title: Analysis of hourly road accident counts using hierarchical clustering and cophenetic correlation coefficient (CPCC)

abstract: Road and traffic accidents are an important concern around the world. Road accidents not only affects the public health with different level of injury but also results in property damage. Data analysis has the capability to identify the different reasons behind road accidents i.e. traffic characteristics, weather characteristics, road characteristics and etc. A variety of research on road accident data analysis has already proves its importance. Some studies focused on identifying factors associated with accident severity while others focused on identifying the associated factors behind accident occurrence. These research analyses used traditional statistical methods as well as data mining methods. Data mining is frequently used method for analyzing road accident data in present research. Trend analysis is another important research area in road accident domain. Trend analysis can assist in identifying the increasing or decreasing accidents rate in different reasons. In this study, we have proposed a method to analyze hourly road accident data using Cophenetic correlation coefficient from Gujarat state in India. The motive of this study is to provide an efficient way to choose the best suitable distance metric to cluster the series of counts data that provide a better clustering result. The result shows that the proposed method is capable of efficiently group the different districts with similar road accident patterns into single cluster or group which can be further used for trend analysis or similar tasks.


title: SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning

abstract: Infectious disease epidemics such as influenza and Ebola pose a serious threat to global public health. It is crucial to characterize the disease and the evolution of the ongoing epidemic efficiently and accurately. Computational epidemiology can model the disease progress and underlying contact network, but suffers from the lack of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance, but is insensible to the underlying contact network and disease model. This paper proposes a novel semi-supervised deep learning framework that integrates the strengths of computational epidemiology and social media mining techniques. Specifically, this framework learns the social media users' health states and intervention actions in real time, which are regularized by the underlying disease model and contact network. Conversely, the learned knowledge from social media can be fed into computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm to substantialize the above interactive learning process iteratively to achieve a consistent stage of the integration. The extensive experimental results demonstrated that our approach can effectively characterize the spatiotemporal disease diffusion, outperforming competing methods by a substantial margin on multiple metrics.


title: Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative

abstract: In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. ::: --- ::: An advanced version is included in arXiv:1703.02000 "Activation Maximization Generative Adversarial Nets". ::: Please refer Section 6 in 1703.02000 for detailed analysis on Inception Score, and refer its appendix for the discussions on Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative.


title: Tinea capitis favosa misdiagnosed as tinea amiantacea.

abstract: INTRODUCTION ::: Favus of the scalp or tinea capitis favosa is a chronic dermatophyte infection of the scalp. In almost cases, favus is caused by Trichophyton schoenleinii, anthropophilic dermatophyte. It is characterized by the presence of scutula and severe alopecia. Besides the classic clinical type of tinea capitis favosa, there are many variant of clinical form which may persist undiagnosed for many years. In this work, we report an atypical form of favus to Trichophyton schoenleinii which was misdiagnosed as tinea amiantacea. ::: ::: ::: CASE-REPORT ::: An 11-year old girl came to the outpatient department of dermatology (day 0) with history of tinea amiantacea treated unsuccessfully with keratolytic shampoo (day - 730). She presented a diffuse scaling of the scalp with thick scaly patches and without scutula or alopecia. A diagnosis of tinea favosa by T. schoenleinii was made by mycological examination. She was treated with griseofulvin and ketoconazole in the form of foaming gel for twelve weeks. Despite treatment, clinical evolution was marked by appearance of permanent alopecia patches. The follow-up mycological examination was negative. ::: ::: ::: CONCLUSION ::: Because of ultimate evolution of favus into alopecia, we emphasize the importance of mycological examination in case of diffuse scaling.


title: Frequency-Converter-Based Hybrid Estimation Method for the Centrifugal Pump Operational State

abstract: Research on utilization of a frequency converter and sensorless methods in the drive system diagnostics has been mostly limited to the motor and to the frequency converter itself. However, it has been shown that a frequency converter also allows the sensorless estimation of the operational state of a centrifugal pump providing new opportunities for the diagnostics and control of pumping systems. In this paper, a novel hybrid estimation method for the centrifugal pump operational state is proposed. It is based on the identification of the process by using those flow rate and head estimates produced by QP-curve-based method, which are known to be relatively accurate. After the identification phase, the process model can be used together with the estimates of the frequency converter for the sensorless operational state estimation of the pump. The method is evaluated by laboratory measurements, and its applicability to industrial pumping and fan systems is also discussed.


title: PPG pulse direction determination algorithm for PPG waveform inversion by wrist rotation

abstract: This paper describes photoplethysmography (PPG)-based pulse direction determination algorithm on a site of the radial artery using a wrist band. It has been well known that PPG is susceptible to noise and motion artifacts in the mobile environment and many research efforts have been made to focus on rejection of the noise and motion artifacts. However, no research has been performed to find PPG pulses when PPG is inverted by wrist movement. We present an algorithm, which accurately yields which direction PPG pulses face regardless of wrist movement. The algorithm is one step closer to robust real-time PPG pulse direction determination for continuous PPG monitoring regardless of body movements.


title: Nonparametric Bayesian Data Analysis

abstract: We review the current state of nonparametric Bayesian inference. The discussion follows a list of important statistical inference problems, including density estimation, regression, survival analysis, hierarchical models and model validation. For each inference problem we review relevant nonparametric Bayesian models and approaches including Dirichlet process (DP) models and variations, Polya trees, wavelet based models, neural network models, spline regression, CART, dependent DP models and model validation with DP and Polya tree extensions of parametric models.


title: Abstract Energy Aware Consolidation for Cloud Computing

abstract: Consolidation of applications in cloud computing environments presents a significant opportunity for energy optimization. As a first step toward enabling energy efficient consolidation, we study the inter-relationships between energy consumption, resource utilization, and performance of consolidated workloads. The study reveals the energy performance trade-offs for consolidation and shows that optimal operating points exist. We model the consolidation problem as a modified bin packing problem and illustrate it with an example. Finally, we outline the challenges in finding effective solutions to the consolidation problem.


title: Preschoolers Monitor the Relative Accuracy of Informants.

abstract: In 2 studies, the sensitivity of 3- and 4-year-olds to the previous accuracy of informants was assessed. Children viewed films in which 2 informants labeled familiar objects with differential accuracy (across the 2 experiments, children were exposed to the following rates of accuracy by the more and less accurate informants, respectively: 100% vs. 0%, 100% vs. 25%, 75% vs. 0%, and 75% vs. 25%). Next, children watched films in which the same 2 informants provided conflicting novel labels for unfamiliar objects. Children were asked to indicate which of the 2 labels was associated with each object. Three-year-olds trusted the more accurate informant only in conditions in which 1 of the 2 informants had been 100% accurate, whereas 4-year-olds trusted the more accurate informant in all conditions tested. These results suggest that 3-year-olds mistrust informants who make a single error, whereas 4-year-olds track the relative frequency of errors when deciding whom to trust.


title: The Calculation of Diffusion Coefficients in Semiconductors

abstract: The intent of the present chapter is to discuss the current status of theoretical work which has been performed in connection with predicting rates of atomic motion in semiconducting materials. This theoretical work, aside from being of intrinsic interest, has some utility for individuals interested in experimental phenomena. Firstly, the calculations provide a means for estimating diffusion properties for systems which have not been studied or for systems for which it is difficult to perform measurements. Secondly, some calculations can often be used as a test of the accuracy of experimental data inasmuch as data for systems which do not conform to calculations based on reasonable models are suspect, and those systems become targets for future work in order to verify or reject the early measurements. As an example, in metal systems theoretical calculations have played a major role confirming the accuracy or inaccuracy of a given set of experimental data. Thirdly, inasmuch as the process of atomic diffusion involves ionic interactions which deviate substantially from equilibrium conditions, experimental data interpreted through various models, in principle, can provide information about the nature of very short-range as well as very long-range forces in crystals.


Intrinsic CanMEDS Competencies Expected of Medical Students During Emergency Medicine Core Rotation: A Needs Assessment


title: The use of big data analytics in the retail industries in South Africa

abstract: The majority of publications around big data analytics are centred on technical algorithms or systems development. While research has been conducted on the use of big data analytics there is an apparent lack of studies which focus on industry-specific usage in South Africa. The purpose of the study was to assess the usage of big data analytics in the retail industries in South Africa. The benefits of using big data analytics are not specific to a particular industry. Retailers, for example, can use big data analytics to gain new insights about their customers in order to inform decision making around pricing and marketing. The usage of big data analytics was assessed by collecting data from interviews with retailers, big data vendors and professional services. The main finding of the study was that South African retailers are not using big data analytics. Some retailers are, however, using big data analytic platforms to improve the speed of processing large amounts of structured data and to deliver information cost effectively. The findings show that South African retailers find it difficult to identify a use case to justify the investment required for using big data analytics. ::: ::: Key words: Big data, data analytics, retail industry.


title: Visualization of brand positioning based on consumer web search information: Using social network analysis

abstract: Purpose ::: ::: ::: ::: ::: Online web searches have played crucial roles in influencing consumers’ purchasing decisions. Web search traffic information enables researchers and practitioners to better understand consumers in terms of their preferences and interests, among other things. The purpose of this paper is to use web search traffic information provided by Google Trends to derive relationships among product brands as well as those between product brands and product attributes to propose a method to enhance the visibility of consumer brand positioning. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This study builds upon the interesting observation that consumers’ behavior in performing simultaneous searches, or searches including two or more keywords, can be converted into data indicating relationships among brands as well as those between brands and their attributes. The study focuses on the cases of hybrid cars and tablet PCs, and applies a social network analysis method to identify these relationships. Time series information on web search traffic is used because it can track these two product groups from the early stages to the present. This step is completed to verify the changes in the status of each brand and in their relationships that occurred in consumers’ minds over time. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Results show that consumers’ web search behaviors reveal the brand positioning and brand-attribute associations in their minds. Specifically, using consumers’ simultaneous search data, the authors derived relationships among brands (brand-brand network) from consumers’ behaviors of searching simultaneously for two brands and the relationships between brands and attributes (brand-product attributes network) from consumers’ behavior of searching simultaneously for a specific brand and certain product attributes. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Theoretically, this study verifies that consumers’ web search traffic information can be used to microscopically identify the positions of individual brands and their relationships in the minds of consumers. Regarding practical applications, this study proposes a method that can be used by companies to track how consumers perceive their brands by performing a simple and cost-effective analysis using the free search traffic information provided by Google.


title: Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification

abstract: Matching pedestrians across multiple camera views, known as human re-identification, is a challenging research problem that has numerous applications in visual surveillance. With the resurgence of Convolutional Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have been proposed for human re-identification with the objective of projecting the images of similar pairs (i.e. same identity) to be closer to each other and those of dissimilar pairs to be distant from each other. However, current networks extract fixed representations for each image regardless of other images which are paired with it and the comparison with other images is done only at the final level. In this setting, the network is at risk of failing to extract finer local patterns that may be essential to distinguish positive pairs from hard negative pairs. In this paper, we propose a gating function to selectively emphasize such fine common local patterns by comparing the mid-level features across pairs of images. This produces flexible representations for the same image according to the images they are paired with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and demonstrate improved performance compared to a baseline Siamese CNN architecture.


title: Scalable Learning of Non-Decomposable Objectives

abstract: Modern retrieval systems are often driven by an underlying machine learning model. The goal of such systems is to identify and possibly rank the few most relevant items for a given query or context. Thus, such systems are typically evaluated using a ranking-based performance metric such as the area under the precision-recall curve, the $F_\beta$ score, precision at fixed recall, etc. Obviously, it is desirable to train such systems to optimize the metric of interest. In practice, due to the scalability limitations of existing approaches for optimizing such objectives, large-scale retrieval systems are instead trained to maximize classification accuracy, in the hope that performance as measured via the true objective will also be favorable. In this work we present a unified framework that, using straightforward building block bounds, allows for highly scalable optimization of a wide range of ranking-based objectives. We demonstrate the advantage of our approach on several real-life retrieval problems that are significantly larger than those considered in the literature, while achieving substantial improvement in performance over the accuracy-objective baseline.


title: Creating Human-like AI Movement in Games Using Imitation Learning

abstract: The way characters move and behave in computer and video games are important factors in their believability, which has an impact on the player’s experience. This project explores Imitation Learning ...


title: Robust semi-supervised nonnegative matrix factorization

abstract: Nonnegative matrix factorization (NMF), which aims at finding parts-based representations of nonnegative data, has been widely applied to a range of applications such as data clustering, pattern recognition and computer vision. Real-world data are often sparse and noisy which may reduce the accuracy of representations. And a small portion of data may have prior label information, which, if utilized, can improve the discriminability of representations. In this paper, we propose a robust semi-supervised nonnegative matrix factorization (RSSN-MF) approach which takes all factors above into consideration. RSSNMF incorporates the label information as an additional constraint to guarantee that the data with the same label have the same representation. It addresses the sparsity of data and accommodates noises and outliers consistently via L 2,1 -norm. An iterative updating optimization scheme is derived to solve RSSNMF's objective function. We have proven the convergence of this optimization scheme by utilizing auxiliary function method and the correctness based on the Karush-Kohn-Tucker condition of optimization theory. Experiments carried on well-known data sets demonstrate the effectiveness of RSSNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.


title: A disciplined approach to adopting agile practices: the agile adoption framework

abstract: Many organizations aspire to adopt agile processes to take advantage of the numerous benefits that they offer to an organization. Those benefits include, but are not limited to, quicker return on investment, better software quality, and higher customer satisfaction. To date, however, there is no structured process (at least that is published in the public domain) that guides organizations in adopting agile practices. To address this situation, we present the agile adoption framework and the innovative approach we have used to implement it. The framework consists of two components: an agile measurement index, and a four-stage process, that together guide and assist the agile adoption efforts of organizations. More specifically, the Sidky Agile Measurement Index (SAMI) encompasses five agile levels that are used to identify the agile potential of projects and organizations. The four-stage process, on the other hand, helps determine (a) whether or not organizations are ready for agile adoption, and (b) guided by their potential, what set of agile practices can and should be introduced. To help substantiate the “goodness” of the Agile Adoption Framework, we presented it to various members of the agile community, and elicited responses through questionnaires. The results of that substantiation effort are encouraging, and are also presented in this paper.


title: Social Entrepreneurship Research: A Source of Explanation, Prediction, and Delight

abstract: Social entrepreneurship, as a practice and a field for scholarly investigation, provides a unique opportunity to challenge, question, and rethink concepts and assumptions from different fields of management and business research. This article puts forward a view of social entrepreneurship as a process that catalyzes social change and addresses important social needs in a way that is not dominated by direct financial benefits for the entrepreneurs. Social entrepreneurship is seen as differing from other forms of entrepreneurship in the relatively higher priority given to promoting social value and development versus capturing economic value. To stimulate future research the authors introduce the concept of embeddedness as a nexus between theoretical perspectives for the study of social entrepreneurship.


title: Stage Aware Performance Modeling of DAG Based in Memory Analytic Platforms

abstract: Spark has grown both in popularity and complexity in recent years. In order to use available resources in an efficient way, users need to understand how the behavior of their applications is affected by the size of the datasets and various configuration settings. Indeed, Spark allows users to specify many configuration parameters and understanding the impact of these choices with respect to the application execution time is not easy. An accurate estimate of application execution time is important for cluster capacity planning and/or runtime scheduling. In this work we propose a gray-box approach to analyze the performance of Spark applications deployed in public cloud infrastructures. The approach is divided into two phases: during application profiling, the application is executed multiple times against different subsets of the input datasets to understand the effect of the data size on the execution time and its dependency on the main configuration parameters. Next, during the estimation phase, we use the data gathered in the first step to predict the execution time of the application, run against the entire dataset. The prediction approach builds several models in order to estimate separately the growth of the time required to execute each stage within the application. Finally, the DAG used by Spark to schedule the execution of stages is analyzed to aggregate the predictions of the stages execution times into the overall application execution time. Both phases are supported by our SLAP open source tool. Experimental results show that our model can effectively and accurately predict application execution time. The approach outperforms pure black-box polynomial regression methods obtaining 1-3% relative error.


title: Finger design automation for industrial robot grippers: A review

abstract: Designing robust end-effector plays a crucial role, in performance of a robot workcell. Design automation of industrial grippers fingers/jaws is therefore of the highest interest in the robot indus ...


title: Scale-free topology of e-mail networks

abstract: We study the topology of e-mail networks with e-mail addresses as nodes and e-mails as links using data from server log files. The resulting network exhibits a scale-free link distribution and pronounced small-world behavior, as observed in other social networks. These observations imply that the spreading of e-mail viruses is greatly facilitated in real e-mail networks compared to random architectures.


title: Investigation of the TiN Gate Electrode With Tunable Work Function and Its Application for FinFET Fabrication

abstract: The titanium nitride (TiN) gate electrode with a tunable work function has successfully been deposited on the sidewalls of upstanding Si-fin channels of FinFETs by using a conventional reactive sputtering. It was found that the work function of the TiN (phiTiN) slightly decreases with increasing nitrogen (N2) gas flow ratio, RN=N2/(Ar+N2) in the sputtering, from 17% to 100%. The experimental threshold voltage (Vth) dependence on the RN shows that the more RN offers the lower Vth for the TiN gate n-channel FinFETs. The composition analysis of the TiN films with different RN showed that the more amount of nitrogen is introduced into the TiN films with increasing RN, which suggests that the lowering of phi TiN with increasing RN should be related to the increase in nitrogen concentration in the TiN film. The desirable Vth shift from -0.22 to 0.22 V was experimentally confirmed by fabricating n+ poly-Si and TiN gate n-channel multi-FinFETs without a channel doping. The developed simple technique for the conformal TiN deposition on the sidewalls of Si-fin channels is very attractive to the TiN gate FinFET fabrication


title: A Merged Interleaved Flyback PFC Converter with Active Clamp and ZVZCS

abstract: Abstract —The design and analysis of a compact flyback AC to DC power supply is described and simulation results are presented. Of particular interest is merging two interleaved power converters such that the main switch in one converter behaves as the clamp switch of the other while ripple cancellation is accomplished. Also, PFC and ZVZCS are achieved with no extra components. Using these approaches, the total number of active and passive components used and consequently the associated power loss, complexity and cost is reduced, which makes the design appealable for low-power high-volume applications. The proposed circuit converts 110VAC to 230VDC at 2.2A load current with an overall simulated efficiency of around 93%. Keywords —Merged, interleaved, flyback, PFC, active clamp, ZVZCS. I. I NTRODUCTION To reduce the weight and size of a power converter, it is desired to use a high switching frequency while it has been reported that low frequency isolated converters have better efficiencies [1]. Lower efficiency at higher frequencies is mainly due to increased switching losses that can be compensated using zero voltage/current switching techniques [2]. In an effort to increase conversion efficiency, [3] proposes an AC to DC converter based on flyback topology that recovers the surge energy generated by parasitic inductances to the output side of the isolation transformer. Additional circuitry is used to achieve zero voltage switching (ZVS) during turn on, and voltage clamping during turn off, of the main switch. In this paper a merged interleaved flyback power factor corrected (PFC) converter with active clamp and zero-voltage zero-current switching (ZVZCS) is introduced. The single-stage conversion proposed here has high power factor, improved efficiency and simple structure with very low number of parts used, which reduces manufacturing cost. Because of these advantages, this converter is recommended for low-power high-volume applications. Major features of this design are: • 1Reduced the total number of components used by merging two parallel power converters and by utilizing single-stage PFC approach. • Reduced the size of input and output filter components by increasing (constant) switching frequency and by interleaving phases. • Increased power rating by paralleling phases (not paralleling switches). • Increased converter output voltage by putting secondary side of the isolation transformers in series (lowered transformer turns ratio). • Suppressed unwanted voltage surge and oscillations across switches using non-dissipative active clamping without using auxiliary circuitry. • ZVZCS operation without using auxiliary circuitry. • PFC functionality without using extra control and current sensing circuitry. II. C


title: Emphasizing Essential Words for Sentiment Classification Based on Recurrent Neural Networks

abstract: With the explosion of online communication and publication, texts become obtainable via forums, chat messages, blogs, book reviews and movie reviews. Usually, these texts are much short and noisy without sufficient statistical signals and enough information for a good semantic analysis. Traditional natural language processing methods such as Bow-of-Word (BOW) based probabilistic latent semantic models fail to achieve high performance due to the short text environment. Recent researches have focused on the correlations between words, i.e., term dependencies, which could be helpful for mining latent semantics hidden in short texts and help people to understand them. Long short-term memory (LSTM) network can capture term dependencies and is able to remember the information for long periods of time. LSTM has been widely used and has obtained promising results in variants of problems of understanding latent semantics of texts. At the same time, by analyzing the texts, we find that a number of keywords contribute greatly to the semantics of the texts. In this paper, we establish a keyword vocabulary and propose an LSTM-based model that is sensitive to the words in the vocabulary; hence, the keywords leverage the semantics of the full document. The proposed model is evaluated in a short-text sentiment analysis task on two datasets: IMDB and SemEval-2016, respectively. Experimental results demonstrate that our model outperforms the baseline LSTM by 1%~2% in terms of accuracy and is effective with significant performance enhancement over several non-recurrent neural network latent semantic models (especially in dealing with short texts). We also incorporate the idea into a variant of LSTM named the gated recurrent unit (GRU) model and achieve good performance, which proves that our method is general enough to improve different deep learning models.


title: A Distributed Framework for NLP-Based Keyword and Keyphrase Extraction From Web Pages and Documents

abstract: The recent growth of the World Wide Web at increasing rate and speed and the number of online available resources populating Internet represent a massive source of knowledge for various research and business interests. Such knowledge is, for the most part, embedded in the textual content of web pages and documents, which is largely represented as unstructured natural language formats. In order to automatically ingest and process such huge amounts of data, single-machine, non-distributed architectures are proving to be inefficient for tasks like Big Data mining and intensive text processing and analysis. Current Natural Language Processing (NLP) systems are growing in complexity, and computational power needs have been significantly increased, requiring solutions such as distributed frameworks and parallel computing programming paradigms. This paper presents a distributed framework for executing NLP related tasks in a parallel environment. This has been achieved by integrating the APIs of the widespread GATE open source NLP platform in a multi-node cluster, built upon the open source Apache Hadoop file system. The proposed framework has been evaluated against a real corpus of web pages and documents.


title: Snapchat Elicits More Jealousy than Facebook: A Comparison of Snapchat and Facebook Use

abstract: Abstract Recent news in the media has suggested that younger people are using popular social media such as Facebook less and are quickly adopting newer media, such as the self-destructing app Snapchat. Snapchat is unique in that it erases messages several seconds after they have been sent, affording its users a higher level of privacy. Yet, little research exists on Snapchat use in general, let alone its broader psychological implications. This article offers a preliminary comparison of Snapchat and Facebook use and psychological effects on romantic jealousy. General motives for using Snapchat and Facebook are examined, as well as the nature of the content that Snapchat users most frequently share. Further, because of the differences in privacy and persistence of information, potential psychological effects in the domain of romantic jealousy are also examined, which has been widely studied on Facebook in the last few years. Findings show that the main difference in motives were that Snapchat was used more...


title: Modeling Consumer Learning from Online Product Reviews

abstract: We propose a structural model to study the effect of online product reviews on consumer purchases of experiential products. Such purchases are characterized by limited repeat purchase behavior of the same product item such as a book title but significant past usage experience with other products of the same type such as books of the same genre. To cope with the uncertainty in quality of the product item, we posit that consumers may learn from their experience with the same type of product and others' experiences with the product item. We model the review credibility as the precision with which product reviews reflect the consumer's own product evaluation. The higher the precision, the more credible the information obtained from product reviews for the consumer, and the larger the effect of reviews on the consumer's choice probabilities. We extend the Bayesian learning framework to model consumer learning on both product quality and review credibility. We apply the model to a panel data set of 1,919 book purchases by 243 consumers. We find that consumers learn more from online reviews of book titles than from their own experience with other books of the same genre. In the counterfactual analysis, we illustrate the profit impact of product reviews and how it varies with the number of reviews. We also study the phenomenon of fake reviews. We find that fake reviews increase consumer uncertainty. The effects of more positive reviews and more numerous reviews on consumer choice are smaller on online retailing platforms that have fake product reviews.


title: Tibia Bone Segmentation in X-ray Images- A Comparative Analysis

abstract: Segmentation techniques in the medical field are used to segment anatomical structures or other region of interest from medical images obtained from different modalities. This paper deals with segmentation techniques like manual thresholding, Otsu thresholding, watershed, traditional active contours and growcut in X-ray modality, for segmenting the tibia bone. This paper analyzes the performance of these algorithms on a database of 48 clinical X-ray images. The images have been obtained from different X-ray machines and vary in their resolution and dimensions. The performance of the algorithms have been measured and validated empirically. General Terms Medical imaging, machine vision, biomedical image analysis.


title: 3: Hydrology and Water Resources Implications of 21 st Century Climate Change for the Hydrology of Washington State

abstract: Pacific Northwest (PNW) hydrology is particularly sensitive to changes in climate because snowmelt dominates seasonal runoff, and temperature changes impact the rain/snow balance. Based on results from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4), we updated previous studies of implications of climate change on PNW hydrology. PNW 21st century hydrology was simulated using 20 Global Climate Models (GCMs) and 2 greenhouse gas emissions scenarios over Washington and the greater Columbia River watershed, with additional focus on the Yakima River watershed and the Puget Sound which are particularly sensitive to climate change. We evaluated projected changes in snow water equivalent (SWE), soil moisture, runoff, and streamflow for A1B and B1 emissions scenarios for the 2020s, 2040s, and 2080s. April 1 SWE is projected to decrease by approximately 38–46% by the 2040s (compared with the mean over water years 1917–2006), based on composite scenarios of B1 and A1B, respectively, which represent average effects of all climate models. In three relatively warm transient watersheds west of the Cascade crest, April 1 SWE is projected to almost completely disappear by the 2080s. By the 2080s, seasonal streamflow timing will shift significantly in both snowmelt dominant and rain–snow mixed watersheds. Annual runoff across the State is projected to increase by 2–3% by the 2040s; these changes are mainly driven by projected increases in winter precipitation.


title: Will you still compile me tomorrow? static cross-version compiler validation

abstract: This paper describes a cross-version compiler validator and measures its effectiveness on the CLR JIT compiler. The validator checks for semantically equivalent assembly language output from various versions of the compiler, including versions across a seven-month time period, across two architectures (x86 and ARM), across two compilation scenarios (JIT and MDIL), and across optimizations levels. For month-to-month comparisons, the validator achieves a false alarm rate of just 2.2%. To help understand reported semantic differences, the validator performs a root-cause analysis on the counterexample traces generated by the underlying automated theorem proving tools. This root-cause analysis groups most of the counterexamples into a small number of buckets, reducing the number of counterexamples analyzed by hand by anywhere from 53% to 96%. The validator ran on over 500,000 methods across a large suite of test programs, finding 12 previously unknown correctness and performance bugs in the CLR compiler.


title: The Enhanced Light Absorptance and Device Application of Nanostructured Black Silicon Fabricated by Metal-assisted Chemical Etching

abstract: We use metal-assisted chemical etching (MCE) method to fabricate nanostructured black silicon on the surface of C-Si. The Si-PIN photoelectronic detector based on this type of black silicon shows excellent device performance with a responsivity of 0.57 A/W at 1060 nm. Silicon nanocone arrays can be created using MCE treatment. These modified surfaces show higher light absorptance in the near-infrared range (800 to 2500 nm) compared to that of C-Si with polished surfaces, and the variations in the absorption spectra of the nanostructured black silicon with different etching processes are obtained. The maximum light absorptance increases significantly up to 95 % in the wavelength range of 400 to 2500 nm. Our recent novel results clearly indicate that nanostructured black silicon made by MCE has potential application in near-infrared photoelectronic detectors.


title: Gender Dysphoria Update

abstract: Concepts of sexuality and gender identity are undergoing re-examination in society. Recent media attention has intensified interest in the area, although reliable information is sometimes lacking. Gender dysphoria, and its extreme form, transsexualism, frequently brings sufferers into contact with psychiatric, social, and mental health professionals, and surgical caregivers. Treatment of these patients often represents a challenge on many levels. Some guidelines for this care are outlined.


title: MRI of normal brain maturation.

abstract: The unprecedented gray/white differentiation obtained with magnetic resonance imaging (MRI) has created a unique opportunity to trace the normal process of myelination. Fifty-nine children referred for evaluation of a nonneurologic problem or a nonspecific neurologic complaint were studied with MRI using spin-echo technique. Children ranged in age from term (40 weeks intrauterine) to 16 years. Scans were reviewed for quantitative and qualitative changes with age. T1 and T2 relaxation times were measured for 13 regions of interest in 37 children. With increasing age a sharp decrease in both T1 and T2 values, most pronounced during the first year of life, was seen. The prolonged relaxation times in the newborn infant correspond to the known high water content of the neonatal brain; the subsequent decline corresponds to the decrease in water content and increase in myelination observed in autopsy studies of infants. Qualitative changes in the MRI appearance of the brain with age using a spin-echo sequence (2 sec repetition time) demonstrated that the process of myelination was most rapid during the first 2-3 years of life. Myelination appeared to occur earliest in the posterior fossa, with the middle cerebellar peduncle identifiable at only 3 months. By the age of 1 year, all major white matter tracts including the corpus callosum, subcortical white matter, and the internal capsule were well defined. However, due to subtle changes in appearance, the refined configuration of the adult brain was not attained until early adolescence.


Automatic Labeling of Document Clusters


title: BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

abstract: We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual data and extracts a bilingual signal from a smaller set of raw-text sentence-aligned data. This is achieved using a novel sampled bag-of-words cross-lingual objective, which is used to regularize two noise-contrastive language models for efficient cross-lingual feature learning. We show that bilingual embeddings learned using the proposed model outperform state-of-the-art methods on a cross-lingual document classification task as well as a lexical translation task on WMT11 data.


title: Investigating the psychometric properties of the Qiyas for L1 Arabic language test using a Rasch measurement framework

abstract: Background ::: This study investigated the psychometric properties of the recently developed Qiyas for L1 Arabic language test using a Rasch measurement framework.


title: Effect of mindfulness meditation on brain–computer interface performance

abstract: Electroencephalogram based brain–computer interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both meditation and music interventions elicited clear expectations for improvement on the BCI task, with the strength of expectation being closely matched. In the main 12 week intervention study, seventy-six healthy volunteers were randomly assigned to three groups: a meditation training group; a music training group; and a no treatment control group. The mindfulness meditation training group obtained a significantly higher BCI accuracy compared to both the music training and no-treatment control groups after the intervention, indicating effects of meditation above and beyond expectancy effects.


title: GCap: Graph-based Automatic Image Captioning

abstract: Given an image, how do we automatically assign keywords to it? In this paper, we propose a novel, graph-based approach (GCap) which outperforms previously reported methods for automatic image captioning. Moreover, it is fast and scales well, with its training and testing time linear to the data set size. We report auto-captioning experiments on the "standard" Corel image database of 680 MBytes, where GCap outperforms recent, successful auto-captioning methods by up to 10 percentage points in captioning accuracy (50% relative improvement).


title: Techniques of Cluster Algorithms in Data Mining

abstract: An overview of cluster analysis techniques from a data mining point of view is given. This is done by a strict separation of the questions of various similarity and distance measures and related optimization criteria for clusterings from the methods to create and modify clusterings themselves. In addition to this general setting and overview, the second focus is used on discussions of the essential ingredients of the i>demographic cluster algorithm of IBM's Intelligent Miner, based i>Condorcet's criterion.


title: SGD: General Analysis and Improved Rates

abstract: We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form mini-batches. This is the first time such an analysis is performed, and most of our variants of SGD were never explicitly considered in the literature before. Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on the variance of the stochastic gradients. By specializing our theorem to different mini-batching strategies, such as sampling with replacement and independent sampling, we derive exact expressions for the stepsize as a function of the mini-batch size. With this we can also determine the mini-batch size that optimizes the total complexity, and show explicitly that as the variance of the stochastic gradient evaluated at the minimum grows, so does the optimal mini-batch size. For zero variance, the optimal mini-batch size is one. Moreover, we prove insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime.


title: Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates

abstract: Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed. In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell. Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper. The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method.


title: Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language

abstract: The expression and experience of human behavior are complex and multimodal and characterized by individual and contextual heterogeneity and variability. Speech and spoken language communication cues offer an important means for measuring and modeling human behavior. Observational research and practice across a variety of domains from commerce to healthcare rely on speech- and language-based informatics for crucial assessment and diagnostic information and for planning and tracking response to an intervention. In this paper, we describe some of the opportunities as well as emerging methodologies and applications of human behavioral signal processing (BSP) technology and algorithms for quantitatively understanding and modeling typical, atypical, and distressed human behavior with a specific focus on speech- and language-based communicative, affective, and social behavior. We describe the three important BSP components of acquiring behavioral data in an ecologically valid manner across laboratory to real-world settings, extracting and analyzing behavioral cues from measured data, and developing models offering predictive and decision-making support. We highlight both the foundational speech and language processing building blocks as well as the novel processing and modeling opportunities. Using examples drawn from specific real-world applications ranging from literacy assessment and autism diagnostics to psychotherapy for addiction and marital well being, we illustrate behavioral informatics applications of these signal processing techniques that contribute to quantifying higher level, often subjectively described, human behavior in a domain-sensitive fashion.


title: Why do arms extract less oxygen than legs during exercise?

abstract: To determine whether conditions for O2 utilization and O2 off-loading from the hemoglobin are different in exercising arms and legs, six cross-country skiers participated in this study. Femoral and subclavian vein blood flow and gases were determined during skiing on a treadmill at approximately 76% maximal O2 uptake (V(O2)max) and at V(O2)max with different techniques: diagonal stride (combined arm and leg exercise), double poling (predominantly arm exercise), and leg skiing (predominantly leg exercise). The percentage of O2 extraction was always higher for the legs than for the arms. At maximal exercise (diagonal stride), the corresponding mean values were 93 and 85% (n = 3; P < 0.05). During exercise, mean arm O2 extraction correlated with the P(O2) value that causes hemoglobin to be 50% saturated (P50: r = 0.93, P < 0.05), but for a given value of P50, O2 extraction was always higher in the legs than in the arms. Mean capillary muscle O2 conductance of the arm during double poling was 14.5 (SD 2.6) ml.min(-1).mmHg(-1), and mean capillary P(O2) was 47.7 (SD 2.6) mmHg. Corresponding values for the legs during maximal exercise were 48.3 (SD 13.0) ml.min(-1).mmHg(-1) and 33.8 (SD 2.6) mmHg, respectively. Because conditions for O2 off-loading from the hemoglobin are similar in leg and arm muscles, the observed differences in maximal arm and leg O2 extraction should be attributed to other factors, such as a higher heterogeneity in blood flow distribution, shorter mean transit time, smaller diffusing area, and larger diffusing distance, in arms than in legs.


title: E-Learning in Higher Education

abstract: This chapter examines the nature of change in Higher Education with respect to the introduction and growth of e-learning. While the ostensible aim is to use e-learning to improve the quality of the learning experience for students, the drivers of change are numerous, and learning quality ranks poorly in relation to most of them. Those of us working to improve student learning, and seeking to exploit e-learning to do so, have to ride each new wave of technological innovation in an attempt to divert it from its more natural course of techno-hype, and drive it towards the quality agenda. We have to build the means for e-learning to evolve and mature as part of the educational change process, so that it achieves its promise of an improved system of higher education. Why is e-learning important for HE? A student who is learning in a way that uses information and communication technologies (ICTs) is using e-learning. These interactive technologies support many different types of capability: internet access to digital versions of materials unavailable locally internet access to search, and transactional services interactive diagnostic or adaptive tutorials interactive educational games remote control access to local physical devices personalised information and guidance for learning support simulations or models of scientific systems communications tools for collaboration with other students and teachers tools for creativity and design virtual reality environments for development and manipulation data analysis, modelling or organisation tools and applications electronic devices to assist disabled learners For each of these, there is a learning application that could be exploited within HE. Each one encompasses a wide range of different types of interaction – internet access to services, for example, includes news services, blogs, online auctions, self-testing sites, etc. Moreover, the list above could be extended further by considering combinations of applications. Imagine, for example, a remotely controlled observatory webcam embedded in an online conference environment for astronomy students; or a computer-aided design device embedded in a role-play environment for students of urban planning.


title: Altered Cortico-Striatal–Thalamic Connectivity in Relation to Spatial Working Memory Capacity in Children with ADHD

abstract: Introduction: Attention deficit hyperactivity disorder (ADHD) captures a heterogeneous group of children, who are characterized by a range of cognitive and behavioral symptoms. Previous resting-state functional connectivity MRI (rs-fcMRI) studies have sought to understand the neural correlates of ADHD by comparing connectivity measurements between those with and without the disorder, focusing primarily on cortical–striatal circuits mediated by the thalamus. To integrate the multiple phenotypic features associated with ADHD and help resolve its heterogeneity, it is helpful to determine how specific circuits relate to unique cognitive domains of the ADHD syndrome. Spatial working memory has been proposed as a key mechanism in the pathophysiology of ADHD. Methods: We correlated the rs-fcMRI of five thalamic regions of interest (ROIs) with spatial span working memory scores in a sample of 67 children aged 7–11 years [ADHD and typically developing children (TDC)]. In an independent dataset, we then examined group differences in thalamo-striatal functional connectivity between 70 ADHD and 89 TDC (7–11 years) from the ADHD-200 dataset. Thalamic ROIs were created based on previous methods that utilize known thalamo-cortical loops and rs-fcMRI to identify functional boundaries in the thalamus. Results/Conclusion: Using these thalamic regions, we found atypical rs-fcMRI between specific thalamic groupings with the basal ganglia. To identify the thalamic connections that relate to spatial working memory in ADHD, only connections identified in both the correlational and comparative analyses were considered. Multiple connections between the thalamus and basal ganglia, particularly between medial and anterior dorsal thalamus and the putamen, were related to spatial working memory and also altered in ADHD. These thalamo-striatal disruptions may be one of multiple atypical neural and cognitive mechanisms that relate to the ADHD clinical phenotype.


title: Cross Validation Framework to Choose Amongst Models and Datasets for Transfer Learning

abstract: One solution to the lack of label problem is to exploit transfer learning, whereby one acquires knowledge from source-domains to improve the learning performance in the target-domain. The main challenge is that the source and target domains may have different distributions. An open problem is how to select the available models (including algorithms and parameters) and importantly, abundance of source-domain data, through statistically reliable methods, thus making transfer learning practical and easy-to-use for real-world applications. To address this challenge, one needs to take into account the difference in both marginal and conditional distributions in the same time, but not just one of them. In this paper, we formulate a new criterion to overcome "double" distribution shift and present a practical approach "Transfer Cross Validation" (TrCV) to select both models and data in a cross validation framework, optimized for transfer learning. The idea is to use density ratio weighting to overcome the difference in marginal distributions and propose a "reverse validation" procedure to quantify how well a model approximates the true conditional distribution of target-domain. The usefulness of TrCV is demonstrated on different cross-domain tasks, including wine quality evaluation, web-user ranking and text categorization. The experiment results show that the proposed method outperforms both traditional cross-validation and one state-of-the-art method which only considers marginal distribution shift. The software and datasets are available from the authors.


title: OPINION Social impairment in schizophrenia: new approaches for treating a persistent problem

abstract: PURPOSE OF REVIEW ::: Deficits in social cognition are an important determinant of social functioning in schizophrenia, a core feature of the illness that persists despite treatment. Recent research in neuroplasticity-based therapeutics shows that neural systems supporting core cognitive skills improve after targeted cognitive training, suggesting that social cognition, and concomitant social functioning, may be improved by targeting the neural systems supporting social cognition. However, the success of this approach depends on the extent to which the social brain is malleable. We review the neural effects of training programs focused on improving social cognition in healthy, schizophrenia, and other clinical samples. ::: ::: ::: RECENT FINDINGS ::: The current literature suggests that the neural mechanisms underlying social cognition show neuroplastic changes after behavioral training and these neural changes confer concomitant benefits to social cognition and social behavior. Most research in schizophrenia has focused on emotion recognition, and although emotion recognition training has behavioral and neural benefits for schizophrenia, more advanced social cognitive processes need to be examined. ::: ::: ::: SUMMARY ::: The data suggest that targeting neural systems underlying social cognition through socially focused behavioral interventions may improve social functioning impairments in schizophrenia. Questions remain regarding how to optimize training, which should be addressed in future work.


title: Fully homomorphic encryption equating to cloud security: An approach

abstract: As the data storage challenge continues to grow for insurers and everyone else, one of the obvious solutions is cloud technology. Storing data on remote servers rather than in-house is definitely a money-saver, but in insurance circles, the worry has been that having critical data reside outside the physical and virtual walls of the insurance enterprise is a risky situation. As the IT field is rapidly moving towards Cloud Computing, software industry's focus is shifting from developing applications for PCs to Data Centers and Clouds that enable millions of users to make use of software simultaneously. "Attempting computation on sensitive data stored on shared servers leaves that data exposed in ways that traditional encryption techniques can't protect against," the article notes. "The main problem is that to manipulate the data, it has to be decoded first". Now a new method, called fully homomorphic encryption (FHE) that performs computation with the encrypted data and send to the client and offers a realistic hope that such calculations can be performed securely in the cloud. Keywords: Cloud computing, fully homomorphic encryption, security threats. I. Introduction "Homomorphic" is an adjective which describes a property of an encryption scheme. That property, in simple terms, is the ability to perform computations on the cipher text without decrypting it first. Our ultimate goal is to used a fully homomorphic encryption scheme E. Let us discuss what it means to be fully homomorphic. At a high-level, the essence of fully homomorphic encryption is simple: given cipher texts that encrypt m1,m2,m3,…………………mt , fully homomorphic encryption should allow anyone (not just the key- holder) to output a cipher text that encrypts f(m1,m2,m3,…………………mi) for any desired function f, as long as that function can be efficiently computed. No information about m1,m2,m3,…………………mt or f(m1,m2,m3,…………………mt) or any intermediate plaintext values, should leak; the inputs, output and intermediate values are always encrypted.


title: Coordination in Categorical Compositional Distributional Semantics

abstract: An open problem with categorical compositional distributional semantics is the representation of words that are considered semantically vacuous from a distributional perspective, such as determiners, prepositions, relative pronouns or coordinators. This paper deals with the topic of coordination between identical syntactic types, which accounts for the majority of coordination cases in language. By exploiting the compact closed structure of the underlying category and Frobenius operators canonically induced over the fixed basis of finite-dimensional vector spaces, we provide a morphism as representation of a coordinator tensor, and we show how it lifts from atomic types to compound types. Linguistic intuitions are provided, and the importance of the Frobenius operators as an addition to the compact closed setting with regard to language is discussed.


title: Latent Semantic Minimal Hashing for Image Retrieval

abstract: Hashing-based similarity search is an important technique for large-scale query-by-example image retrieval system, since it provides fast search with computation and memory efficiency. However, it is a challenge work to design compact codes to represent original features with good performance. Recently, a lot of unsupervised hashing methods have been proposed to focus on preserving geometric structure similarity of the data in the original feature space, but they have not yet fully refined image features and explored the latent semantic feature embedding in the data simultaneously. To address the problem, in this paper, a novel joint binary codes learning method is proposed to combine image feature to latent semantic feature with minimum encoding loss, which is referred as latent semantic minimal hashing . The latent semantic feature is learned based on matrix decomposition to refine original feature, thereby it makes the learned feature more discriminative. Moreover, a minimum encoding loss is combined with latent semantic feature learning process simultaneously, so as to guarantee the obtained binary codes are discriminative as well. Extensive experiments on several well-known large databases demonstrate that the proposed method outperforms most state-of-the-art hashing methods.


title: Towards a Design Theory of Blended Learning Curriculum

abstract: The purpose of this article is to develop a design theory of blended learning curriculum in ways of establishing a model for designing such a curriculum and a model for designing an activity in a blended learning curriculum as well as demonstrating how these models can be utilized in a curriculum design. It first attempts to define what the essence of blended learning is by drawing on definitions of previous studies. Then, it goes on to identify the characteristics and rationales of blended learning. Finally, it exemplifies the devised BLC activity model, which is supported by the BLC design model and the BLC process model.


title: Predicting the co-evolution of event and Knowledge Graphs

abstract: Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Knowledge graphs are typically treated as static: A knowledge graph grows more links when more facts become available but the ground truth values associated with links is considered time invariant. In this paper we address the issue of knowledge graphs where triple states depend on time. We assume that changes in the knowledge graph always arrive in form of events, in the sense that the events are the gateway to the knowledge graph. We train an event prediction model which uses both knowledge graph background information and information on recent events. By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well. Our experiments demonstrate that our approach performs well in a clinical application, a recommendation engine and a sensor network application.


title: Blood pressure levels and risk of cardiovascular disease mortality among Japanese men and women: the Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC Study)

abstract: Objective:To examine the association of blood pressure (BP) with cardiovascular mortality in real-world settings and investigate whether that association varied by use of antihypertensive medication at baseline.Methods:Data from 27 728 Japanese men and women, aged 40–79 years, free of stroke, corona


title: High Gain Wide-Band U-Shaped Patch Antennas With Modified Ground Planes

abstract: A high gain wideband U-shaped patch antenna with two equal arms on poly tetra fluoro ethylene (PTFE) substrate is presented. An inverted U-shaped slot is introduced on the circular or square shaped ground plane just under the U-shaped patch. In this communication the effect of size and shape of the ground plane on impedance bandwidth is studied. Maximum impedance bandwidth of 86.79% (4.5–11.4 GHz) is obtained with circular shaped ground plane with diameter 36 mm. The highest gain achieved is 4.1 dBi. The simulated results are confirmed experimentally. The proposed antenna is simple in structure compared to the regular stacked or coplanar parasitic patch antennas. It is highly suitable for wireless communications.


title: A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems

abstract: We present a biologically inspired architecture for incremental learning that remains resource-efficient even in the face of very high data dimensionalities (>1000) that are typically associated with perceptual problems. In particular, we investigate how a new perceptual (object) class can be added to a trained architecture without retraining, while avoiding the well-known catastrophic forgetting effects typically associated with such scenarios. At the heart of the presented architecture lies a generative description of the perceptual space by a self-organized approach which at the same time approximates the neighborhood relations in this space on a two-dimensional plane. This approximation, which closely imitates the topographic organization of the visual cortex, allows an efficient local update rule for incremental learning even in the face of very high dimensionalities, which we demonstrate by tests on the well-known MNIST benchmark. We complement the model by adding a biologically plausible short-term memory system, allowing it to retain excellent classification accuracy even under incremental learning in progress. The short-term memory is additionally used to reinforce new data statistics by replaying previously stored samples during dedicated “sleep” phases.


title: Secure and Reliable Internet of Things Systems for Healthcare

abstract: Proposals and some implementations of Internet of Things (IoT) systems for healthcare are described. Implications of current European Union legislation, the new General Data Protection Regulation, for the security and reliability of healthcare IoT systems and for the privacy of users of these systems are presented. Analytics of healthcare IoT data for the requirements of evidence based healthcare is outlined. Threats to the security and reliability of healthcare IoT systems and to the privacy of the users of these systems, security and reliability requirements, and solutions for security and enhanced reliability are described. Visions for future healthcare IoT are presented and some future research directions are proposed.


title: Missing Data: Our View of the State

abstract: Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.


title: Edge Drawing: A Heuristic Approach to Robust Real-Time Edge Detection

abstract: We propose a new edge detection algorithm that works by computing a set of anchor edge points in an image and then linking these anchor points by drawing edges between them. The resulting edge map consists of perfect contiguous, one pixel wide edges. The performance tests show that our algorithm is up to 16% faster than the fastest known edge detection algorithm, i.e., OpenCV implementation of the Canny edge detector. We believe that our edge detector is a novel step in edge detection and would be very suitable for the next generation real-time image processing and computer vision applications.


title: Modeling and Control of PV Charger System With SEPIC Converter

abstract: The photovoltaic (PV) stand-alone system requires a battery charger for energy storage. This paper presents the modeling and controller design of the PV charger system implemented with the single-ended primary inductance converter (SEPIC). The designed SEPIC employs the peak-current-mode control with the current command generated from the input PV voltage regulating loop, where the voltage command is determined by both the PV module maximum power point tracking (MPPT) control loop and the battery charging loop. The control objective is to balance the power flow from the PV module to the battery and the load such that the PV power is utilized effectively and the battery is charged with three charging stages. This paper gives a detailed modeling of the SEPIC with the PV module input and peak-current-mode control first. Accordingly, the PV voltage controller, as well as the adaptive MPPT controller, is designed. An 80-W prototype system is built. The effectiveness of the proposed methods is proved with some simulation and experimental results.


title: Challenges in the Design of High-Speed Clock and Data Recovery Circuits

abstract: This article describes the challenges in the design of monolithic clock and data recovery circuits used in high-speed transceivers. Following an overview of general issues, the task of phase detection for random data is addressed. Next, Hogge (1985), Alexander (1975), and half-rate phase detectors are introduced and their trade-offs outlined. Finally, a number of clock and data recovery architectures are presented.


title: Detecting Kernel-Level Rootkits Using Data Structure Invariants

abstract: Rootkits affect system security by modifying kernel data structures to achieve a variety of malicious goals. While early rootkits modified control data structures, such as the system call table and values of function pointers, recent work has demonstrated rootkits that maliciously modify noncontrol data. Most prior techniques for rootkit detection have focused solely on detecting control data modifications and, therefore, fail to detect such rootkits. This paper presents a novel technique to detect rootkits that modify both control and noncontrol data. The main idea is to externally observe the execution of the kernel during an inference phase and hypothesize invariants on kernel data structures. A rootkit detection phase uses these invariants as specifications of data structure integrity. During this phase, violation of invariants indicates an infection. We have implemented Gibraltar, a prototype tool that infers kernel data structure invariants and uses them to detect rootkits. Experiments show that Gibraltar can effectively detect previously known rootkits, including those that modify noncontrol data structures.


title: Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games

abstract: In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.


title: ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

abstract: Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. ::: To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. ::: We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper.


title: Discriminative key-component models for interaction detection and recognition

abstract: We present key-component models for selecting important temporal instants or human poses from video sequences.Human interaction key-component models are learned from weakly supervised data.We demonstrate empirical results on the VIRAT and UT-Interaction datasets. Not all frames are equal - selecting a subset of discriminative frames from a video can improve performance at detecting and recognizing human interactions. In this paper we present models for categorizing a video into one of a number of predefined interactions or for detecting these interactions in a long video sequence. The models represent the interaction by a set of key temporal moments and the spatial structures they entail. For instance: two people approaching each other, then extending their hands before engaging in a "handshaking" interaction. Learning the model parameters requires only weak supervision in the form of an overall label for the interaction. Experimental results on the UT-Interaction and VIRAT datasets verify the efficacy of these structured models for human interactions.


title: Customer satisfaction in E-Commerce : A case study of China and Bangladesh

abstract: As information technology and the popularity of Internet technology and in-depth applications, e-commerce is at unprecedented pace. People become more and more the focus of attention. At present, r ...


title: A k-nearest neighbor based algorithm for multi-label classification

abstract: In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, a multi-label lazy learning approach named ML-kNN is presented, which is derived from the traditional k-nearest neighbor (kNN) algorithm. In detail, for each new instance, its k-nearest neighbors are firstly identified. After that, according to the label sets of these neighboring instances, maximum a posteriori (MAP) principle is utilized to determine the label set for the new instance. Experiments on a real-world multi-label bioinformatic data show that ML-kNN is highly comparable to existing multi-label learning algorithms.


title: Unsupervised Modeling of Twitter Conversations

abstract: We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium.


eview opamine and training-related working-memory improvement


title: Using legos to interest high school students and imtrove k12 stem education

abstract: Wichita State University is actively using LEGOs to encourage science math engineering and technology (SMET). There are two major thrusts in our efforts. The college of engineering uses LEGO blocks to simulate a factory environment in the building of LEGO airplanes. This participative demonstration has been used at middle school, high school, and college classes. LEGOs are used to present four manufacturing scenarios of traditional, cellular, pull, and single piece flow manufacturing. The demonstration presents to students how the design of a factory has significant impact on the success of the company. It also encourages students to pursue engineering careers. The college of education uses robotics as a vehicle to integrate technology and engineering into math and science preservice and inservice teacher education.. The purpose is to develop technologically astute and competent teachers who are capable of integrating technology into their curriculum to improve the teaching and learning of their students. This paper will discuss each effort, the collaboration between the two, and provide examples of success.


title: Walkability of local communities: using geographic information systems to objectively assess relevant environmental attributes.

abstract: Geographic Information Systems (GIS) can be used to objectively measure features of the built environment that may influence adults’ physical activity, which is an important determinant of chronic disease. We describe how a previously developed index of walkability was operationalised in an Australian context, using available spatial data. The index was used to generate a stratified sampling frame for the selection of households from 32 communities for the PLACE (Physical Activity in Localities and Community Environments) study. GIS data have the potential to be used to construct measures of environmental attributes and to develop indices of walkability for cities, regions or local communities.


title: Happiness is everything, or is it? Explorations on the meaning of psychological well-being.

abstract: Reigning measures of psychological well-being have little theoretical grounding, despite an extensive literature on the contours of positive functioning. Aspects of well-being derived from this literature (i.e., self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life, and personal growth) were operationalized. Three hundred and twenty-one men and women, divided among young, middle-aged, and older adults, rated themselves on these measures along with six instruments prominent in earlier studies (i.e., affect balance, life satisfaction, self-esteem, morale, locus of control, depression). Results revealed that positive relations with others, autonomy, purpose in life, and personal growth were not strongly tied to prior assessment indexes, thereby supporting the claim that key aspects of positive functioning have not been represented in the empirical arena. Furthermore, age profiles revealed a more differentiated pattern of well-being than is evident in prior research.


title: Research on Explosive Formulation Performance Parameter Prediction Method Based on GEP

abstract: Definition of homology gene, mutual exclusion gene, formulations gene and compatriots gene set has been given based on the characteristics of the energetic formulation components in this paper. The complex formula chromosome resolution rules have been designed to solve the energetic formulation component burst speed estimate problem combined with the GEP theories and test techniques. The test results showed that the performance prediction error of the detonation velocity is less than 3%.


title: Satellite Oil Spill Detection Using Artificial Neural Networks

abstract: Oil spills represent a major threat to ocean ecosystems and their health. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection employing two different Artificial Neural Networks (ANN), used in sequence. The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and used to drive a second ANN which classifies objects into oil spills or look-alikes. The proposed algorithm was trained using 97 ERS-2 SAR and ENVSAT ASAR images of individual verified oil spills or/and look-alikes. The algorithm was validated using a large dataset comprising full-swath images and correctly identified 91.6% of reported oil spills and 98.3% of look-alike phenomena. The segmentation stage of the new technique outperformed the established edge detection and adaptive thresholding approaches. An analysis of feature descriptors highlighted the importance of image gradient information in the classification stage.


title: Toad Glandular Secretions and Skin Extractions as Anti-Inflammatory and Anticancer Agents

abstract: Toad glandular secretions and skin extractions contain many natural agents which may provide a unique resource for novel drug development. The dried secretion from the auricular and skin glands of Chinese toad (Bufo bufo gargarizans) is named Chansu, which has been used in Traditional Chinese Medicine (TCM) for treating infection and inflammation for hundreds of years. The sterilized hot water extraction of dried toad skin is named Huachansu (Cinobufacini) which was developed for treating hepatitis B virus (HBV) and several types of cancers. However, the mechanisms of action of Chansu, Huachansu, and their constituents within are not well reported. Existing studies have suggested that their anti-inflammation and anticancer potential were via targeting Nuclear Factor (NF)-κB and its signalling pathways which are crucial hallmarks of inflammation and cancer in various experimental models. Here, we review some current studies of Chansu, Huachansu, and their compounds in terms of their use as both anti-inflammatory and anticancer agents. We also explored the potential use of toad glandular secretions and skin extractions as alternate resources for treating human cancers in combinational therapies.


title: Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation

abstract: Prior art search or recommending citations for a patent application is a challenging task. Many approaches have been proposed and shown to be useful for prior art search. However, most of these methods do not consider the network structure for integrating and diffusion of different kinds of information present among tied patents in the citation network. In this paper, we propose a method based on a time-aware random walk on a weighted network of patent citations, the weights of which are characterized by contextual similarity relations between two nodes on the network. The goal of the random walker is to find influential documents in the citation network of a query patent, which can serve as candidates for drawing query terms and bigrams for query refinement. The experimental results on CLEF-IP datasets (CLEF-IP 2010 and CLEF-IP 2011) show the effectiveness of encoding contextual similarities (common classification codes, common inventor, and common applicant) between nodes in the citation network. Our proposed approach can achieve significantly better results in terms of recall and Mean Average Precision rates compared to strong baselines of prior art search.


Agents in Object-Oriented Software Engineering


title: Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services

abstract: Bringing smartness into tourism destinations requires dynamically interconnecting stakeholders through a technological platform on which information relating to tourism activities could be exchanged instantly. Instant information exchange has also created extremely large data sets known as Big Data that may be analysed computationally to reveal patterns and trends. Smart Tourism Destinations should make an optimal use of Big Data by offering right services that suit users’ preference at the right time. In relation thereto, this paper aims at contributing to the understanding on how Smart Tourism Destinations could potentially enhance tourism experience through offering products/services that are more personalised to meet each of visitor’s unique needs and preferences. Understanding the needs, wishes and desires of travellers becomes increasingly critical for the competitiveness of destinations. Therefore, the findings in the present research are insightful for number of tourism destinations.


title: Diffusion of innovation: customer relationship management adoption in supply chain organizations

abstract: The successful diffusion of innovations in rapidly changing supply chain technological environments is essential to support operations and supply chain management functions. In this paper we conceptualize and develop a framework for research into the diffusion of innovations in organizations pertaining to software adoption in supply chain management. Incorporating Task-Technology Fit theory with a network externalities model, we develop a novel approach in customer relations management (CRM) software adoption. An empirical study using Partial Least Squares (PLS) on data from US supply chain managers is utilized to confirm the usability of the proposed framework as well as confirming the efficacy of the proposed combined methodological approach suggested in this paper. The results also show the direct and indirect network externalities of system quality, ease of use, system reliability, and authority can moderate the CRM adoption relationship in organizations, suggesting the possibility that other moderating network externality variables may impact CRM adoption.


title: What's the Risk? A Simple Approach for Estimating Adjusted Risk Measures from Nonlinear Models Including Logistic Regression

abstract: OBJECTIVE ::: To develop and validate a general method (called regression risk analysis) to estimate adjusted risk measures from logistic and other nonlinear multiple regression models. We show how to estimate standard errors for these estimates. These measures could supplant various approximations (e.g., adjusted odds ratio [AOR]) that may diverge, especially when outcomes are common. ::: ::: ::: STUDY DESIGN ::: Regression risk analysis estimates were compared with internal standards as well as with Mantel-Haenszel estimates, Poisson and log-binomial regressions, and a widely used (but flawed) equation to calculate adjusted risk ratios (ARR) from AOR. ::: ::: ::: DATA COLLECTION ::: Data sets produced using Monte Carlo simulations. ::: ::: ::: PRINCIPAL FINDINGS ::: Regression risk analysis accurately estimates ARR and differences directly from multiple regression models, even when confounders are continuous, distributions are skewed, outcomes are common, and effect size is large. It is statistically sound and intuitive, and has properties favoring it over other methods in many cases. ::: ::: ::: CONCLUSIONS ::: Regression risk analysis should be the new standard for presenting findings from multiple regression analysis of dichotomous outcomes for cross-sectional, cohort, and population-based case-control studies, particularly when outcomes are common or effect size is large.


title: Part Of Speech Tagging and Chunking with HMM and CRF

abstract: In this paper we propose an approach to Part of Speech (PoS) tagging using a combination of Hidden Markov Model and error driven learning. For the NLPAI joint task, we also implement a chunker using Conditional Random Fields (CRFs). The results for the PoS tagging and chunking task are separately reported along with the results of the joint task.


title: Class-D Amplifier Power Stage With PWM Feedback Loop

abstract: This paper presents a second-order pulsewidth modulation (PWM) feedback loop to improve power supply rejection (PSR) of any open-loop PWM class-D amplifiers (CDAs). PSR of the audio amplifier has always been a key parameter in mobile phone applications. In contrast to class-AB amplifiers, the poor PSR performance has always been the major drawback for CDAs with a half-bridge connected power stage. The proposed PWM feedback loop is fabricated using GLOBALFOUNDRIES' 0.18-μm CMOS process technology. The measured PSR is more than 80 dB and the measured total harmonic distortion is less than 0.04% with a 1-kHz input sinusoidal test tone.


title: Modeling Information Diffusion in Implicit Networks

abstract: Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffusion processes over social networks, the underlying phenomena are the result of a complex web of interactions among numerous participants. Here we develop the Linear Influence Model where rather than requiring the knowledge of the social network and then modeling the diffusion by predicting which node will influence which other nodes in the network, we focus on modeling the global influence of a node on the rate of diffusion through the (implicit) network. We model the number of newly infected nodes as a function of which other nodes got infected in the past. For each node we estimate an influence function that quantifies how many subsequent infections can be attributed to the influence of that node over time. A nonparametric formulation of the model leads to a simple least squares problem that can be solved on large datasets. We validate our model on a set of 500 million tweets and a set of 170 million news articles and blog posts. We show that the Linear Influence Model accurately models influences of nodes and reliably predicts the temporal dynamics of information diffusion. We find that patterns of influence of individual participants differ significantly depending on the type of the node and the topic of the information.


title: Joint Pairwise Learning and Image Clustering Based on a Siamese CNN

abstract: How to use a deep convolutional neural network (CNN) to efficiently and effectively learn representations of a large unlabeled set of images and group them into clusters remains a challenging problem. To address this problem, we propose a Siamese clustering CNN (SC-CNN) to iteratively learn discriminative representations for image clustering. Based on the proposed SC-CNN, we employ a mini-batch-based joint pairwise representation learning and clustering scheme to make the computation and storage cost efficient for large-scale image clustering on a personal computer with a commercial GPU graphic card. On top of SC-CNN, the proposed pairwise learning scheme effectively learns discriminative representations by appropriately selecting same-cluster and different-cluster image pairs from the results of each clustering iteration. Experimental results demonstrate that the proposed method outperforms start-of-the-art clustering schemes in clustering accuracy on public image sets.


title: Learning Semantics-Guided Visual Attention for Few-Shot Image Classification

abstract: We propose a deep learning framework for few-shot image classification, which exploits information across label semantics and image domains, so that regions of interest can be properly attended for improved classification. The proposed semantics-guided attention module is able to focus on most relevant regions in an image, while the attended image samples allow data augmentation and alleviate possible overfitting during FSL training. Promising performances are presented in our experiments, in which we consider both closed and open-world settings. The former considers the test input belong to the categories of few shots only, while the latter requires recognition of all categories of interest.


title: A Component-Based Dynamic Link Support for Safety-Critical Embedded Systems

abstract: Safety-critical embedded systems have to undergo rigorous development process in order to ensure that their function will not compromise humans or environment where they operate. Therefore, they rely on simple and proven-in-use design. However, with growing software complexity, maintenance becomes very important aspect in safety domain. Recent approaches for managing maintenance allow to perform changes on software at design-time, which implies that the whole system has to be rebuilt when the application software changes. In this paper, we describe more flexible solution for updating the application software. We apply the component-based paradigm to construct the application software, i.e. we define a model of a software function that can be dynamically linked with the entire operating system (OS). In order to avoid the usage of the OS-provided support for dynamic linking, we design software functions as position-independent and relocation-free binaries with well-defined interfaces. With the help of component-based paradigm we show how to simplify the link support and make it suitable for safety domain.


title: Fast Random Walk Graph Kernel

abstract: Random walk graph kernel has been used as an important tool for various data mining tasks including classification and similarity computation. Despite its usefulness, however, it suffers from the expensive computational cost which is at least O(n) or O(m) for graphs with n nodes and m edges. In this paper, we propose Ark, a set of fast algorithms for random walk graph kernel computation. Ark is based on the observation that real graphs have much lower intrinsic ranks, compared with the orders of the graphs. Ark exploits the low rank structure to quickly compute random walk graph kernels in O(n) or O(m) time. Experimental results show that our method is up to 97,865× faster than the existing algorithms, while providing more than 91.3% of the accuracies.


Proposal Manipulation of 3 D Objects in Immersive Virtual Environments


title: Self-adaptive UIs: Integrated Model-Driven Development of UIs and Their Adaptations

abstract: Self-adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. In classical model-driven UI development (MDUID) approaches, self-adaptivity and context management introduce additional complexity since self-adaptation features are distributed in a cross-cutting manner at various locations in the models. This results in a tightly interwoven model landscape that is hard to understand and maintain. In this paper, we present an integrated model-driven development method where a classical model-driven development of UIs is coupled with a separate model-driven development of UI adaptation rules and context-of-use. We base our approach on the core UI modeling language IFML, and focus on a new modeling language for adaptation rules, called AdaptUI. We show how generated UI code is coupled with adaptation services generated from AdaptUI adaptation rules and integrated in an overall UI framework. This allows runtime UI adaptation realized by an automatic reaction to context-of-use changes. The benefit of our approach is demonstrated by a case study, showing the development of self-adaptive UIs for a university library application, utilizing the Angular 2 JavaScript framework.


title: An IoT environmental data collection system for fungal detection in crop fields

abstract: There is a need for a system which provides real-time local environmental data in rural crop fields for the detection and management of fungal diseases. This paper presents the design of an Internet of Things (IoT) system consisting of a device capable of sending real-time environmental data to cloud storage and a machine learning algorithm to predict environmental conditions for fungal detection and prevention. The stored environmental data on conditions such as air temperature, relative air humidity, wind speed, and rain fall is accessed and processed by a remote computer for analysis and management purposes. A machine learning algorithm using Support Vector Machine regression (SVMr) was developed to process the raw data and predict short-term (day-to-day) air temperature, relative air humidity, and wind speed values to assist in predicting the presence and spread of harmful fungal diseases through the local crop field. Together, the environmental data and environmental predictions made easily accessible by this IoT system will ultimately assist crop field managers by facilitating better management and prevention of fungal disease spread.