lightweight edge computing platform

abstract: Recent trends show that deploying low cost devices with lightweight virtualisation services is an attractive alternative for supporting the computational requirements at the network edge. Examples include inherently supporting the computational needs for local applications like smart cities and applications with stringent Quality of Service (QoS) requirements which are naturally hard to satisfy by traditional cloud infrastructures or supporting multi-access edge computing requirements of network in the box type solutions. The implementation of such a platform demands precise knowledge of several key system parameters, including the load that a service can tolerate and the number of service instances that a device can host. In this paper, we introduce PiCasso, a platform for lightweight service orchestration at the edges, and discuss the benchmarking results aimed at identifying the critical parameters that PiCasso needs to take into consideration.


title: A morphable model for the synthesis of 3D faces

abstract: In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance. Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.


title: Balance recovery of a quadruped robot

abstract: Maintaining the balance or stability of legged robots in natural terrains is a challenging problem. Besides the inherent unstable characteristics of legged robots, the sources of instability are the irregularities of the ground surface and also the external pushes. In this paper, a push recovery framework for restoring the robot balance against external unknown disturbances will be demonstrated. It is assumed that the magnitude of exerted pushes is not large enough to use a reactive stepping strategy. In the comparison with previous methods, which a simplified model such as point mass model is used as the model of the robot for studying the push recovery problem, the whole body dynamic model will be utilized in present work. This enhances the capability of the robot to exploit all of the DOFs to recover its balance. To do so, an explicit dynamic model of a quadruped robot will be derived. The balance controller is based on the computation of the appropriate acceleration of the main body. It is calculated to return the robot to its desired position after the perturbation. This acceleration should be chosen under the stability and friction conditions. To calculate main body acceleration, an optimization problem is defined so that the stability, friction condition considered as its constraints. The simulation results show the effectiveness of the proposed algorithm. The robot can restore its balance against the large disturbance solely through the adjustment of the position and orientation of main body.


title: Joint Representation Learning for Location-Based Social Networks with Multi-Grained Sequential Contexts

abstract: This article studies the problem of learning effective representations for Location-Based Social Networks (LBSN), which is useful in many tasks such as location recommendation and link prediction. Existing network embedding methods mainly focus on capturing topology patterns reflected in social connections, while check-in sequences, the most important data type in LBSNs, are not directly modeled by these models. In this article, we propose a representation learning method for LBSNs called as JRLM++, which models check-in sequences together with social connections. To capture sequential relatedness, JRLM++ characterizes two levels of sequential contexts, namely fine-grained and coarse-grained contexts. We present a learning algorithm tailored to the hierarchical architecture of the proposed model. We conduct extensive experiments on two important applications using real-world datasets. The experimental results demonstrate the superiority of our model. The proposed model can generate effective representations for both users and locations in the same embedding space, which can be further utilized to improve multiple LBSN tasks.


title: EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.

abstract: Abstract We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications.


title: Exploration from Demonstration for Interactive Reinforcement Learning

abstract: Reinforcement Learning (RL) has been effectively used to solve complex problems given careful design of the problem and algorithm parameters. However standard RL approaches do not scale particularly well with the size of the problem and often require extensive engineering on the part of the designer to minimize the search space. To alleviate this problem, we present a model-free policy-based approach called Exploration from Demonstration (EfD) that uses human demonstrations to guide search space exploration. We use statistical measures of RL algorithms to provide feedback to the user about the agent's uncertainty and use this to solicit targeted demonstrations useful from the agent's perspective. The demonstrations are used to learn an exploration policy that actively guides the agent towards important aspects of the problem. We instantiate our approach in a gridworld and a popular arcade game and validate its performance under different experimental conditions. We show how EfD scales to large problems and provides convergence speed-ups over traditional exploration and interactive learning methods.


Evaluation Dataset (DT-Grade) and Word Weighting Approach towards Constructed Short Answers Assessment in Tutorial Dialogue Context


title: Positive psychology: An introduction

abstract: A science of positive subjective experience, positive individual traits, and positive institutions promises to improve quality of life and prevent the pathologies that arise when life is barren and meaningless, The exclusive focus on pathology that has dominated so much of our discipline results in a model of the human being lacking the positive features that make life worth living. Hope, wisdom, creativity, future mindedness, courage, spirituality, responsibility, and perseverance are ignored or explained as transformations of more authentic negative impulses. The 15 articles in this millennial issue of the American Psychologist discuss such issues as what enables happiness, the effects of autonomy and self-regulation, how optimism and hope affect health, what constitutes wisdom, and how talent and creativity come to fruition. The authors outline a framework for a science of positive psychology, point to gaps in our knowledge, and predict that the next century will see a science and profession that will come to understand and build the factors that allow individuals, communities, and societies to flourish.


title: Machine learning approach for forecasting crop yield based on climatic parameters

abstract: With the impact of climate change in India, majority of the agricultural crops are being badly affected interms of their performance over a period of last two decades. Predicting the crop yield well ahead of its harvest would help the policy makers and farmers for taking appropriate measures for marketing and storage. Such predictions will also help the associated industries for planning the logistics of their business. Several methods of predicting and modeling crop yields have been developed in the past with varying rateof success, as these don't take into account characteristicsoftheweather, a n d aremostly empirical. In the present study a software tool named ‘Crop Advisor’ has been developed as an user friendly web page for predicting the influence of climatic parameters on the crop yields.C4.5 algorithm is used to find out the most influencing climatic parameter on the crop yields of selected crops in selected districts of Madhya Pradesh. This software provides an indication of relative influence of different climatic parameters on the crop yield, other agro-input parameters responsible for crop yield are not considered in this tool, since, application of these input parameters varies with individual fields in space and time.


title: Smartphone malware detection: From a survey towards taxonomy

abstract: Smartphone malwares are serious threat. Malware detector is the primary tool to protect Smartphones against malwares. The malware detector efficiency is based on the technique it uses. In this paper, we survey the current state of the art of Smartphone malware detection techniques. Those techniques have been classified into a structured taxonomy based on 3 rules. Those rules are inferred and compiled from literature review. The rules are: reference behaviour, analysis approach and malware behaviour representation. According to reference behaviour rule, Smartphone malware detection techniques divided into two main classes: signature-based and anomaly-based. In side these classes, ramifications are then derived according to analysis approach rule and malware behaviour representation rule.