title: Hierarchical part detection with deep neural networks

abstract: Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection. In a single network we first detect the object and then regress to part location proposals based only on the feature representation inside the object. Experiments show that our hierarchical approach outperforms a network which directly regresses the part locations. We also show that our approach obtains part detection accuracy comparable or better than state-of-the-art on the CUB-200 bird and Fashionista clothing item datasets with only a fraction of the number of part proposals.


title: Anomaly Detection In Cellular Network Data Using Big Data Analytics

abstract: Anomaly detection is a key component in which perturbations from a normal behavior suggests a misconfigured/ mismatched data in related systems. In this paper, we present a call detail record based anomaly detection method (CADM) that analyzes the users's calling activities and detects the abnormal behavior of user movements in a real cellular network. CADM is capable of detecting the location of the site that an anomaly has occurred. We evaluate the proposed CADM by performing experiments over the call-detail records of AVEA, a mobile service provider in Turkey.


title: A Formal Approach to Identifying Security Vulnerabilities in Telecommunication Networks

abstract: The number of security attacks on the telecommunication networks is constantly increasing. To prevent them, the telecom sector is looking for new automated techniques facilitating a discovery of potential network vulnerabilities and rectification of them. In this paper, we propose an approach for identifying potential attack scenarios and defining recommendations for preventing them. The approach is formalised in the Event-B framework. It allows us to not only formalise the analysed part of the network architecture and verify consistency of the control and data flow of the associated services but also employ model checking to generate and analyse attack scenarios. By applying the proposed approach, the designers can systematically explore network vulnerabilities and propose recommendations for attack prevention.


title: Survival model predictive accuracy and ROC curves.

abstract: The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.


title: Three-dimensional shape reconstruction of abdominal aortic aneurysm

abstract: This paper presents an effective computational technique for reconstructing a three-dimensional shape of an abdominal aortic aneurysm (AAA), from a limited number of computed tomography (CT) images. The three-dimensional template geometry of a healthy abdominal aorta is used as a priori knowledge, and the template geometry is deformed by extended free-form deformation (EFFD), to generate a patient-specific AAA geometry. A two-step optimization scheme is devised to find an optimal set of EFFD parameters that match the cross-section of a deformed template with an AAA contour shown in a CT image. The geometric continuity of a deformed model is maintained by raising the order of the polynomial function used in EFFD. Experimental results show that the proposed method creates the three-dimensional shape of AAA suitable for structural finite element analysis and computational fluid dynamics for medical diagnosis.


title: MapReduce Online

abstract: MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire output of each map and reduce task before it can be consumed. In this paper, we propose a modified MapReduce architecture that allows data to be pipelined between operators. This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We present a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see "early returns" from a job as it is being computed. Our Hadoop Online Prototype (HOP) also supports continuous queries, which enable MapReduce programs to be written for applications such as event monitoring and stream processing. HOP retains the fault tolerance properties of Hadoop and can run unmodified user-defined MapReduce programs.


title: Sparse PCA via Bipartite Matchings

abstract: We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. Such components can be computed one by one, repeatedly solving the single-component problem and deflating the input data matrix, but this greedy procedure is suboptimal. We present a novel algorithm for sparse PCA that jointly optimizes multiple disjoint components. The extracted features capture variance that lies within a multiplicative factor arbitrarily close to 1 from the optimal. Our algorithm is combinatorial and computes the desired components by solving multiple instances of the bipartite maximum weight matching problem. Its complexity grows as a low order polynomial in the ambient dimension of the input data, but exponentially in its rank. However, it can be effectively applied on a low-dimensional sketch of the input data. We evaluate our algorithm on real datasets and empirically demonstrate that in many cases it outperforms existing, deflation-based approaches.


title: Continuous speech recognition using hidden Markov models

abstract: The use of hidden Markov models (HMMs) in continuous speech recognition is reviewed. Markov models are presented as a generalization of their predecessor technology, dynamic programming. A unified view is offered in which both linguistic decoding and acoustic matching are integrated into a single, optimal network search framework. Advances in recognition architectures are discussed. The fundamentals of Viterbi beam search, the dominant search algorithm used today in speed recognition, are presented. Approaches to estimating the probabilities associated with an HMM model are examined. The HMM-supervised training paradigm is examined. Several examples of successful HMM-based speech recognition systems are reviewed. >


Encyclopedia of Artificial Intelligence (3 Volumes)


title: A multilevel banded graph cuts method for fast image segmentation

abstract: In the short time since publication of Boykov and Jolly's seminal paper [2001], graph cuts have become well established as a leading method in 2D and 3D semi-automated image segmentation. Although this approach is computationally feasible for many tasks, the memory overhead and supralinear time complexity of leading algorithms results in an excessive computational burden for high-resolution data. In this paper, we introduce a multilevel banded heuristic for computation of graph cuts that is motivated by the well-known narrow band algorithm in level set computation. We perform a number of numerical experiments to show that this heuristic drastically reduces both the running time and the memory consumption of graph cuts while producing nearly the same segmentation result as the conventional graph cuts. Additionally, we are able to characterize the type of segmentation target for which our multilevel banded heuristic yields different results from the conventional graph cuts. The proposed method has been applied to both 2D and 3D images with promising results.


title: CASH DEMAND FORECASTING FOR ATM USING NEURAL NETWORKS AND SUPPORT VECTOR REGRESSION ALGORITHMS

abstract: In this paper two different methods are used to forecast the daily cash demand for automatic teller machines (ATM). The first method is based on flexible artificial neural network (ANN). The gener- alization properties of this ANN were improved using special adaptive regularization term. The second forecasting method employs the support vector regression (SPR) algorithm. Performed simulation studies and experimental tests showed tolerable forecasting capacities using the both proposed methods. Despite the today's overenthusiastic beliefs about the capabilities of SPR, our investigation showed however that for this application slightly better result can be achieved using forecasting method based on flexible ANN. At this stage the forecasting schema based on flexible ANN is in the implementing phase for intel- ligent cash management in ATM network .


title: Revolve: A Versatile Simulator for Online Robot Evolution

abstract: Developing robotic systems that can evolve in real-time and real-space is a long term objective with technological as well as algorithmic milestones on the road. Technological prerequisites include advanced 3D-printing, automated assembly, and robust sensors and actuators. The necessary evolutionary mechanisms need not wait for these, they can be developed and investigated in simulations. In this paper, we present a system to simulate online evolution of constructible robots, where (1) the population members (robots) concurrently exist and evolve their morphologies and controllers, (2) all robots can be physically constructed. Experiments with this simulator provide us with insights into differences of using online and offline evolutionary setups.


Population Growth and Economic Development in Africa


title: A Systematic Approach of Dataset Definition for a Supervised Machine Learning Using NFR Framework

abstract: Non-functional requirements describe important constraints upon the software development and should therefore be considered and specified as early as possible during the system analysis. Effective elicitation of requirements is arguably among the most important of the resulting recommended RE practices. Recent research has shown that artificial intelligence techniques such as Machine Learning and Text Mining perform the automatic extraction and classification of quality attributes from text documents with relevant results. This paper aims to define a systematic process of dataset generation through NFR Framework catalogues improving the NFR's classification process using Machine Learning techniques. A well-known dataset (Promise) was used to evaluate the precision of our approach reaching interesting results. Regarding to security and performance we obtained a precision and recall ranging between ~85% and ~98%. And we achievement a F1 above ~79% when classified the security, performance and usability together.


title: Dual Band Metamaterial Antenna For LTE/Bluetooth/WiMAX System

abstract: A compact metamaterial inspired antenna operate at LTE, Bluetooth and WiMAX frequency band is introduced in this paper. For the lower band, the design utilizes an outer square metallic strip forcing the patch to radiate as an equivalent magnetic-current loop. For the upper band, another magnetic current loop is created by adding metamaterial structure near the feed line on the patch. The metamaterial inspired antenna dimension of 42 × 32 mm2 compatible to wireless devices. Finite integration technique based CST Microwave Studio simulator has been used to design and numerical investigation as well as lumped circuit model of the metamaterial antenna is explained with proper mathematical derivation. The achieved measured dual band operation of the conventional antenna are sequentially, 0.561~0.578 GHz, 2.346~2.906 GHz, and 2.91~3.49 GHz, whereas the metamaterial inspired antenna shows dual-band operation from 0.60~0.64 GHz, 2.67~3.40 GHz and 3.61~3.67 GHz, respectively. Therefore, the metamaterial antenna is applicable for LTE and WiMAX applications. Besides, the measured metamaterial antenna gains of 0.15~3.81 dBi and 3.47~3.75 dBi, respectively for the frequency band of 2.67~3.40 GHz and 3.61~3.67 GHz.


title: The evolution of an orbiting sample container for potential Mars sample return

abstract: Although NASA has no specific plans at this time to return samples from Mars, the Program Formulation Office of the Mars Exploration Program sponsors ongoing mission concept studies, systems analyses, and technology investments which explore different strategies for the potential return of samples from Mars, consistent with the charter of the program and stated priorities of the science community. A critical component of such a campaign would be an Orbiting Sample container (OS), which would contain the Mars samples to be returned to Earth. This paper discusses the most recent efforts by the JPL's Mars Formulation Office to mature an OS design planned for use on a potential Mars Sample Return (MSR) mission. Similar to the “Decadal Study Architecture” [1], the current MSR architecture envisions as a three-mission campaign with each mission serving a critical role towards returning Martian rock and atmospheric samples back to Earth. An OS would be a central piece of hardware in the proposed MSR architecture due to its interfaces to all the three missions of the potential campaign. Additionally, numerous stakeholders and subsystems such as science and planetary protection impose challenging requirements on the OS's functions and capabilities. As a result, designing an OS that meets all the requirements is challenging and quite complex. The story of the OS's evolution from black box concept thru to the current-and-still-maturing baseline design is the focus of this paper. From the OS's launch off Earth aboard a Sample Retrieval Lander (SRL) through to return to Earth, the design and functional requirements generated by and for each stage of the OS's mission are discussed. Then, with an understanding of what the OS would be required to do, a mapping of the main requirements to the design features of the current OS concept is explained. Many tests and analyses have been conducted to support and validate the current OS design. Results from test and analysis in the areas of aerothermal, impact dynamics, optical tracking, and radio electromagnetics are presented.


title: Microsatellite analysis reveals a progressive widening of the genetic basis in the elite durum wheat germplasm

abstract: It has been argued that the level of genetic diversity in the modern durum wheat (Triticum turgidum L. var. durum) elite germplasm may have declined due to the high selection pressure applied in breeding programs. In this study, 58 accessions covering a wide spectrum of genetic diversity of the cultivated durum wheat gene pool were characterized with 70 microsatellite loci (or simple sequence repeats, SSRs). On average, SSRs detected 5.6 different allelic variants per locus, with a mean diversity index (DI) equal to 0.56, thus revealing a diversity content comparable to those previously observed with SSRs in other small-grain cereal gene pools. The mean genetic similarity value was equal to 0.44. A highly diagnostic SSR set has been identified. A high variation in allele size was detected among SSR loci, suggesting a different suitability of these loci for estimating genetic diversity. The B genome was characterized by an overall polymorphism significantly higher than that of the A genome. Genetic diversity is organised in well-distinct sub-groups identified by the corresponding foundation-genotypes. A large portion (92.7%) of the molecular variation detected within the group of 45 modern cvs was accounted for by SSR alleles tracing back to ten foundation-genotypes; among those, the most recent CIMMYT-derived founders were genetically distant from the old Mediterranean ones. On the other hand, rare alleles were abundant, suggesting that a large number of genetic introgressions contributed to the foundation of the well-diversified germplasm herein considered. The profiles of recently released varieties indicate that the level of genetic diversity present in the modern durum wheat germplasm has actually increased over time.


title: High-Voltage Ultra Wideband Coaxial Load With Distributed Ceramic-Carbon-Rod Resistor

abstract: An ultra wideband coaxial termination load is developed using a distributed ceramic-carbon-rod resistor for high-voltage fast transient impulse measurements. The difficulty in the development of a high-voltage ultra wideband termination device is a compromise in the size of the internal resistive element for optimizing performance between high-voltage insulation and wideband frequency operation. A smaller resistor shows better high-frequency performance but worse high-voltage insulation characteristics. In this letter, we propose a logarithmically tapered coaxial load device having wideband and high-voltage properties simultaneously. For high-voltage operation exceeding tens of kilovolts, a ceramic-carbon-rod resistor with distributed parameters is used as the termination element. For wideband frequency operation, we constructed a linearly decreasing coaxial characteristic impedance along the solid rod resistor for compensating impedance mismatch and maintaining consistent synthetic impedance in the entire load device. The implemented coaxial load shows a return loss under -18 dB from dc to 10 GHz, peak impedance variation under 8% with converged value of 54 Ω, and insulation performance up to 20 kV with a rise time of 100 ps.


A Survey Of Effort Estimation Techniques For The Software Development


title: Performance Analysis and Optimization for SpMV on GPU Using Probabilistic Modeling

abstract: This paper presents a unique method of performance analysis and optimization for sparse matrix-vector multiplication (SpMV) on GPU. This method has wide adaptability for different types of sparse matrices and is different from existing methods which only adapt to some particular sparse matrices. In addition, our method does not need additional benchmarks to get optimized parameters, which are calculated directly through the probability mass function (PMF). We make the following contributions. (1) We present a PMF to analyze precisely the distribution pattern of non-zero elements in a sparse matrix. The PMF can provide theoretical basis for the compression of a sparse matrix. (2) Compression efficiency of COO, CSR, ELL, and HYB can be analyzed precisely through the PMF, and combined with the hardware parameters of GPU, the performance of SpMV based on COO, CSR, ELL, and HYB can be estimated. Furthermore, the most appropriate format for SpMV can be selected according to estimated value of the performance. Experiments prove that the theoretical estimated values and the tested values have high consistency. (3) For HYB, the optimal segmentation threshold can be found through the PMF to achieve the optimal performance for SpMV. Our performance modeling and analysis are very accurate. The order of magnitude of the estimated speedup and that of the tested speedup for each of the ten tested sparse matrices based on the three formats COO, CSR, and ELL are the same. The percentage of relative difference between an estimated value and a tested value is less than 20 percent for over 80 percent cases. The performance improvement of our algorithm is also effective. The average performance improvement of the optimal solution for HYB is over 15 percent compared with that of the automatic solution provided by CUSPARSE lib.


title: Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models

abstract: Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics- and maneuver-based approaches. These two methods were combined for the reason that the physics-based trajectory prediction method could ensure accuracy in the short term with the consideration of vehicle running dynamic parameters, and the maneuver-based prediction approach has a long-term insight into future trajectories with maneuver estimation. In this study, the interactive multiple model trajectory prediction (IMMTP) method is proposed by combining the two predicting models. The probability of each model in the interactive multiple models could recursively adjust according to the predicting variance of each model. In addition, prediction uncertainty is considered by employing unscented Kalman filters in the physics-based prediction model. To the maneuver-based method, random elements for uncertainty are introduced to the trajectory of each maneuver inferred by using the dynamic Bayesian network. The approach is applied and analyzed in the lane-changing scenario by using naturalistic driving data. Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon.


Divided loop colostomy that does not prolapse


title: A Longitudinal Study of Adult Life Experiences and Developmental Outcomes.

abstract: The purpose of this study was to test the hypothesis that selected life experiences are related to developmental outcomes. To test this hypothesis, answers to questions from the National Longitudinal Survey of the High School Class of 1972 were used. Developmental outcomes were operationalized through the four dependent variables of personal agency, political participation, concern for social inequality, and social action participation. Seven independent variables representing life experiences were marriage, parenting, unemployment, type of employer, work with people, educational attainment, and location. After controlling for initial differences, each of the seven variables used to operationalize life experience was found to be significantly related to one or more of the four dependent variables representing developmental outcomes. These results were interpreted as presenting convincing support for the connection between life experience, learning, and developmental outcomes.


title: A Step-Up Transformerless, ZV–ZCS High-Gain DC/DC Converter With Output Voltage Regulation Using Modular Step-Up Resonant Cells for DC Grid in Wind Systems

abstract: The use of medium voltage dc (MVDC) grid in offshore wind farms has been presented as an alternative solution to eliminate the conventional bulky low frequency step-up transformers used in the medium voltage ac grid. In order to reduce the transmission losses, the trend is to increase the wind turbine output voltage to at least thousands of volts. In this paper, a variable frequency controlled MVDC converter with modular approach of combining multiple step-up resonant circuits and multistring of switches configuration is proposed for MVDC grid. The proposed converter has the following features: 1) the multistring arrangement of the switches allows much lower voltage stress across each transistor; 2) the modular step-up resonant circuits are able to achieve high voltage gain so that transformer with large turns ratio is not required; 3) zero voltage switching turn-on and zero current switching turn-off are achieved in all the switches; 4) the output MVDC level is controlled by variable frequency control of the step-up converter; and 5) circulating energy in all the resonant circuit modules is minimized through close-to-resonant operation for different load conditions. Simulation results are provided on a 4 kVac/50 kV, 2 MW converter for a wide range of load conditions. Experimental results are provided on a laboratory-scale 200 V/1.6 kV proof-of-concept prototype to highlight the merits of this paper.


Measurement of intimacy: conceptual and methodological issues of studying close relationships


title: Detecting Intrusions through System Call Sequence and Argument Analysis

abstract: We describe an unsupervised host-based intrusion detection system based on system call arguments and sequences. We define a set of anomaly detection models for the individual parameters of the call. We then describe a clustering process that helps to better fit models to system call arguments and creates interrelations among different arguments of a system call. Finally, we add a behavioral Markov model in order to capture time correlations and abnormal behaviors. The whole system needs no prior knowledge input; it has a good signal-to-noise ratio, and it is also able to correctly contextualize alarms, giving the user more information to understand whether a true or false positive happened, and to detect global variations over the entire execution flow, as opposed to punctual ones over individual instances.


title: #Proana: Pro-Eating Disorder Socialization on Twitter.

abstract: Abstract Purpose Pro–eating disorder (ED) online movements support engagement with ED lifestyles and are associated with negative health consequences for adolescents with EDs. Twitter is a popular social media site among adolescents that provides a unique setting for Pro-ED content to be publicly exchanged. The purpose of this study was to investigate Pro-ED Twitter profiles' references to EDs and how their social connections (followers) reference EDs. Methods A purposeful sample of 45 Pro-ED profiles was selected from Twitter. Profile information, all tweets, and a random sample of 100 of their followers' profile information were collected for content analysis using the Twitter Application Programming Interface. A codebook based on ED screening guidelines was applied to evaluate ED references. For each Pro-ED profile, proportion of tweets with ED references and proportion of followers with ED references in their own profile were evaluated. Results In total, our 45 Pro-ED profiles generated 4,245 tweets for analysis. A median of 36.4% of profiles' tweets contained ED references. Pro-ED profiles had a median of 173 followers, and a median of 44.5% of followers had ED references. Pro-ED profiles with more tweets with ED references also tended to have more followers with ED references (β = .37, p Conclusions Findings suggest that profiles which self-identify as Pro-ED express disordered eating patterns through tweets and have an audience of followers, many of whom also reference ED in their own profiles. ED socialization on Twitter might provide social support, but in the Pro-ED context this activity might also reinforce an ED identity.


title: Generative Models from the perspective of Continual Learning

abstract: Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tuning. We used two quantitative metrics to estimate the generation quality and memory ability. We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10). We found that among all models, the original GAN performs best and among Continual Learning strategies, generative replay outperforms all other methods. Even if we found satisfactory combinations on MNIST and Fashion MNIST, training generative models sequentially on CIFAR10 is particularly instable, and remains a challenge. Our code is available online1.


title: Punctuated eternal inflation via AdS/CFT

abstract: The work is an attempt to model a scenario of inflation in the framework of anti-de Sitter/conformal field theory duality, a potentially complete nonperturbative description of quantum gravity. We study bubble geometries with de Sitter interiors within an ambient Schwarzschild anti-de Sitter black hole spacetime and the properties of the corresponding states in the dual conformal field theory. It is argued the viable bubble states can be identified with a subset of the black hole microstates. Consistency checks are performed and a number of implications regarding cosmology are discussed including how the key problems or paradoxes of conventional eternal inflation are overcome in this scenario.


title: Learning distributed word representation with multi-contextual mixed embedding

abstract: Learning distributed word representations has been a popular method for various natural language processing applications such as word analogy and similarity, document classification and sentiment analysis. However, most existing word embedding models only exploit a shallow slide window as the context to predict the target word. Because the semantic of each word is also influenced by its global context, as the distributional models usually induced the word representations from the global co-occurrence matrix, the window-based models are insufficient to capture semantic knowledge. In this paper, we propose a novel hybrid model called mixed word embedding (MWE) based on the well-known word2vec toolbox. Specifically, the proposed MWE model combines the two variants of word2vec, i.e., SKIP-GRAM and CBOW, in a seamless way via sharing a common encoding structure, which is able to capture the syntax information of words more accurately. Furthermore, it incorporates a global text vector into the CBOW variant so as to capture more semantic information. Our MWE preserves the same time complexity as the SKIP-GRAM. To evaluate our MWE model efficiently and adaptively, we study our model on linguistic and application perspectives with both English and Chinese dataset. For linguistics, we conduct empirical studies on word analogies and similarities. The learned latent representations on both document classification and sentiment analysis are considered for application point of view of this work. The experimental results show that our MWE model is very competitive in all tasks as compared with the state-of-the-art word embedding models such as CBOW, SKIP-GRAM, and GloVe.


title: A novel Blood Pressure estimation method combing Pulse Wave Transit Time model and neural network model

abstract: Blood Pressure (BP) measurement can assist doctors to assess patients' cardiovascular status and diagnose heart diseases. Pulse Wave Transit Time (PWTT) model is one frequently used BP estimation method to monitor BP continuously in clinics. However, individual variations may influence the measurement accuracy of PWTT model. Focusing on above promble, this paper proposes a novel BP estimation method combining a classical PWTT model and a neural network model. The novel method is composed of five steps: signal pre-processing, feature extraction, initial PWTT model selection, model correction by neural network model, and final PWTT model identification. A validation experiment based on 10 patients from Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database showed that the BP estimation results by our method had a minimum mean of error readout value 5 mmHg with a standard deviation of error readout value ±8mmHg. As a result, both the diastolic blood pressure and systolic blood pressure estimation by our method can meet clinical requirements.


Comparative Spatial Analysis of Positional Accuracy of OpenStreetMap and Proprietary Geodata


title: Sticky Actuator: Free-Form Planar Actuators for Animated Objects

abstract: We propose soft planar actuators enhanced by free-form fabrication that are suitable for making everyday objects move. The actuator consists of one or more inflatable pouches with an adhesive back. We have developed a machine for the fabrication of free-from pouches; squares, circles and ribbons are all possible. The deformation of the pouches can provide linear, rotational, and more complicated motion corresponding to the pouch's geometry. We also provide a both manual and programmable control system. In a user study, we organized a hands-on workshop of actuated origami for children. The results show that the combination of the actuator and classic materials can enhance rapid prototyping of animated objects.


title: Market value and patent citations

abstract: We explore the usefulness of patent citations as a measure of the "importance" of a firm's patents, as indicated by the stock market valuation of the firm's intangible stock of knowledge. Using patents and citations for 1963--1995, we estimate Tobin's q equations on the ratios of R&D to assets stocks, patents to R&D, and citations to patents. We find that each ratio significantly affects market value, with an extra citation per patent boosting market value by 3%. Further findings indicate that "unpredictable" citations have a stronger effect than the predictable portion, and that self-citations are more valuable than external citations.


title: Mbps experimental acoustic through-tissue communications: MEAT-COMMS

abstract: Methods for digital, phase-coherent acoustic communication date to at least the work of Stojanjovic, et al [20], and the added robustness afforded by improved phase tracking and compensation of Johnson, et al [21]. This work explores the use of such methods for communications through tissue for potential biomedical applications, using the tremendous bandwidth available in commercial medical ultrasound transducers. While long-range ocean acoustic experiments have been at rates of under 100kbps, typically on the order of 1- 10kbps, data rates in excess of 120Mb/s have been achieved over cm-scale distances in ultrasonic testbeds [19]. This paper describes experimental transmission of digital communication signals through samples of real pork tissue and beef liver, achieving data rates of 20-30Mbps, demonstrating the possibility of real-time video-rate data transmission through tissue for inbody ultrasonic communications with implanted medical devices.


title: Accelerating chip design with machine learning: From pre-silicon to post-silicon

abstract: At sub-22nm regime, chip designs have to go through hundreds to thousands of steps and tasks before shipment. Many tasks are data and simulation intensive, thereby demanding significant amount of resources and time. Unlike conventional methodologies relying on experiences to manually handle data and extract models, recent advances in machine learning techniques enable the successful applications in various complex tasks to accelerate modern chip designs, ranging from pre-silicon verification to post-silicon validation and tuning. The goals are to reduce the amount of time and efforts to process and understand data through automatic and effective learning and enhancing from examples. In this paper we review and discuss several application cases of machine learning techniques, including pre-silicon hotspot detection through classification, post-silicon variation extraction and bug localization through inference, and post-silicon timing tuning through iterative learning and optimization, so as to leverage the potentials and inspire more future innovations.


title: ANANAS - A Framework For Analyzing Android Applications

abstract: Android is an open software platform for mobile devices with a large market share in the smart phone sector. The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform. ANANAS is an expandable and modular framework for analyzing Android applications. It takes care of common needs for dynamic malware analysis and provides an interface for the development of plugins. Adaptability and expandability have been main design goals during the development process. An abstraction layer for simple user interaction and phone event simulation is also part of the framework. It allows an analyst to script the required user simulation or phone events on demand or adjust the simulation to his needs. Six plugins have been developed for ANANAS. They represent well known techniques for malware analysis, such as system call hooking and network traffic analysis. The focus clearly lies on dynamic analysis, as five of the six plugins are dynamic analysis methods.


title: SoccerStories: A Kick-off for Visual Soccer Analysis

abstract: This article presents SoccerStories, a visualization interface to support analysts in exploring soccer data and communicating interesting insights. Currently, most analyses on such data relate to statistics on individual players or teams. However, soccer analysts we collaborated with consider that quantitative analysis alone does not convey the right picture of the game, as context, player positions and phases of player actions are the most relevant aspects. We designed SoccerStories to support the current practice of soccer analysts and to enrich it, both in the analysis and communication stages. Our system provides an overview+detail interface of game phases, and their aggregation into a series of connected visualizations, each visualization being tailored for actions such as a series of passes or a goal attempt. To evaluate our tool, we ran two qualitative user studies on recent games using SoccerStories with data from one of the world's leading live sports data providers. The first study resulted in a series of four articles on soccer tactics, by a tactics analyst, who said he would not have been able to write these otherwise. The second study consisted in an exploratory follow-up to investigate design alternatives for embedding soccer phases into word-sized graphics. For both experiments, we received a very enthusiastic feedback and participants consider further use of SoccerStories to enhance their current workflow.


title: Semisupervised Neural Networks for Efficient Hyperspectral Image Classification

abstract: A framework for semisupervised remote sensing image classification based on neural networks is presented. The methodology consists of adding a flexible embedding regularizer to the loss function used for training neural networks. Training is done using stochastic gradient descent with additional balancing constraints to avoid falling into local minima. The method constitutes a generalization of both supervised and unsupervised methods and can handle millions of unlabeled samples. Therefore, the proposed approach gives rise to an operational classifier, as opposed to previously presented transductive or Laplacian support vector machines (TSVM or LapSVM, respectively). The proposed methodology constitutes a general framework for building computationally efficient semisupervised methods. The method is compared with LapSVM and TSVM in semisupervised scenarios, to SVM in supervised settings, and to online and batch k-means for unsupervised learning. Results demonstrate the improved classification accuracy and scalability of this approach on several hyperspectral image classification problems.


title: Two-dimensional grating light modulator for projection display

abstract: A novel two-dimensional (2D) phase grating light modulator for projection display is proposed. It consists of an upper moveable grating, a bottom mirror, and four supporting posts between them. After the driving voltage is applied to the modulator, the upper grating will move down, which induces a phase difference and, therefore, leads to a controlled variation of its diffraction pattern. Optical characteristics of the modulator and the modulator array are analyzed with Fourier optics theory. The analysis shows the incident light will be switched from its zero order diffraction fringe to the first order diffraction fringe when the phase difference between the moveable grating and the bottom mirror changes from 2π to π. The diffraction pattern of the light modulator array is the coherent superposition of all single modulators. A 16×16 modulator array is fabricated by surface micromachining technology. The test result shows that the device works well when it is actuated by a voltage with a 1 kHz frequency and 10V amplitude. Both theoretical analysis and experiment results indicate that the 2D phase grating light modulator has potential application in a projection display system.


title: Intra color-shape classification for traffic sign recognition

abstract: This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller subclasses. Within these subclasses, all redundant information except the pictogram is discarded for feature selection since the pictogram contains critical information for road users. Principle Component Analysis (PCA) is applied to extract salient points for traffic sign dimensionality reduction. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. These features are fed into RBFNN for training with a proposed weight updating scheme based on Lyapunov stability theory. The performance of the proposed system is evaluated with Malaysian road signs with promising recognition rate.


title: STAR: articulation training for young children

abstract: The Speech Training, Assessment, and Remediation (STAR) system is intended to assist Speech and Language Pathologists in treating children with articulation problems. The system is embedded in an interactive video game that is set in a spaceship and involves teaching aliens to “understand” selected words by spoken example. The sequence of events leads children through a series of successively more difficult speech production tasks, beginning with CV syllables and progressing to words/phrases. Word selection is further tailored to emphasize the contrastive nature of phonemes by the use of minimal pairs (e.g., run/won) in production sets. To assess children’s speech, a discrete hidden Markov model recognition engine is used[1]. Phone models were trained on the CMU Kids database[2]. Performance of the HMM recognizer was compared to perceptual ratings of speech recorded from children who substitute /w/ for /r/. The difference in log likelihood between /r/ and /w/ models correlates well with perceptual ratings of utterances containing substitution errors, but very poorly for correctly articulated examples. The poor correlation between perceptual and machine ratings for correctly articulated utterances may be due to very restricted variance in the perceptual data for those utterances.


title: Numerically stable, single-pass, parallel statistics algorithms

abstract: Statistical analysis is widely used for countless scientific applications in order to analyze and infer meaning from data. A key challenge of any statistical analysis package aimed at large-scale, distributed data is to address the orthogonal issues of parallel scalability and numerical stability. In this paper we derive a series of formulas that allow for single-pass, yet numerically robust, pairwise parallel and incremental updates of both arbitrary-order centered statistical moments and co-moments. Using these formulas, we have built an open source parallel statistics framework that performs principal component analysis (PCA) in addition to computing descriptive, correlative, and multi-correlative statistics. The results of a scalability study demonstrate numerically stable, near-optimal scalability on up to 128 processes and results are presented in which the statistical framework is used to process large-scale turbulent combustion simulation data with 1500 processes.


title: Supervised Deep Feature Extraction for Hyperspectral Image Classification

abstract: Hyperspectral image classification has become a research focus in recent literature. However, well-designed features are still open issues that impact on the performance of classifiers. In this paper, a novel supervised deep feature extraction method based on siamese convolutional neural network (S-CNN) is proposed to improve the performance of hyperspectral image classification. First, a CNN with five layers is designed to directly extract deep features from hyperspectral cube, where the CNN can be intended as a nonlinear transformation function. Then, the siamese network composed by two CNNs is trained to learn features that show a low intraclass and high interclass variability. The important characteristic of the presented approach is that the S-CNN is supervised with a margin ranking loss function, which can extract more discriminative features for classification tasks. To demonstrate the effectiveness of the proposed feature extraction method, the features extracted from three widely used hyperspectral data sets are fed into a linear support vector machine (SVM) classifier. The experimental results demonstrate that the proposed feature extraction method in conjunction with a linear SVM classifier can obtain better classification performance than that of the conventional methods.


title: Encoding Spatial Relations from Natural Language

abstract: Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world. In particular, spatial relations are encoded in a way that is inconsistent with human spatial reasoning and lacking invariance to viewpoint changes. We present a system capable of capturing the semantics of spatial relations such as behind, left of, etc from natural language. Our key contributions are a novel multi-modal objective based on generating images of scenes from their textual descriptions, and a new dataset on which to train it. We demonstrate that internal representations are robust to meaning preserving transformations of descriptions (paraphrase invariance), while viewpoint invariance is an emergent property of the system.


title: Why doesn't it work?: voice-driven interfaces and young children's communication repair strategies

abstract: In this study, we examine the conversational repair strategies that preschoolers use to correct communication breakdowns with a voice-driven interface. We conducted a two-week deployment in the homes of 14 preschoolers of a tablet game that included a broken voice-driven mini-game. We collected 107 audio samples of these children's (unsuccessful) attempts to communicate with the mini-game. We found that children tried a common set of repair strategies, including repeating themselves and experimenting with the tone and pronunciation of their words. Children were persistent, rarely giving up on the interaction, asking for help, or showing frustration. When parents participated in the interaction, they moved through four phases of engagement: first making suggestions, then intervening, then making statements of resignation, and finally pronouncing that the interaction could not be repaired. Designers should anticipate that in this context, children will borrow behaviors from person-to-person communication, such as pivoting strategies to probe the source of failed communication and structuring communication into turn-taking attempts.


title: A Fast HOG Descriptor Using Lookup Table and Integral Image

abstract: The histogram of oriented gradients (HOG) is a widely used feature descriptor in computer vision for the purpose of object detection. In the paper, a modified HOG descriptor is described, it uses a lookup table and the method of integral image to speed up the detection performance by a factor of 5~10. By exploiting the special hardware features of a given platform(e.g. a digital signal processor), further improvement can be made to the HOG descriptor in order to have real-time object detection and tracking.


title: A successive approximation ADC with resistor-capacitor hybrid structure

abstract: This paper presents a 10-bit 50MS/s successive approximation register (SAR) ADC with low input capacitance that uses an on-chip resistive ladder to arrange a new switching scheme. The proposed arrangement not only reduces the total input capacitance, but also performs the predictive capacitor switching sequence to further reduce the power consumption. Therefore, the proposed SAR ADC has the features of small area and low power consumption. Compared to conventional SAR ADCs, the proposed ADC reduces the input capacitance to 512 fF for 10-bit resolution. This work is fabricated in TSMC 90-nm 1P9M CMOS process. This prototype chip consumes 0.703 mW from a 1.2-V supply and the ENOB is 9.3 bits at 50 MS/s sampling rate. The resultant FoM is 28 fJ/conversion-step.


Solvent effects on phytochemical constituent profiles and antioxidant activities, using four different extraction formulations for analysis of Bucida buceras L. and Phoradendron californicum


Non-Invasive Glucose Measurement by Use of Metabolic Heat Conformation Method


title: A large-scale empirical analysis of chinese web passwords

abstract: Users speaking different languages may prefer different patterns in creating their passwords, and thus knowledge on English passwords cannot help to guess passwords from other languages well. Research has already shown Chinese passwords are one of the most difficult ones to guess. We believe that the conclusion is biased because, to the best of our knowledge, little empirical study has examined regional differences of passwords on a large scale, especially on Chinese passwords. In this paper, we study the differences between passwords from Chinese and English speaking users, leveraging over 100 million leaked and publicly available passwords from Chinese and international websites in recent years. We found that Chinese prefer digits when composing their passwords while English users prefer letters, especially lowercase letters. However, their strength against password guessing is similar. Second, we observe that both users prefer to use the patterns that they are familiar with, e.g., Chinese Pinyins for Chinese and English words for English users. Third, we observe that both Chinese and English users prefer their conventional format when they use dates to construct passwords. Based on these observations, we improve a PCFG (Probabilistic Context-Free Grammar) based password guessing method by inserting Pinyins (about 2.3% more entries) into the attack dictionary and insert our observed composition rules into the guessing rule set. As a result, our experiments show that the efficiency of password guessing increases by 34%.


title: Capturing the mood: facebook and face-to-face encounters in the workplace

abstract: What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.


A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning


title: Grand challenges in information security: process and output

abstract: Even casual observers can see that our society's computing infrastructure has significant security problems. Technical sources such as CERT, BugTraq, and the Risks Digest - not to mention the popular media - regularly catalog critical vulnerabilities in deployed software. Unless we figure out how to build trustworthy systems in the real world, we're in trouble. Recognizing that fact, the Computing Research Association (CRA, www.cra.org), with support from the US National Science Foundation, recently drafted its Grand Research Challenges in security and assurance, intent on galvanizing the field by focusing attention and stimulating progress on these problems.


title: At Light Speed: Advances in Optogenetic Systems for Regulating Cell Signaling and Behavior.

abstract: Cells are bombarded by extrinsic signals that dynamically change in time and space. Such dynamic variations can exert profound effects on behaviors, including cellular signaling, organismal development, stem cell differentiation, normal tissue function, and disease processes such as cancer. Although classical genetic tools are well suited to introduce binary perturbations, new approaches have been necessary to investigate how dynamic signal variation may regulate cell behavior. This fundamental question is increasingly being addressed with optogenetics, a field focused on engineering and harnessing light-sensitive proteins to interface with cellular signaling pathways. Channelrhodopsins initially defined optogenetics; however, through recent use of light-responsive proteins with myriad spectral and functional properties, practical applications of optogenetics currently encompass cell signaling, subcellular localization, and gene regulation. Now, important questions regarding signal integration within branch points of signaling networks, asymmetric cell responses to spatially restricted signals, and effects of signal dosage versus duration can be addressed. This review summarizes emerging technologies and applications within the expanding field of optogenetics.


title: Collaborative Link-Aware Protocols for Energy-Efficient and QoS Wireless Body Area Networks Using Integrated Sensors

abstract: Integrated sensor systems are the promising solutions to the multiprobe design of biomedical sensors for energy efficient wireless body area networks (WBANs). To improve energy efficiency and communication quality-of-service (QoS) of integrated sensor hubs, a cross-layered energy-aware resource allocation (CLEAR) is proposed to exploit the characteristics of medium access control (MAC) and physical (PHY) layers of WBANs. The proposed MAC protocol applies a twin-token bucket model and real-time scheduling to the active superframe interleaving and beacon shifting techniques defined in the IEEE 802.15.6 standard. In addition, the proposed period transformation controlling task end-to-end delay improves the QoS while reducing packet collisions. The proposed PHY protocol is a hybrid of the transmit power control (TPC) and link adaption (LA) strategies, which coordinates the receiver and transmitter with link parameters including data rate, bit error ratio, receiver sensitivity, transmission power, modulation, etc. It adapts the link quality due to human postures or movements to meet the required QoS while minimizing the energy consumption of the transceiver front-end. Experimental results show that CLEAR outperforms existing methods in terms of packet delivery ratio, delay, bandwidth utilization, and energy consumption. In particular, CLEAR reduces average end-to-end delay by 21% as compared to existing methods, and saves an average of 18% of energy consumption over the TPC and LA-based methods at a power of −25 dBm while achieving a maximum 0.05 packet error ratio.


title: Calcium ions as a factor of cell-cell cooperation in hepatocyte cultures.

abstract: Ultradian protein synthesis rhythm was used as a marker of cell cooperation in synchronous dense and non-synchronous sparse hepatocyte cultures. Phenylephrine (2 microM, 2 min), an alpha (1)-adrenoreceptor agonist, which exerts [Ca(2+)](cyt)elevation from intracellular stores, affected protein synthesis rhythm in sparse cultures, i.e. initiated cooperative activity of the cells. The same effect was produced by 2,5-di(tertiary-butyl)-1,4-benzohydroquinone (10 microM, 2 min), which increases [Ca(2+)](cyt)by a non-receptor pathway. Pretreatment of dense cultures with the intracellular calcium chelator, 1,2-bis (2-aminophenoxy) ethane-N,N,N',N'- tetraacetic acid (acetoxymethyl) ester (BAPTA-AM) at 10-20 microM for, 30-60 min resulted in loss of the rhythm of protein synthesis, i.e. loss of cooperative activity between the cells. The medium conditioned by control dense cultures initiated rhythm in sparse cultures, whereas the conditioned medium of cultures pretreated with BAPTA-AM did not. [Ca(2+)](cyt)increase is known to occur with monosialoganglioside GM1 treatment. By ELISA estimation, the GM1 content in 3 h conditioned medium was similar in control dense cultures to that in cultures pretreated with BAPTA-AM. Bearing in mind data on the Ca(2+)-dependence of vesicle formation and shedding, the conditioned medium was separated by 150000 g centrifugation to supernatant containing monomers and micelles, and a pellet containing vesicular form of gangliosides. Only the latter initiated cooperative activity of the cells of sparse cultures. These cultures were also synchronized by GM1-containing liposomes at lower concentrations than added free GM1, 0.0003 and 0.06 microM respectively. Thus, GM1 and calcium are both involved in cell-cell synchronization. Activation of gangliosides, including GM1 and elevation of [Ca(2+)](cyt,)is known to lead to changes of protein kinase activity and protein phosphorylation resulting in modulation of oscillations in protein metabolism.


title: Supply chain contract evolution

abstract: Summary This paper draws together theories from organisational and neo-institutional literatures to address the evolution of supply chain contracts. Using a longitudinal case study of the Norwegian State Railways, we examine how firms move through the stages in an inter-organisational process of supply chain contract evolution and how they can cooperate to ensure efficiency and equity in their contractual relationship. The findings suggest that inefficient and inequitable initial contracts can occur, in part, because of the cognitive shortcomings in human decision-making processes that reveal themselves early in the arrangement before learning and trust building can accumulate. We then reveal how parties can renegotiate towards a more equitable and efficient supply chain contract.


title: Passivity-based whole-body balancing for torque-controlled humanoid robots in multi-contact scenarios

abstract: This work presents a new control approach to multi-contact balancing for torque-controlled humanoid robots. The controller includes a non-strict task hierarchy, which allows the robot to use a subset of its end effectors for balancing while the remaining ones can be used for interacting with the environment. The controller creates a passive and compliant behavior for regulating the center of mass (CoM) location, hip orientation and the poses of each end effector assigned to the interaction task. This is achieved by applying a suitable wrench (force and torque) at each one of the end effectors used for interaction. The contact wrenches at the balancing end effectors are chosen such that the sum of the balancing and interaction wrenches produce the desired wrench at the CoM. The algorithm requires the solution of an optimization problem, which distributes the CoM wrench to the end effectors taking into account constraints for unilaterality, friction and position of the center of pressure. Furthermore, the feedback controller is combined with a feedforward control in order to improve performance while tracking a predefined trajectory, leading to a control structure similar to a PD+ control. The controller is evaluated in several experiments with the humanoid robot TORO.


A generic solution to polygon clipping


title: Muscular Movement Model-Based Automatic 3D/4D Facial Expression Recognition

abstract: Facial expression is an important channel for human nonverbal communication. This paper presents a novel and effective approach to automatic 3D/4D facial expression recognition based on the muscular movement model (MMM). In contrast to most of existing methods, the MMM deals with such an issue in the viewpoint of anatomy. It first automatically segments the input 3D face (frame) by localizing the corresponding points within each muscular region of the reference using iterative closest normal point. A set of features with multiple differential quantities, including ${coordinate}$ , ${normal,}$ and ${shape\,index}$ values, are then extracted to describe the geometry deformation of each segmented region. Meanwhile, we analyze the importance of these muscular areas, and a score level fusion strategy is exploited to optimize their weights by the genetic algorithm in the learning step. The support vector machine and the hidden Markov model are finally used to predict the expression label in 3D and 4D, respectively. The experiments are conducted on the BU-3DFE and BU-4DFE databases, and the results achieved clearly demonstrate the effectiveness of the proposed method.


title: Trust-Aware Collaborative Filtering with a Denoising Autoencoder

abstract: Collaborative filtering is one of the most successful and extensive methods used by recommender systems for predicting the preferences of users. However, traditional collaborative filtering only uses rating information to model the user, the data sparsity problem and the cold start problem will severely reduce the recommendation performance. To overcome these problems, we propose two neural network models to improve recommendations. The first one called TDAE uses a denoising autoencoder to integrate the ratings and the explicit trust relationships between users in the social networks in order to model the preferences of users more accurately. However, the explicit trust information is very sparse, which limits the performance of this model. Therefore, we propose a second method called TDAE++ for extracting the implicit trust relationships between users with similarity measures, where we employ both the explicit and implicit trust information together to improve the quality of recommendations. Finally, we inject the trust information into both the input and the hidden layer in order to fuse these two types of different information to learn more reliable semantic representations of users. Comprehensive experiments based on three popular data sets verify that our proposed models perform better than other state-of-the-art approaches in common recommendation tasks.


title: Probabilistic Model-Agnostic Meta-Learning

abstract: Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e.g., a classifier) for that task that is accurate. In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution. Our approach extends model-agnostic meta-learning, which adapts to new tasks via gradient descent, to incorporate a parameter distribution that is trained via a variational lower bound. At meta-test time, our algorithm adapts via a simple procedure that injects noise into gradient descent, and at meta-training time, the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior. Our experimental results show that our method can sample plausible classifiers and regressors in ambiguous few-shot learning problems.


title: Evaluation of query expansion using MeSH in PubMed

abstract: This paper investigates the effectiveness of using MeSH® in PubMed through its automatic query expansion process: Automatic Term Mapping (ATM). We run Boolean searches based on a collection of 55 topics and about 160,000 MEDLINE® citations used in the 2006 and 2007 TREC Genomics Tracks. For each topic, we first automatically construct a query by selecting keywords from the question. Next, each query is expanded by ATM, which assigns different search tags to terms in the query. Three search tags: [MeSH Terms], [Text Words], and [All Fields] are chosen to be studied after expansion because they all make use of the MeSH field of indexed MEDLINE citations. Furthermore, we characterize the two different mechanisms by which the MeSH field is used. Retrieval results using MeSH after expansion are compared to those solely based on the words in MEDLINE title and abstracts. The aggregate retrieval performance is assessed using both F-measure and mean rank precision. Experimental results suggest that query expansion using MeSH in PubMed can generally improve retrieval performance, but the improvement may not affect end PubMed users in realistic situations.


title: Prevalence and associated factors of Internet gaming disorder among community dwelling adults in Macao, China

abstract: Background and aims Internet gaming disorder (IGD) has been mainly studied among adolescents, and no research to date has examined its prevalence in general Chinese adult populations. This study estimated the prevalence of probable IGD in community-dwelling adults in Macao, China. Associations between IGD and psychological distress (i.e., depression and anxiety) as well as IGD and character strength (i.e., psychological resilience and purpose in life) were also tested. Methods A random, representative sample of 1,000 Chinese residents (44% males; mean age = 40.0) was surveyed using a telephone poll design from October to November 2016. Results The estimated prevalence of probable IGD was 2.0% of the overall sample and 4.3% among the recent gamers (n = 473), with no statistically significant sex and age effects observed (p > .05). The two most prevalent IGD symptoms were mood modification and continued engagement, despite negative consequences. Probable IGD respondents were more vulnerable to psychological distress (25.0% and 45.0% for moderate or above levels of depression and anxiety, respectively) than their non-IGD counterparts. They also reported a lower level of psychological resilience than non-IGD respondents. No significant buffering effect of the two character strength variables on the distress-IGD relationship was found. Discussion and conclusions These results provide empirical evidence that IGD is a mental health threat not only to adolescents but also to adults. IGD was significantly associated with psychological distress, which should be addressed in conjunction with IGD symptoms in interventions. Inclusion of gamers of both sexes and different age groups in future prevention programs is also recommended.


Understanding DMA


title: Issues in Strategic Decision Modelling

abstract: [Spreadsheet] Models are invaluable tools for strategic planning. Models help key decision makers develop a shared conceptual understanding of complex decisions, identify sensitivity factors and test management scenarios. Different modelling approaches are specialist areas in themselves. Model development can be onerous, expensive, time consuming, and often bewildering. It is also an iterative process where the true magnitude of the effort, time and data required is often not fully understood until well into the process. This paper explores the traditional approaches to strategic planning modelling commonly used in organisations and considers the application of a real-options approach to match and benefit from the increasing uncertainty in today's rapidly changing world.


title: SDN-WISE: Design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks

abstract: In this paper SDN-WISE, a Software Defined Networking (SDN) solution for WIreless SEnsor networks, is introduced. Differently from the existing SDN solutions for wireless sensor networks, SDN-WISE is stateful and pursues two objectives: (i) to reduce the amount of information exchanged between sensor nodes and the SDN network controller, and (ii) to make sensor nodes programmable as finite state machines so enabling them to run operations that cannot be supported by stateless solutions. A detailed description of SDN-WISE is provided in this paper. SDN-WISE offers APIs that allow software developers to implement the SDN Controller using the programming language they prefer. This represents a major advantage of SDN-WISE as compared to existing solutions because it increases flexibility and simplicity in network programming. A prototype of SDN-WISE has been implemented and is described in this paper. Such implementation contains the modules that allow a real SDN Controller to manage an OMNeT++ simulated network. Finally, the paper illustrates the results obtained through an experimental testbed which has been developed to evaluate the performance of SDN-WISE in several operating conditions.


title: Advances in interior permanent magnet (IPM) motor drives

abstract: In recent years, high efficiency electric motors are demanded to meet the challenges of energy hungry world with limited fossil fuel sources. Interior permanent magnet (IPM) motors have been increasingly developed and widely used to meet the high efficiency and performance requirements. It is because; an IPM is a highly efficient synchronous motor with built-in advantageous features. This is due to advances in high-energy permanent magnets, smart inverters and intelligent controllers. This paper provides an outline of a modern IPM motor in an electronic world. On-line and soft starting of an IPM motor are provided. Highlights of IPM motors include applications in Japanese air conditioners and hybrid electric vehicles, which are two examples of ac motor drives in domestic heat pumps and passenger automobiles for the competitive market place


title: Captology: the study of computers as persuasive technologies

abstract: "Captology" is a newly coined word that describes the study of Computers As Persuasive Technologies (see Figure 1).


title: Short Paper Real-Time Ray Tracing Using Nvidia OptiX

abstract: Modern GPUs with their several hundred cores and more accessible programming models are becoming attractive devices for compute-intensive applications. They are particularly well suited for applications, such as image processing, where the end result is intended to be displayed via the graphics card. One of the more versatile and powerful graphics techniques is ray tracing. However, tracing each ray of light in a scene is very computational expensive and have traditionally been preprocessed on CPUs over hours, if not days. In this paper, Nvidia’s new OptiX ray tracing engine is used to show how the power of modern graphics cards, such as the Nvidia Quadro FX 5800, can be harnessed to ray trace several scenes that represent real-life applications in real-time speeds ranging from 20.63 to 67.15 fps. Near-perfect speedup is demonstrated on dual GPUs for scenes with complex geometries. The impact on ray tracing of the recently announced Nvidia Fermi processor, is also discussed.


title: Breaching the Human Firewall: Social engineering in Phishing and Spear-Phishing Emails

abstract: We examined the influence of three social engineering strategies on users' judgments of how safe it is to click on a link in an email. The three strategies examined were authority, scarcity and social proof, and the emails were either genuine, phishing or spear-phishing. Of the three strategies, the use of authority was the most effective strategy in convincing users that a link in an email was safe. When detecting phishing and spear-phishing emails, users performed the worst when the emails used the authority principle and performed best when social proof was present. Overall, users struggled to distinguish between genuine and spear-phishing emails. Finally, users who were less impulsive in making decisions generally were less likely to judge a link as safe in the fraudulent emails. Implications for education and training are discussed.


title: A feedback framework for improved chord recognition based on NMF-based approximate note transcription

abstract: This paper presents a feedback framework that can improve chord recognition for music audio signals by performing approximate note transcription with Bayesian non-negative matrix factorization (NMF) using prior knowledge on chords. Although the names and note compositions of chords are intrinsically linked with each other (e.g., C major chords are highly likely to include C, E, and G notes, and those notes are highly likely to be in C major chords), chord recognition and note transcription (multipitch analysis) have been studied independently. To solve this chicken-and-egg problem, our framework iterates chord recognition and approximate note transcription using each other's results. More specifically, we first perform approximate note transcription based on Bayesian NMF that forces basis spectra to respectively correspond to different semitone-level pitches covering the whole range. We then execute chord recognition based on Bayesian hidden Markov models (HMMs) that use chroma features obtained from the activation patterns of those pitches. To improve note transcription, we again perform Bayesian NMF that encourages certain kinds of pitches in each chord region to be activated. Experimental results showed that our feedback framework gradually improved the accuracy of chord recognition.


title: Statistical data cleaning for deep learning of automation tasks from demonstrations

abstract: Automation using deep learning from demonstrations requires many training examples. Gathering this data is time consuming and expensive, and human demonstrators are prone to inconsistencies and errors that can delay or degrade learning. This paper explores how characterizing supervisor inconsistency and correcting for this noise can improve task performance with a limited budget of data. We consider a planar part extraction task (separating one part from a group) where human operators provide demonstrations by teleoperating a 2DOF robot. We analyze 30, 000 image-control pairs from 480 trajectories. After error corrections, trained CNN models show an improvement of 11.2% upon the baseline in mean absolute success rate.


title: From enterprise models to dimensional models: a methodology for data warehouse and data mart design

abstract: This paper describes a method for developing dimensional models from traditional Entity Relationship models. This can be used to design data warehouses and data marts based on enterprise data models. The first step of the method involves classifying entities in the data model into a number of categories. The second step involves identifying hierarchies that exist in the model. The final step involves collapsing these hierarchies and aggregating transaction data to form dimensional models. A number of design alternatives are presented, including a flat schema, a terraced schema, a star schema and a snowflake schema. We also define a new type of schema called a star cluster schema. This is a restricted form of snowflake schema, which minimises the number of tables while avoiding overlap between different dimensional hierarchies. Individual schemas can be collected together to form constellations or galaxies. We illustrate the method using a simple example.


title: Medication adherence in patients with type 2 diabetes mellitus treated at primary health clinics in Malaysia

abstract: PURPOSE ::: Diabetes mellitus is a growing global health problem that affects patients of all ages. Even though diabetes mellitus is recognized as a major chronic illness, adherence to antidiabetic medicines has often been found to be unsatisfactory. This study was conducted to assess adherence to medications and to identify factors that are associated with nonadherence in type 2 diabetes mellitus (T2DM) patients at Primary Health Clinics of the Ministry of Health in Malaysia. ::: ::: ::: MATERIALS AND METHODS ::: The cross-sectional survey was carried out among T2DM patients to assess adherence to medication in primary health clinics. Adherence was measured by using the Medication Compliance Questionnaire that consists of a total of seven questions. Other data, such as patient demographics, treatment, outcome, and comorbidities were also collected from patient medical records. ::: ::: ::: RESULTS ::: A total of 557 patients were recruited in the study. Approximately 53% of patients in the study population were nonadherent. Logistic regression analysis was performed to predict the factors associated with nonadherence. Variables associated with nonadherence were age, odds ratio 0.967 (95% confidence interval [CI]: 0.948-0.986); medication knowledge, odds ratio 0.965 (95% CI: 0.946-0.984); and comorbidities, odds ratio 1.781 (95% CI: 1.064-2.981). ::: ::: ::: CONCLUSION ::: Adherence to medication in T2DM patients in the primary health clinics was found to be poor. This is a cause of concern, because nonadherence could lead to a worsening of disease. Improving medication knowledge by paying particular attention to different age groups and patients with comorbidities could help improve adherence.


title: Classification of Research Papers using Citation Links and Citation Types: Towards Automatic Review Article Generation

abstract: We are investigating automatic generation of a review (or survey) article in a specific subject domain. In a research paper, there are passages where the author describes the essence of a cited paper and the differences between the current paper and the cited paper (we call them citing areas). These passages can be considered as a kind of summary of the cited paper from the current author's viewpoint. We can know the state of the art in a specific subject domain from the collection of citing areas. FUrther, if these citing areas are properly classified and organized, they can act 8.', a kind of a review article. In our previous research, we proposed the automatic extraction of citing areas. Then, with the information in the citing areas, we automatically identified the types of citation relationships that indicate the reasons for citation (we call them citation types). Citation types offer a useful clue for organizing citing areas. In addition, to support writing a review article, it is necessary to take account of the contents of the papers together with the citation links and citation types. In this paper, we propose several methods for classifying papers automatically. We found that our proposed methods BCCT-C, the bibliographic coupling considering only type C citations, which pointed out the problems or gaps in related works, are more effective than others. We also implemented a prototype system to support writing a review article, which is based on our proposed method.


title: Modeling Ethno-religious Conflicts as Prisoner's Dilemma Game in Graphs

abstract: In this paper, we present and analyze a multi-agentgame theoretic model of conflicts in multi-cultural societies. Two salient factors responsible for violence in multi-cultural societies (that are identified in the social sciences literature) are (a) ethnoreligiousidentity of the population and (b) spatial structure(distribution) of the population. It has also been experimentally shown by Lumsden that multi-cultural conflict can be viewed as a Prisoner’s Dilemma (PD) game. Using the above observations, we model the multi-cultural conflict problem as a variant of the repeated PD game in graphs. The graph consists of labeled nodes corresponding to the different ethno-religious types and the topology of the graph encode the spatial distribution and interaction of the population. The agents play the game with neighbors of their opponent type and they update their strategiesbased on neighbors of their same type. This strategy updatedynamics with different update neighborhood from game playing neighborhood distinguishes our model from conventional models of PD games in graphs. We present simulation results showing the effect of various parameters of our model to the propensity ofconflict in a population consisting of two ethno-religious groups.


title: Some Experiences on Applying Deep Learning to Speech Signal and Natural Language Processing

abstract: With the advent of deep learning there has been a major paradigm shift in how speech signal and text processing techniques work. In this paper, we would like to share our experiences/recent works on applying deep neural networks (DNNs) to adaptive microphone array speech enhancement, sentiment analysis, speech recognition, language recognition and text-to-speech (TTS). In those tasks, many different DNN structures and learning strategies were involved including DNN, convolutional neural network (CNN), recurrent neural network (RNN) and long-short-term memory (LSTM). Experimental results all show DNNs are quite promising.


title: AUTOMATIC RECTIFICATION OF PERSPECTIVE DISTORTION FROM A SINGLE IMAGE USING PLANE HOMOGRAPHY

abstract: Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the distortion of a single image without losing any desired information is one of the challenging task in the field of Computer Vision. We consider the problem of estimating perspective distortion from a single still image of an unstructured environment and to make perspective correction which is both quantitatively accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A method based on plane homography and transformation is used to make perspective correction. The algorithm infers frontier information directly from the images, without any reference objects or prior knowledge of the camera parameters. The frontiers are detected using geometric context based segmentation. The goal of this paper is to present a framework providing fully automatic and fast perspective correction.


title: A single-layer folded substrate integrated waveguide (SIW) filter

abstract: A novel single-layer folded filter using the substrate integrated waveguide (SIW) technique is presented in this paper. A prototype at X-band for the proposed filter has been fabricated using standard printed-circuit-board (PCB) process, and is measured with a vector network analyzer (VNA). The measured results show the proposed filter has a narrow band-pass bandwidth, small insert loss and good frequency selective performances. The proposed filter has a compact size and it is suitable for designing microwave and millimeter-wave circuits.


title: Low cost transformer isolated boost half-bridge micro-inverter for single-phase grid-connected photovoltaic system

abstract: This paper presents a low cost high efficiency transformer isolated micro-inverter for single-phase grid-connected photovoltaic (PV) system. The proposed micro-inverter is composed of two stages, an isolated dc-dc converter stage and an inverter stage with a dc link. A high frequency transformer isolated high voltage gain boost half-bridge dc-dc converter is used at the first stage to achieve maximum power point tracking (MPPT) and to step up the PV voltage to the high voltage dc-link. A pulse width modulated (PWM) full-bridge inverter with LCL filter is used at the second stage to output the synchronized sinusoid current with unity power factor to the grid. By utilizing the transformer leakage inductance, two primary side switches can achieve zero voltage switching (ZVS). A 210 W prototype of the proposed micro-inverter has been built and tested. The efficiency of the proposed boost half-bridge dc-dc converter has been measured according to the PV curve, which is up to 98.2%. Experimental results are provided to demonstrate the validity and features of the proposed circuit.


title: Multi-source and multicore automotive ECUs - OS protection mechanisms and scheduling

abstract: As the demand for computing power is quickly increasing in the automotive domain, car manufacturers and tier-one suppliers are gradually introducing multicore ECUs in their electronic architectures. Additionally, these multicore ECUs offer new features such as higher levels of parallelism which ease the respect of the safety requirements such as the ISO 26262 and the implementation of other automotive use-cases. These new features involve also more complexity in the design, development and verification of the software applications. Hence, OEMs and suppliers will require new tools and methodologies for deployment and validation. In this paper, we review the operating system protection mechanisms (e.g., memory, timing), needed for multi-source software in a safety critical context, with a clear focus on AUTOSAR OS which is the upcoming defacto standard for automotive ECUs. Then, we identify the main use-cases for automotive multicore ECUs and present solutions for the scheduling in a context where there are hundreds of software components and only a few OS tasks are allowed. Finally, experiments aim to assess the load level that can be reached on realistic case-studies.


title: Dynamic simulation model of pneumatic actuator with artificial muscle

abstract: Antagonistic actuator with pneumatic artificial muscles (PAMs) is a kinematic structure that consists of two pneumatic muscles acting in opposition to each other, connected through the chain gear. The resulting position of the actuator can be determined by the angle of arm of the load attached to a shaft. Stiffness position of shaft in the given direction is determined by size of pressure of the relevant pneumatic muscle, which is loaded in tension and rotation (position) of shaft is proportional to pressure difference in individual muscle. The main drawback of this actuator is that its dynamic behavior is highly nonlinear. Due to this knowledge of the muscle properties it is necessary to use the model of the muscle. This muscle model of the system is based on modified Hill's basic model which consists of a variable damper and a variable spring connected in parallel. All the work on dynamic model was created in Matlab/Simulink environment.


Segmentations with Explanations for Outage Analysis


Ordinary people associate addiction with loss of free will


SPICE Modeling of Magnetic Tunnel Junctions written by Spin Transfer Torque 2 1


title: ISA mapper: a compute and hardware agnostic deep learning compiler

abstract: Domain specific accelerators present new challenges for code generation onto novel instruction sets, communication fabrics, and memory architectures. We introduce a shared intermediate representation to describe both deep learning programs and hardware capabilities, then formulate and apply instruction mapping to determine how a computation can be performed on a hardware system. Our scheduler chooses a specific mapping and determines data movement and computation order. With this system, we demonstrate automated extraction of matrix multiplication kernels from recent deep learning operations. We demonstrate 2--5X better performance on GEMM and GRU execution versus state-of-the-art on new hardware and up to 85% of state-of-the-art performance on existing hardware.


title: Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?

abstract: This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways? We develop novel orthogonality regularizations on training deep CNNs, utilizing various advanced analytical tools such as mutual coherence and restricted isometry property. These plug-and-play regularizations can be conveniently incorporated into training almost any CNN without extra hassle. We then benchmark their effects on state-of-the-art models: ResNet, WideResNet, and ResNeXt, on several most popular computer vision datasets: CIFAR-10, CIFAR-100, SVHN and ImageNet. We observe consistent performance gains after applying those proposed regularizations, in terms of both the final accuracies achieved, and faster and more stable convergences. We have made our codes and pre-trained models publicly available: this https URL.


title: Absence of disconnexion syndrome in callosal agenesis and early callosotomy: Brain reorganization or lack of structural specificity during ontogeny?

abstract: Abstract Four acallosal subjects, one child, aged 5, and three adults, as well as five epileptic patients who underwent callosotomy between the ages of 6–21 years, were tested on a variety of intra- and intermanual tasks in a study aimed at elucidating the developmental aspects of callosal plasticity. The performance of the clinical sample was compared to that of 48 normal children, aged 5–12 years, an age span generally considered to coincide with the final stages of callosal maturation. As previously reported, interhemispheric integration improved with increasing age in the normal sample. The two patients having undergone callosotomy in childhood performed as well as their normal peers, whereas the three others who had the operation in late adolescence or adulthood showed the typical disconnexion deficits reported in the literature. The acallosal subjects, including the youngest one, outperformed all groups. We speculate that the remarkable plasticity seen in the acallosals and the young callosotomized patients may be related to a critical period in development coinciding with a phase of synaptic overproduction and redundancy that would favor the reinforcement of alternative neural pathways. The compensatory mechanisms appear to become more limited in late adolescence when synaptic distribution presumably assumes adult patterns.


title: The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: A review with emphasis on a reanalysis of previous studies: LF HRV and sympathetic cardiac tone

abstract: This article evaluates the suitability of low frequency (LF) heart rate variability (HRV) as an index of sympathetic cardiac control and the LF/high frequency (HF) ratio as an index of autonomic balance. It includes a comprehensive literature review and a reanalysis of some previous studies on autonomic cardiovascular regulation. The following sources of evidence are addressed: effects of manipulations affecting sympathetic and vagal activity on HRV, predictions of group differences in cardiac autonomic regulation from HRV, relationships between HRV and other cardiac parameters, and the theoretical and mathematical bases of the concept of autonomic balance. Available data challenge the interpretation of the LF and LF/HF ratio as indices of sympathetic cardiac control and autonomic balance, respectively, and suggest that the HRV power spectrum, including its LF component, is mainly determined by the parasympathetic system.


title: One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks

abstract: We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects. This problem presents a number of major challenges. Video demonstrations without teleoperation are easy for humans to provide, but do not provide any direct supervision. Learning policies from raw pixels enables full generality but calls for large function approximators with many parameters to be learned. Finally, compound tasks can require impractical amounts of demonstration data, when treated as a monolithic skill. To address these challenges, we propose a method that learns both how to learn primitive behaviors from video demonstrations and how to dynamically compose these behaviors to perform multi-stage tasks by"watching"a human demonstrator. Our results on a simulated Sawyer robot and real PR2 robot illustrate our method for learning a variety of order fulfillment and kitchen serving tasks with novel objects and raw pixel inputs.


title: Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction

abstract: In many real-life applications it is crucial to be able to, given a collection of link states of a network in a certain time period, accurately predict the link state of the network at a future time. This is known as dynamic link prediction, which compared to its static counterpart is more complex, as capturing the temporal characteristics is a non-trivial task. This explains while still majority of today's research in network representation learning focuses on static setting ignoring temporal information. In this work, we focus on one such case and aim at extending node2vec, representation learning method successfully applied for static link prediction, to a dynamic setup. This extended method is applied and validated on several real-life networks with different properties. Results show that taking into account dynamic aspect outperforms static approach. Additionally, based on the network properties, recommendations are given for the node2vec parameters.


title: AUNet: Breast Mass Segmentation of Whole Mammograms.

abstract: Deep learning based segmentation has seen rapid development lately in both natural and medical image processing. However, its application to mammographic mass segmentation is still a challenging task due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. In this study, we propose a new network, AUNet, for the breast mass segmentation. Different from most methods that need to extract mass-centered image patches, AUNet could directly process the whole mammograms. Furthermore, it introduces an asymmetrical structure to the traditional encoder-decoder segmentation architecture and proposes a new upsampling block, Attention Up (AU) Block. Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three existing fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.


title: Mid-air tactile stimulation using laser-induced thermoelastic effects: The first study for indirect radiation

abstract: This paper reports our recent finding that a laser that is radiated on a thin light-absorbing elastic medium attached on the skin can elicit a tactile sensation of mechanical tap. Laser radiation to the elastic medium creates inner elastic waves on the basis of thermoelastic effects, which subsequently move the medium and stimulate the skin. We characterize the associated stimulus by measuring its physical properties. In addition, the perceptual identity of the stimulus is confirmed by comparing it to mechanical and electrical stimuli by means of perceptual spaces. All evidence claims that indirect laser radiation conveys a sensation of short mechanical tap with little individual difference. To the best of our knowledge, this is the first study that discovers the possibility of using indirect laser radiation for mid-air tactile rendering.


title: Seventh sense technology

abstract: Seventh Sense Technology is a technology used for the human electronic interaction using natural hand gestures. The hand gestures can be used to control any micro controller based devices or robots at distant places using Zigbee or GSM Module or Satellite transceiver. The "Seventh Sense Technology" is easy to implement with reduced implementation cost and it opens a wide area of research for the next generation and a greater development in almost all areas of current interest. Seventh Sense Technology uses the Sixth Sense Technology and its advancements to control the autonomous robots or any microcontroller based devices on human will. Currently autonomous robots or any microcontroller based devices are designed for a single purpose, but with this technology robots and those devices can be made to achieve multidimensional purposes. Even the costlier sensors can be replaced with the low cost night vision cameras. It opens a way to control robots or any microcontroller based devices at any distance with natural hand gestures of human beings. It also has the provision of customizable virtual keyboard for giving more actions to distant devices. It is going to be a greater field of research that also paves a lot of attention in this current world of technology.