title: FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos

abstract: State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e.g., 60Mpixels/s for HD video). This paper introduces FAST (Free Adaptive Super-resolution via Transfer), a framework to accelerate any SR algorithm applied to compressed videos. FAST exploits the temporal correlation between adjacent frames such that SR is only applied to a subset of frames; SR pixels are then transferred to the other frames. The transferring process has negligible computation cost as it uses information already embedded in the compressed video (e.g., motion vectors and residual). Adaptive processing is used to retain accuracy when the temporal correlation is not present (e.g., occlusions). FAST accelerates state-of-the-art SR algorithms by up to 15x with a visual quality loss of 0.2dB. FAST is an important step towards real-time SR algorithms for ultra-HD displays and energy constrained devices (e.g., phones and tablets).


title: Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

abstract: We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.


title: Zero-Shot Object Detection

abstract: We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training. We work with a challenging set of object classes, not restricting ourselves to similar and/or fine-grained categories cf. prior works on zero-shot classification. We follow a principled approach by first adapting visual-semantic embeddings for ZSD. We then discuss the problems associated with selecting a background class and motivate two background-aware approaches for learning robust detectors. One of these models uses a fixed background class and the other is based on iterative latent assignments. We also outline the challenge associated with using a limited number of training classes and propose a solution based on dense sampling of the semantic label space using auxiliary data with a large number of categories. We propose novel splits of two standard detection datasets - MSCOCO and VisualGenome and discuss extensive empirical results to highlight the benefits of the proposed methods. We provide useful insights into the algorithm and conclude by posing some open questions to encourage further research.


title: The HMM-based speech synthesis system (HTS) version 2.0.

abstract: A statistical parametric speech synthesis system based on hidden Markov models (HMMs) has grown in popularity over the last few years. This system simultaneously models spectrum, excitation, and duration of speech using context-dependent HMMs and generates speech waveforms from the HMMs themselves. Since December 2002, we have publicly released an open-source software toolkit named HMM-based speech synthesis system (HTS) to provide a research and development platform for the speech synthesis community. In December 2006, HTS version 2.0 was released. This version includes a number of new features which are useful for both speech synthesis researchers and developers. This paper describes HTS version 2.0 in detail, as well as future release plans.


title: Broadband Circularly Polarized Microstrip Antenna with Coplanar Parasitic Ring Slot Patch for L-Band Satellite System Application

abstract: A novel capacitive-coupled probe-fed circularly polarized (CP) microstrip antenna with coplanar parasitic ring slot patch has been proposed for the International Maritime Satellite (INMARSAT) and the Global Positioning System (GPS) applications. The key feature of the design is employing capacitive-coupled four-probe feeds to increase impedance bandwidth and adopting coplanar parasitic ring slot patch to enhance CP bandwidth. The feed network is compact and neat using only T-junction power dividers and phase shifters with shunted short-circuited microstrip stubs for lightning protection. The characteristics of the proposed antenna have been studied by simulation and experiment. The final antenna can produce a ${ S}_{11} 14 dB impedance bandwidth of nearly 27%, a 3-dB axial-ratio bandwidth of nearly 16%, and a gain of higher than 8 dB in the whole maritime satellite communication work band.


title: Marketing strategy : taxonomy and frameworks

abstract: Purpose – The purpose of this paper is to present taxonomy of marketing strategy concepts and integrative frameworks that differentiate and integrate its formulation and implementation processes.Design/methodology/approach – The paper is conceptual based on a review of academic literature on marketing strategy chronicled in major marketing journals January 1990‐April 2006. We present selected references classified by key marketing strategy topics for further pursuit by interested readers. Also, the paper reflects our experience and views based on practices chronicled in corporate case studies and trade journals.Findings – The literature casts marketing strategy formulation and implementation in the context of strategic planning and marketing strategy process models. The focus of the strategic planning model is on achieving corporate financial objectives through the implementation of product, pricing, promotion, and place (distribution) programs. The focus of the marketing strategy process model is on the ...


title: Continuous Semantic Topic Embedding Model Using Variational Autoencoder

abstract: This paper proposes the continuous semantic topic embedding model (CSTEM) which finds latent topic variables in documents using continuous semantic distance function between the topics and the words by means of the variational autoencoder(VAE). The semantic distance could be represented by any symmetric bell-shaped geometric distance function on the Euclidean space, for which the Mahalanobis distance is used in this paper. In order for the semantic distance to perform more properly, we newly introduce an additional model parameter for each word to take out the global factor from this distance indicating how likely it occurs regardless of its topic. It certainly improves the problem that the Gaussian distribution which is used in previous topic model with continuous word embedding could not explain the semantic relation correctly and helps to obtain the higher topic coherence. Through the experiments with the dataset of 20 Newsgroup, NIPS papers and CNN/Dailymail corpus, the performance of the recent state-of-the-art models is accomplished by our model as well as generating topic embedding vectors which makes possible to observe where the topic vectors are embedded with the word vectors in the real Euclidean space and how the topics are related each other semantically.


title: Scientific software production: incentives and collaboration

abstract: Software plays an increasingly critical role in science, including data analysis, simulations, and managing workflows. Unlike other technologies supporting science, software can be copied and distributed at essentially no cost, potentially opening the door to unprecedented levels of sharing and collaborative innovation. Yet we do not have a clear picture of how software development for science fits into the day-to-day practice of science, or how well the methods and incentives of its production facilitate realization of this potential. We report the results of a multiple-case study of software development in three fields: high energy physics, structural biology, and microbiology. In each case, we identify a typical publication, and use qualitative methods to explore the production of the software used in the science represented by the publication. We identify several different production systems, characterized primarily by differences in incentive structures. We identify ways in which incentives are matched and mismatched with the needs of the science fields, especially with respect to collaboration.


title: Enhancing LambdaMART Using Oblivious Trees

abstract: Learning to rank is a machine learning technique broadly used in many areas such as document retrieval, collaborative filtering or question answering. We present experimental results which suggest that the performance of the current state-of-the-art learning to rank algorithm LambdaMART, when used for document retrieval for search engines, can be improved if standard regression trees are replaced by oblivious trees. This paper provides a comparison of both variants and our results demonstrate that the use of oblivious trees can improve the performance by more than $2.2\%$. Additional experimental analysis of the influence of a number of features and of a size of the training set is also provided and confirms the desirability of properties of oblivious decision trees.


title: Image Forgery Localization via Integrating Tampering Possibility Maps

abstract: Over the past decade, many efforts have been made in passive image forensics. Although it is able to detect tampered images at high accuracies based on some carefully designed mechanisms, localization of the tampered regions in a fake image still presents many challenges, especially when the type of tampering operation is unknown. Some researchers have realized that it is necessary to integrate different forensic approaches in order to obtain better localization performance. However, several important issues have not been comprehensively studied, for example, how to select and improve/readjust proper forensic approaches, and how to fuse the detection results of different forensic approaches to obtain good localization results. In this paper, we propose a framework to improve the performance of forgery localization via integrating tampering possibility maps. In the proposed framework, we first select and improve two existing forensic approaches, i.e., statistical feature-based detector and copy-move forgery detector, and then adjust their results to obtain tampering possibility maps. After investigating the properties of possibility maps and comparing various fusion schemes, we finally propose a simple yet very effective strategy to integrate the tampering possibility maps to obtain the final localization results. The extensive experiments show that the two improved approaches used in our framework significantly outperform the state-of-the-art techniques, and the proposed fusion results achieve the best $\mathrm {F_{1}}$ -score in the IEEE IFS-TC Image Forensics Challenge.


title: Experimental Evaluation of Mutation Testing Approaches to Python Programs

abstract: Mutation testing of Python programs raises a problem of incompetent mutants. Incompetent mutants cause execution errors due to inconsistency of types that cannot be resolved before run-time. We present a practical approach in which incompetent mutants can be generated, but the solution is transparent for a user and incompetent mutants are detected by a mutation system during test execution. Experiments with 20 traditional and object-oriented operators confirmed that the overhead can be accepted. The paper presents an experimental evaluation of the first- and higher-order mutation. Four algorithms to the 2nd and 3rd order mutant generation were applied. The impact of code coverage consideration on the process efficiency is discussed. The experiments were supported by the MutPy system for mutation testing of Python programs.


title: Dual-Band Implantable Antenna With Open-End Slots on Ground

abstract: We present a dual-band implantable planar inverted-F antenna. Besides the resonance at 2.45-GHz Industrial, Scientific, and Medical (ISM) band, three closely spaced resonant frequencies are created at the lower band by adding open-end slots in the ground plane, resulting in a broad bandwidth covering Medical Implant Communication Services (MICS) band at 402 MHz and ISM band at 433 MHz. The measured frequencies range from 356 to 610 MHz (52.6%) at the lower band and from 2.42 to 2.53 GHz (4.4%) at the upper band for |S11| less than - 10 dB. The antenna occupies a compact size of 487.8 mm 3 (19 mm × 19.4 mm × 1.27 mm).


title: Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study

abstract: Circular economy (CE) is a term that exists since the 1970s and has acquired greater importance in the past few years, partly due to the scarcity of natural resources available in the environment and changes in consumer behavior. Cutting-edge technologies such as big data and internet of things (IoT) have the potential to leverage the adoption of CE concepts by organizations and society, becoming more present in our daily lives. Therefore, it is fundamentally important for researchers interested in this subject to understand the status quo of studies being undertaken worldwide and to have the overall picture of it. We conducted a bibliometric literature review from the Scopus Database over the period of 2006---2015 focusing on the application of big data/IoT on the context of CE. This produced the combination of 30,557 CE documents with 32,550 unique big data/IoT studies resulting in 70 matching publications that went through content and social network analysis with the use of `R' statistical tool. We then compared it to some current industry initiatives. Bibliometrics findings indicate China and USA are the most interested countries in the area and reveal a context with significant opportunities for research. In addition, large producers of greenhouse gas emissions, such as Brazil and Russia, still lack studies in the area. Also, a disconnection between important industry initiatives and scientific research seems to exist. The results can be useful for institutions and researchers worldwide to understand potential research gaps and to focus future investments/studies in the field.


title: The SocialTrust framework for trusted social information management : Architecture and algorithms

abstract: Social information systems are a promising new paradigm for large-scale distributed information management, as evidenced by the success of large-scale information sharing communities, social media sites, and web-based social networks. But the increasing reliance on these social systems also places individuals and their computer systems at risk, creating opportunities for malicious participants to exploit the tight social fabric of these networks. With these problems in mind, this manuscript presents the SocialTrust framework for enabling trusted social information management in Internet-scale social information systems. Concretely, we study online social networks, consider a number of vulnerabilities inherent in online social networks, and introduce the SocialTrust framework for supporting tamper-resilient trust establishment. We study three key factors for trust establishment in online social networks - trust group feedback, distinguishing the user's relationship quality from trust, and tracking user behavior - and describe a principled approach for assessing each component. In addition to the SocialTrust framework, which provides a network-wide perspective on the trust of all users, we describe a personalized extension called mySocialTrust, which provides a user-centric trust perspective that can be optimized for individual users within the network. Finally, we experimentally evaluate the SocialTrust framework using real online social networking data consisting of millions of MySpace profiles and relationships. While other trust aggregation approaches have been developed and implemented by others, we note that it is rare to find such a large-scale experimental evaluation that carefully considers the important factors impacting the trust framework. We find that SocialTrust supports robust trust establishment even in the presence of large-scale collusion by malicious participants.


title: Cognitive and emotional influences in anterior cingulate cortex

abstract: Anterior cingulate cortex (ACC) is a part of the brain’s limbic system. Classically, this region has been related to affect, on the basis of lesion studies in humans and in animals. In the late 1980s, neuroimaging research indicated that ACC was active in many studies of cognition. The findings from EEG studies of a focal area of negativity in scalp electrodes following an error response led to the idea that ACC might be the brain’s error detection and correction device. In this article, these various findings are reviewed in relation to the idea that ACC is a part of a circuit involved in a form of attention that serves to regulate both cognitive and emotional processing. Neuroimaging studies showing that separate areas of ACC are involved in cognition and emotion are discussed and related to results showing that the error negativity is influenced by affect and motivation. In addition, the development of the emotional and cognitive roles of ACC are discussed, and how the success of this regulation in controlling responses might be correlated with cingulate size. Finally, some theories are considered about how the different subdivisions of ACC might interact with other cortical structures as a part of the circuits involved in the regulation of mental and emotional activity.


title: Data Mining of the US FDA’s Adverse Events Reporting System Database to Evaluate Drug–Drug Interactions Associated with Statin-Induced Rhabdomyolysis

abstract: Background and Objective ::: With the rise in polypharmacy, it is increasingly important to identify drug–drug interactions (DDIs) that cause serious adverse events in a timely manner. The purpose of the study was to investigate the utility of systematic data mining of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database for interactions between statins (HMG-CoA reductase inhibitors) and other drugs that underlie a higher risk for rhabdomyolysis.


title: GFR prediction using the MDRD and Cockcroft and Gault equations in patients with end-stage renal disease

abstract: Background. Although prediction equations are recommended to determine GFR and creatinine clearance (CrCl), neither the MDRD equations nor the Cockcroft and Gault formula have been validated for the low levels of GFR present in end-stage renal disease (ESRD). The accuracy of the MDRD equations and the Cockcroft and Gault formula in predicting GFR and CrCl, respectively, was examined in patients with ESRD and its relationship to the basal GFR and two markers of malnutrition, urinary creatinine and body fat determined. Methods. Inulin clearance (C in ) was measured in 26 non-diabetic patients with ESRD and the 24 h CrCl determined. GFR was predicted using three equations derived from the MDRD study population containing four to six variables. Both CrCl and GFR were predicted from the Cockcroft and Gault formula. Estimates of bias and precision were obtained and Bland and Altman analysis performed. Body fat was measured by DEXA scan. Results. The predicted GFR (MDRD) was 10% lower than C in (8.83±0.71ml/min/1.73m2) with all three MDRD equations, showing a similar degree of precision and bias. C in gave a negative correlation with the difference between the predicted GFR (MDRD) and the measured GFR. The predicted GFR (MDRD) underestimated GFR when C in >8 ml/min/1.73 m but overestimated GFR when C in <8ml/min/1.73m 2 . The Cockcroft and Gault formula overestimated CrCl by 14% and overestimated C in by 35%. C in gave a negative correlation with the difference between the predicted GFR (Cockcroft and Gault) and measured GFR, overestimating GFR when C in <13 ml/min/ 1.73m 2 . The overestimation of GFR by the MDRD equation was not associated with urinary creatinine excretion. However, both Cockcroft and Gault and the MDRD predictions showed a positive, but weak, correlation with body fat. Conclusion. The MDRD equations were more accurate in predicting the group mean GFR in patients with ESRD than the Cockcroft and Gault formula. However, the predicted GFR using either formula was related to the basal GFR and percentage body fat.


title: Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum

abstract: The efforts to increasing the quantity and quality of rice production are obstructed by the paddy disease. This research attempted to identify the four major paddy diseases in Indonesia (leaf blast, brown spot, bacterial leaf blight, and tungro) using fractal descriptors to analyze the texture of the lesions. The lesion images were extracted manually. The descriptors of ‘S’ component of each lesion images then used in classification process using probabilistic neural networks. This techniques achieved at least 83.00% accuracy when identifying the diseases. This method has a potential to be used as one of the feature if it combined with other features, especially when two diseases with relatively same color involved.


title: Optimizing the decomposition of time series using evolutionary algorithms: soil moisture analytics

abstract: Soil moisture plays a crucial part in earth science, with impact on agriculture, ecology, hydrology, landslides, and water resources. Extremes in soil moisture, which we denote as peaks and valleys, caused by heavy rainfalls and subsequent dry weather, are very important when predicting future soil moisture or even landslides. Existing methods, like moving averages, have limitations when it comes to smoothing time series data while preserving peaks and valleys. In this work, we propose a novel method, HyperSTL, for extrema-preserving smoothing of soil moisture time series. The method optimizes an existing time series decomposition technique, Seasonal Decomposition of Time Series by Loess (STL). HyperSTL optimizes STL's control parameters, which we call hyperparameters, using an objective function over the decomposed components. We demonstrate in experiments with nine soil moisture datasets that using HyperSTL generally results in improved predictions compared to using other smoothing methods.


title: The Cretaceous-Tertiary boundary in a sector of the south limb of the Miranda-Treviño synclinal: the first appearance of chelonia and archosauria in the Basque country

abstract: Abstract This paper presents a study of the Cretaceous-Tertiary boundary in the Miranda-Trevino Synclinorium (Basque-Cantabric basin) where two main sequences have been recognised; the lower is regressive and theupper one has a marked transgressive trend. They are separated by a low angled un-conformity which is indicative of the Maastrichtian-Danian boundary. The lower sequence (S1) indicates a clear transition between the shoreface and backshore sub-environments. Bone accumulations are located in the sandy sediments of the upper foreshore and backshore, associated with ferrugenous hardgrounds and gypsum efflorescences. The upper sequence (S2) indicates the beginning of a marine transgression. It is composed a dominantly carbonate-rich sediments and the fauna suggests a deposition in environmental conditions ranging from stenohaline, to littoral, and to a coastal lagoon and inner carbonaterich platform. In the lower sequence (Maastrichtian Unit S 1 U 3 ), the following herpetofauna have been identified; an eusuchian alligator, and two dinosaurs (an ornithopod and a sauropod). Several plates of chelonia have been identified as? Adocus sp.


title: +44.0.20.7950.4402 (Europe).Income, Health, and Well-Being Around the World: Evidence From the Gallup World Poll

abstract: During 2006, the Gallup Organization conducted a World Poll that used an identical questionnaire for national samples of adults from 132 countries. I analyze the data on life satisfaction (happiness) and on health satisfaction and look at their relationships with national income, age, and life-expectancy. Average happiness is strongly related to per capita national income; each doubling of income is associated with a near one point increase in life satisfaction on a scale from 0 to 10. Unlike most previous findings, the effect holds across the range of international incomes; if anything, it is slightly stronger among rich countries. Conditional on national income, recent economic growth makes people unhappier, improvements in life-expectancy make them happier, but life-expectancy itself has little effect. Age has an internationally inconsistent relationship with happiness. National income moderates the effects of aging on self-reported health, and the decline in health satisfaction and rise in disability with age are much stronger in poor countries than in rich countries. In line with earlier findings, people in much of Eastern Europe and in the countries of the former Soviet Union are particularly unhappy and particularly dissatisfied with their health, and older people in those countries are much less satisfied with their lives and with their health than are younger people. HIV prevalence in Africa has little effect on Africans' life or health satisfaction; the fraction of Kenyans who are satisfied with their personal health is the same as the fraction of Britons and higher than the fraction of Americans. The US ranks 81st out of 115 countries in the fraction of people who have confidence in their healthcare system, and has a lower score than countries such as India, Iran, Malawi, or Sierra Leone. While the strong relationship between life-satisfaction and income gives some credence to the measures, as do the low levels of life and health satisfaction in Eastern Europe and the countries of the former Soviet Union, the lack of correlations between life and health satisfaction and health measures shows that happiness (or self-reported health) measures cannot be regarded as useful summary indicators of human welfare in international comparisons.


title: WHERE DOES TAM RESIDE IN THE BRAIN? THE NEURAL MECHANISMS UNDERLYING TECHNOLOGY ADOPTION

abstract: Toward materializing the recently identified potential of cognitive neuroscience for IS research (Dimoka, Pavlou and Davis 2007), this paper demonstrates how functional neuroimaging tools can enhance our understanding of IS theories. Specifically, this study aims to uncover the neural mechanisms that underlie technology adoption by identifying the brain areas activated when users interact with websites that differ on their level of usefulness and ease of use. Besides localizing the neural correlates of the TAM constructs, this study helps understand their nature and dimensionality, as well as uncover hidden processes associated with intentions to use a system. The study also identifies certain technological antecedents of the TAM constructs, and shows that the brain activations associated with perceived usefulness and perceived ease of use predict selfreported intentions to use a system. The paper concludes by discussing the study’s implications for underscoring the potential of functional neuroimaging for IS research and the TAM literature.


title: Controller Area Network (CAN) schedulability analysis: Refuted, revisited and revised

abstract: Controller Area Network (CAN) is used extensively in automotive applications, with in excess of 400 million CAN enabled microcontrollers manufactured each year. In 1994 schedulability analysis was developed for CAN, showing how worst-case response times of CAN messages could be calculated and hence guarantees provided that message response times would not exceed their deadlines. This seminal research has been cited in over 200 subsequent papers and transferred to industry in the form of commercial CAN schedulability analysis tools. These tools have been used by a large number of major automotive manufacturers in the design of in-vehicle networks for a wide range of cars, millions of which have been manufactured during the last decade. ::: ::: This paper shows that the original schedulability analysis given for CAN messages is flawed. It may provide guarantees for messages that will in fact miss their deadlines in the worst-case. This paper provides revised analysis resolving the problems with the original approach. Further, it highlights that the priority assignment policy, previously claimed to be optimal for CAN, is not in fact optimal and cites a method of obtaining an optimal priority ordering that is applicable to CAN. The paper discusses the possible impact on commercial CAN systems designed and developed using flawed schedulability analysis and makes recommendations for the revision of CAN schedulability analysis tools.


title: Super-Bit Locality-Sensitive Hashing

abstract: Sign-random-projection locality-sensitive hashing (SRP-LSH) is a probabilistic dimension reduction method which provides an unbiased estimate of angular similarity, yet suffers from the large variance of its estimation. In this work, we propose the Super-Bit locality-sensitive hashing (SBLSH). It is easy to implement, which orthogonalizes the random projection vectors in batches, and it is theoretically guaranteed that SBLSH also provides an unbiased estimate of angular similarity, yet with a smaller variance when the angle to estimate is within (0, π/2]. The extensive experiments on real data well validate that given the same length of binary code, SBLSH may achieve significant mean squared error reduction in estimating pairwise angular similarity. Moreover, SBLSH shows the superiority over SRP-LSH in approximate nearest neighbor (ANN) retrieval experiments.


title: Selfadjoint Operators and Generalized Central Algorithms in Frechet Spaces

abstract: The paper gives an extension of the fundamental principles of selfadjoint operators in Frechet-Hilbert spaces, countable-Hilbert and nu- clear Frechet spaces. Generalizations of the well known theorems of von Neumann, Hellinger-Toeplitz, Friedrichs and Ritz are obtained. Definitions of generalized central and generalized spline algorithms are given. The re- striction A 1 of a selfadjoint operator A defined on a dense set D(A) of the Hilbert space H to the Frechet space D(A 1 ) is substantiated. The extended Ritz method is used for obtaining an approximate solution of the equation A 1 u = f in the Frechet space D(A 1 ). It is proved that approximate so- lutions of this equation constructed by the extended Ritz method do not depend on the number of norms that generate the topology of the space D(A 1 ). Hence this approximate method is both a generalized central and generalized spline algorithm. Examples of selfadjoint and positive definite elliptic dierential operators satisfying the above conditions are given. The validity of theoretical results in the case of a harmonic oscillator operator is confirmed by numerical cal- culations.


title: Achieving 5As in Cloud Centric Cognitive Cellular Networks

abstract: The growing density of cellular users is placing an unprecedented demand on radio spectrum. Cognitive cellular networks can alleviate spectrum demand through dynamic spectrum access. In order to be fully functional, cellular users not only need spectrum access but also require network access. However, a cellular carrier grants network access only to accredited users. In this paper, we focus on the problem of opportunistic network access for unaccredited users by spontaneous provisioning in the context of cognitive cellular networks. We achieve this for both single and group users by extending the traditional 3A security framework - authentication, authorization, and accounting - to a 5A security paradigm. This entails the addition of network access and user assertion. We design several simulations based on a proof-of-principle prototype to validate our approach against multiple traffic models. The results show our approach can deliver at least 13% improvement in user provisioning time compared to conventional schemes.


title: Cutting the electric bill for internet-scale systems

abstract: Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this paper, we characterize the variation due to fluctuating electricity prices and argue that existing distributed systems should be able to exploit this variation for significant economic gains. Electricity prices exhibit both temporal and geographic variation, due to regional demand differences, transmission inefficiencies, and generation diversity. Starting with historical electricity prices, for twenty nine locations in the US, and network traffic data collected on Akamai's CDN, we use simulation to quantify the possible economic gains for a realistic workload. Our results imply that existing systems may be able to save millions of dollars a year in electricity costs, by being cognizant of locational computation cost differences.


title: Tool-use: An open window into body representation and its plasticity

abstract: Over the last decades, scientists have questioned the origin of the exquisite human mastery of tools. Seminal studies in monkeys, healthy participants and brain-damaged patients have primarily focused on the plastic changes that tool-use induces on spatial representations. More recently, we focused on the modifications tool-use must exert on the sensorimotor system and highlighted plastic changes at the level of the body representation used by the brain to control our movements, i.e., the Body Schema. Evidence is emerging for tool-use to affect also more visually and conceptually based representations of the body, such as the Body Image. Here we offer a critical review of the way different tool-use paradigms have been, and should be, used to try disentangling the critical features that are responsible for tool incorporation into different body representations. We will conclude that tool-use may offer a very valuable means to investigate high-order body representations and their plasticity.


title: Deep Hashing for Scalable Image Search

abstract: In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods, which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the non-linear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) and multi-label SDH by including a discriminative term into the objective function of DH, which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes with the single-label and multi-label settings, respectively. Extensive experimental results on eight widely used image search data sets show that our proposed methods achieve very competitive results with the state-of-the-arts.


title: Review Conversational Reading Comprehension

abstract: Inspired by conversational reading comprehension (CRC), this paper studies a novel task of leveraging reviews as a source to build an agent that can answer multi-turn questions from potential consumers of online businesses. We first build a review CRC dataset and then propose a novel task-aware pre-tuning step running between language model (e.g., BERT) pre-training and domain-specific fine-tuning. The proposed pre-tuning requires no data annotation, but can greatly enhance the performance on our end task. Experimental results show that the proposed approach is highly effective and has competitive performance as the supervised approach. The dataset is available at \url{https://github.com/howardhsu/RCRC}


title: Reversible GANs for Memory-Efficient Image-To-Image Translation

abstract: The Pix2pix and CycleGAN losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks. We extend this framework by exploring approximately invertible architectures which are well suited to these losses. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep. We are able to demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.


title: Image processing techniques for insect shape detection in field crops

abstract: In agriculture, crop pest detection is considered as one of the challenging tasks for the farmers. An automatic insect detection system using machine vision and image analysis provides better identification of crop insects on early stage with reduced time and greater accuracy which helps farmers to increase the crop yield. In the present work, digital image processing techniques were applied for crop insects images to perform preprocessing, segmentation and feature extraction to detect the shape of insects in the sugarcane crop. Sobel edge detection is applied to segment the insect image against background. In feature extraction, shape of the insect can be recognized by nine geometric shape features. This insect shape identification method performs well and achieves high accuracy for sugarcane crop insects with round (circle), oval, triangle and rectangle shapes. The present work was implemented in MATLAB 2015b using Image Processing Toolbox.


title: FreeDetector: Device-Free Occupancy Detection with Commodity WiFi

abstract: Occupancy detection is playing a critical role to improve the efficiency of building management system and optimize personalized thermal comfort, among many other emerging applications. Conventional occupancy detection methods, such as Passive Infra-Red (PIR) and camera, have several drawbacks including low accuracy, high intrusiveness and extra infrastructure. In this work, we propose FreeDetector, a device-free occupancy detection scheme that is able to detect human presence accurately just using existing commodity WiFi routers. We upgrade the firmware of the routers so that the channel state information (CSI) data in PHY layer can be obtained directly from them. With only two routers, FreeDetector is able to reveal the variations in CSI data caused by human presence. We leverage signal tendency index (STI) to analyze the shape similarity of adjacent time series CSI curves. The most representative subset of subcarriers is selected by greedy algorithm and we utilize machine learning algorithm to construct a detection classifier. Extensive experiments are conducted and the results demonstrate that FreeDetector is able to provide outstanding occupancy detection service in terms of both accuracy and efficiency.


title: Deep Learning for Confidence Information in Stereo and ToF Data Fusion

abstract: This paper proposes a novel framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and a stereo vision system. The key problem of balancing between the two sources of information is solved by extracting confidence maps for both sources using deep learning. We introduce a novel synthetic dataset accurately representing the data acquired by the proposed setup and use it to train a Convolutional Neural Network architecture. The machine learning framework estimates the reliability of both data sources at each pixel location. The two depth fields are finally fused enforcing the local consistency of depth data taking into account the confidence information. Experimental results show that the proposed approach increases the accuracy of the depth estimation.


title: Comparing context-aware recommender systems in terms of accuracy and diversity

abstract: Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable "best bet" when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.


title: Data aggregation and roadside unit placement for a vanet traffic information system

abstract: In this paper we investigate how a VANET-based traffic information system can overcome the two key problems of strictly limited bandwidth and minimal initial deployment. First, we present a domain specific aggregation scheme in order to minimize the required overall bandwidth. Then we propose a genetic algorithm which is able to identify good positions for static roadside units in order to cope with the highly partitioned nature of a VANET in an early deployment stage. A tailored toolchain allows to optimize the placement with respect to an application-centric objective function, based on travel time savings. By means of simulation we assess the performance of the resulting traffic information system and the optimization strategy.


Processability and development of syntax and agreement in the interlanguage of learners of Arabic as a foreign language


title: Interdisciplinary research issues in music information retrieval: ISMIR 2000–2002

abstract: Music Information Retrieval (MIR) is an interdisciplinary research area that has grown out of the need to manage burgeoning collections of music in digital form. Its diverse disciplinary communities, exemplified by the recently established ISMIR conference series, have yet to articulate a common research agenda or agree on methodological principles and metrics of success. In order for MIR to succeed, researchers need to work with real user communities and develop research resources such as reference music collections, so that the wide variety of techniques being developed in MIR can be meaningfully compared with one another. Out of these efforts, a common MIR practice can emerge.


title: Impact of increased penetration of photovoltaic generation on power systems

abstract: Present renewable portfolio standards are changing power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. With the increase in penetration of PV resources, power systems are expected to experience a change in dynamic and operational characteristics. This paper studies the impact of increased penetration of PV systems on static performance as well as transient stability of a large power system, in particular the transmission system. Utility scale and residential rooftop PVs are added to the aforementioned system to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The studied system is a large test system representing a portion of the Western U.S. interconnection. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance.


title: A PFC Modified Landsman Converter-Based PWM-Dimmable RGB HB-LED Driver for Large Area Projection Applications

abstract: In this paper for power factor correction (PFC), a modified Landsman converter is introduced. The application is targeted for high-brightness (HB) projection application with brightness control for HB red–green–blue (HB-RGB) light-emitting diode (LED). A pulse-width modulation (PWM) technique is used for intensity control to achieve brightness control of the LED driver. The proposed PFC converter is used to feed a dual output flyback dc–dc converter that supplies power to the forced cooling unit and the LED module with galvanic isolation. The brightness control of LED is performed by synchronous buck converter. The modified Landsman converter operates in discontinuous output inductor current mode for PFC at universal ac mains. A prototype of the proposed LED driver is experimentally verified. The performance of proposed driver is evaluated at full and light-load condition for universal ac mains (90–265 V) over a full range of brightness. The power quality parameters are found within the acceptable limits of harmonic standard IEC 61000-3-2 for universal ac mains.


title: The Intestinal Microbiome in Early Life: Health and Disease

abstract: Human microbial colonization begins at birth and continues to develop and modulate in species abundance for about three years, until the microbiota becomes adult-like. During the same time period, children experience significant developmental changes that influence their current health status as well as their immune system. An ever-expanding number of articles associate several diseases with early life imbalances of the gut microbiota, also referred to as gut microbial dysbiosis. Whether early life dysbiosis precedes and plays a role in disease pathogenesis, or simply originates from the disease process itself is a question that is beginning to be answered in a few diseases, including IBD, obesity and asthma. This review describes the gut microbiome structure and function during the formative first years of life, as well as the environmental factors that determine its composition. It also aims to discuss the recent advances in understanding the role of the early life gut microbiota in the development of immune-mediated, metabolic, and neurological diseases. A greater understanding of how the early life gut microbiota impacts our immune development could potentially lead to novel microbial-derived therapies that target disease prevention at an early age.


title: Community detection in large‐scale networks: a survey and empirical evaluation

abstract: Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. In this review, we evaluated eight state-of-the-art and five traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth communities. These 13 algorithms were empirically compared using goodness metrics that measure the structural properties of the identified communities, as well as performance metrics that evaluate these communities against the ground-truth. Our results show that these two types of metrics are not equivalent. That is, an algorithm may perform well in terms of goodness metrics, but poorly in terms of performance metrics, or vice versa. © 2014 The Authors. WIREs Computational


title: Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach

abstract: Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic diseases, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients’ medical history, recent and more distant, as described in their Electronic Health Records (EHRs). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVMs), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: $K$ -LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large data sets from the Boston Medical Center, the largest safety-net hospital system in New England.


title: A Constrained Coalition Formation Game for Multihop D2D Content Uploading

abstract: This paper investigates relay-based schemes in cellular systems, where multihop device-to-device (D2D) communications are exploited for content uploading toward the eNodeB. All user equipments (UEs) are sources of their own content and form a multihop D2D chain, with the head of the chain being in charge of uploading all the generated content to the eNodeB. By pooling the cellular radio resources assigned to the D2D chain and by using high-quality short-range radio links, the proposed cooperative content uploading scheme guarantees lower upload delays than in the traditional cellular mode operation. To model the D2D chain formation in a cell and to best characterize self-interested users concerned about their own payoff, a constrained coalition formation game is defined, where each UE is a player whose cost is identified as the content upload time. The solution of the game determines the stable feasible partition for the UEs in the cell. We demonstrate through simulations that with this solution the content uploading time is reduced by 52% with respect to the traditional cellular mode.


title: Optogenetic stimulation of a hippocampal engram activates fear memory recall

abstract: The activation of a population of hippocampal neurons thought to encode a specific fear memory is shown to elicit freezing behaviour in mice. Several studies have used ablation strategies to demonstrate that certain neuronal populations in the brain are needed for memory expression, but whether a particular ensemble is sufficient to elicit a behavioural outcome from a particular memory has remained unexplored. Now, Susumu Tonegawa and colleagues use optogenetics to demonstrate that a particular, targeted memory 'engram', or group of cells, that was active during fear-learning is sufficient to drive freezing behaviour in mice during subsequent reactivations. A specific memory is thought to be encoded by a sparse population of neurons1,2. These neurons can be tagged during learning for subsequent identification3 and manipulation4,5,6. Moreover, their ablation or inactivation results in reduced memory expression, suggesting their necessity in mnemonic processes. However, the question of sufficiency remains: it is unclear whether it is possible to elicit the behavioural output of a specific memory by directly activating a population of neurons that was active during learning. Here we show in mice that optogenetic reactivation of hippocampal neurons activated during fear conditioning is sufficient to induce freezing behaviour. We labelled a population of hippocampal dentate gyrus neurons activated during fear learning with channelrhodopsin-2 (ChR2)7,8 and later optically reactivated these neurons in a different context. The mice showed increased freezing only upon light stimulation, indicating light-induced fear memory recall. This freezing was not detected in non-fear-conditioned mice expressing ChR2 in a similar proportion of cells, nor in fear-conditioned mice with cells labelled by enhanced yellow fluorescent protein instead of ChR2. Finally, activation of cells labelled in a context not associated with fear did not evoke freezing in mice that were previously fear conditioned in a different context, suggesting that light-induced fear memory recall is context specific. Together, our findings indicate that activating a sparse but specific ensemble of hippocampal neurons that contribute to a memory engram is sufficient for the recall of that memory. Moreover, our experimental approach offers a general method of mapping cellular populations bearing memory engrams.


title: Automatic Locating of Anthropometric Landmarks on 3D Human Models

abstract: We present an algorithm for automatic locating of anthropometric landmarks on 3D human scans. Our method is based on learning landmark characteristics and the spatial relationships between them from a set of human scans where the landmarks are identified. The learned information is formulated by a pairwise Markov network. Each node of the network is a random variable corresponding to the position of a landmark. The edges of the network represent correlations between the positions of landmark pairs. Probabilistic inference is then performed over the Markov network to locate the landmarks. We evaluated the algorithm on 30 human models with different shapes. The results showed good accuracy for most of the landmarks.


MeTA: A Unified Toolkit for Text Retrieval and Analysis


title: An audio-visual corpus for speech perception and automatic speech recognition

abstract: An audio-visual corpus has been collected to support the use of common material in speech perception and automatic speech recognition studies. The corpus consists of high-quality audio and video recordings of 1000 sentences spoken by each of 34 talkers. Sentences are simple, syntactically identical phrases such as “place green at B 4 now.” Intelligibility tests using the audio signals suggest that the material is easily identifiable in quiet and low levels of stationary noise. The annotated corpus is available on the web for research use.


title: Enhancing the Tourism Experience through Mobile Augmented Reality: Challenges and Prospects

abstract: This paper discusses the use of Augmented Reality (AR) applications for the needs of tourism. It describes the technology's evolution from pilot applications into commercial mobile applications. We address the technical aspects of mobile AR application development, emphasizing the technologies that render the delivery of augmented reality content possible and experientially superior. We examine the state of the art, providing an analysis concerning the development and the objectives of each application. Acknowledging the various technological limitations hindering AR's substantial end-user adoption, the paper proposes a model for developing AR mobile applications for the field of tourism, aiming to release AR's full potential within the field.


title: Boa: Ultra-Large-Scale Software Repository and Source-Code Mining

abstract: In today's software-centric world, ultra-large-scale software repositories, such as SourceForge, GitHub, and Google Code, are the new library of Alexandria. They contain an enormous corpus of software and related information. Scientists and engineers alike are interested in analyzing this wealth of information. However, systematic extraction and analysis of relevant data from these repositories for testing hypotheses is hard, and best left for mining software repository (MSR) expertse Specifically, mining source code yields significant insights into software development artifacts and processes. Unfortunately, mining source code at a large scale remains a difficult task. Previous approaches had to either limit the scope of the projects studied, limit the scope of the mining task to be more coarse grained, or sacrifice studying the history of the code. In this article we address mining source code: (a) at a very large scale; (b) at a fine-grained level of detail; and (c) with full history information. To address these challenges, we present domain-specific language features for source-code mining in our language and infrastructure called Boa. The goal of Boa is to ease testing MSR-related hypotheses. Our evaluation demonstrates that Boa substantially reduces programming efforts, thus lowering the barrier to entry. We also show drastic improvements in scalability.


title: Immune disease and HLA associations with myasthenia gravis.

abstract: In the late 1950's laboratory and clinical evidence suggested that myasthenia gravis was an autoimmune disorder. Since then a voluminous literature has developed documenting the many immunological abnormalities that occur in this condition. Recent findings point to a central disorder of immunoregulation. It is postulated that the disease occurs as a result of host genetic and environmental influences-the latter being, as yet unidentified and possibly a virus.


title: Precision open-ended coaxial probes for in vivo and ex vivo dielectric spectroscopy of biological tissues at microwave frequencies

abstract: Hermetic stainless-steel open-ended coaxial probes have been designed for precision dielectric spectroscopy of biological tissue, such as breast tissue, over the 0.5-20-GHz frequency range. Robust data-processing techniques have also been developed for extracting the unknown permittivity of the tissue under test from the reflection coefficient measured with the precision probe and a vector network analyzer. The first technique, referred to as a reflection-coefficient deembedding method, converts the reflection coefficient measured at the probe's calibration plane to the desired aperture-plane reflection coefficient. The second technique uses a rational function model to solve the inverse problem, i.e., to convert the aperture-plane reflection coefficient to the tissue permittivity. The results of the characterization and validation studies demonstrate that these precision probes, used with the prescribed measurement protocols and data-processing techniques, provide highly accurate and reliable in vivo and ex vivo biological tissue measurements, including breast tissue spectroscopy.


title: Robust Fault Isolation of Uncertain Linear Systems: An LMI Approach

abstract: Abstract This contribution deals with the design of fault isolation observer for linear systems in the presence of both model uncertainty and disturbances. The FDI problem is investigated with the aid of frequency domain and LMI techniques. Based on the theory of polytopic observers, an optimization problem under LMI constraints is derived, which allows one to incorporate the plant polytopic uncertainty into the optimal robust observer design. In order to increase the robustness of residual evaluation, a frequency domain residual index is introduced and adaptive fault thresholds are derived. A simulation example illustrates the proposed approach.


title: A hierarchical, HMM-based automatic evaluation of OCR accuracy for a digital library of books

abstract: A number of projects are creating searchable digital libraries of printed books. These include the Million Book Project, the Google Book project and similar efforts from Yahoo and Microsoft. Content-based on line book retrieval usually requires first converting printed text into machine readable (e.g. ASCII) text using an optical character recognition (OCR) engine and then doing full text search on the results. Many of these books are old and there are a variety of processing steps that are required to create an end to end system. Changing any step (including the scanning process) can affect OCR performance and hence a good automatic statistical evaluation of OCR performance on book length material is needed. Evaluating OCR performance on the entire book is non-trivial. The only easily obtainable ground truth (the Gutenberg e-texts) must be automatically aligned with the OCR output over the entire length of a book. This may be viewed as equivalent to the problem of aligning two large (easily a million long) sequences. The problem is further complicated by OCR errors as well as the possibility of large chunks of missing material in one of the sequences. We propose a Hidden Markov Model (HMM) based hierarchical alignment algorithm to align OCR output and the ground truth for books. We believe this is the first work to automatically align a whole book without using any book structure information. The alignment process works by breaking up the problem of aligning two long sequences into the problem of aligning many smaller subsequences. This can be rapidly and effectively done. Experimental results show that our hierarchical alignment approach works very well even if OCR output has a high recognition error rate. Finally, we evaluate the performance of a commercial OCR engine over a large dataset of books based on the alignment results.


title: Why we twitter: understanding microblogging usage and communities

abstract: Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.


title: Fine-grained Concept Linking using Neural Networks in Healthcare

abstract: To unlock the wealth of the healthcare data, we often need to link the real-world text snippets to the referred medical concepts described by the canonical descriptions. However, existing healthcare concept linking methods, such as dictionary-based and simple machine learning methods, are not effective due to the word discrepancy between the text snippet and the canonical concept description, and the overlapping concept meaning among the fine-grained concepts. To address these challenges, we propose a Neural Concept Linking (NCL) approach for accurate concept linking using systematically integrated neural networks. We call the novel neural network architecture as the COMposite AttentIonal encode-Decode neural network (COM-AID). COM-AID performs an encode-decode process that encodes a concept into a vector and decodes the vector into a text snippet with the help of two devised contexts. On the one hand, it injects the textual context into the neural network through the attention mechanism, so that the word discrepancy can be overcome from the semantic perspective. On the other hand, it incorporates the structural context into the neural network through the attention mechanism, so that minor concept meaning differences can be enlarged and effectively differentiated. Empirical studies on two real-world datasets confirm that the NCL produces accurate concept linking results and significantly outperforms state-of-the-art techniques.


title: Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques

abstract: Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99.62%, and 90.74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.


title: Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test

abstract: The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.


title: Leveraging Uncertainty to Rethink Loss Functions and Evaluation Measures for Egocentric Action Anticipation

abstract: Current action anticipation approaches often neglect the intrinsic uncertainty of future predictions when loss functions or evaluation measures are designed. The uncertainty of future observations is especially relevant in the context of egocentric visual data, which is naturally exposed to a great deal of variability. Considering the problem of egocentric action anticipation, we investigate how loss functions and evaluation measures can be designed to explicitly take into account the natural multi-modality of future events. In particular, we discuss suitable measures to evaluate egocentric action anticipation and study how loss functions can be defined to incorporate the uncertainty arising from the prediction of future events. Experiments performed on the EPIC-KITCHENS dataset show that the proposed loss function allows improving the results of both egocentric action anticipation and recognition methods.


title: The 2018 NVIDIA AI City Challenge

abstract: The NVIDIA AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these videos to provide actionable insights. These insights will benefit a wide variety of agencies, from traffic control to public safety. The second edition of the NVIDIA AI City Challenge, being organized as a CVPR workshop, provided a forum to more than 70 academic and industrial research teams to compete and solve real-world problems using traffic camera video data. The Challenge was launched with three tracks — speed estimation, anomaly detection, and vehicle re-identification. Each track was chosen in consultation with traffic and public safety officials based on the value of potential solutions. With the largest available dataset for such tasks, and ground truth for each track, the Challenge enabled 22 teams to evaluate their solutions. Given how complex these tasks are, the results are encouraging and reflect increased value addition year over year for the Challenge.


title: Antifungal efficacy of Capsicum frutescens L. extracts against some prevalent fungal strains associated with groundnut storage.

abstract: The antifungal potential of aqueous leaf and fruit extracts of Capsicum frutescens against four ::: major fungal strains associated with groundnut storage was evaluated. These seed-borne fungi, ::: namely Aspergillus flavus, A. niger, Penicillium sp. and Rhizopus sp. were isolated by standard ::: agar plate method and identified by macroscopic and microscopic features. The minimum ::: inhibitory concentrations (MIC) and minimum fungicidal concentration (MFC) of C. frutescens ::: extracts were determined. MIC values of the fruit extract were lower compared to the leaf ::: extract. At MIC, leaf extract showed strong activity against A. flavus (88.06%), while fruit ::: extract against A. niger (88.33%) in the well diffusion method. Groundnut seeds treated with ::: C.frutescens fruit extract (10mg/ml) showed a higher rate of fungal inhibition. The present ::: results suggest that groundnuts treated with C. frutescens fruit extracts are capable of ::: preventing fungal infection to a certain extent.


title: Critical nursing and health care aide behaviors in care of the nursing home resident dying with dementia

abstract: With the aging of the population, dying with dementia will become one of the most common ways in which older adults will end their final years of life, particularly for those living in a nursing home. Though individuals living with dementia have complex care needs and would benefit from a palliative approach to care, they have traditionally not been recipients of such care. An important aspect of determining quality in end-of-life care is the identification of expert practices, processes or behaviors that may help achieve this care. However, for those living with dementia in nursing homes, we have a limited understanding of how to best support expert end of life care. To redress this gap in knowledge, the purpose of this study was to examine and describe expert care of the individual with dementia approaching death from the perspective of nurses and health care aides (HCAs) identified by their peers as having special expertise in caring for this population. A qualitative research design known as Interpretative Description was used to conduct the study. Expert nurses and HCAs were identified through a two-phase nomination process. Individual semi-structured interviews were conducted with consenting participants. Data were analyzed using constant comparative analysis to determine the key critical behaviors. Analysis of data collected from expert nurses (n = 8) and HCAs (n = 7) revealed six critical behaviors when caring for residents dying with dementia. All nurses and HCAs unanimously endorsed that the overarching goal of care is similar for all residents who are actively dying; to achieve comfort. The six critical behaviors in caring for residents dying with dementia included: 1) recognizing and responding to changes in a resident’s pattern of behavior; 2) attending to the person; 3) working with the family; 4) engaging with others; 5) responding after the death has occurred; and 6) having a positive attitude toward care of the dying. The critical behaviors described by nurses and HCAs in this study provides emerging evidence of best practices in care of those with dementia and their families, particularly near the end of life within the nursing home setting.


title: Extracellular matrix-based biomaterial scaffolds and the host response

abstract: Abstract Extracellular matrix (ECM) collectively represents a class of naturally derived proteinaceous biomaterials purified from harvested organs and tissues with increasing scientific focus and utility in tissue engineering and repair. This interest stems predominantly from the largely unproven concept that processed ECM biomaterials as natural tissue-derived matrices better integrate with host tissue than purely synthetic biomaterials. Nearly every tissue type has been decellularized and processed for re-use as tissue-derived ECM protein implants and scaffolds. To date, however, little consensus exists for defining ECM compositions or sources that best constitute decellularized biomaterials that might better heal, integrate with host tissues and avoid the foreign body response (FBR). Metrics used to assess ECM performance in biomaterial implants are arbitrary and contextually specific by convention. Few comparisons for in vivo host responses to ECM implants from different sources are published. This review discusses current ECM-derived biomaterials characterization methods including relationships between ECM material compositions from different sources, properties and host tissue response as implants. Relevant preclinical in vivo models are compared along with their associated advantages and limitations, and the current state of various metrics used to define material integration and biocompatibility are discussed. Commonly applied applications of these ECM-derived biomaterials as stand-alone implanted matrices and devices are compared with respect to host tissue responses.


Feasibility and acceptability of a universal screening and referral protocol for gender-based violence with women seeking care in health clinics in Dadaab refugee camps in Kenya


title: An Auto-exposure algorithm for detecting high contrast lighting conditions

abstract: The proposed algorithm provides fast and accurate auto-exposure capability for digital still cameras. For normal lighting conditions, the number of preview frames and the exposure error are within 3.5 frames and 3.92%. For high contrast lighting conditions, the number of preview frames and the exposure error are within 8.8 frames and 6.56%. Furthermore, it offers accurate detection for both back lit and excessive front lit conditions at the same time and make proper exposure to the main object.


title: Motion Graph for Character Animation: Design Considerations

abstract: Animating human character has become an active research area in computer graphics. It is really important for development of virtual environment applications such as computer games and virtual reality. One of the popular methods to animate the character is by using motion graph. Since motion graph is the main focus of this research, we investigate the preliminary work of motion graph and discuss about the main components of motion graph like distance metrics and motion transition. These two components will be taken into consideration during the process of development of motion graph. In this paper, we will also present a general framework and future plan of this study.


title: Automated Irrigation System-IoT Based Approach

abstract: Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.


title: On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results

abstract: We study in this paper the rate of convergence for learning distributions with the Generative Adversarial Networks (GAN) framework, which subsumes Wasserstein, Sobolev and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions, under a collection of objective evaluation metrics. On the nonparametric end, we investigate the minimax optimal rates and fundamental difficulty of the density estimation under the adversarial framework. On the parametric end, we establish theory for neural network classes, that characterizes the interplay between the choice of generator and discriminator. We investigate how to improve the GAN framework with better theoretical guarantee through the lens of regularization. We discover and isolate a new notion of regularization, called the \textit{generator/discriminator pair regularization}, that sheds light on the advantage of GAN compared to classic parametric and nonparametric approaches for density estimation.


title: Vulvar and Vaginal Atrophy: Physiology, Clinical Presentation, and Treatment Considerations

abstract: Vulvovaginal atrophy is a common condition associated with decreased estrogenization of the vaginal tissue. Symptoms include vaginal dryness, irritation, itching, soreness, burning, dyspareunia, discharge, urinary frequency, and urgency. It can occur at any time in a woman's life cycle, although more commonly in the postmenopausal phase, during which the prevalence is approximately 50%. Despite the high prevalence and the substantial effect on quality of life, vulvovaginal atrophy often remains underreported and undertreated. This article aims to review the physiology, clinical presentation, assessment, and current recommendations for treatment, including aspects of effectiveness and safety of local vaginal estrogen therapies.


title: A Survey of Reinforcement Learning in Relational Domains

abstract: Reinforcement learning has developed into a primary approach for learning control strategies for autonomous agents. However, most of the work has focused on the algorithmic aspect, i.e. various ways of computing value functions and policies. Usually the representational aspects were limited to the use of attribute-value or propositional languages to describe states, actions etc. A recent direction - under the general name of relational reinforcement learning - is concerned with upgrading the representation of reinforcement learning methods to the first-order case, being able to speak, reason and learn about objects and relations between objects. This survey aims at presenting an introduction to this new field, starting from the classical reinforcement learning framework. We will describe the main motivations and challenges, and give a comprehensive survey of methods that have been proposed in the literature. The aim is to give a complete survey of the available literature, of the underlying motivations and of the implications if the new methods for learning in large, relational and probabilistic environments.


title: Tensor-based approach to accelerate deformable part models

abstract: This article provides next step towards solving speed bottleneck of any system that intensively uses convolutions operations (e.g. CNN). Method described in the article is applied on deformable part models (DPM) algorithm. Method described here is based on multidimensional tensors and provides efficient tradeoff between DPM performance and accuracy. Experiments on various databases, including Pascal VOC, show that the proposed method allows decreasing a number of convolutions up to 4.5 times compared with DPM v.5, while maintaining similar accuracy. If insignificant accuracy degradation is allowable, higher computational gain can be achieved. The method consists of filters tensor decomposition and convolutions shortening using the decomposed filter. Mathematical overview of the proposed method as well as simulation results are provided.


title: Associations of serum levels of advanced glycation end products with nutrition markers and anemia in patients with chronic kidney disease.

abstract: Background: Advanced glycation end product (AGE) levels are elevated in patients with decreased renal function and may contribute to the excessive cardiovascular disease in this population. However, their relation to nutrition, anemia, and micro inflammation is not well characterized. The aim of this study is to determine their relationship in patients with chronic kidney disease (CKD). Methods: The studied group consisted of 203 subjects: 159 patients with CKD 1–5 and 44 healthy control subjects. AGE levels were assessed by spectrofluorimetry, and routine biochemical parameters were measured using standard methods. Results: AGE levels were significantly increased in CKD patients compared with controls (3.9 ± 1.7 × 105 AU in CKD 1–5 patients vs. 3.2 ± 0.48 × 105 AU in controls, p < 0.0001). AGE levels increased from CKD 3. AGE levels were positively associated with age, albumin, prealbumin, and orosomucoid, and were negatively associated with hemoglobin and estimated glomerular filtration rate. In multipl...


BEATnIk: an algorithm to Automatic generation of educational description of movies


title: Can perceived risks affect the relationship of switching costs and customer loyalty in e‐commerce?

abstract: Purpose – The purpose of this paper is to verify the relationship between switching costs and customer loyalty in e‐commerce.Design/methodology/approach – The study conducted an empirical research. A total of 425 online shopping customers were invited from northeastern USA as samples.Findings – The findings show that switching costs positively influence customer loyalty. In addition, perceived risks will affect the relationship of switching costs and customer loyalty. For customers with low perceived risks, switching costs are also positively associated with customer loyalty. However, for customers with high perceived risks, the relationship of switching costs and customer loyalty is weak or negative.Research limitations/implications – One limitation is that mostly students were selected in the sample. The insights of this study can further validate the previous studies about the relationship of switching costs and customer loyalty, and suggest that perceived risks can be a moderating factor affecting thi...


title: An FMCW radar sensor for human gesture recognition in the presence of multiple targets

abstract: Hand gesture recognition is an emerging application of portable radar sensors. Most of existing works rely on Doppler-radar sensors for non-contact detection of hand motions. However, Doppler-radar-based hand gesture recognition utilizes only the Doppler effect to extract motion features, which imposes a stringent requirement that only one motion is in the field of view of the sensor, leading to limitations in practical applications. In this paper, we investigate the feasibility of using an FMCW radar sensor with range-Doppler processing to recognize human gestures when multiple moving subjects are present in the sensor's field of view. A custom-designed portable 5.8-GHz FMCW radar is used. By using range-Doppler processing in the range-Doppler domain, experimental results enable to verify the effectiveness of the proposed radar sensor in hand gesture identification with the existence of interferences from surrounding moving targets.


title: A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

abstract: Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.


title: A real time, wearable ECG and blood pressure monitoring system

abstract: In this paper, a wearable medical device for Electrocardiogram (ECG) and blood pressure monitoring is presented. Its basis is a validated and certified existent medical device. This device was evolved in order to allow new physiological signal monitoring and real time assessment of blood pressure levels by adding a photoplethismography (PPG) probe and the incorporation of new algorithms in an online interface. The device presents accuracies of 94.6% for systolic blood pressure and 92.3% for systolic blood pressure when compared with reference values obtained from a commercial electronic Sphygmomanometer.


title: Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WSNs during Wormhole Attack

abstract: Wireless sensor networks are networks having non wired infrastructure and dynamic topology. In OSI model each layer is prone to various attacks, which halts the performance of a network .In this paper several attacks on four layers of OSI model are discussed and security mechanism is described to prevent attack in network layer i.e wormhole attack. In Wormhole attack two or more malicious nodes makes a covert channel which attracts the traffic towards itself by depicting a low latency link and then start dropping and replaying packets in the multi-path route. This paper proposes promiscuous mode method to detect and isolate the malicious node during wormhole attack by using Ad-hoc on demand distance vector routing protocol (AODV) with omnidirectional antenna. The methodology implemented notifies that the nodes which are not participating in multi-path routing generates an alarm message during delay and then detects and isolate the malicious node from network. We also notice that not only the same kind of attacks but also the same kind of countermeasures can appear in multiple layer. For example, misbehavior detection techniques can be applied to almost all the layers we discussed.


title: Australia's National Bowel Cancer Screening Program: does it work for Indigenous Australians?

abstract: BACKGROUND ::: Despite a lower incidence of bowel cancer overall, Indigenous Australians are more likely to be diagnosed at an advanced stage when prognosis is poor. Bowel cancer screening is an effective means of reducing incidence and mortality from bowel cancer through early identification and prompt treatment. In 2006, Australia began rolling out a population-based National Bowel Cancer Screening Program (NBCSP) using the Faecal Occult Blood Test. Initial evaluation of the program revealed substantial disparities in bowel cancer screening uptake with Indigenous Australians significantly less likely to participate in screening than the non-Indigenous population.This paper critically reviews characteristics of the program which may contribute to the discrepancy in screening uptake, and includes an analysis of organisational, structural, and socio-cultural barriers that play a part in the poorer participation of Indigenous and other disadvantaged and minority groups. ::: ::: ::: METHODS ::: A search was undertaken of peer-reviewed journal articles, government reports, and other grey literature using electronic databases and citation snowballing. Articles were critically evaluated for relevance to themes that addressed the research questions. ::: ::: ::: RESULTS ::: The NBCSP is not reaching many Indigenous Australians in the target group, with factors contributing to sub-optimal participation including how participants are selected, the way the screening kit is distributed, the nature of the test and comprehensiveness of its contents, cultural perceptions of cancer and prevailing low levels of knowledge and awareness of bowel cancer and the importance of screening. ::: ::: ::: CONCLUSIONS ::: Our findings suggest that the population-based approach to implementing bowel cancer screening to the Australian population unintentionally excludes vulnerable minorities, particularly Indigenous and other culturally and linguistically diverse groups. This potentially contributes to exacerbating the already widening disparities in cancer outcomes that exist among Indigenous Australians. Modifications to the program are recommended to facilitate access and participation by Indigenous and other minority populations. Further research is also needed to understand the needs and social and cultural sensitivities of these groups around cancer screening and inform alternative approaches to bowel cancer screening.


Author Profiling with Bidirectional RNNs using Attention with GRUs


title: A DCA ALGORITHM FOR INDOOR PERSONAL COMMUNICATIONS

abstract: This paper proposes the implementation of a combined DCA and call admissions control policy on indoor picocellular base stations to provide increased capacity whilst maintaining a ‘fair’ QoS (Quality of Service) for users on each base station.


title: A Deep Neural Model Of Emotion Appraisal

abstract: Emotional concepts play a huge role in our daily life since they take part into many cognitive processes: from the perception of the environment around us to different learning processes and natural communication. Social robots need to communicate with humans, which increased also the popularity of affective embodied models that adopt different emotional concepts in many everyday tasks. However, there is still a gap between the development of these solutions and the integration and development of a complex emotion appraisal system, which is much necessary for true social robots. In this paper, we propose a deep neural model which is designed in the light of different aspects of developmental learning of emotional concepts to provide an integrated solution for internal and external emotion appraisal. We evaluate the performance of the proposed model with different challenging corpora and compare it with state-of-the-art models for external emotion appraisal. To extend the evaluation of the proposed model, we designed and collected a novel dataset based on a Human-Robot Interaction (HRI) scenario. We deployed the model in an iCub robot and evaluated the capability of the robot to learn and describe the affective behavior of different persons based on observation. The performed experiments demonstrate that the proposed model is competitive with the state of the art in describing emotion behavior in general. In addition, it is able to generate internal emotional concepts that evolve through time: it continuously forms and updates the formed emotional concepts, which is a step towards creating an emotional appraisal model grounded in the robot experiences.


title: A Soft Switching Full Bridge Converter With Reduced Parasitic Oscillation in a Wide Load Range

abstract: A phase-shift zero voltage switching (ZVS) pulse width modulation full-bridge converter with reduced parasitic oscillation across the rectifier is proposed. The introduced auxiliary transformer used as a voltage source forces the primary current commutating in advance. Parasitic voltage oscillation at secondary is well reduced and there is no duty cycle loss. ZVS of primary switches can be achieved in much wide load range with the help of an auxiliary coupled inductor, and the main transformer continuously transfers energy to load in a whole switching cycle, resulting in no circulating time, and ripple current flowing through the output filter inductor decreases significantly. In this paper, operation principle of the circuit is analyzed in detail. Besides, discussion of key characteristics and design considerations are also included. Finally, experimental results from a 480-W/48-V, 100-kHz prototype are presented to confirm the superior features of the proposed converter.


title: Analog CMOS-based Resistive Processing Unit for Deep Neural Network Training

abstract: Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations running on CPU/GPU. However, currently available device candidates based on non-volatile memory technologies do not satisfy all the requirements to realize the RPU concept. Here, we propose an analog CMOS-based RPU design (CMOS RPU) which can store and process data locally and can be operated in a massively parallel manner. We analyze various properties of the CMOS RPU to evaluate the functionality and feasibility for acceleration of DNN training.


title: Intelligent Digital Learning: Agent-Based Recommender System

abstract: In the context of intelligent digital learning, we propose an agent-based recommender system that aims to help learners overcome their gaps by suggesting relevant learning resources. The main idea is to provide them with appropriate support in order to make their learning experience more effective. To this end we design an agent-based cooperative system where autonomous agents are able to update recommendation data and to improve the recommender outcome on behalf of their past experiences in the learning platform.


title: Predicting stock market index using fusion of machine learning techniques

abstract: Two stage fusion model comprising three machine learning techniques is used.Emphasis is on adequacy of information given to prediction models.First stage provides future value of statistical parameters helping the later stage.Two stage fusion model helps in decreasing overall prediction error. The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10years of historical data of these two indices. The predictions are made for 1-10, 15 and 30days in advance. The paper proposes two stage fusion approach involving Support Vector Regression (SVR) in the first stage. The second stage of the fusion approach uses Artificial Neural Network (ANN), Random Forest (RF) and SVR resulting into SVR-ANN, SVR-RF and SVR-SVR fusion prediction models. The prediction performance of these hybrid models is compared with the single stage scenarios where ANN, RF and SVR are used single-handedly. Ten technical indicators are selected as the inputs to each of the prediction models.


title: TOSSIM: accurate and scalable simulation of entire TinyOS applications

abstract: Accurate and scalable simulation has historically been a key enabling factor for systems research. We present TOSSIM, a simulator for TinyOS wireless sensor networks. By exploiting the sensor network domain and TinyOS's design, TOSSIM can capture network behavior at a high fidelity while scaling to thousands of nodes. By using a probabilistic bit error model for the network, TOSSIM remains simple and efficient, but expressive enough to capture a wide range of network interactions. Using TOSSIM, we have discovered several bugs in TinyOS, ranging from network bit-level MAC interactions to queue overflows in an ad-hoc routing protocol. Through these and other evaluations, we show that detailed, scalable sensor network simulation is possible.


title: Vehicular Multi-Access Edge Computing With Licensed Sub-6 GHz, IEEE 802.11p and mmWave

abstract: With the rapid increase of vehicular Internet of things applications, it is urgent to design a mobile edge computing architecture, which is possible to distribute and process a large amount of contents with vehicles on the road. From a communication perspective, the current cellular technology faces challenges due to the limited bandwidth in a dense vehicle environment. In this paper, we propose a multi-access edge computing framework and the corresponding communication protocol which integrates licensed Sub-6 GHz band, IEEE 802.11p, and millimeter wave (mmWave) communications for the content distribution and processing in vehicular networks. The proposed protocol uses a cluster-based approach, where a fuzzy logic-based algorithm is employed to select efficient gateway nodes which bridge the licensed Sub-6 GHz communication and the mmWave communication in order to maximize the overall network throughput. IEEE 802.11p vehicle-to-vehicle communication is used to share information among vehicles in order to achieve efficient clustering. We conduct extensive simulations to evaluate the performance of the proposed protocol under various network conditions. Simulation results show that the proposed protocol can achieve significant improvements in various scenarios compared with the existing approaches.