title: Infrastructure-Less Indoor Localization Using the Microphone, Magnetometer and Light Sensor of a Smartphone

abstract: In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user’s location in an indoor environment. A multivariate model is applied to find the user’s location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth’s magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of information.


title: Segmentation Guided Image-to-Image Translation with Adversarial Networks

abstract: Recently image-to-image translation has received increasing attention, which aims to map images in one domain to another specific one. Existing methods mainly solve this task via a deep generative model, and focus on exploring the relationship between different domains. However, these methods neglect to utilize higher-level and instance-specific information to guide the training process, leading to a great deal of unrealistic generated images of low quality. Existing methods also lack of spatial controllability during translation. To address these challenge, we propose a novel Segmentation Guided Generative Adversarial Networks (SGGAN), which leverages semantic segmentation to further boost the generation performance and provide spatial mapping. In particular, a segmentor network is designed to impose semantic information on the generated images. Experimental results on multi-domain face image translation task empirically demonstrate our ability of the spatial modification and our superiority in image quality over several state-of-the-art methods.


title: Noname manuscript No. (will be inserted by the editor) GATE Teamware: A Web-based, Collaborative Text Annotation Framework

abstract: This paper presents GATE Teamware--an open-source, web-based, collaborative text annotation framework. It enables users to carry out complex corpus annotation projects, involving distributed annotator teams. Different user roles are provided (annotator, manager, administrator) with customisable user interface functionalities, in order to support the complex workflows and user interactions that occur in corpus annotation projects. Documents may be pre-processed automatically, so that human annotators can begin with text that has already been pre-annotated and thus making them more efficient. The user interface is simple to learn, aimed at non-experts, and runs in an ordinary web browser, without need of additional software installation. GATE Teamware has been evaluated through the creation of several gold standard corpora and internal projects, as well as through external evaluation in commercial and EU text annotation projects. It is available as on-demand service on GateCloud.net, as well as open-source for self-installation.


title: Towards Natural Interactive Question Answering

abstract: The specification discloses a circuit which produces a gradual increase or decrease in the alternating current power applied to a series connected load. Double time constant gating circuitry and capacitor integration may be utilized in combination with self-heated thermistors to effect a gradual change in the conduction duration of a series connected control element.


title: Systematic mapping studies in software engineering

abstract: BACKGROUND: A software engineering systematic map is a defined method to build a classification scheme and structure a software engineering field of interest. The analysis of results focuses on frequencies of publications for categories within the scheme. Thereby, the coverage of the research field can be determined. Different facets of the scheme can also be combined to answer more specific research questions. ::: ::: OBJECTIVE: We describe how to conduct a systematic mapping study in software engineering and provide guidelines. We also compare systematic maps and systematic reviews to clarify how to chose between them. This comparison leads to a set of guidelines for systematic maps. ::: ::: METHOD: We have defined a systematic mapping process and applied it to complete a systematic mapping study. Furthermore, we compare systematic maps with systematic reviews by systematically analyzing existing systematic reviews. ::: ::: RESULTS: We describe a process for software engineering systematic mapping studies and compare it to systematic reviews. Based on this, guidelines for conducting systematic maps are defined. ::: ::: CONCLUSIONS: Systematic maps and reviews are different in terms of goals, breadth, validity issues and implications. Thus, they should be used complementarily and require different methods (e.g., for analysis).


title: Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning

abstract: Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of depression relies on costly clinician assessment using survey instruments which may not objectively reflect the fluctuation of daily behavior. Self-administered surveys, such as the Quick Inventory of Depressive Symptomatology (QIDS) commonly used to monitor depression, may show disparities from clinical decision. Smartphones provide easy access to many behavioral parameters, and Fitbit wrist bands are becoming another important tool to assess variables such as heart rates and sleep efficiency that are complementary to smartphone sensors. However, data used to identify depression indicators have been limited to a single platform either iPhone, or Android, or Fitbit alone due to the variation in their methods of data collection. The present work represents a large-scale effort to collect and integrate data from mobile phones, wearable devices, and self reports in depression analysis by designing a new machine learning approach. This approach constructs sparse mappings from sensing variables collected by various tools to two separate targets: self-reported QIDS scores and clinical assessment of depression severity. We propose a so-called heterogeneous multi-task feature learning method that jointly builds inference models for related tasks but of different types including classification and regression tasks. The proposed method was evaluated using data collected from 103 college students and could predict the QIDS score with an R2 reaching 0.44 and depression severity with an F1-score as high as 0.77. By imposing appropriate regularizers, our approach identified strong depression indicators such as time staying at home and total time asleep.


title: Catheter Ablation of Ventricular Tachycardia in Patients With MitraClip Device: Preliminary Findings

abstract: INTRODUCTION ::: Patients with mitral regurgitation are increasingly treated by percutaneous implantation of a MitraClip device (Abbott Park, IL, USA). We investigate the feasibility and safety of the transmitral catheter route for catheter ablation of ventricular tachycardia (VT) in these patients. ::: ::: ::: METHODS ::: The mitral valve with the MitraClip in situ was crossed under transesophageal 3-dimensional echocardiographic and fluoroscopic guidance using a steerable sheath for ablation of the left ventricle. ::: ::: ::: RESULTS ::: Five patients (all males, median age 74.0 ± 16.0 years) who had previously a MitraClip implanted were referred for catheter ablation of VT. The left ventricular ejection fraction was 29.0% ± 24.0%. One patient had both an atrial septal defect and a left atrial appendage occluder device in addition to a MitraClip. The duration between MitraClip implantation and ablation was 1019.0 ± 783.0 days. After transseptal puncture, ablation catheter was successfully steered through the mitral valve with the use of fluoroscopy. A complete high-density map of the substrate in sinus rhythm could be obtained in all patients using multipolar mapping catheters. In 1 patient, mapping was carried out using a mini-basket catheter. Procedural endpoints, noninducibility of all VTs, and abolition of all late potentials were achieved in all patients. Procedure time was 255.0 ± 52.5 minute, fluoroscopy time was 23.0 ± 7.3, and the radiation dose was 61.0 ± 37.5 Gycm2 . No mitral insufficiency or worsening of regurgitation was documented after the procedure. ::: ::: ::: CONCLUSIONS ::: This is the first report demonstrating the feasibility and safety of VT ablation in patients with a MitraClip device using the anterograde transmitral catheter route.


title: Can We Translate Letters?

abstract: Current statistical machine translation systems handle the translation process as the transformation of a string of symbols into another string of symbols. Normally the symbols dealt with are the words in different languages, sometimes with some additional information included, like morphological data. In this work we try to push the approach to the limit, working not on the level of words, but treating both the source and target sentences as a string of letters. We try to find out if a nearly unmodified state-of-the-art translation system is able to cope with the problem and whether it is capable to further generalize translation rules, for example at the level of word suffixes and translation of unseen words. Experiments are carried out for the translation of Catalan to Spanish.


title: Highly Accurate Debye Models for Normal and Malignant Breast Tissue Dielectric Properties at Microwave Frequencies

abstract: The finite difference time domain (FDTD) method is widely used as a computational tool for development, validation, and optimization of emerging microwave breast cancer detection and treatment techniques. When expressed in terms of Debye parameters, dispersive breast tissue dielectric properties can be efficiently incorporated into FDTD codes. Previously, we experimentally characterized the dielectric properties of a large number of excised normal and malignant breast tissue samples from 0.5 to 20 GHz. We subdivided the large database of normal tissue data into three groups based on the percent adipose tissue present in a particular sample. In addition, we formed a group of all cancer samples that contained at least 30% malignant tissue. We summarized the data using one-pole Cole-Cole models that were rigorously fit to the median dielectric properties of the three normal tissue groups and one malignant tissue group. In this letter, we present computationally simpler one- and two-pole Debye models that retain the high accuracy of the Cole-Cole models. Model parameters are derived for two sets of frequency ranges: the entire measurement frequency range from 0.5 to 20 GHz, and the 3.1-10.6 GHz FCC band allocated for ultrawideband medical applications. The proposed Debye models provide a means for creating computationally efficient FDTD breast models with realistic wideband dielectric properties derived from the largest and most comprehensive experimental study conducted to date on human breast tissue.


title: LIFESTYLE FINDER Intelligent User Profiling Using Large-Scale Demographic Data 1

abstract: A number of approaches have been advanced for taking data about a user's likes and dislikes and generating a general profile of the user. These profiles can be used to retrieve documents matching user interests; recommend music, movies, or other similar products; or carry out other tasks in a specialized fashion. This article presents a fundamentally new method for generating user profiles that takes advantage of a large-scale database of demographic data. These data are used to generalize user-specified data along the patterns common across the population, including areas not represented in the user's original data. I describe the method in detail and present its implementation in the LIFESTYLE FINDER agent, an internet-based experiment testing our approach on more than 20,006 users worldwide.


title: Towards Passive Political Opinion Polling using Twitter

abstract: Social media platforms, such as Twitter, provide a forum for political communication where politicians broadcast messages and where the general public engages in the discussion of pertinent political issues. The open nature of Twitter, together with its large volume of traffic, makes it a useful resource for new forms of 'passive' opinion polling , i.e. automatically monitoring and de- tecting which key issues the general public is concerned about and inferring their voting intentions. In this paper, we present a number of case studies for the automatic analysis of UK political tweets. We investigate the automated senti- ment analysis of tweets from UK Members of Parliament (MPs) towards the main political parties. We then investigate using the volume and sentiment of the tweets from other users as a proxy for their voting intention and compare the results against existing poll data. Finally we conduct automatic identifica- tion of the key topics discussed by both the MPs and users on Twitter and com- pare them with the main political issues identified in traditional opinion polls. We describe our data collection methods, analysis tools and evaluation frame- work and discuss our results and the factors affecting their accuracy.


title: Multivariate lattices for encrypted image processing

abstract: Images are inherently sensitive signals that require privacy-preserving solutions when processed in an untrusted environment, but their efficient encrypted processing is particularly challenging due to their structure and size. This work introduces a new cryptographic hard problem called m-RLWE (multivariate Ring Learning with Errors) extending RLWE. It gives support to lattice cryptosystems that allow for encrypted processing of multidimensional signals. We show an example cryptosystem and prove that it outperforms its RLWE counterpart in terms of security against basis-reduction attacks, efficiency and cipher expansion for encrypted image processing.


title: Bayesian Active Distance Metric Learning

abstract: Distance metric learning is an important component for many tasks, such as statistical classification and content-based image retrieval. Existing approaches for learning distance metrics from pairwise constraints typically suffer from two major problems. First, most algorithms only offer point estimation of the distance metric and can therefore be unreliable when the number of training examples is small. Second, since these algorithms generally select their training examples at random, they can be inefficient if labeling effort is limited. This paper presents a Bayesian framework for distance metric learning that estimates a posterior distribution for the distance metric from labeled pair-wise constraints. We describe an efficient algorithm based on the variational method for the proposed Bayesian approach. Furthermore, we apply the proposed Bayesian framework to active distance metric learning by selecting those unlabeled example pairs with the greatest uncertainty in relative distance. Experiments in classification demonstrate that the proposed framework achieves higher classification accuracy and identifies more informative training examples than the non-Bayesian approach and state-of-the-art distance metric learning algorithms.


title: The Development of a Virtual world for Learning Newtonian Mechanics

abstract: We are collaboratively designing “ScienceSpace,” a collection of virtual worlds designed to explore the potential utility of physical immersion and multisensory perception to enhance science education. This paper describes the creation and formative evaluation of NewtonWorld, a virtual environment for investigating the kinematics and dynamics of one-dimensional motion. Through this research, we are developing design heuristics, assessment methodologies, and insights about multisensory learning generalizable to a wide range of educational environments. We are also gaining an understanding the strengths and weaknesses of virtual reality as a vehicle for learning.


Generating Multiple Hypotheses for Human 3D Pose Consistent with 2D Joint Detections


title: Lactobacillus delbrueckii subsp. lactis DSM7290 pepG gene encodes a novel cysteine aminopeptidase

abstract: A number of Escherichia coli clones were isolated from a Lactobacillus delbrueckii subsp. lactis gene library capable of hydrolysing the chromogenic substrate Gly-Ala-β-naphthylamide (Gly-Ala-βNA). Some of the recombinant plasmids carried by these clones have been shown to encode the cysteine aminopeptidase gene pepC. Nucleotide sequence analyses of the plasmid inserts of the remaining clones resulted in the identification of two adjacent ORFs encoding proteins exhibiting a high degree of similarity between themselves (72.6%) and with PepC. One gene, designated pepG, was overexpressed in E. coli and the crude extracts obtained were shown to be peptidolytically active both against chromogenic substrates and peptides, and in a Salmonella typhimurium growth test. PepC and PepG activities were compared using chromogenic βNA and p-nitroanilide substrates and leucine or proline-containing peptides were applied in growth experiments of recombinant Sal. typhimurium. The results indicate that the enzymes, although structurally related, have different substrate preferences. No enzyme activity could be ascribed to the second ORF (orfW), despite the production of a visible protein using a T7 RNA polymerase system. Primer extension analysis, using mRNA isolated from Lb. delbrueckii subsp. lactis DSM7290 did establish that orfW was transcribed.


title: Patient Acceptance of Sexual Orientation and Gender Identity Questions on Intake Forms in Outpatient Clinics: A Pragmatic Randomized Multisite Trial

abstract: Objectives ::: To (1) test whether patient attitudes toward intake forms at three Midwestern outpatient clinics are significantly more negative among those who are asked to complete SOGI questions versus those who are not; and (2) gain an in-depth understanding of patient concerns about SOGI questions. ::: ::: Study Setting ::: Data were collected between 6/29/2015 and 2/29/2016 from new patients (N = 491) who presented at three outpatient clinics in a large academic medical center. This study was originally a quality improvement project, and later, institutional review board approval was obtained for secondary data analysis. ::: ::: Study Design ::: Two-stage mixed-methods study. (1) Experimental: New patients at three sites were randomly assigned to complete either routine intake forms (control) or routine intake forms with SOGI questions (experimental); and (2) qualitative: interviews with patients who responded negatively to SOGI questions. ::: ::: Principal Findings ::: There were no significant differences in patient attitudes between experimental and control groups (p > .05). Of those who received SOGI questions, only 3 percent reported being distressed, upset, or offended by the SOGI questions. ::: ::: Conclusions ::: Collection of SOGI data as a part of the routine clinical patient intake process is not distressing to 97 percent of patients who are heterosexual, cisgender, and older than 50 years.


title: Investigating Haptic Perception of and Physiological Responses to Air Vortex Rings on a User's Cheek

abstract: Haptic perception is one of the primary means of interaction with the world. Recent research on affective haptics suggests that it can affect emotional and behavioral responses. In this study, we evaluate user perceptions of haptic stimuli generated by air vortex rings on the cheek and investigate the effects on their physiological responses. To develop a cheek haptic display, we investigated and found that the cheek had enough resolution to perceive the differences in haptic stimuli in a two-point discrimination threshold test of the face. Additionally, the intensities of the haptic stimuli for experiments were determined by investigating the subjective impressions of different stimuli pairs. Finally, we conducted experiments to evaluate quantitatively the effects of four different combinations of haptic stimuli on the physiological responses in terms of stress modification, brainwave activities, task performance, and subjective assessment. The results suggest that different stimuli affect physiological responses and task performance.


title: Supply Chain Evolution for Sustainability-Focused Firms: Content Analysis towards Socially and Environmentally Friendliness

abstract: The objective of this research was to understand how the organizational structure of sustainability-focused companies changes over time as the companies become more environmentally, economically, and socially sustainable. We applied trend analysis to the sustainability Scores, and vertical integration level of the companies. Our sample consisted of ten sustainability-focused companies from the industrials industry. We used the content analysis of annual reports to calculate sustainability development scores and applied the Fan and Lang’s method to determine the vertical integration level of the companies. The study results demonstrated an increasing trend in both vertical integration and sustainability development of industrials industry companies over a 15-year of period. Furthermore, the companies became more vertically integrated as their environmental, economical, and social sustainability increased.


title: Scalable high efficiency video coding (SHEVC) performance evaluation

abstract: Digital video applications cover today a very large range of multimedia application: messaging, HD video telephony and video conferencing. etc. These heterogeneity motivated some years ago the development of the scalable video coding. Therefore, the Joint Collaborative Team on Video Coding (JCT-VC) has developed a new Scalable High efficiency Video Coding standard (SHVC) as an extension of HEVC. The SHVC may compress numerous video sequences of various resolutions as a single bit-stream using the inter layer prediction tools. In order to highlight the features' coding efficiency; several analyses were made using numerous High Definition (HD) sequences with various scenarios. In this paper an analysis study has done on the efficiency of SHVC. Results show that multicast coding using SHVC standard provides an average bit rate reduction of more than 30% compared to simulcast. These results demonstrate the improvement in coding efficiency of the SHVC solution with the introduction of inter-layer prediction.


title: Spectral classification using fuzzy feature sampling

abstract: Classifying biomedical spectra is often difficult due to the bir voluminous nature; typically, only a small subset of spectral features is discriminatory, while the large majority tends to have a confounding effect on pattern classifiers. We present a two-pronged approach to dealing with this issue. First, we describe an iterative technique whereby many classifier instances operate on different feature subsets. A fuzzy feature sampling method is used to identify discriminatory feature subsets. Second, subsets are aggregated using a fuzzy logic based method. Using a biomedical dataset, we empirically demonstrate that this two-pronged approach produces superior classification accuracies compared against a set of classifier benchmarks.


title: Deep Age Distribution Learning for Apparent Age Estimation

abstract: Apparent age estimation has attracted more and more researchers since its potential applications in the real world. Apparent age estimation differs from chronological age estimation that in apparent age estimation each facial image is labelled by multiple individuals, the mean age is the ground truth age and the uncertainty is introduced by the standard deviation. In this paper, we propose a novel method called Deep Age Distribution Learning(DADL) to deal with such situation. According to the given mean age and the standard deviation, we generate a Gaussian age distribution for each facial image as the training target instead of the single age. DADL first detects the facial region in image and aligns the facial image. Then, it uses deep Convolutional Neural Network(CNN) pre-trained based on the VGGFace and fine-tuned on the age dataset to extract the predicted age distribution. Finally it uses ensemble method to get the result. Our DADL method got a good performance in ChaLearn Looking at People 2016-Track 1: Age Estimation and ranked the 2nd place among 105 registered participants.


title: Image enhancement using a fusion framework of histogram equalization and laplacian pyramid

abstract: The image enhancement methods based on histogram equalization (HE) often fail to improve local information and sometimes have the fatal flaw of over-enhancement when a quantum jump occurs in the cumulative distribution function of the histogram. To overcome these shortcomings, we propose an image enhancement method based on a modified Laplacian pyramid framework that decomposes an image into band-pass images to improve both the global contrast and local information. For the global contrast, a novel robust HE is proposed to provide a well-balanced mapping function which effectively suppresses the quantum jump. For the local information, noise-reduced and adaptively gained high-pass images are applied to the resultant image. In qualitative and quantitative comparisons through experimental results, the proposed method shows natural and robust image quality and suitability for video sequences, achieving generally higher performance when compared to existing methods.


title: Heart Disease Prediction System using Associative Classification and Genetic Algorithm

abstract: Associative classification is a recent and rewarding technique which integrates association rule mining and classification to a model for prediction and achieves maximum accuracy. Associative classifiers are especially fit to applications where maximum accuracy is desired to a model for prediction. There are many domains such as medical where the maximum accuracy of the model is desired. Heart disease is a single largest cause of death in developed countries and one of the main contributors to disease burden in developing countries. Mortality data from the registrar general of India shows that heart disease are a major cause of death in India, and in Andhra Pradesh coronary heart disease cause about 30%of deaths in rural areas. Hence there is a need to develop a decision support system for predicting heart disease of a patient. In this paper we propose efficient associative classification algorithm using genetic approach for heart disease prediction. The main motivation for using genetic algorithm in the discovery of high level prediction rules is that the discovered rules are highly comprehensible, having high predictive accuracy and of high interestingness values. Experimental Results show that most of the classifier rules help in the best prediction of heart disease which even helps doctors in their diagnosis decisions.


title: Electronics and optoelectronics of two-dimensional transition metal dichalcogenides.

abstract: The remarkable properties of graphene have renewed interest in inorganic, two-dimensional materials with unique electronic and optical attributes. Transition metal dichalcogenides (TMDCs) are layered materials with strong in-plane bonding and weak out-of-plane interactions enabling exfoliation into two-dimensional layers of single unit cell thickness. Although TMDCs have been studied for decades, recent advances in nanoscale materials characterization and device fabrication have opened up new opportunities for two-dimensional layers of thin TMDCs in nanoelectronics and optoelectronics. TMDCs such as MoS(2), MoSe(2), WS(2) and WSe(2) have sizable bandgaps that change from indirect to direct in single layers, allowing applications such as transistors, photodetectors and electroluminescent devices. We review the historical development of TMDCs, methods for preparing atomically thin layers, their electronic and optical properties, and prospects for future advances in electronics and optoelectronics.


Short Papers Fast Random Sampling of Triangular Meshes


title: Computer self-efficacy: development of a measure and initial test

abstract: This paper discusses the role of individuals' beliefs about their abilities to competently use computers (computer self-efficacy) in the determination of computer use. A survey of Canadian managers and professionals was conducted to develop and validate a measure of computer self-efficacy and to assess both its impacts and antecedents. Computer self- efficacy was found to exert a significant influence on individuals' expectations of the outcomes of using computers, their emotional reactions to computers (affect and anxiety), as well as their actual computer use. An individual's self-efficacy and outcome expecta- tions were found to be positively influenced by the encouragement of others in their work group, as well as others' use of computers. Thus, self-efficacy represents an important individual trait, which moderates organizational influences (such as encouragement and support) on an individual's decision to use computers. Understanding self-efficacy, then, is important to the successful implementation of systems in organizations. The existence of a reliable and valid measure of self-efficacy makes assessment possible and should have implications for organizational support, training, and implementation.


title: Jitter in ring oscillators

abstract: This paper describes a theoretical framework for analyzing and predicting jitter in ring oscillators. These oscillators are becoming more popular in low jitter applications, but the mechanisms underlying their jitter performance have been poorly understood. A comparison with analysis techniques for harmonic and relaxation oscillators shows that a different approach is needed to design for low jitter in rings. The approach taken is in the time domain, considering each delay stage to add a uniform amount of phase in a varying an amount of time. The theory is applied to experimental results from ring oscillators of several different lengths. >


title: Investigation of the CasCor Family of Learning Algorithms

abstract: Abstract Six learning algorithms are investigated and compared empirically. All of them are based on variants of the candidate training idea of the Cascade Correlation method. The comparison was performed using 42 different datasets from the PROBEN1 benchmark collection. The results indicate: (1) for these problems it is slightly better not to cascade the hidden units; (2) error minimization candidate training is better than covariance maximization for regression problems but may be a little worse for classification problems; (3) for most learning tasks, considering validation set errors during the selection of the best candidate will not lead to improved networks, but for a few tasks it will. © 1997 Elsevier Science Ltd.


title: CNN-Based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss

abstract: Learning based approaches have not yet achieved their full potential in optical flow estimation, where their performance still trails heuristic approaches. In this paper, we present a CNN based patch matching approach for optical flow estimation. An important contribution of our approach is a novel thresholded loss for Siamese networks. We demonstrate that our loss performs clearly better than existing losses. It also allows to speed up training by a factor of 2 in our tests. Furthermore, we present a novel way for calculating CNN based features for different image scales, which performs better than existing methods. We also discuss new ways of evaluating the robustness of trained features for the application of patch matching for optical flow. An interesting discovery in our paper is that low-pass filtering of feature maps can increase the robustness of features created by CNNs. We proved the competitive performance of our approach by submitting it to the KITTI 2012, KITTI 2015 and MPI-Sintel evaluation portals where we obtained state-of-the-art results on all three datasets.


Interleukin-1 induced lung neutrophil accumulation and oxygen metabolite mediated lung leak in rats


title: Imperative functional programming

abstract: We present a new model, based on monads, for performing input/output in a non-strict, purely functional language. It is composable, extensible, efficient, requires no extensions to the type system, and extends smoothly to incorporate mixed-language working and in-place array updates.


title: Updating P300: An integrative theory of P3a and P3b

abstract: The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.


title: A Framework for Dynamic Image Sampling Based on Supervised Learning

abstract: Sparse sampling schemes have the potential to dramatically reduce image acquisition time while simultaneously reducing radiation damage to samples. However, for a sparse sampling scheme to be useful it is important that we are able to reconstruct the underlying object with sufficient clarity using the sparse measurements. In dynamic sampling, each new measurement location is selected based on information obtained from previous measurements. Therefore, dynamic sampling schemes have the potential to dramatically reduce the number of measurements needed for high fidelity reconstructions. However, most existing dynamic sampling methods for point-wise measurement acquisition tend to be computationally expensive and are therefore too slow for practical applications. ::: In this paper, we present a framework for dynamic sampling based on machine learning techniques, which we call a supervised learning approach for dynamic sampling (SLADS). In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements. SLADS is fast because we use a simple regression function to compute the ERD, and it is accurate because the regression function is trained using data sets that are representative of the specific application. In addition, we introduce a method to terminate dynamic sampling at a desired level of distortion, and we extended the SLADS methodology to sample groups of pixels at each step. Finally, we present results on computationally-generated synthetic data and experimentally-collected data to demonstrate a dramatic improvement over state-of-the-art static sampling methods.


title: Software aging in the eucalyptus cloud computing infrastructure: Characterization and rejuvenation

abstract: The need for high reliability, availability and performance has significantly increased in modern applications, that handle rapidly growing demands while providing uninterruptible services. Cloud computing systems fundamentally provide access to large pools of data and computational resources. Eucalyptus is a software framework largely used to implement private clouds and hybrid-style Infrastructure as a Service. It implements the Amazon Web Service (AWS) API, allowing interoperability with other AWS-based services. This article investigates the software aging effects in the Eucalyptus framework, considering workloads composed of intensive requests for remote storage attachment and virtual machine instantiations. We found problems that may be harmful to system dependability and performance, specifically regarding to RAM memory and swap space exhaustion, besides highly excessive CPU utilization by the virtual machines. We also present an approach that applies time series analysis to schedule rejuvenation, so as to reduce the downtime by predicting the proper moment to perform the rejuvenation. We experimentally evaluate our approach using an Eucalyptus test bed. The results show that our approach achieves higher availability, when compared to a threshold-triggered rejuvenation method based on continuous monitoring of resources utilization.


title: A Survey on Black Hole Attacks and Comparative Analysis of Various IDS Schemes in MANET

abstract: A wireless communication system which do not require any fixed infrastructure for the establishment of its configuration is called Mobile Adhoc Network(MANET).This infrastructure leads to the misbehavior of some nodes which attack and degrade the performance of the network. In this paper we perform an analysis of various schemes that can be applied to improve the performance of MANET when a black hole attack occurs and provide a solution using anomaly based IDS scheme.


title: Multiple instance classification: Review, taxonomy and comparative study

abstract: Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problem have been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e., leaving out other learning tasks such as regression). In order to perform our study, we implemented fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods.


title: Object-Based Features for House Detection from RGB High-Resolution Images

abstract: Automatic building extraction from satellite images, an open research topic in remote sensing, continues to represent a challenge and has received substantial attention for decades. This paper presents an object-based and machine learning-based approach for automatic house detection from RGB high-resolution images. The images are first segmented by an algorithm combing a thresholding watershed transformation and hierarchical merging, and then shadows and vegetation are eliminated from the initial segmented regions to generate building candidates. Subsequently, the candidate regions are subjected to feature extraction to generate training data. In order to capture the characteristics of house regions well, we propose two kinds of new features, namely edge regularity indices (ERI) and shadow line indices (SLI). Finally, three classifiers, namely AdaBoost, random forests, and Support Vector Machine (SVM), are employed to identify houses from test images and quality assessments are conducted. The experiments show that our method is effective and applicable for house identification. The proposed ERI and SLI features can improve the precision and recall by 5.6% and 11.2%, respectively.


title: Design and implementation of maximum power point tracking solar charge controller

abstract: Solar or photovoltaic (PV) system is an alternative clean energy resource that has received much attention in the research and industries. Solar charge controller (CC) is the heart of a solar system. Three common types of charge controller are ON/Off, pulse width modulation (PWM) and maximum power point tracking (MPPT). MPPT is getting very much popularity nowadays for its high capability of power extracting from solar panel. This paper is presenting here an improved design of MPPT solar charge controller using Aurduino. The proposed technique significantly reduce system power loss and increase the efficiency. Experimented highest efficiency 97.75% is recorded from the proposed system. Additionally, some new features are added like smart device charging, wireless data logging and protections from high voltage disconnect (HVD), low voltage disconnect (LVD), over current protection, short circuit protection and reverse polarity protection.


title: Learning robust cortico-cortical associations with the basal ganglia: An integrative review

abstract: Abstract This article focuses on the interaction between the basal ganglia (BG) and prefrontal cortex (PFC). The BG are a group of nuclei at the base of the forebrain that are highly connected with cortex. A century of research suggests that the role of the BG is not exclusively motor, and that the BG also play an important role in learning and memory. In this review article, we argue that one important role of the BG is to train connections between posterior cortical areas and frontal cortical regions that are responsible for automatic behavior after extensive training. According to this view, one effect of BG trial-and-error learning is to activate the correct frontal areas shortly after posterior associative cortex activation, thus allowing for Hebbian learning of robust, fast, and efficient cortico-cortical processing. This hypothesized process is general, and the content of the learned associations depends on the specific areas involved (e.g., associations involving premotor areas would be more closely related to behavior than associations involving the PFC). We review experiments aimed at pinpointing the function of the BG and the frontal cortex and show that these results are consistent with the view that the BG is a general purpose trainer for cortico-cortical connections. We conclude with a discussion of some implications of the integrative framework and how this can help better understand the role of the BG in many different tasks.


title: Image Based Fashion Product Recommendation with Deep Learning

abstract: We develop a two-stage deep learning framework that recommends fashion images based on other input images of similar style. For that purpose, a neural network classifier is used as a data-driven, visually-aware feature extractor. The latter then serves as input for similarity-based recommendations using a ranking algorithm. Our approach is tested on the publicly available Fashion dataset. Initialization strategies using transfer learning from larger product databases are presented. Combined with more traditional content-based recommendation systems, our framework can help to increase robustness and performance, for example, by better matching a particular customer style.


title: DIEDA: discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification

abstract: Face and kinship verification using facial images is a novel and challenging problem in computer vision. In this paper, we propose a new system that uses discriminative information, which is based on the exponential discriminant analysis (DIEDA) combined with multiple scale descriptors. The histograms of different patches are concatenated to form a high dimensional feature vector, which represents a specific descriptor at a given scale. The projected histograms for each zone use the cosine similarity metric to reduce the feature vector dimensionality. Lastly, zone scores corresponding to various descriptors at different scales are fused and verified by using a classifier. This paper exploits discriminative side information for face and kinship verification in the wild (image pairs are from the same person or not). To tackle this problem, we take examples of the face samples with unlabeled kin relations from the labeled face in the wild dataset as the reference set. We create an optimized function by minimizing the interclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kinship relation) with the DIEDA approach. Experimental results on three publicly available face and kinship datasets show the superior performance of the proposed system over other state-of-the-art techniques.


title: The mathematics of cancer: integrating quantitative models

abstract: This Review discusses mathematical modelling approaches in cancer research. These models can complement experimental and clinical studies, but can also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research.


title: Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback

abstract: We study the online influence maximization problem in social networks under the independent cascade model. Specifically, we aim to learn the set of "best influencers" in a social network online while repeatedly interacting with it. We address the challenges of (i) combinatorial action space, since the number of feasible influencer sets grows exponentially with the maximum number of influencers, and (ii) limited feedback, since only the influenced portion of the network is observed. Under a stochastic semi-bandit feedback, we propose and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our bounds on the cumulative regret are polynomial in all quantities of interest, achieve near-optimal dependence on the number of interactions and reflect the topology of the network and the activation probabilities of its edges, thereby giving insights on the problem complexity. To the best of our knowledge, these are the first such results. Our experiments show that in several representative graph topologies, the regret of IMLinUCB scales as suggested by our upper bounds. IMLinUCB permits linear generalization and thus is both statistically and computationally suitable for large-scale problems. Our experiments also show that IMLinUCB with linear generalization can lead to low regret in real-world online influence maximization.


title: Learning temporal features using LSTM-CNN architecture for face anti-spoofing

abstract: Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93% vs. 7.34%), and hand-crafted features (5.93% vs. 10.00%) on CASIA dataset.


title: Social MatchUP -: a Memory-like Virtual Reality Game for the Enhancement of Social Skills in Children with Neurodevelopmental Disorders

abstract: This paper presents Social MatchUP, a multiplayer Virtual Reality game for children with Neurodevelopmental Disorders (NDD). Shared virtual reality environments (SVREs) allow NDD children to interact in the same virtual space, but without the possible discomfort or fear caused by having a real person in front of them. Social MatchUP is a simple Concentration-like game, run on smartphones, where players should communicate to match up all the pairs of images they are given. Because every player can only interact with half of the pictures, but can see what his companion is doing, the game improves social and communication skill, and can be used also as a learning tool. A simple and easy-to-use customization tool was also developed to let therapists and teachers adapt the game context to the needs of the children they take care of.


title: FAtiMA Modular: Towards an Agent Architecture with a Generic Appraisal Framework

abstract: This paper presents a generic and flexible architecture for emotional agents, with what we consider to be the minimum set of functionalities that allows us to implement and compare different appraisal theories in a given scenario. FAtiMA Modular, the architecture proposed is composed of a core algorithm and by a set of components that add particular functionality (either in terms of appraisal or behaviour) to the architecture, which makes the architecture more flexible and easier to extend.


title: Sensors for Robotic Hands: A Survey of State of the Art

abstract: Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors. We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands.


A study of term weighting schemes using class information for text classification


title: Inferring Dockless Shared Bike Distribution in New Cities

abstract: Recently, dockless shared bike services have achieved great success and reinvented bike sharing business in China. When expanding bike sharing business into a new city, most start-ups always wish to find out how to cover the whole city with a suitable bike distribution. In this paper, we study the problem of inferring bike distribution in new cities, which is challenging. As no dockless bikes are deployed in the new city, we propose to learn insights on bike distribution from cities populated with dockless bikes. We exploit multi-source data to identify important features that affect bike distributions and develop a novel inference model combining Factor Analysis and Convolutional Neural Network techniques. The extensive experiments on real-life datasets show that the proposed solution provides significantly more accurate inference results compared with competitive prediction methods.


title: Beyond NER: Towards Semantics in Clinical Text

abstract: While clinical text NLP systems have become very effective in recognizing named entities in clinical text and mapping them to standardized terminologies in the normalization process, there remains a gap in the ability of extractors to combine entities together into a complete semantic representation of medical concepts that contain multiple attributes each of which has its own set of allowed named entities or values. Furthermore, additional domain knowledge may be required to determine the semantics of particular tokens in the text that take on special meanings in relation to this concept. This research proposes an approach that provides ontological mappings of the surface forms of medical concepts that are of the UMLS semantic class signs/symptoms. The mappings are used to extract and encode the constituent set of named entities into interoperable semantic structures that can be linked to other structured and unstructured data for reuse in research and analysis.


Random Sample Consensus (RANSAC)


title: Growth and Tuberization of Hydroponically Grown Potatoes

abstract: Three hydroponic systems (aeroponics, aerohydroponics, and deep-water culture) were compared for the production of potato (Solanum tuberosum) seed tubers. Aerohydroponics was designed to improve the root zone environment of aeroponics by maintaining root contact with nutrient solution in the lower part of the beds, while intermittently spraying roots in the upper part. Root vitality, shoot fresh and dry weight, and total leaf area were significantly highest when cv. Superior, a medium early-maturing cultivar, was grown in the aeroponic system. This better plant growth in the aeroponic system was accompanied by rapid changes of solution pH and EC, and early tuberization. However, with cv. Atlantic, a mid-late maturing cultivar, there were no significant differences in shoot weight and leaf area among the hydroponic systems. The first tuberization was observed in aeroponics on 26–30 and 43–53 days after transplanting for cvs Superior and Atlantic, respectively. Tuberization in aerohydroponics and deep-water culture system occurred about 3–4 and 6–8 days later, respectively. The number of tubers produced was greatest in the deep-water culture system, but the total tuber weight per plant was the least in this system. For cv. Atlantic, the number of tubers 30 g between aerohydroponics and aeroponics. For cv. Superior, there was no difference in the size distribution of tubers between the two aeroponic systems. It could be concluded that deep-water culture system could be used to produce many small tubers (1–5 g) for plant propagation. However, the reduced number of large tubers above 5 g weight in the deep-water culture system, may favor use of either aeroponics or aerohydroponics. These two systems produced a similar number of tubers in each size group for the medium-early season cv. Superior, whereas aerohydroponics produced more tubers than aeroponics for the mid-late cultivar Atlantic.


title: Preventive Interventions for ADHD: A Neurodevelopmental Perspective

abstract: It is proposed that the time is ripe for the development of secondary preventive interventions for attention-deficit/hyperactivity disorder (ADHD). By targeting preschool children, a developmental stage during which ADHD symptoms first become evident in most children with the disorder, many of the adverse long-term consequences that typify the trajectory of ADHD may be avoided. A dynamic/interactive model of the biological and environmental factors that contribute to the emergence and persistence of ADHD throughout the lifespan is proposed. Based on this model, it is argued that environmental influences and physical exercise can be used to enhance neural growth and development, which in turn should have an enduring and long-term impact on the trajectory of ADHD. Central to this notion are 2 hypotheses: 1) environmental influences can facilitate structural and functional brain development, and 2) changes in brain structure and function are directly related to ADHD severity over the course of development and the degree to which the disorder persists or remits with time. We present experimental and correlational data supporting the first hypothesis and longitudinal data in individuals with ADHD supporting the second. The case is made for initiating such an intervention during the preschool years, when the brain is likely to be more “plastic” and perhaps susceptible to lasting modifications, and before complicating factors, such as comorbid psychiatric disorders, academic failure, and poor social and family relationships emerge, making successful treatment more difficult. Finally, we review recent studies in young children with ADHD that might fall under the umbrella of secondary prevention.


title: The Blurring Boundaries of Work-Related and Personal Media Use: A Grounded Theory Study on the Employee's Perspective

abstract: The ongoing consumerization of information technology (IT) demands to rethink the traditional paradigms of media use at the workplace. Past research has focused on the use of company IT resources for personal purposes (e.g., ‘cyberloafing’) and primarily emphasized the adverse effects. This study takes a wider perspective and includes the use of personal IT resources for company purposes as well as other mixed forms of personal and work-related media use. We conducted interviews with 14 employees in 5 different firms and used a grounded theory approach to explore the blurring of boundaries between work and private life discussed in the wider social science literature. Our study demonstrates different attitudes of employees towards work-life blurring and suggests that social and mobile IT has a catalyst role in this process. We contribute to the past literature by providing a conceptual model for the context, the conditions and the consequences of this phenomenon. Practical implications are discussed.


title: Workflow Scheduling to Minimize Data Movement Using Multi-constraint Graph Partitioning

abstract: Among scheduling algorithms of scientific workflows, the graph partitioning is a technique to minimize data transfer between nodes or clusters. However, when the graph partitioning is simply applied to a complex workflow DAG, tasks in each parallel phase are not always evenly assigned to computation nodes since the graph partitioning algorithm is not aware of edge directions that represent task dependencies. Thus, we propose a new method of task assignment based on Multi-Constraint Graph Partitioning. This method relates the dimension of weight vectors to the rank of a task phase defined by traversing the task graph. Our algorithm is implemented in the Pwrake workflow system and evaluated the performance of the Montage workflow using a computer cluster. The result shows that the file size accessed from remote nodes is reduced from 88% to 14% of the total file size accessed during the workflow and that the elapsed time is reduced by 31%.


title: Analysis of multi-hop emergency message propagation in vehicular ad hoc networks

abstract: Vehicular Ad Hoc Networks (VANETs) are attracting the attention of researchers, industry, and governments for their potential of significantly increasing the safety level on the road. In order to understand whether VANETs can actually realize this goal, in this paper we analyze the dynamics of multi-hop emergency message dissemination in VANETs. Under a probabilistic wireless channel model that accounts for interference, we derive lower bounds on the probability that a car at distance d from the source ofthe emergency message correctly receives the message within time t. Besides d and t, this probability depends also on 1-hop channel reliability, which we model as a probability value p, and on the message dissemination strategy. Our bounds are derived for an idealized dissemination strategy which ignores interference, and for two provably near-optimal dissemination strategies under protocol interference. The bounds derived in the first part of the paper are used to carefully analyze the tradeoff between the safety level on the road (modeled by parameters d and t), and the value of 1-hop message reliability p. The analysis of this tradeoff discloses several interesting insights that can be very useful in the design of practical emergency message dissemination strategies.


title: New Ultra-Wide Stopband Low-Pass Filter Using Transformed Radial Stubs

abstract: A new type of low-pass filter (LPF) with ultra-wide band rejection and compact size by using a proposed transformed radial stubs (TRSs) is introduced and investigated. The operating mechanism of the filter is investigated based on proposed equivalent-circuit model. The implemented LPF with 1-dB cutoff frequency fc of 3 GHz demonstrates stopband rejection up to 8 fc i.e., 24 GHz. The skirt selectivity is achieved up to -144 dB per octave through multiple-zero generation using TRSs. The measured passband insertion loss is less than 1.5 dB. I/O loading cells are used to further extend the deep stopband to more than 13 fc i.e., above 40 GHz. The size of the four-cell LPF is only 0.31 λg × 0.24 λg, (λg is the guide wavelength at center cutoff frequency) without using any lumped elements.


title: Fully secure key-policy attribute-based encryption with constant-size ciphertexts and fast decryption

abstract: Attribute-based encryption (ABE), introduced by Sahai and Waters, is a promising cryptographic primitive, which has been widely applied to implement fine-grained access control system for encrypted data. In its key-policy flavor, attribute sets are used to annotate ciphertexts and secret keys are associated with access structures that specify which ciphertexts a user is entitled to decrypt. In most existing key-policy attribute-based encryption (KP-ABE) constructions, the size of the ciphertext is proportional to the number of attributes associated with it and the decryption cost is proportional to the number of attributes used during decryption. In this paper, we present a new construction of KP-ABE. Our proposed construction is the first KP-ABE scheme, which has the following features simultaneously: expressive (i.e., supporting arbitrary monotonic access structures); fully secure in the standard model; constant-size ciphertexts and fast decryption. The downside of our construction is that secret keys have quadratic size in the number of attributes.


title: An Accurate, Low-Voltage, CMOS Switching Power Supply With Adaptive On-Time Pulse-Frequency Modulation (PFM) Control

abstract: Integrated switching power supplies with multimode control are gaining popularity in state-of-the-art portable applications like cellular phones, personal digital assistants (PDAs), etc., because of their ability to adapt to various loading conditions and therefore achieve high efficiency over a wide load-current range, which is critical for extended battery life. Constant-frequency, pulsewidth modulated (PWM) switching converters, for instance, have poor light-load efficiencies because of higher switching losses while pulse-frequency modulation (PFM) control in discontinuous-conduction mode (DCM) is more efficient at light loads because the switching frequency and associated switching losses are scaled down with load current. This paper presents the design and integrated circuit prototype results of an 83% power efficient 0.5-V 50-mA CMOS PFM buck (step-down) dc-dc converter with a novel adaptive on-time scheme that generates a 27-mV output ripple voltage from a 1.4- to 4.2-V input supply (battery-compatible range). The output ripple voltage variation and steady-state accuracy of the proposed supply was 5 mV (22-27 mV) and 0.6% whereas its constant on-time counterpart was 45 mV (10-55 mV) and 3.6%, respectively. The proposed control scheme provides an accurate power supply while achieving 2%-10% higher power efficiency than conventional fixed on-time schemes with little circuit complexity added, which is critical during light-loading conditions, where quiescent current plays a pivotal role in determining efficiency and battery-life performance


title: EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

abstract: Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level - Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1 + 2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that"learn"over time.


title: Exploring a user-defined gesture vocabulary for descriptive mid-air interactions

abstract: Gesturing provides an alternative interaction input for design that is more natural and intuitive. However, standard input devices do not completely reflect natural hand motions in design. A key challenge lies in how gesturing can contribute to human–computer interaction, as well as understanding the patterns in gestures. This paper aims to analyze human gestures to define a gesture vocabulary for descriptive mid-air interactions in a virtual reality environment. We conducted experiments with twenty participants describing two chairs (simple and abstract) with different levels of complexity. This paper presents a detailed analysis of gesture distribution and hand preferences for each description task. Comparisons are drawn between the proposed approach to the definition of a vocabulary using combined gestures (GestAlt) and previously suggested methods. The findings state that GestAlt is successful in describing the employed gestures in both tasks (60% of all gestures for simple chair and 69% for abstract chair). The findings can be applied to the development of an intuitive mid-air interface using gesture recognition.


title: 3D printing of modified-release aminosalicylate (4-ASA and 5-ASA) tablets.

abstract: The aim of this study was to explore the potential of fused-deposition 3-dimensional printing (FDM 3DP) to produce modified-release drug loaded tablets. Two aminosalicylate isomers used in the treatment of inflammatory bowel disease (IBD), 5-aminosalicylic acid (5-ASA, mesalazine) and 4-aminosalicylic acid (4-ASA), were selected as model drugs. Commercially produced polyvinyl alcohol (PVA) filaments were loaded with the drugs in an ethanolic drug solution. A final drug-loading of 0.06% w/w and 0.25% w/w was achieved for the 5-ASA and 4-ASA strands, respectively. 10.5mm diameter tablets of both PVA/4-ASA and PVA/5-ASA were subsequently printed using an FDM 3D printer, and varying the weight and densities of the printed tablets was achieved by selecting the infill percentage in the printer software. The tablets were mechanically strong, and the FDM 3D printing was shown to be an effective process for the manufacture of the drug, 5-ASA. Significant thermal degradation of the active 4-ASA (50%) occurred during printing, however, indicating that the method may not be appropriate for drugs when printing at high temperatures exceeding those of the degradation point. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) of the formulated blends confirmed these findings while highlighting the potential of thermal analytical techniques to anticipate drug degradation issues in the 3D printing process. The results of the dissolution tests conducted in modified Hank's bicarbonate buffer showed that release profiles for both drugs were dependent on both the drug itself and on the infill percentage of the tablet. Our work here demonstrates the potential role of FDM 3DP as an efficient and low-cost alternative method of manufacturing individually tailored oral drug dosage, and also for production of modified-release formulations.


Sinks, sustainability, and conservation incentives


title: Democratising open data, complexity science and collective

abstract: Abstract ::: The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project’s own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of self-organization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed. ::: Graphical abstract


Couples Behavior Modeling and Annotation Using Low-Resource LSTM Language Models


title: In Search of the Ideal Female Umbilicus

abstract: The umbilicus is essential to the aesthetic appearance of the abdomen. However, little research exists on what characterizes an aesthetically pleasing umbilicus. Often, the plastic surgeon is called on to reconstruct the umbilicus. Although a variety of reconstructive methods are available, no real standards define the appearance of an aesthetically pleasing umbilicus. To ascertain the characteristics of an aesthetically pleasing umbilicus, a photographic analysis was undertaken using 147 female participants between the ages of 18 and 62 years. Each subject's age, height, and weight were recorded. Photographs of each participant's umbilicus were then taken from 30 cm and 50 cm away. The photographs were reviewed by the authors, and each umbilicus was categorized on the basis of size, shape, hooding, or protrusion. The photographs were then reviewed by a panel and given a score between 1 and 10 to rate attractiveness. Using linear regression analysis, the effects of body mass index, shape, hooding, and umbilical protrusion on attractiveness were assessed. The T- or vertically shaped umbilicus with superior hooding consistently scored the highest in aesthetic appeal, whereas the presence of any degree of protrusion and a horizontal orientation or distorted shape detracted from the score. Those with a large umbilicus tended to score consistently lower than those with a smaller configuration. A desirable goal in umbilical reconstruction is, thus, to create a small T or vertically oriented umbilicus with the addition of a superior hood or shelf.


title: Assessing women's satisfaction with family planning services in Mozambique

abstract: BACKGROUND ::: The contraceptive prevalence rate in Mozambique was estimated as 11.3% in the last Demographic and Health Survey. The impact of family planning (FP) on women's health and on the reduction of maternal mortality is well known. ::: ::: ::: METHODS ::: Acknowledging the importance of user satisfaction in the utilisation of health services, exit interviews were used to assess women's satisfaction with FP services in Mozambique. The survey, conducted in 174 health facilities, was representative at the national level, covered all provinces, and both urban and rural areas. ::: ::: ::: RESULTS ::: Overall, 86% of respondents were satisfied with FP services, but issues such as insufficient supplies of oral contraceptives and the low quality of healthcare provider/client interactions were given as reasons for women's dissatisfaction. ::: ::: ::: CONCLUSION ::: Defined actions at the level of health service provision are needed to tackle the identified issues and ensure improved satisfaction with, and better utilisation of, FP services in Mozambique.


title: Multi-temporal wheat disease detection by multi-spectral remote sensing

abstract: For the implementation of site-specific fungicide applications, the spatio-temporal dynamics of crop diseases must be well known. Remote sensing can be a useful tool to monitor the heterogeneity of crop vitality within agricultural sites. However, the identification of fungal infections at an early growth stage is essential. This study examines the potential of multi-spectral remote sensing for a multi-temporal analysis of crop diseases. Within an experimental field, a 6 ha plot of winter wheat was grown, containing all possible infective stages of the powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita) pathogens. Three high-resolution remote sensing images were used to execute a spatio-temporal analysis of the infection dynamics. A decision tree, using mixture tuned matched filtering (MTMF) results and the Normalized Difference Vegetation Index (NDVI), was applied to classify the data into areas showing different levels of disease severity. Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8%, whereas the scenes from May 28th and June 20th achieved considerably higher accuracies of 65.9% and 88.6% respectively. The results showed that high-resolution multi-spectral data are generally suitable to detect in-field heterogeneities of crop vigour but are only moderately suitable for early detection of crop infections.


title: A Scalable Framework for Trajectory Prediction

abstract: Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems, such as location-based advertising, route planning, traffic management, and early warning systems. In the last few years, the widespread use of GPS navigation systems and wireless communication technology enabled vehicles has resulted in huge volumes of trajectory data. The task of utilizing these data employing spatio-temporal techniques for TP in an efficient and accurate manner is an ongoing research problem. Existing TP approaches are limited to the short-term predictions. Moreover, they cannot handle a large volume of trajectory data for long-term prediction. To address these limitations, we propose a scalable clustering and Markov chain-based hybrid framework, called Traj-clusiVAT-based TP, for both short- and long-term TPs, which can handle a large number of overlapping trajectories in a dense road network. Traj-clusiVAT can also determine the number of clusters, which represent different movement behaviors in input trajectory data. In our experiments, we compare our proposed approach with a mixed Markov model-based scheme and a trajectory clustering, NETSCAN-based TP method for both short- and long-term TPs. We performed our experiments on two real, vehicle trajectory datasets, including a large-scale trajectory dataset consisting of 3.28 million trajectories obtained from 15 061 taxis in Singapore over a period of one month. The experimental results on two real trajectory datasets show that our proposed approach outperforms the existing approaches in terms of both short- and long-term prediction performances, based on the prediction accuracy and distance error (in km).


title: Generative Adversarial Networks as Variational Training of Energy Based Models

abstract: In this paper, we study deep generative models for effective unsupervised learning. We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density $p(\mathbf{x})$ is approximated by a variational distribution $q(\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two step procedure: given $p(\mathbf{x})$, $q(\mathbf{x})$ is updated to maximize the lower bound; $p(\mathbf{x})$ is then updated one step with samples drawn from $q(\mathbf{x})$ to decrease the lower bound. VGAN is inspired by the generative adversarial networks (GANs), where $p(\mathbf{x})$ corresponds to the discriminator and $q(\mathbf{x})$ corresponds to the generator, but with several notable differences. We hence name our model variational GANs (VGANs). VGAN provides a practical solution to training deep EBMs in high dimensional space, by eliminating the need of MCMC sampling. From this view, we are also able to identify causes to the difficulty of training GANs and propose viable solutions. \footnote{Experimental code is available at https://github.com/Shuangfei/vgan}


title: Analysis of n-dimensional Quadtrees Using the Hausdorff F ’ ractal Dimension

abstract: There is mounting evidence [Man77, SchSI] that real datasets are statistically self-similar, and thus, ‘fractal’. This is an important insight since it permits a compact statistical description of spatial datasets; subsequently, as we show, it also forms the basis for the theoretical analysis of spatial access methods, without using the typical, but unrealistic, uniformity assumption. In this paper, we focus on the estimation of the number of quadtree blocks that a real, spatial dataset will require. Using the the wellknown Hausdorff fractal dimension, we derive some closed formulas which allow us to predict the number of quadtree blocks, given some few parameters. Using our formulas, it is possible to predict the space overhead and the response time of linear quadtrees/z-ordering [OM88], which are widely used in practice. In order to verify our analytical model, we performed *This work was partially supported by the National Science Foundation under Grants No. CDR8803012, EEC-94-02384, IRI-8958546 and IRI-9205273), with matching funds from Empress Software Inc. and Thinking Machines Inc. Some of the work was performed while he was visiting AT&T Bell Labor+ tories, Murray Hill, NJ. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 22nd VLDB Conference Mumbai(Bombay), India, 1996 Volker Gaede Institut fiir Wirtschaftsinformatik Humboldt-Universitgt zu Berlin Spandauer Str. 1 10178 Berlin, Germany gaede@wiwi.hu-berlin.de an extensive experimental investigation using several real datasets coming from different domains. In these experiments, we found that our analytical model agrees well with our experiments as well as with older empirical observations on 2-d [Gae95b] and 3-d [ACF+94] data.


title: Pedestrian Parsing via Deep Decompositional Network

abstract: We propose a new Deep Decompositional Network (DDN) for parsing pedestrian images into semantic regions, such as hair, head, body, arms, and legs, where the pedestrians can be heavily occluded. Unlike existing methods based on template matching or Bayesian inference, our approach directly maps low-level visual features to the label maps of body parts with DDN, which is able to accurately estimate complex pose variations with good robustness to occlusions and background clutters. DDN jointly estimates occluded regions and segments body parts by stacking three types of hidden layers: occlusion estimation layers, completion layers, and decomposition layers. The occlusion estimation layers estimate a binary mask, indicating which part of a pedestrian is invisible. The completion layers synthesize low-level features of the invisible part from the original features and the occlusion mask. The decomposition layers directly transform the synthesized visual features to label maps. We devise a new strategy to pre-train these hidden layers, and then fine-tune the entire network using the stochastic gradient descent. Experimental results show that our approach achieves better segmentation accuracy than the state-of-the-art methods on pedestrian images with or without occlusions. Another important contribution of this paper is that it provides a large scale benchmark human parsing dataset that includes 3,673 annotated samples collected from 171 surveillance videos. It is 20 times larger than existing public datasets.


title: Fovea center detection based on the retina anatomy and mathematical morphology

abstract: In this work, we present a new fovea center detection method for color eye fundus images. This method is based on known anatomical constraints on the relative locations of retina structures, and mathematical morphology. The detection of this anatomical feature is a prerequisite for the computer aided diagnosis of several retinal diseases, such as Diabetic Macular Edema. The proposed method is adaptive to local illumination changes, and it is robust to local disturbances introduced by pathologies in digital color eye fundus images (e.g. exudates). Our experimental results using the DRIVE image database indicate that our method is able to detect the fovea center in 37 out of 37 images (i.e. with a success rate of 100%). Using the DIARETDB1 database, our method was able to detect the fovea center in 92.13% of all tested cases (i.e. in 82 out of 89 images). These results indicate that our approach potentially can achieve a better performance than comparable methods proposed in the literature.


title: A low-power temperature-compensated CMOS peaking current reference in subthreshold region

abstract: In this paper, a new method to achieve very small current reference levels on integrated circuits with immunity to temperature variations using peaking current source with MOSFETs operating in subthreshold region is proposed. By adding a source degeneration resistor to the conventional peaking current source architecture, a zero temperature coefficient current can be generated. The proposed low-power circuit operating in the weak inversion region is designed, simulated, and fabricated in a 0.18-μm standard CMOS process. Measurement results verify the circuit operation with about 5% variation over the span of −40° C to +100° C (industrial temperature grade). The supplied current is designed to be 1.5μΑ.


title: Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram

abstract: The aim of this paper is to detect abnormal events in video streams, a challenging but important subject in video surveillance. We propose a novel algorithm to address this problem. The algorithm is based on an image descriptor and a nonlinear classification method. We introduce a histogram of optical flow orientation as a descriptor encoding the moving information of each video frame. The nonlinear one-class support vector machine classification algorithm, following a learning period characterizing the normal behavior of training frames, detects abnormal events in the current frame. Further, a fast version of the detection algorithm is designed by fusing the optical flow computation with a background subtraction step. We finally apply the method to detect abnormal events on several benchmark data sets, and show promising results.


title: A Chinese Text Corrector Based on Seq2Seq Model

abstract: In this paper, we build a Chinese text corrector which can correct spelling mistakes precisely in Chinese texts. Our motivation is inspired by the recently proposed seq2seq model which consider the text corrector as a sequence learning problem. To begin with, we propose a biased-decoding method to improve the bilingual evaluation understudy (BLEU) score of our model. Secondly, we adopt a more reasonable OOV token scheme, which enhances the robustness of our correction mechanism. Moreover, to test the performance of our proposed model thoroughly, we establish a corpus which includes 600,000 sentences from news data of Sogou Labs. Experiments show that our corrector model can achieve better corrector results based on the corpus.


title: Can we trust digital image forensics?

abstract: Compared to the prominent role digital images play in nowadays multimedia society, research in the field of image authenticity is still in its infancy. Only recently, research on digital image forensics has gained attention by addressing tamper detection and image source identification. However, most publications in this emerging field still lack rigorous discussions of robustness against strategic counterfeiters, who anticipate the existence of forensic techniques. As a result, the question of trustworthiness of digital image forensics arises. This work will take a closer look at two state-of-the-art forensic methods and proposes two counter-techniques; one to perform resampling operations undetectably and another one to forge traces of image origin. Implications for future image forensic systems will be discussed.


title: Cascade: crowdsourcing taxonomy creation

abstract: Taxonomies are a useful and ubiquitous way of organizing information. However, creating organizational hierarchies is difficult because the process requires a global understanding of the objects to be categorized. Usually one is created by an individual or a small group of people working together for hours or even days. Unfortunately, this centralized approach does not work well for the large, quickly changing datasets found on the web. Cascade is an automated workflow that allows crowd workers to spend as little at 20 seconds each while collectively making a taxonomy. We evaluate Cascade and show that on three datasets its quality is 80-90% of that of experts. Cascade has a competitive cost to expert information architects, despite taking six times more human labor. Fortunately, this labor can be parallelized such that Cascade will run in as fast as four minutes instead of hours or days.


title: A General Statistical Framework for Designing Strategy-proof Assignment Mechanisms

abstract: We develop a statistical framework for the design of a strategy-proof assignment mechanism that closely approximates a target outcome rule. The framework can handle settings with and without money, and allows the designer to employ techniques from machine learning to control the space of strategy-proof mechanisms searched over, by providing a rule class with appropriate capacity. We solve a sample-based optimization problem over a space of mechanisms that correspond to agent-independent price functions (virtual prices in the case of settings without money), subject to a feasibility constraint on the sample. A transformation is applied to the obtained mechanism to ensure feasibility on all type profiles, and strategy-proofness. We derive a sample complexity bound for our approach in terms of the capacity of the chosen rule class and provide applications for our results.


title: A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception

abstract: While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neuronal systems. Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial. However, while CNNs capture key properties of the average responses of cortical neurons, they fail to explain other properties of these neurons. For one, CNNs typically require large quantities of labeled input data for training. Our own brains, in contrast, rarely have access to this kind of supervision, so to the extent that representations are similar between CNNs and brains, this similarity must arise via different training paths. In addition, neurons in visual cortex produce complex time-varying responses even to static inputs, and they dynamically tune themselves to temporal regularities in the visual environment. We argue that these differences are clues to fundamental differences between the computations performed in the brain and in deep networks. To begin to close the gap, here we study the emergent properties of a previously-described recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single unit response dynamics to complex perceptual motion illusions. These results suggest potentially deep connections between recurrent predictive neural network models and the brain, providing new leads that can enrich both fields.


Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models


title: Iterative Integration of Visual Insights during Scalable Patent Search and Analysis

abstract: Patents are of growing importance in current economic markets. Analyzing patent information has, therefore, become a common task for many interest groups. As a prerequisite for patent analysis, extensive search for relevant patent information is essential. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. Already the amount of patent data to be analyzed poses challenges with respect to scalability. Further scalability issues arise concerning the diversity of users and the large variety of analysis tasks. With "PatViz”, a system for interactive analysis of patent information has been developed addressing scalability at various levels. PatViz provides a visual environment allowing for interactive reintegration of insights into subsequent search iterations, thereby bridging the gap between search and analytic processes. Because of its extensibility, we expect that the approach we have taken can be employed in different problem domains that require high quality of search results regarding their completeness.


title: What is the common thread of creativity? Its dialectical relation to intelligence and wisdom

abstract: Creativity refers to the potential to produce novel ideas that are task-appropriate and high in quality. Creativity in a societal context is best understood in terms of a dialectical relation to intelligence and wisdom. In particular, intelligence forms the thesis of such a dialectic. Intelligence largely is used to advance existing societal agendas. Creativity forms the antithesis of the dialectic, questioning and often opposing societal agendas, as well as proposing new ones. Wisdom forms the synthesis of the dialectic, balancing the old with the new. Wise people recognize the need to balance intelligence with creativity to achieve both stability and change within a societal context.


title: Process monitoring in the intensive care unit: Assessing patient mobility through activity analysis with a non-invasive mobility sensor

abstract: Throughout a patient’s stay in the Intensive Care Unit (ICU), accurate measurement of patient mobility, as part of routine care, is helpful in understanding the harmful effects of bedrest [1]. However, mobility is typically measured through observation by a trained and dedicated observer, which is extremely limiting. In this work, we present a video-based automated mobility measurement system called NIMS: Non-Invasive Mobility Sensor. Our main contributions are: (1) a novel multi-person tracking methodology designed for complex environments with occlusion and pose variations, and (2) an application of human-activity attributes in a clinical setting. We demonstrate NIMS on data collected from an active patient room in an adult ICU and show a high inter-rater reliability using a weighted Kappa statistic of 0.86 for automatic prediction of the highest level of patient mobility as compared to clinical experts.


title: Ordinal Regression Methods: Survey and Experimental Study

abstract: Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can be faced using standard nominal classification techniques, there are several algorithms which can specifically benefit from the ordering information. Therefore, this paper is aimed at reviewing the state of the art on these techniques and proposing a taxonomy based on how the models are constructed to take the order into account. Furthermore, a thorough experimental study is proposed to check if the use of the order information improves the performance of the models obtained, considering some of the approaches within the taxonomy. The results confirm that ordering information benefits ordinal models improving their accuracy and the closeness of the predictions to actual targets in the ordinal scale.


title: Fully automatic face normalization and single sample face recognition in unconstrained environments

abstract: We present a fully automatic face normalization and recognition system.It normalizes the face images for both in-plane and out-of-plane pose variations.The performance of AAM fitting is improved using a novel initialization technique.HOG and Gabor features are fused using CCA to have more discriminative features.The proposed system recognizes non-frontal faces using only a single gallery sample. Single sample face recognition have become an important problem because of the limitations on the availability of gallery images. In many real-world applications such as passport or driver license identification, there is only a single facial image per subject available. The variations between the single gallery face image and the probe face images, captured in unconstrained environments, make the single sample face recognition even more difficult. In this paper, we present a fully automatic face recognition system robust to most common face variations in unconstrained environments. Our proposed system is capable of recognizing faces from non-frontal views and under different illumination conditions using only a single gallery sample for each subject. It normalizes the face images for both in-plane and out-of-plane pose variations using an enhanced technique based on active appearance models (AAMs). We improve the performance of AAM fitting, not only by training it with in-the-wild images and using a powerful optimization technique, but also by initializing the AAM with estimates of the locations of the facial landmarks obtained by a method based on flexible mixture of parts. The proposed initialization technique results in significant improvement of AAM fitting to non-frontal poses and makes the normalization process robust, fast and reliable. Owing to the proper alignment of the face images, made possible by this approach, we can use local feature descriptors, such as Histograms of Oriented Gradients (HOG), for matching. The use of HOG features makes the system robust against illumination variations. In order to improve the discriminating information content of the feature vectors, we also extract Gabor features from the normalized face images and fuse them with HOG features using Canonical Correlation Analysis (CCA). Experimental results performed on various databases outperform the state-of-the-art methods and show the effectiveness of our proposed method in normalization and recognition of face images obtained in unconstrained environments.


title: Network Partition and Voltage Coordination Control for Distribution Networks With High Penetration of Distributed PV Units

abstract: In order to solve the voltage violation problem caused by high penetration of photovoltaic (PV) units in distribution power networks, this paper proposes a double-layer voltage control strategy based on the distribution network partition. By optimizing the active and reactive power outputs of PV units, the minimization objective of PV active power curtailment and network active power loss can be achieved. In the basis of community detection algorithm, a novel cluster performance index based on the electrical distance and regional voltage regulation capability is presented to partition a distribution network into several clusters. The proposed double-layer voltage control strategy combines the cluster autonomous optimization and distributed intercluster coordination optimization in different time scales. The cluster autonomous optimization can eliminate intracluster voltage violation rapidly by alternately updating the intracluster optimal solution and the virtual slack bus voltage. The distributed intercluster coordination optimization with a long-time scale is based on the alternating direction method of multipliers and realizes global optimal control through finite boundary data exchange among adjacent clusters. The effectiveness and feasibility of the proposed method have been demonstrated via simulation tests on a real 10.5 kV feeder in China and the IEEE 123-bus system.