title: Temporal Relation Classification in Persian and English contexts

abstract: This paper introduces the first pattern-based Persian Temporal Relation Classifier (PTRC) that finds the type of temporal relations between pairs of events in the Persian texts. The proposed system uses support vector machines (SVMs) equipped by combinations of simple, convolution tree, and string subsequence kernels (SSK). In order to evaluate the algorithm, we have developed a Persian TimeBank (PTB) corpus. PTRC not only increases the performance of the classification by applying new features and SSK, but also alleviates the probable adverse effects of the Free Word Orderness (FWO) of Persian on temporal relation classification. We have also applied our proposed algorithm to two standard corpora on English (i.e., TimeBank and TempEval-2) to measure the efficiency of the new features and SSK. The experiments show the accuracies of 65.6%, 59.53%, 50.2%, and 62.17% on an augmented version of PTB, TimeBank, tasks E and F of TempEval-2, respectively. Consequently, we have achieved the third best result on TimeBank, and the second best result on the task F of TempEval-2.


Hyperlink Analysis : Techniques and Applications


title: Efficient Algorithms for Discovering Association Rules

abstract: Association rules are statements of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B". Agrawal, Imielinski, and Swami introduced the problem of mining association rules from large collections of data, and gave a method based on successive passes over the database. We give an improved algorithm for the problem. The method is based on careful combinatorial analysis of the information obtained in previous passes; this makes it possible to eliminate unnecessary candidate rules. Experiments on a university course enrollment database indicate that the method outperforms the previous one by a factor of 5. We also show that sampling is in general a very efficient way of finding such rules.


title: Predicting personality with social media

abstract: Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the results of predicting personality traits through machine learning. We then discuss the implications this has for social media design, interface design, and broader domains.


title: Multistage Gene Normalization and SVM-Based Ranking for Protein Interactor Extraction in Full-Text Articles

abstract: The interactor normalization task (INT) is to identify genes that play the interactor role in protein-protein interactions (PPIs), to map these genes to unique IDs, and to rank them according to their normalized confidence. INT has two subtasks: gene normalization (GN) and interactor ranking. The main difficulties of INT GN are identifying genes across species and using full papers instead of abstracts. To tackle these problems, we developed a multistage GN algorithm and a ranking method, which exploit information in different parts of a paper. Our system achieved a promising AUC of 0.43471. Using the multistage GN algorithm, we have been able to improve system performance (AUC) by 1.719 percent compared to a one-stage GN algorithm. Our experimental results also show that with full text, versus abstract only, INT AUC performance was 22.6 percent higher.


title: Zika Virus Associated with Microcephaly.

abstract: A widespread epidemic of Zika virus (ZIKV) infection was reported in 2015 in South and Central America and the Caribbean. A major concern associated with this infection is the apparent increased incidence of microcephaly in fetuses born to mothers infected with ZIKV. In this report, we describe the case of an expectant mother who had a febrile illness with rash at the end of the first trimester of pregnancy while she was living in Brazil. Ultrasonography performed at 29 weeks of gestation revealed microcephaly with calcifications in the fetal brain and placenta. After the mother requested termination of the pregnancy, a fetal autopsy was performed. Micrencephaly (an abnormally small brain) was observed, with almost complete agyria, hydrocephalus, and multifocal dystrophic calcifications in the cortex and subcortical white matter, with associated cortical displacement and mild focal inflammation. ZIKV was found in the fetal brain tissue on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay, with consistent findings on electron microscopy. The complete genome of ZIKV was recovered from the fetal brain.


title: Fundamental limits of DNA storage systems

abstract: Due to its longevity and enormous information density, DNA is an attractive medium for archival storage. In this work, we study the fundamental limits and tradeoffs of DNA-based storage systems under a simple model, motivated by current technological constraints on DNA synthesis and sequencing. Our model captures two key distinctive aspects of DNA storage systems: (1) the data is written onto many short DNA molecules that are stored in an unordered way and (2) the data is read by randomly sampling from this DNA pool. Under this model, we characterize the storage capacity, and show that a simple index-based coding scheme is optimal.


title: EasyLiving: Technologies for Intelligent Environments

abstract: The EasyLiving project is concerned with development of an architecture and technologies for intelligent environments which allow the dynamic aggregation of diverse I/O devices into a single coherent user experience. Components of such a system include middleware (to facilitate distributed computing), world modelling (to provide location-based context), perception (to collect information about world state), and service description (to support decomposition of device control, internal logic, and user interface). This paper describes the current research in each of these areas, highlighting some common requirements for any intelligent environment.


title: Variational Composite Autoencoders

abstract: Learning in the latent variable model is challenging in the presence of the complex data structure or the intractable latent variable. Previous variational autoencoders can be low effective due to the straightforward encoder-decoder structure. In this paper, we propose a variational composite autoencoder to sidestep this issue by amortizing on top of the hierarchical latent variable model. The experimental results confirm the advantages of our model.


title: The problematic use of Information and Communication Technologies (ICT) in adolescents by the cross sectional JOITIC study

abstract: Background ::: The emerging field of Information and Communications Technology (ICT) has brought about new interaction styles. Its excessive use may lead to addictive behaviours. ::: ::: The objective is to determine the prevalence of the problematic use of ICT such as Internet, mobile phones and video games, among adolescents enrolled in mandatory Secondary Education (ESO in Spanish) and to examine associated factors.


Crowdsourced Corpus with Entity Salience Annotations


title: A STRUCTURAL EQUATIONS MODELING OF PURCHASING DECISION THROUGH E-COMMERCE

abstract: The purpose of the study is to explore the factors influencing customer buying decision through Intern et shopping. Several factors such as information quali ty, firm’s reputation, perceived ease of payment, s ites design, benefit of online shopping, and trust that influence customer decision to purchase from e-commerce sites were analyzed. Factors such as those mention ed above, which are commonly considered influencing purhasing decision through online shopping in other countries were hypothesized to be true in the case of Indonesia. A random sample comprised of 171 Indonesian people who have been buying goods/services through e-commerce sites at least once, were collec ted via online questionnaires. To test the hypothes is, the data were examined using Structural Equations Modeling (SEM) which is basically a combination of Confirmatory Factor Analysis (CFA), and linear Regression. The results suggest that information qualit y, perceived ease of payment, benefits of online shopp ing, and trust affect online purchase decision significantly. Close attention need to be placed on these factors to increase online sales. The most significant influence comes from trust. Indonesian people still lack of trust toward online commerce, so it is very important to gain customer trust to increase s ales. E-commerce’s business owners are encouraged to develop sites that can meet the expectation of pote ntial customer, provides ease of payment system, pr ovide detailed and actual information and responsible for customer personal information and transaction reco rds. This paper outlined the key factors influencing onl ine shopping intention in Indonesia and pioneered t he building of an integrated research framework to und erstand how consumers make purchase decision toward online shopping; a relatively new way of shopping i n the country.


Exemplary Automotive Attack Scenarios : Trojan Horses for Electronic Throttle Control System ( ETC ) and Replay Attacks on the Power Window System


title: Internet On-Ramp The Sequence Manipulation Suite: JavaScript

abstract: JavaScript is an object-based scripting language that can be interpreted by most commonly used Web browsers, including Netscape® Navigator® and Internet Explorer®. In conjunction with HTML form elements, JavaScript can be used to make flexible and easy-to-use applications that can be accessed by anyone connected to the Internet (3). The Sequence Manipulation Suite (http://www.ualberta.ca/~stothard/javascript/) is a collection of freely available JavaScript applications for molecular biologists. It consists of over 30 utilities for analyzing and manipulating sequence data, including the following:


title: The patellar extensor apparatus of the knee

abstract: The patellar extensor apparatus is composed of the quadriceps muscles that converge to a central tendon that inserts on and invests the patella. It continues by the patellar tendon to act on the tibial tuberosity and thereby extends the leg at the knee. The structure can be thought of as a chain with pathology able to occur at each level. Pathological processes are generally caused by the great force experienced at each level both chronically and acutely. The forces are, however, greatly modified by the particular geometries present at each level. The various pathological processes and factors that modify them are reviewed at each level, beginning with the quadriceps musculature and ending at the terminal point of action, the tibial tuberosity.


title: Evaluating a Convolutional Neural Network on Short-Wave Infra-Red Images

abstract: Machine learning algorithms, both traditional and neuralnetwork-based, have been tested against RGB facial images for years, but these algorithms are prone to fail when illumination conditions are insufficient, for example, at night or when images are taken from long distances. Short-Wave Infra-Red (SWIR) illumination provides a much higher intensity and a much more ambient structure than visible light, which makes it better suited for face recognition in different conditions. However, current neural networks require lots of training data, which is not available in the SWIR domain. In this paper, we examine the ability of a convolutional neural network, specifically, the VGG Face network, which was trained on visible spectrum images, to work on SWIR images. Utilizing a dataset containing both RGB and SWIR images, we hypothesize that the VGG Face network will perform well both on facial images taken in RGB and SWIR wavelengths. We expect that the features extracted with VGG Face are independent of the actual wavelengths that the images were taken with. Thus, face recognition with VGG Face is possible between the RGB and SWIR domains. We find that VGG Face performs reasonable on some of the SWIR wavelengths. We can almost reach the same recognition performance when using composite images built from three SWIR wavelengths probing on RGB.


title: A Survey Report on Text Classification with Different Term Weighing Methods and Comparison between Classification Algorithms

abstract: Text classification approach gaining more importance because of the accessibility of large number of electronic documents from a variety of resource. Text categorization is the task of assigning predefined categories to documents. It is the method of finding interesting regularities in large textual, where interesting means non trivial, hidden, previously unknown and potentially useful. The goal of text mining is to enable users to extract information from textual resource and deals with operation such as retrieval, classification, clustering, data mining, natural language preprocessing and machine learning techniques together to classify different pattern. In text classification, term weighting methods design appropriate weights to the given terms to improve the text classification performance. This paper surveys of text classification, process of text classification different term weighing methods and comparisons between different classification algorithms.


title: Comparison of similarity metrics for refactoring detection

abstract: Identifying refactorings in software archives has been an active research topic in the last decade, mainly because it is a prerequisite for various software evolution analyses (e.g., error detection, capturing intent of change, capturing and replaying changes, and relating refactorings and software metrics). Many of these techniques rely on similarity measures to identify structurally equivalent code, however, up until now the effect of this similarity measure on the performance of the refactoring identification algorithm is largely unexplored. In this paper we replicate a well-known experiment from Weisgerber and Diehl, plugging in three different similarity measures (text-based, AST-based, token-based). We look at the overlap of the results obtained by the different metrics, and we compare the results using recall and the computation time. We conclude that the different result sets have a large overlap and that the three metrics perform with a comparable quality.


title: A keyword search system using open source software

abstract: Provides an overview of a speech-to-text (STT) and keyword search (KWS) system architecture build primarily on the top of the Kaldi toolkit and expands on a few highlights. The system was developed as a part of the research efforts of the Radical team while participating in the IARPA Babel program. Our aim was to develop a general system pipeline which could be easily and rapidly deployed in any language, independently on the language script and phonological and linguistic features of the language.


title: Putting Descartes before the horse: quantum theories of consciousness: comment on “Consciousness, biology, and quantum hypotheses” by Baars & Edelman

abstract: Despite rapidly advancing work across the sciences, the means by which neurochemical brain activity engenders subjective conscious experience (such as the pain of a stubbed toe, or of a lingering regret) can still seem entirely mysterious, and perhaps requiring scientific revolution rather than evolution. Quantum ‘theories’ of consciousness capitalize on this suspicion but, in the absence of generating testable predictions or unifying insights, they presently do little to advance our understanding of this fundamental biological property.


title: Mercury: A Messaging Framework for Modular UI Components

abstract: In recent years, the entity--component--system pattern has become a fundamental feature of the software architectures of game-development environments such as Unity and Unreal, which are used extensively in developing 3D user interfaces. In these systems, UI components typically respond to events, requiring programmers to write application-specific callback functions. In some cases, components are organized in a hierarchy that is used to propagate events among vertically connected components. When components need to communicate horizontally, programmers must connect those components manually and register/unregister events as needed. Moreover, events and callback signatures may be incompatible, making modular UIs cumbersome to build and share within or across applications. To address these problems, we introduce a messaging framework, Mercury, to facilitate communication among components. We provide an overview of Mercury, outline its underlying protocol and how it propagates messages to responders using relay nodes, describe a reference implementation in Unity, and present example systems built using Mercury to explain its advantages.


title: IMG2DSM: Height Simulation From Single Imagery Using Conditional Generative Adversarial Net

abstract: This letter proposes a groundbreaking approach in the remote-sensing community to simulating the digital surface model (DSM) from a single optical image. This novel technique uses conditional generative adversarial networks whose architecture is based on an encoder–decoder network with skip connections (generator) and penalizing structures at the scale of image patches (discriminator). The network is trained on scenes where both the DSM and optical data are available to establish an image-to-DSM translation rule. The trained network is then utilized to simulate elevation information on target scenes where no corresponding elevation information exists. The capability of the approach is evaluated both visually (in terms of photographic interpretation) and quantitatively (in terms of reconstruction errors and classification accuracies) on subdecimeter spatial resolution data sets captured over Vaihingen, Potsdam, and Stockholm. The results confirm the promising performance of the proposed framework.


title: The relativity of absolute judgements

abstract: Suppose that, with rather few exceptions, the assignment of a number to a stimulus on one trial of a magnitude estimation or category judgement experiment serves as the point of reference for choosing an appropriate assignment on the next trial. This principle of relative judgement—relative to the immediate context—is developed to generate models for both magnitude estimation and category judgement experiments. It is applied, in particular, to the explanation of three, hitherto unrelated, phenomena: these are (a) the limited transmission of information in category judgements; (b) sequential constraints on the resolving power of category judgements; and (c) the autocorrelation of successive numerical magnitude estimates. Finally, some comparison is made between the principle of relative judgement introduced here and other contemporary ideas which have been addressed to one or more of these three critical phenomena.


title: www.elsevier.com/locate/patrec An introduction to ROC analysis

abstract: Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.


title: Regaining Lost Cycles with HotCalls: A Fast Interface for SGX Secure Enclaves

abstract: Intel's SGX secure execution technology allows running computations on secret data using untrusted servers. While recent work showed how to port applications and large-scale computations to run under SGX, the performance implications of using the technology remains an open question. We present the first comprehensive quantitative study to evaluate the performance of SGX. We show that straightforward use of SGX library primitives for calling functions add between 8,200 - 17,000 cycles overhead, compared to 150 cycles of a typical system call. We quantify the performance impact of these library calls and show that in applications with high system calls frequency, such as memcached, openVPN, and lighttpd, which all have high bandwidth network requirements, the performance degradation may be as high as 79%. We investigate the sources of this performance degradation by leveraging a new set of microbenchmarks for SGX-specific operations such as enclave entry-calls and out-calls, and encrypted memory I/O accesses. We leverage the insights we gain from these analyses to design a new SGX interface framework HotCalls. HotCalls are based on a synchronization spin-lock mechanism and provide a 13-27x speedup over the default interface. It can easily be integrated into existing code, making it a practical solution. Compared to a baseline SGX implementation of memcached, openVPN, and lighttpd - we show that using the new interface boosts the throughput by 2.6-3.7x, and reduces application latency by 62-74%.


title: Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques

abstract: Plant disease analysis is one of the critical tasks in the field of agriculture. Automatic identification and classification of plant diseases can be supportive to agriculture yield maximization. In this paper we compare performance of several Machine Learning techniques for identifying and classifying plant disease patterns from leaf images. A three-phase framework has been implemented for this purpose. First, image segmentation is performed to identify the diseased regions. Then, features are extracted from segmented regions using standard feature extraction techniques. These features are then used for classification into disease type. Experimental results indicate that our proposed technique is significantly better than other techniques used for Plant Disease Identification and Support Vector Machines outperforms other techniques for classification of diseases.


title: Centroid-Based Document Classification: Analysis Experimental Results

abstract: In this paper we present a simple linear-time centroid-based document classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our experiments show that this centroidbased classifier consistently and substantially outperforms other algorithms such as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets. Our analysis shows that the similarity measure used by the centroid-based scheme allows it to classify a new document based on how closely its behavior matches the behavior of the documents belonging to different classes. This matching allows it to dynamically adjust for classes with different densities and accounts for dependencies between the terms in the different classes


title: Printed small-size monopole antenna for LTE/WWAN smartphone application

abstract: In this communication, we propose a original small-size multiband antenna with a coupling feed for LTE/WWAN/ HIPERLAN2/ IEEE 802.11a operation in the smart phones. The obtained impedance bandwidth of the proposed antenna (S11<;-6dB) over the operating bands can attain about 520/1545 MHz for the smart phone application, respectively. Ultimately, up to four resonances are achieved to cover the desired 698-960 /1710-2690 MHz and the HIPERLAN2/ IEEE 802.11a band. With an additional parasitic part, it is successfully achieved to cover the HIPERLAN2/IEEE 802.11a band. In general, the simulated results are consistent with the measured results. In the same time, the radiation efficiency, antenna gain and body SAR of the proposed antenna make it good enough for the practical smart phone.


title: Finding a roadmap to achieve large neuromorphic hardware systems

abstract: Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are meeting hard physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Towards this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore's law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.


title: Fast, Cheap and Out of Control: A Robot Invasion of the Solar System

abstract: Complex systems and complex missions take years of planning and force launches to become incredibly expensive. The longer the planning and the more expensive the mission, the more catastrophic if it fails. The solution has always been to plan better, add redundancy, test thoroughly and use high quality components. Based on our experience in building ground based mobile robots (legged and wheeled) we argue here for cheap, fast missions using large numbers of mass produced simple autonomous robots that are small by today's standards (1 to 2 Kg). We argue that the time between mission conception and implementation can be radically reduced, that launch mass can be slashed, that totally autonomous robots can be more reliable than ground controlled robots, and that large numbers of robots can change the tradeoff between reliability of individual components and overall mission success. Lastly, we suggest that within a few years it will be possible at modest cost to invade a planet with millions of tiny robots.


title: Using Data-Driven Discovery of Better Student Models to Improve Student Learning

abstract: Deep analysis of domain content yields novel insights and can be used to produce better courses. Aspects of such analysis can be performed by applying AI and statistical algorithms to student data collected from educational technology and better cognitive models can be discovered and empirically validated in terms of more accurate predictions of student learning. However, can such improved models yield improved student learning? This paper reports positively on progress in closing this loop. We demonstrate that a tutor unit, redesigned based on data-driven cognitive model improvements, helped students reach mastery more efficiently. In particular, it produced better learning on the problem-decomposition planning skills that were the focus of the cognitive model improvements.


title: A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

abstract: One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.


title: Emotional state detection based on EMG and EOG biosignals: A short survey

abstract: With its acquisition of muscular potentials linked to displays of affective state, facial Electromyography (EMG) is the most fitting physiological signal for performing emotional expressions detection. EMG and computer vision-based facial recognition have very different strengths and weaknesses, but their information may complement each other. Additionally, Electrooculographic (EOG) signals can enable gaze tracking and detection of eye movements, but blinks and saccades cannot directly indicate emotional state. In this paper, we survey and analyze the methods, strengths and challenges of using these biosignal sources for detecting emotional states, and then we propose a framework that combines simultaneously facial expression detection, using EMG, with saccade detection using EOGs, to classify four basic expressions: neutral, sad, happy, and angry.


title: Improving Energy Efficiency and Reducing Code Size with RISC-V Compressed

abstract: Delivering the instruction stream can be the largest source of energy consumption in a processor, yet loosely-encoded RISC instruction sets are wasteful of instruction bandwidth. Aiming to improve the performance and energy efficiency of the RISC-V ISA, this thesis proposes RISC-V Compressed (RVC), a variable-length instruction set extension. RVC is a superset of the RISC-V ISA, encoding the most frequent instructions in half the size of a RISCV instruction; the remaining functionality is still accessible with full-length instructions. RVC programs are 25% smaller than RISC-V programs, fetch 25% fewer instruction bits than RISCV programs, and incur fewer instruction cache misses. Its code size is competitive with other compressed RISCs. RVC is expected to improve the performance and energy per operation of RISC-V.


title: Doubly robust covariate shift correction

abstract: Covariate shift correction allows one to perform supervised learning even when the distribution of the covariates on the training set does not match that on the test set. This is achieved by re-weighting observations. Such a strategy removes bias, potentially at the expense of greatly increased variance. We propose a simple strategy for removing bias while retaining small variance. It uses a biased, low variance estimate as a prior and corrects the final estimate relative to the prior. We prove that this yields an efficient estimator and demonstrate good experimental performance.


title: Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes

abstract: Abstract Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things.


title: Death to the Privacy Calculus

abstract: The “privacy calculus” has been used extensively to describe how people make privacy-related decisions. At the same time, many researchers have found that such decisions are often anything but calculated. More recently, the privacy calculus has been used in service of machine learning approaches to privacy. This position paper discusses the practical and ethical questions that arise from this use of the privacy calculus.


title: China's Impact on Africa – The Role of Trade, FDI and Aid

abstract: We investigate the impact of Chinese activities in sub-Saharan African countries with respect to the growth performance of economies in that region. Using a Solow-type growth model and panel data for the period 1991 to 2011, we find that African economies that export natural resources have benefited from positive terms-of-trade effects. In addition, there is evidence for displacement effects of African firms due to competition from China. Chinese foreign investment and aid in Africa does not have an impact on growth.


title: Variation of Individualism and Collectivism within and between 20 Countries: A Typological Analysis

abstract: With data from a 20-nation study (N = 2,533), the authors investigated how individual patterns of endorsement of individualist and collectivist attitudes are distributed within and across national contexts. A cluster analysis performed on individual scores of self-reliance (individualist dimension), group-oriented interdependence (collectivist dimension), and competitiveness (individualist or collectivist dimension) yielded a typology of four constrained combinations of these dimensions. Despite the prevalence of a typology group within a given country, variability was observed in all countries. Self-reliant non-competitors and interdependent non-competitors were prevalent among participants from Western nations, whereas self-reliant competitors and interdependent competitors were more common in non-Western countries. These findings emphasize the benefits for cross-cultural research of a typological approach based on combinations of individualist and collectivist dimensions.


title: Serum Resistin and Kidney Function: A Family-Based Study in Non-Diabetic, Untreated Individuals

abstract: Background ::: High serum resistin levels have been associated with kidney dysfunction. Most of these studies have been carried out in individuals with severe kidney impairment, diabetes, cardiovascular disease and related treatments. Thus, the observed association might have been influenced by these confounders. Our aim was to study the relationship between serum resistin, urinary albumin/creatinine ratio (ACR) and glomerular filtration rate (GFR) in a family-based sample, the Gargano Family Study (GFS) of 635 non diabetic, untreated Whites. ::: ::: ::: Methods ::: A linear mixed effects model and bivariate analyses were used to evaluate the phenotypic and genetic relations between serum resistin and both ACR and eGFR. All analyses were adjusted for sex, age, age squared, BMI, systolic blood pressure, smoking habits and physical exercise. ::: ::: ::: Results ::: After adjustments, resistin levels were slightly positively associated with ACR (β±SE = 0.049±0.023, p = 0.035) and inversely related to eGFR (β±SE = −1.43±0.61, p = 0.018) levels. These associations remained significant when either eGFR or ACR were, reciprocally, added as covariates. A genetic correlation (ρg = −0.31±0.12; adjusted p = 0.013) was observed between resistin and eGFR (but not ACR) levels. ::: ::: ::: Conclusion ::: Serum resistin levels are independently associated with ACR and eGFR in untreated non-diabetic individuals. Serum resistin and eGFR share also some common genetic background. Our data strongly suggest that resistin plays a role in modulating kidney function.


title: Fast MATLAB assembly of FEM matrices in 2D and 3D: Edge elements

abstract: We propose an effective and flexible way to assemble finite element stiffness and mass matrices in MATLAB. We apply this for problems discretized by edge finite elements. Typical edge finite elements are Raviart-Thomas elements used in discretizations of H ( div ) spaces and Nedelec elements in discretizations of H ( curl ) spaces. We explain vectorization ideas and comment on a freely available MATLAB code which is fast and scalable with respect to time.


title: Fit or unfit: analysis and prediction of 'closed questions' on stack overflow

abstract: Stack Overflow is widely regarded as the most popular Community driven Question Answering (CQA) website for programmers. Questions posted on Stack Overflow which are not related to programming topics, are marked as `closed' by experienced users and community moderators. A question can be `closed' for five reasons -- duplicate, off-topic, subjective, not a real question and too localized. In this work, we present the first study of `closed' questions on Stack Overflow. We download 4 years of publicly available data which contains 3.4 Million questions. We first analyze and characterize the complete set of 0.1 Million `closed' questions. Next, we use a machine learning framework and build a predictive model to identify a `closed' question at the time of question creation. One of our key findings is that despite being marked as `closed', subjective questions contain high information value and are very popular with the users. We observe an increasing trend in the percentage of closed questions over time and find that this increase is positively correlated to the number of newly registered users. In addition, we also see a decrease in community participation to mark a `closed' question which has led to an increase in moderation job time. We also find that questions closed with the Duplicate and Off Topic labels are relatively more prone to reputation gaming. Our analysis suggests broader implications for content quality maintenance on CQA websites. For the `closed' question prediction task, we make use of multiple genres of feature sets based on - user profile, community process, textual style and question content. We use a state-of-art machine learning classifier based on an ensemble framework and achieve an overall accuracy of 70.3%. Analysis of the feature space reveals that `closed' questions are relatively less informative and descriptive than non-`closed' questions. To the best of our knowledge, this is the first experimental study to analyze and predict `closed' questions on Stack Overflow.


title: Deep learning with Elastic Averaging SGD

abstract: We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm. We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM. We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM. We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings. Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient.


title: Text Classification for Organizational Researchers

abstract: Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their ow...


title: Blame and coercion: together again for the first time

abstract: C#, Dart, Pyret, Racket, TypeScript, VB: many recent languages integrate dynamic and static types via gradual typing. We systematically develop three calculi for gradual typing and the relations between them, building on and strengthening previous work. The calculi are: λB, based on the blame calculus of Wadler and Findler (2009); λC, inspired by the coercion calculus of Henglein (1994); λS inspired by the space-efficient calculus of Herman, Tomb, and Flanagan (2006) and the threesome calculus of Siek and Wadler (2010). While λB is little changed from previous work, λC and λS are new. Together, λB, λC, and λS provide a coherent foundation for design, implementation, and optimisation of gradual types. We define translations from λB to λC and from λC to λS. Much previous work lacked proofs of correctness or had weak correctness criteria; here we demonstrate the strongest correctness criterion one could hope for, that each of the translations is fully abstract. Each of the calculi reinforces the design of the others: λC has a particularly simple definition, and the subtle definition of blame safety for λB is justified by the simple definition of blame safety for λC. Our calculus λS is implementation-ready: the first space-efficient calculus that is both straightforward to implement and easy to understand. We give two applications: first, using full abstraction from λC to λS to validate the challenging part of full abstraction between λB and λC; and, second, using full abstraction from λB to λS to easily establish the Fundamental Property of Casts, which required a custom bisimulation and six lemmas in earlier work.


title: Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology

abstract: Abstract Technology acceptance research has tended to focus on instrumental beliefs such as perceived usefulness and perceived ease of use as drivers of usage intentions, with technology characteristics as major external stimuli. Behavioral sciences and individual psychology, however, suggest that social influences and personal traits such as individual innovativeness are potentially important determinants of adoption as well, and may be a more important element in potential adopters' decisions. This paper models and tests these relationships in non-work settings among several latent constructs such as intention to adopt wireless mobile technology, social influences, and personal innovativeness. Structural equation analysis reveals strong causal relationships between the social influences, personal innovativeness and the perceptual beliefs—usefulness and ease of use, which in turn impact adoption intentions. The paper concludes with some important implications for both theory research and implementation strategies.


A Study of Barriers to E-Commerce Adoption among SMEs in Malaysia *


title: Large-scale Incremental Processing Using Distributed Transactions and Notifications

abstract: Updating an index of the web as documents are crawled requires continuously transforming a large repository of existing documents as new documents arrive. This task is one example of a class of data processing tasks that transform a large repository of data via small, independent mutations. These tasks lie in a gap between the capabilities of existing infrastructure. Databases do not meet the storage or throughput requirements of these tasks: Google's indexing system stores tens of petabytes of data and processes billions of updates per day on thousands of machines. MapReduce and other batch-processing systems cannot process small updates individually as they rely on creating large batches for efficiency. ::: ::: We have built Percolator, a system for incrementally processing updates to a large data set, and deployed it to create the Google web search index. By replacing a batch-based indexing system with an indexing system based on incremental processing using Percolator, we process the same number of documents per day, while reducing the average age of documents in Google search results by 50%.


title: Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

abstract: Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment. We exhaustively evaluate various design choices, identify performance bottlenecks, and more importantly propose multiple orthogonal ways to boost performance. (b) Based on our analysis, we propose a novel hierarchical, parallel and multi-scale residual architecture that yields large performance improvement over the standard bottleneck block while having the same number of parameters, thus bridging the gap between the original network and its binarized counterpart. (c) We perform a large number of ablation studies that shed light on the properties and the performance of the proposed block. (d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance. Code can be downloaded from https://www.adrianbulat.com/binary-cnn-landmarks


title: A Submicrowatt Implantable Capacitive Sensor System for Biomedical Applications

abstract: This brief presents a submicrowatt, offset-free, and implantable system for a biomedical capacitive sensor. The system is powered by a 13.56-MHz radio frequency signal and performs sensor signal amplification, analog-to-digital conversion, and load-shift keying uplink data transmission within 640 $\mu\hbox{s}$ . An ultralow-power capacitance-to-digital converter (CDC) is designed by replacing a power-hungry operational amplifier with a subthreshold inverter in a switched-capacitor amplifier (SC-amp) . A fast-response-gain compensation method is employed to reduce the gain error of the SC-amp while achieving high energy efficiency for the CDC. To eliminate the offset, a two-step autocalibration is applied. The application-specific integrated circuit is implemented in the Taiwan Semiconductor Manufacturing Company 90-nm complementary metal–oxide–semiconductor technology, and the whole system achieves an 8.02 effective number of bits with 9-bit linearity while consuming only 5.5 $\mu\hbox{W}$ .


title: The Role of Information Provision in Economic Evaluations of Newborn Bloodspot Screening: A Systematic Review

abstract: The extent to which economic evaluations have included the healthcare resource and outcome-related implications of information provision in national newborn bloodspot screening programmes (NBSPs) is not currently known. To identify if, and how, information provision has been incorporated into published economic evaluations of NBSPs. A systematic review of economic evaluations of NBSPs (up to November 2014) was conducted. Three electronic databases were searched (Ovid: Medline, Embase, CINAHL) using an electronic search strategy combining a published economic search filter with terms related to national NBSPs and screening-related technologies. These electronic searches were supplemented by searching the NHS Economic Evaluations Database (NHS EED) and hand-searching identified study reference lists. The results were tabulated and summarised as part of a narrative synthesis. A total of 27 economic evaluations [screening-related technologies (n = 11) and NBSPs (n = 16)] were identified. The majority of economic evaluations did not quantify the impact of information provision in terms of healthcare costs or outcomes. Five studies did include an estimate of the time cost associated with information provision. Four studies included a value to reflect the disutility associated with parental anxiety caused by false-positive results, which was used as a proxy for the impact of imperfect information. A limited evidence base currently quantifies the impact of information provision on the healthcare costs and impact on the users of NBSPs; the parents of newborns. We suggest that economic evaluations of expanded NBSPs need to take account of information provision otherwise the impact on healthcare costs and the outcomes for newborns and their parents may be underestimated.


title: Review on trajectory similarity measures

abstract: The availability of devices that can be used to track moving objects has increased dramatically leading to a great growth in movement data from almost every application domain. Therefore, there has been an increasing interest in proposing new methodologies for indexing, classifying, clustering, querying and measuring similarity between moving objects' data. One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. In this paper, we present a comparative study between widely used trajectory similarity measures observing the advantages and disadvantages of these measures.


title: A miniaturized three way power divider

abstract: A miniaturized three-way Wilkinson power divider that provides equal power split over a wide bandwidth implemented on a single-layer printed circuit board (PCB) is presented. In this work, the bent microstrip line replaces the straight microstrip line of the conventional power divider, thus, the size of the proposed power divider is 44.75 mm times 70 mm, which is much smaller than the conventional proposed power divider. Good experimental performances of the proposed power divider, including an even power split, low loss, and good isolation between the output ports are obtained. Measured results show that VSWR of the input and three output ports is less than 1.6 across the bandwidth from 0.7 to 1.1 GHz.


A Survey of Arabic Text Representation and Classification Methods


title: Perineal Nodular Induration (“Biker’s Nodule”): Report of Two Cases With Fine-Needle Aspiration Cytology and Immunohistochemical Study

abstract: Perineal nodular induration (PNI) is a fibroblastic pseudotumor that presents almost exclusively in male cyclists. It develops in the soft tissues of the perineum immediately posterior to the scrotum, as a bilateral or single, central or lateralized mass. Although well known to sport medicine specialists, it is a scarcely documented entity in the pathology literature. We present 2 cases of PNI with fine-needle aspiration cytology and immunohistochemistry. They consisted of a paucicellular fibroblastic proliferation containing CD34-reactive spindle and epithelioid cells, small foci of fibrinoid degeneration, numerous blood vessels, and entrapped groups of mature fat cells. Our cases show that the histopathological features of PNI are more varied than those previously described and its immunohistochemical profile is wider. A central cystic focus and a zonal pattern are not consistent features of this entity. The lesional cells can express CD34, a hitherto unreported immunohistochemical finding.


title: Towards Comparability of Linguistic Graph Banks for Semantic Parsing

abstract: We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource ...


title: A Language Independent Algorithm for Single and Multiple Document Summarization

abstract: This paper discusses a language independent algorithm for single and multiple document summarization.


title: A Study on the Stability of SPMT

abstract: Currently, large vessels and structures are manufactured into set of blocks, then assembled on-site. Large scale ships that weigh thousands of tons are built in a short period by making set of large blocks and assembled on a dock or a land. When a transporter encounters a slope during the process of transporting blocks, the heavy goods loaded on the transporter can be tilted. Further, if the vehicle moves down the slope in this state then it can cause an accident of overturn of loaded goods. The research has been taken into account to calculate the center of gravity of the transporter carrying heavy objects on a leveled surface or the three dimensions. In addition, ZMP (Zero Moment Point) is used to calculate the allowable slope degree that objects are predicted to overturn. Through the simulation, the objects` stability is tested when it is climbing the slope.


title: Analyzing Enterprise Architecture in National Governments: The Cases of Denmark and the Netherlands

abstract: National enterprise architectures (NEA) promise to fill the gap between policy and implementation. NEAs are embedded within an institutional environment consisting of active players capable of responding strategically and innovatively to architectural initiatives, which might complicate NEA adoption. In this paper we analyze the efforts of two European national governments in developing enterprise architecture. Grounded in institutional theory and practice we develop an analytical framework and use this framework to analyze the efforts of two countries, Denmark and the Netherlands. Our framework and analysis draws the attention to the need to take a broader perspective on enterprise architecture, especially governance aspects determine the adoption and diffusion of NEA


CHAPTER 10 Children and Digital Media


Light-weight Ontologies for Scrutable User


title: Backstepping Controller Design to Track Maximum Power in Photovoltaic Systems

abstract: This work presents a new control method to track the maximum power point of a grid-connected photovoltaic (PV) system. A backstepping controller is designed to be applied to a buck-boost DC-DC converter in order to achieve an optimal PV array output voltage. This nonlinear control is based on Lyapunov functions assuring the local stability of the system. Control reference voltages are initially estimated by a regression plane, avoiding local maximum and adjusted with a modified perturb and observe method (P&O). Thus, the maximum power extraction of the generating system is guaranteed. Finally, a DC-AC converter is controlled to supply AC current in the point of common coupling (PCC) of the electrical network. The performance of the developed system has been analyzed by means a simulation platform in Matlab/Simulink helped by SymPowerSystem Blockset. Results testify the validity of the designed control method.


title: Double-Sided SIW Leaky-Wave Antenna With Increased Directivity in the $E$ -Plane

abstract: The control of the radiation properties of a double-sided leaky-wave antenna (LWA) is demonstrated in this communication. To this aim, a standard substrate integrated waveguide (SIW) transmission line working with the TE20 mode is used. Due to the asymmetry of the radiated fields at both sides of the SIW, the equivalent magnetic currents generated are added in-phase at the center of the antenna, thus contributing to the radiation and improving the directivity in the $E$ -plane. Moreover, the complex propagation constant of the SIW LWA is properly controlled by means of the separation $P$ between posts, which mainly determines the leakage rate, and by the width $W$ of SIW, which mainly determines the pointing angle. In order to demonstrate this control, three prototypes radiating at different pointing angles (10°, 30°, and 50°) and same beamwidth (10°) have been manufactured and measured at the design frequency of 15 GHz.


title: Linear Spectral Clustering Superpixel

abstract: In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts-based formulation of image segmentation is adopted using a distance metric that measures both the color similarity and the space proximity between image pixels. However, rather than directly using the traditional eigen-based algorithm, we approximate the similarity metric through a deliberately designed kernel function such that pixel values can be explicitly mapped to a high-dimensional feature space. We then apply the conclusion that by appropriately weighting each point in this feature space, the objective functions of the weighted K-means and the normalized cuts share the same optimum points. Consequently, it is possible to optimize the cost function of the normalized cuts by iteratively applying simple K-means clustering in the proposed feature space. LSC possesses linear computational complexity and high memory efficiency, since it avoids both the decomposition of the affinity matrix and the generation of the large kernel matrix. By utilizing the underlying mathematical equivalence between the two types of seemingly different methods, LSC successfully preserves global image structures through efficient local operations. Experimental results show that LSC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation. The applicability of LSC is further demonstrated in two related computer vision tasks.


title: A low cost mobile ECG monitoring device using two active dry electrodes

abstract: Mobile electrocardiograph (ECG) monitoring provides crucial information about the cardiac rhythm and system anomaly helping doctors to efficiently diagnose heart diseases. This paper presents the development of a low cost mobile ECG monitoring device, which early warms abnormalities throughout long-term ECG observation. For user convenience, two active dry electrodes are used as touch sensors. A buffer circuit is applied to remove the over-sensitivity effect of the electrodes. Experimental results indicated that ECG quality when using active dry electrodes is equivalent to that of wet Ag/AgCl electrodes application.


title: Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

abstract: Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition to spectral information, digital surface model (DSM) and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP) values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%). The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.


title: OpenStreetMap for the disaster management in Bangladesh

abstract: OpenStreetMap (OSM) is a world-wide campaign for developing open source maps. The prospect of such open source maps is very bright in commercial, educational and political points of views. With the burgeoning growth of mobile phones, the necessity of location based services is increasing day by day. Starting from the 3G mobile applications to cheap SMS-based services, we need maps everywhere. Various applications of digital maps are also seen in different national and international issues. Hence, OSM has been growing at a rapid pace in different parts of the world. However, it is a bit challenging task to develop this sort of voluntary efforts in the developing third world countries, like Bangladesh. In this paper, we discuss the possible fields of disaster responses in Bangladesh where OSM can play a vital role. We also discuss about the problems we face while developing OSM for this region. Finally, we talk about a number of ways which can be instrumental while trying to make free open source maps avoiding all the difficulties.


title: Survey of Face Detection on Low-Quality Images

abstract: Face detection is a well-explored problem. Many challenges on face detectors like extreme pose, illumination, low resolution and small scales are studied in the previous work. However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems. In this paper, we first review the current state-of-the-art face detectors and their performance on benchmark dataset FDDB, and compare the design protocols of the algorithms. Secondly, we investigate their performance degradation while testing on low-quality images with different levels of blur, noise, and contrast. Our results demonstrate that both hand-crafted and deep-learning based face detectors are not robust enough for low-quality images. It inspires researchers to produce more robust design for face detection in the wild.


title: Pinpointing: Precise Head- and Eye-Based Target Selection for Augmented Reality

abstract: Head and eye movement can be leveraged to improve the user's interaction repertoire for wearable displays. Head movements are deliberate and accurate, and provide the current state-of-the-art pointing technique. Eye gaze can potentially be faster and more ergonomic, but suffers from low accuracy due to calibration errors and drift of wearable eye-tracking sensors. This work investigates precise, multimodal selection techniques using head motion and eye gaze. A comparison of speed and pointing accuracy reveals the relative merits of each method, including the achievable target size for robust selection. We demonstrate and discuss example applications for augmented reality, including compact menus with deep structure, and a proof-of-concept method for on-line correction of calibration drift.


title: I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence

abstract: Recent advances in artificial intelligence (AI) have increased the opportunities for users to interact with the technology. Now, users can even collaborate with AI in creative activities such as art. To understand the user experience in this new user--AI collaboration, we designed a prototype, DuetDraw, an AI interface that allows users and the AI agent to draw pictures collaboratively. We conducted a user study employing both quantitative and qualitative methods. Thirty participants performed a series of drawing tasks with the think-aloud method, followed by post-hoc surveys and interviews. Our findings are as follows: (1) Users were significantly more content with DuetDraw when the tool gave detailed instructions. (2) While users always wanted to lead the task, they also wanted the AI to explain its intentions but only when the users wanted it to do so. (3) Although users rated the AI relatively low in predictability, controllability, and comprehensibility, they enjoyed their interactions with it during the task. Based on these findings, we discuss implications for user interfaces where users can collaborate with AI in creative works.


title: Content-based processing and analysis of endoscopic images and videos: A survey

abstract: In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with real-time assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.


title: A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

abstract: Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12–14% of the average trip consumption, of which 7–9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.


title: Functional Asymmetry of the Sodium-d-Glucose Cotransporter Expressed in Yeast Secretory Vesicles

abstract: The sodium-D-glucose cotransporter (SGLT1) was expressed in a yeast mutant strain NY 17 (sec6-4) that accumulates secretory vesicles at a nonpermissive temperature because of a block in the delivery of these vesicles to the plasma membrane. By differential centrifugation a microsomal fraction enriched in secretory vesicles was prepared with a high specific activity of the vanadate-sensitive H+-ATPase and invertase. In this membrane fraction one protein band of an apparent molecular weight of 55 kDa representing the nonglycosylated SGLT1 protein could be detected by immunochemical analysis. In addition, higher molecular weight protein bands probably representing dimers and aggregates were found. In transport studies with the microsomes D-glucose fluxes showed asymmetric properties: efflux experiments revealed the typical properties of the SGLT1 such as sodium dependence, inhibition by phlorizin and potential dependence. Influx of D-glucose showed no dependence on sodium and was not inhibited by phlorizin. Furthermore, the transporter exhibited a striking asymmetry with regard to the D-glucose affinity and the sugar specificity. These results suggest that the orientation of the SGLT1 expressed in yeast secretory vesicles is, indeed, inverted with regard to its configuration in the plasma membrane of epithelial cells. Moreover, there are striking functional differences between the periplasmic and cytoplasmic face of the transporter.


title: PorchLight: a tag-based approach to bug triaging

abstract: Bug triaging is an important activity in any software development project. It involves developers working through the set of unassigned bugs, determining for each of the bugs whether it represents a new issue that should receive attention, and, if so, assigning it to a developer and a milestone. Current tools provide only minimal support for bug triaging and especially break down when developers must triage a large number of bug reports, since those reports can only be viewed one-by-one. This paper presents PorchLight, a novel tool that uses tags, attached to individual bug reports by queries expressed in a specialized bug query language, to organize bug reports into sets so developers can explore, work with, and ultimately assign bugs effectively in meaningful groups. We describe the challenges in supporting bug triaging, the design decisions upon which PorchLight rests, and the technical aspects of the implementation. We conclude with an early evaluation that involved six professional developers who assessed PorchLight and its potential for their day-to-day triaging duties.


title: Stress Detection Using Wearable Physiological and Sociometric Sensors

abstract: Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.


title: Evaluation of gate drive overvoltage management methods for enhancement mode gallium nitride transistors

abstract: Gallium nitride (GaN) power devices are commercially available from a number of suppliers and the market is growing. The predominant GaN technology is normally-off enhancement mode (e-mode) power transistors. One of the main design challenges of implementing e-mode GaN transistors is the gate drive circuit, in particular, the management of the GaN transistor's peak gate voltage. In this paper, we will discuss gate drive design fundamentals for e-mode GaN transistors, evaluate various methods for managing gate overvoltage, and propose a novel solution to effectively manage gate overvoltage in half-bridge configurations.


title: Attending to All Mention Pairs for Full Abstract Biological Relation Extraction

abstract: Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or require a large context to disambiguate. We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction. We encode full paper abstracts using an efficient self-attention encoder and form pairwise predictions between all mentions with a bi-affine operation. An entity-pair wise pooling aggregates mention pair scores to make a final prediction while alleviating training noise by performing within document multi-instance learning. We improve our model's performance by jointly training the model to predict named entities and adding an additional corpus of weakly labeled data. We demonstrate our model's effectiveness by achieving the state of the art on the Biocreative V Chemical Disease Relation dataset for models without KB resources, outperforming ensembles of models which use hand-crafted features and additional linguistic resources.


title: Paraphrase-Supervised Models of Compositionality

abstract: Compositional vector space models of meaning promise new solutions to stubborn language understanding problems. This paper makes two contributions toward this end: (i) it uses automatically-extracted paraphrase examples as a source of supervision for training compositional models, replacing previous work which relied on manual annotations used for the same purpose, and (ii) develops a context-aware model for scoring phrasal compositionality. Experimental results indicate that these multiple sources of information can be used to learn partial semantic supervision that matches previous techniques in intrinsic evaluation tasks. Our approaches are also evaluated for their impact on a machine translation system where we show improvements in translation quality, demonstrating that compositionality in interpretation correlates with compositionality in translation.


title: Six Degree of Freedom Sensing for Docking Using IR LED Emitters and Receivers

abstract: Six DOF offset sensing between two plates is important for automatic docking mechanisms. This paper presents an easy and inexpensive implementation of such a system using four commercial-off-the-shelf (COTS) infrared (IR) light emitting diode (LED) emitters and two COTS IR receivers on each of two docking plates. The angular intensity distribution of an emitter and the sensitivity distribution of a receiver allow for estimation of the angle and distance between them. Simple experiments have been conducted indicating that such a setup is able to give positional offset in any of 6 degrees of error (x, y, z, pitch, roll, and yaw) within a range. A theoretical framework is also established using least squares minimization. The theoretical framework is general and applies to other configurations of emitter and receiver parts and positioning.


title: Process Discovery using Inductive Miner and Decomposition

abstract: This report presents a submission to the Process Discovery Contest. The contest is dedicated to the assessment of tools and techniques that discover business process models from event logs. The objective is to compare the efficiency of techniques to discover process models that provide a proper balance between "overfitting" and "underfitting". In the context of the Process Discovery Contest, process discovery is turned into a classification task with a training set and a test set; where a process model needs to decide whether traces are fitting or not. In this report, we first show how we use two discovery techniques, namely: Inductive Miner and Decomposition, to discover process models from the training set using ProM tool. Second, we show how we use replay results to 1) check the rediscoverability of models, and to 2) classify unseen traces (in test logs) as fitting or not. Then, we discuss the classification results of validation logs, the complexity of discovered models, and their impact on the selection of models for submission. The report ends with the pictures of the submitted process models.


title: Monte Carlo Tree Search in Lines of Action

abstract: The success of Monte Carlo tree search (MCTS) in many games, where αβ-based search has failed, naturally raises the question whether Monte Carlo simulations will eventually also outperform traditional game-tree search in game domains where αβ -based search is now successful. The forte of αβ-based search are highly tactical deterministic game domains with a small to moderate branching factor, where efficient yet knowledge-rich evaluation functions can be applied effectively. In this paper, we describe an MCTS-based program for playing the game Lines of Action (LOA), which is a highly tactical slow-progression game exhibiting many of the properties difficult for MCTS. The program uses an improved MCTS variant that allows it to both prove the game-theoretical value of nodes in a search tree and to focus its simulations better using domain knowledge. This results in simulations superior in both handling tactics and ensuring game progression. Using the improved MCTS variant, our program is able to outperform even the world's strongest αβ-based LOA program. This is an important milestone for MCTS because the traditional game-tree search approach has been considered to be the better suited for playing LOA.


title: A Modular Biped Wall-Climbing Robot With High Mobility and Manipulating Function

abstract: High-rise tasks such as cleaning, painting, inspection, and maintenance on walls of large buildings or other structures require robots with climbing and manipulating skills. Motivated by these potential applications and inspired by the climbing motion of inchworms, we have developed a biped wall-climbing robot-W-Climbot. Built with a modular approach, the robot consists of five joint modules connected in series and two suction modules mounted at the two ends. With this configuration and biped climbing mode, W-Climbot not only has superior mobility on smooth walls, but also has the function of attaching to and manipulating objects equivalent to a “mobile manipulator.” In this paper, we address several fundamental issues with this novel wall-climbing robot, including system development, analysis of suction force, basic climbing gaits, overcoming obstacles, and transiting among walls. A series of comprehensive and challenging experiments with the robot climbing on walls and performing a manipulation task have been conducted to demonstrate its superior climbing ability and manipulation function. The analytical and experimental results have shown that W-Climbot represents a significant advancement in the development of wall-climbing robots.


title: Speech Intelligibility Prediction Based on Mutual Information

abstract: This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in predicting the intelligibility of speech signals contaminated by additive noise and potentially non-linearly processed using time-frequency weighting.


title: Integrating esthetic dentistry and space closure in patients with missing maxillary lateral incisors.

abstract: T plans for patients with missing max illary lateral incisors have traditionally included either space closure or space reopen ing.1-9 The most common objections to orthodon tic space closure are that the treatment outcome may not look “natural”, that retention is difficult, and that the functional occlusion may be com promised. Many clinicians have therefore pre ferred to create space for missing lateral incisors with single-tooth implants or resin-bonded bridges.8,9 In the past, however, neither approach produced results that were entirely satisfactory from an esthetic and functional standpoint. Now, with the possibility of esthetic tooth restorations using bonded ceramics and resin composites, together with various procedures for at-home or in-office tooth whitening, the profes sion is changing its way of thinking.10 The pur pose of this article is to show the considerable improvement that can be achieved today with the space-closure alternative by combining tech niques from esthetic dentistry and carefully detailed orthodontic treatment. Such treatment


title: An Inventory for Measuring Depression

abstract: The difficulties inherent in obtaining consistent and adequate diagnoses for the purposes of research and therapy have been pointed out by a number of authors. Pasamanick12in a recent article viewed the low interclinician agreement on diagnosis as an indictment of the present state of psychiatry and called for "the development of objective, measurable and verifiable criteria of classification based not on personal or parochial considerations, but on behavioral and other objectively measurable manifestations." Attempts by other investigators to subject clinical observations and judgments to objective measurement have resulted in a wide variety of psychiatric rating scales.4,15These have been well summarized in a review article by Lorr11on "Rating Scales and Check Lists for the Evaluation of Psychopathology." In the area of psychological testing, a variety of paper-and-pencil tests have been devised for the purpose of measuring specific


title: Human aggression: A social-cognitive view

abstract: Aggressive and violent behavior by one human being toward another is not a new phenomenon. It was prevalent among our hunter/gatherer ancestors 25,000 years ago, among the Greek, Egyptian, and Roman societies two to three thousand years ago, among most societies in the last two centuries, and in almost every society today. Periodically, events such as the Holocaust, the Columbine, Colorado school shootings; or the terrorist attack on the World Trade Center on September 11, 2001 make people wonder 'what the world is coming to'; but although technology has made mass violence easier to accomplish, it is doubtful that people are more prone to be violent today than thousands of years ago. In fact, the portion of the world's population who behave violently today is probably lower than in most previous times. Nevertheless, the prevalence of aggressive and violent behavior today is sufficient to make it a social problem worthy of attention around the world.


title: Solving Problems with Semidefinite and Related Constraints Using Interior-Point Methods for Nonlinear Programming. this volume

abstract: In this paper, we describe how to reformulate a problem that has second-order cone and/or semidefiniteness constraints in order to solve it using a general-purpose interior-point algorithm for nonlinear programming. The resulting problems are smooth and convex, and numerical results from the DIMACS Implementation Challenge problems and SDPLib are provided.


title: Chapter 1 Morphogenetic Engineering: Reconciling Self-Organization and Architecture

abstract: Generally, phenomena of spontaneous pattern formation are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both architectured and self-organized. Can we understand their precise self-formation capabilities and integrate them with technological planning? Can physical systems be endowed with information, or informational systems be embedded in physics, to create autonomous morphologies and functions? This book is the first initiative of its kind toward establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of “self-architecturing” systems. Particular emphasis is set on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science—while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies.


Fully Integrated Biopotential Acquisition Analog Front-End IC


title: Effect of a Nontechnical Skills Intervention on First-Year Student Registered Nurse Anesthetists' Skills During Crisis Simulation.

abstract: Simulation-based education provides a safe place for student registered nurse anesthetists to practice non-technical skills before entering the clinical arena. An anesthetist's lack of nontechnical skills contributes to adverse patient outcomes. The purpose of this study was to determine whether an educational intervention on nontechnical skills could improve the performance of nontechnical skills during anesthesia crisis simulation with a group of first-year student registered nurse anesthetists. Thirty-two first-year students volunteered for this quasi-experimental study. Each subject was videotaped and rated as he or she performed 6 simulated crisis scenarios: 3 scenarios before the intervention and 3 after the intervention. Findings revealed that the nontechnical skills mean posttest score was greater than pretest scores: t (df = 31) = 1.99, P = .028. The mean gain in scores for standardized nontechnical skills were significantly greater than those for standardized technical skills: t (df = 30) = 1.81, P = .04. In conclusion, a 3-hour educational intervention on nontechnical skills resulted in significant improvement. Nontechnical skills therefore are not acquired through experience, but rather through instruction. An educational intervention using the Anaesthetists' Non-Technical Skills system is a valuable tool in the measurement of nontechnical skills assessment of first-year student registered nurse anesthetists.


title: Eating with our eyes: From visual hunger to digital satiation

abstract: Abstract One of the brain’s key roles is to facilitate foraging and feeding. It is presumably no coincidence, then, that the mouth is situated close to the brain in most animal species. However, the environments in which our brains evolved were far less plentiful in terms of the availability of food resources (i.e., nutriments) than is the case for those of us living in the Western world today. The growing obesity crisis is but one of the signs that humankind is not doing such a great job in terms of optimizing the contemporary food landscape. While the blame here is often put at the doors of the global food companies – offering addictive foods, designed to hit ‘the bliss point’ in terms of the pleasurable ingredients (sugar, salt, fat, etc.), and the ease of access to calorie-rich foods – we wonder whether there aren’t other implicit cues in our environments that might be triggering hunger more often than is perhaps good for us. Here, we take a closer look at the potential role of vision; Specifically, we question the impact that our increasing exposure to images of desirable foods (what is often labelled ‘food porn’, or ‘gastroporn’) via digital interfaces might be having, and ask whether it might not inadvertently be exacerbating our desire for food (what we call ‘visual hunger’). We review the growing body of cognitive neuroscience research demonstrating the profound effect that viewing such images can have on neural activity, physiological and psychological responses, and visual attention, especially in the ‘hungry’ brain.


title: A Low Complexity Antenna Switching for Joint Wireless Information and Energy Transfer in MIMO Relay Channels

abstract: In this paper, we investigate a low-complexity technique for simultaneous wireless information and energy transfer in multiple-input multiple-output relay channels. The proposed technique exploits the array configuration at the relay node and uses the antenna elements either for conventional decoding or for rectifying (rectennas). In order to keep the complexity low, a dynamic antenna switching between decoding/rectifying is proposed based on the principles of the generalized selection combiner (GSC); the L strongest paths are allocated for decoding while the remaining channel paths for rectifying (and vice versa). The optimal L as well as the allocation strategy that minimizes the outage probability are investigated via theoretical and numerical results. In addition, two performance bounds that provide the optimal performance without the limitation of GSC are proposed by solving a linear programming and a binary knapsack problem, respectively. The proposed technique is extended to scenarios with multi-user interference, where a zero-forcing receiver is used at the relay node; closed-forms expressions for the outage probability are also derived.


title: Backstepping control design for two-wheeled self balancing robot

abstract: This paper presents different control schemes for tracking the pitch and yaw angle of a two-wheeled robot (TWR). Mathematical model of the TWR is developed using Lagrangian and Kirchhoff's current and voltage laws. The nonlinear system of TWR is first linearized at equilibrium point and then decomposed into two subsystems, a fourth and a second order system. The novelty discussed in this paper is the transformation of the fourth order system into strictly feedback form using input output linearization and is used to control the body pitch angle. The second order system is used to control the body yaw angle. The control strategies consisting of linear quadratic regulator (LQR) and iterative backstepping are developed to track the desired response. The simulation results ensure better performance for backstepping scheme as compared to the traditional LQR.


title: Photographic image synthesis with improved U-net

abstract: Photographic image synthesis is the process of creating images that are accurate representations of a real scene with some form of image description and is a new application field o f deep l earning technology. T he U-net is a frequently used convolutional network architecture in deep learning technology and is mainly used for image segmentation. Currently, the U-net has been used for photographic image synthesis. However, the attenuation of image description information, the low memory ca-pacity of the network and the checkerboard artifacts in the output images cause the low quality of the images synthesized by the U-net. In order to solve the above problems, this paper presents an improved U-net (IUN) for photographic image synthesis. In IUN, the image description map is resized to the multiple resolutions and the resized images are merged into the different layers of IUN for enhancing the image description information. The resize-convolution layers in IUN replaces the deconvolution layers in the U-net to reduce the checkerboard artifacts. The proposed learnable residual blocks increase the memory capacity of the network. IUN can be easy trained end-to-end with the image description map and the corresponding photographic image. In experiments on datasets, two types of image description information, semantic and sketch, are used to synthesize the photographic images. Compared to the optimization-based networks, cascaded refinement networks (CRNs) and other methods, IUN gives more realistic results. Additionally, our network achieves a balance between memory capacity and time cost.


title: An analysis of player affect transitions in survival horror games

abstract: The trend of multimodal interaction in interactive gaming has grown significantly as demonstrated for example by the wide acceptance of the Wii Remote and the Kinect as tools not just for commercial games but for game research as well. Furthermore, using the player’s affective state as an additional input for game manipulation has opened the realm of affective gaming. In this paper, we analyzed the affective states of players prior to and after witnessing a scary event in a survival horror game. Player affect data were collected through our own affect annotation tool that allows the player to report his affect labels while watching his recorded gameplay and facial expressions. The affect data were then used for training prediction models with the player’s brainwave and heart rate signals, as well as keyboard–mouse activities collected during gameplay. Our results show that (i) players are likely to get more fearful of a scary event when they are in the suspense state and that (ii) heart rate is a good candidate for detecting player affect. Using our results, game designers can maximize the fear level of the player by slowly building tension until the suspense state and showing a scary event after that. We believe that this approach can be applied to the analyses of different sets of emotions in other games as well.


title: Anti-Cheating: Detecting Self-Inflicted and Impersonator Cheaters for Remote Health Monitoring Systems with Wearable Sensors

abstract: In remote health monitoring of patient's physical activity, ensuring correctness and authenticity of the received data is essential. Although many activity monitoring systems, devices and techniques have been developed, preventing patient cheating of an activity monitor has been a primarily unaddressed challenge across the board. Patients can manually shake an activity monitor device (sensor) with their hand and watch their physical activity points or rewards increase, we define this as "self-inflicted" cheating. A second type of cheating, "impersonator" cheating, is when subjects hand the activity sensor over to a friend or second party to wear and perform physical activity on their behalf. In this paper, we propose two novel methods based on classification algorithms to address the cheating problems. The first classification framework improves the correctness of our data by detecting self-inflicted cheatings. The second technique is an advanced classification scheme that extracts and learns unique patient-specific activity patterns from prior data collected on a patient to distinguish the true subject from an impersonator. We tested our proposed techniques on Wanda, a remote health monitoring system used in our Women's Heart Health study of 90 African American women at risk of cardiovascular disease. We were able to distinguish cheating from other physical activities such as walking and running, as well as other common activities of daily living such as driving and playing video games. The self-inflicted cheating classifier achieved an accuracy of above 90% and an AUC of 99%. The impersonator cheater framework results in an average accuracy of above 90% and an average AUC of 94%. Our results provide insight into the randomness of cheating activities, successfully detects cheaters, and attempts to build more context-aware remote activity monitors that more accurately capture patient activity.


title: Experiments with SVM to classify opinions in different domains

abstract: Recently, opinion mining is receiving more attention due to the abundance of forums, blogs, e-commerce web sites, news reports and additional web sources where people tend to express their opinions. Opinion mining is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In this paper we explore this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes. We have accomplished experiments with different features on three corpora. Two of them have already been used in several works. The last one has been built from Amazon.com specifically for this paper in order to prove the feasibility of the SVM for different domains.