title: A tale of two cities: Analyzing road accidents with big spatial data

abstract: With the growing number of Open Data initiatives and the increased volume of related data, new forms of civic engagement are emerging. This engagement leads to novel applications and problem solving approaches. Useful insights derived from exploiting publicly available data and open source tools ultimately result in the enhancement of daily life in our communities. In this paper, we analyze publicly available road accident data for the cities of Fredericton and Laval. A traffic accident data processing and analysis pipeline is built using big data systems and big data spatial frameworks. We present a comparative analysis of traffic accidents in these two cities. Random forest classification models are trained to predict if an accident has casualties. The predictive models are used to provide insights into important factors affecting accidents with fatalities or injuries. Results of the study can be used to establish meaningful safe driving policy suggestions, aid in making emergency dispatch decisions, inform accident management procedures or even assist with urban planning.


title: Adapting Attention-Based Neural Network to Low-Resource Mongolian-Chinese Machine Translation

abstract: Neural machine translation (NMT) has shown very promising results for some resourceful languages like En-Fr and En-De. The success partly relies on the availability of large scale and high quality parallel corpora. We research on how to adapt NMT to very low-resource Mongolian-Chinese machine translation by introducing attention mechanism, sub-words translation, monolingual data and a NMT correction model. We proposed a sub-words model to address the out-of-vocabulary (OOV) problem in attention-based NMT model. Monolingual data help alleviate the low-resource problem. Besides, we explore a Chinese NMT correction model to enhance the translation performance. The experiments show that the adapted Mongolian-Chinese attention-based NMT machine translation obtains an improvement of 1.70 BLEU points over the phrased-based statistical machine translation baseline and 3.86 BLEU points over normal NMT baseline on an open training set.


title: Severity Analyses of Single-Vehicle Crashes Based on Rough Set Theory

abstract: A single-vehicle crash is a typical pattern of traffic accidents and tends to cause heavy loss. The purpose of this study is to identify the factors significantly influencing single-vehicle crash injury severity, using a data selected from Beijing city for a 4-year period. Rough set theory was applied to complete the injury severity analysis, and followed by applying cross-validation method to estimate the prediction accuracy of extraction rules. Results show that it is effective for analyzing the severity of Single-vehicle crashes with rough set theory.


title: Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation

abstract: Detecting pedestrians is still a challenging task for automotive vision systems due to the extreme variability of targets, lighting conditions, occlusion, and high-speed vehicle motion. Much research has been focused on this problem in the last ten years and detectors based on classifiers have gained a special place among the different approaches presented. This paper presents a state-of-the-art pedestrian detection system based on a two-stage classifier. Candidates are extracted with a Haar cascade classifier trained with the Daimler Detection Benchmark data set and then validated through a part-based histogram-of-oriented-gradient (HOG) classifier with the aim of lowering the number of false positives. The surviving candidates are then filtered with feature-based tracking to enhance the recognition robustness and improve the results' stability. The system has been implemented on a prototype vehicle and offers high performance in terms of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system relies on the combination of a HOG part-based approach, tracking based on a specific optimized feature, and porting on a real prototype.


title: Modeling paddle-aided stair-climbing for a mobile robot based on eccentric paddle mechanism

abstract: To gain high mobility on challenging terrains, a mobile robot based on eccentric paddle mechanism (ePaddle) with locomotion versatility has been proposed. In this paper, a paddle-aided stair-climbing motion is presented for this ePaddle-based robot. The robot can roll on the stair as a traditional wheeled vehicle and also can climb up the stair under the help of its paddles. Robot-stair interaction modes are presented and typical feasible postures of the robot in stair-climbing are discussed. Frictional requirements for the robot to hold a desired posture are evaluated by modelling statics of the robot. Analyzed results reveal that two critical scenarios in wheeled mode occur when the front-wheel is at the bottom of the riser, and when the rear-wheel is at the top of the riser, respectively. In contrast, frictional requirements of the paddle-aided stair-climbing postures confirm that the robot can climb up the stair with all feasible postures by touching the stair with the paddle, which verifies the effectiveness of the proposed paddle-aided stair-climbing.


title: Positioning with Bluetooth

abstract: An evaluation of Bluetooth positioning in a general positioning platform is presented. The evaluation a Bluetooth based positioning system was implemented in order to complement the theoretical evaluation with empirical tests. Three different ways of positioning with Bluetooth have been developed. With a registered positioning service, a Bluetooth device has an active role in the positioning task, where a connected device to look up respective position in a database uses the unique address of the device. It is also possible to forward a position gained from the positioning platforms using the peer-to-peer characteristics in Bluetooth. The theoretical time requirements for a positioning system based on Bluetooth are discussed. Empirical tests show that these requirements hold.


title: Sketching and Neural Networks

abstract: High-dimensional sparse data present computational and statistical challenges for supervised learning. We propose compact linear sketches for reducing the dimensionality of the input, followed by a single layer neural network. We show that any sparse polynomial function can be computed, on nearly all sparse binary vectors, by a single layer neural network that takes a compact sketch of the vector as input. Consequently, when a set of sparse binary vectors is approximately separable using a sparse polynomial, there exists a single-layer neural network that takes a short sketch as input and correctly classifies nearly all the points. Previous work has proposed using sketches to reduce dimensionality while preserving the hypothesis class. However, the sketch size has an exponential dependence on the degree in the case of polynomial classifiers. In stark contrast, our approach of using improper learning, using a larger hypothesis class allows the sketch size to have a logarithmic dependence on the degree. Even in the linear case, our approach allows us to improve on the pesky $O({1}/{{\gamma}^2})$ dependence of random projections, on the margin $\gamma$. We empirically show that our approach leads to more compact neural networks than related methods such as feature hashing at equal or better performance.


title: Improving Security Screening: A Comparison of Multistatic Radar Configurations for Human Body Imaging

abstract: Active millimeter-wave radar can be used for imaging concealed objects and detecting contraband at security checkpoints. Current state-of-theart systems are based on monostatic or quasimonostatic configurations that can misrepresent areas of the target when the specular reflection is oriented away from the incident direction. This article presents a fully multistatic millimeter-wave imaging architecture for human-body screening. Transmitters placed off the receiving aperture panel allow for maximizing the surface of the human body that can be reconstructed. A reduced number of transmitters allows for fast imaging, which minimizes the target motion effects. Multiple three-dimensional (3-D) simulation-based examples are presented to validate the proposed system.


title: A Data Stream Publish/Subscribe Architecture with Self-adapting Queries

abstract: In data stream applications, streams typically arise from a geographically distributed collection of producers and may be queried by consumers, which may be distributed as well. In such a setting, a query can be seen as a subscription asking to be informed of all tuples that satisfy a specific condition. We propose to support the publishing and querying of distributed data streams by a publish/subscribe architecture. ::: ::: To enable such a system to scale to a large number of producers and consumers requires the introduction of republishers which collect together data streams and make the merged stream available. If republishers consume from other republishers, a hierarchy of republishers results. ::: ::: We present a formalism that allows distributed data streams, published by independent stream producers, to be integrated as views on a mediated schema. We use the formalism to develop methods to adapt query plans to changes in the set of available data streams and allow consumers to dynamically change which streams they subscribe to.


title: Recent developments in consumer credit risk assessment

abstract: Consumer credit risk assessment involves the use of risk assessment tools to manage a borrower’s account from the time of pre-screening a potential application through to the management of the account during its life and possible write-off. The riskiness of lending to a credit applicant is usually estimated using a logistic regression model though researchers have considered many other types of classifier and whilst preliminary evidence suggest support vector machines seem to be the most accurate, data quality issues may prevent these laboratory based results from being achieved in practice. The training of a classifier on a sample of accepted applicants rather than on a sample representative of the applicant population seems not to result in bias though it does result in difficulties in setting the cut off. Profit scoring is a promising line of research and the Basel 2 accord has had profound implications for the way in which credit applicants are assessed and bank policies adopted.


title: Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing

abstract: Mobile crowdsensing can enable numerous attractive novel sensing applications due to the prominent advantages such as wide spatiotemporal coverage, low cost, good scalability, pervasive application scenarios, etc. In mobile crowdsensing applications, incentive mechanisms are necessary to stimulate more potential smartphone users and to achieve good service quality. In this paper, we focus on exploring truthful incentive mechanisms for a novel and practical scenario where the tasks are time window dependent, and the platform has strong requirement of data integrity. We present a universal system model for this scenario based on reverse auction framework and formulate the problem as the Social Optimization User Selection (SOUS) problem. We design two incentive mechanisms, MST and MMT . In single time window case, we design an optimal algorithm based on dynamic programming to select users. Then we determine the payment for each user by VCG auction; while in multiple time window case, we show the general SOUS problem is NP-hard, and we design MMT based on greedy approach, which approximates the optimal solution within a factor of $\mathbf{In}|{\mathcal W}|+1$ , where $|{\mathcal W}|$ is the length of sensing time window defined by the platform. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve high computation efficiency, individual rationality and truthfulness.


title: Indian Medicinal Plants: A Potential Source for Anticandidal Drugs

abstract: Alcoholic and aqueous crude extracts of 37 traditionally-used medicinal plants were screened for antifungal activity against the pathogenic yeast, Candida albicans, and dermatophytes, Epidermophyton floccosum, Microsporum gypseum and Trichophyton rubrum. Alcoholic extracts of these 37 plants were demonstrated to have antifungal activity. Twenty-one extracts showed strong activity and the remaining 16 plants exhibited moderate activity against Candida albicans and to one or more dermatophytes. Such activity was detected in only 16 aqueous extracts. Alcoholic extracts of five medicinal plants were selected for further studies on the basis of their strong anticandidal activity. Minimum inhibitory concentration (MIC) values of these five extracts revealed their varied anticandidal potency. MIC values ranged between 4-9 mg/ml. Comparatively high potency was observed in the extracts of Plumbago zeylanica (4mg/ml) followed by Terminalia bellirica and Phyllanthus emblica (7 mg/ml), each Holarrhena antidysentrica ...


title: Spatial-temporal wind field prediction by Artificial Neural Networks

abstract: The prediction of near surface wind speed is becoming increasingly vital for the operation of electrical energy grids as the capacity of installed wind power grows. The majority of predictive wind speed modeling has focused on point-based time-series forecasting. Effectively balancing demand and supply in the presence of distributed wind turbine electricity generation, however, requires the prediction of wind fields in space and time. Additionally, predictions of full wind fields are particularly useful for future power planning such as the optimization of electricity power supply systems. In this paper, we propose a composite artificial neural network (ANN) model to predict the 6-hour and 24-hour ahead average wind speed over a large area (~3.15*106 km2). The ANN model consists of a convolutional input layer, a Long Short-Term Memory (LSTM) hidden layer, and a transposed convolutional layer as the output layer. We compare the ANN model with two non-parametric models, a null persistence model and a mean value model, and find that the ANN model has substantially smaller error than each of these models. Additionally, the ANN model also generally performs better than integrated autoregressive moving average models, which are trained for optimal performance in specific locations.


title: ForesTexter: An efficient random forest algorithm for imbalanced text categorization

abstract: In this paper, we propose a new random forest (RF) based ensemble method, ForesTexter, to solve the imbalanced text categorization problems. RF has shown great success in many real-world applications. However, the problem of learning from text data with class imbalance is a relatively new challenge that needs to be addressed. A RF algorithm tends to use a simple random sampling of features in building their decision trees. As a result, it selects many subspaces that contain few, if any, informative features for the minority class. Furthermore, the Gini measure for data splitting is considered to be skew sensitive and bias towards the majority class. Due to the inherent complex characteristics of imbalanced text datasets, learning RF from such data requires new approaches to overcome challenges related to feature subspace selection and cut-point choice while performing node splitting. To this end, we propose a new tree induction method that selects splits, both feature subspace selection and splitting criterion, for RF on imbalanced text data. The key idea is to stratify features into two groups and to generate effective term weighting for the features. One group contains positive features for the minority class and the other one contains the negative features for the majority class. Then, for feature subspace selection, we effectively select features from each group based on the term weights. The advantage of our approach is that each subspace contains adequate informative features for both minority and majority classes. One difference between our proposed tree induction method and the classical RF method is that our method uses Support Vector Machines (SVM) classifier to split the training data into smaller and more balance subsets at each tree node, and then successively retrains the SVM classifiers on the data partitions to refine the model while moving down the tree. In this way, we force the classifiers to learn from refined feature subspaces and data subsets to fit the imbalanced data better. Hence, the tree model becomes more robust for text categorization task with imbalanced dataset. Experimental results on various benchmark imbalanced text datasets (Reuters-21578, Ohsumed, and imbalanced 20 newsgroup) consistently demonstrate the effectiveness of our proposed ForesTexter method. The performance of our proposed approach is competitive against the standard random forest and different variants of SVM algorithms.


title: Online Display Advertising: Targeting and Obtrusiveness

abstract: We use data from a large-scale field experiment to explore what influences the effectiveness of online advertising. We find that matching an ad to website content and increasing an ad's obtrusiveness independently increase purchase intent. However, in combination, these two strategies are ineffective. Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: the negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income and for categories where privacy matters most. Our results suggest a possible explanation for the growing bifurcation in Internet advertising between highly targeted plain text ads and more visually striking but less targeted ads.


title: Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter

abstract: Consider the ridge estimate (λ) for β in the model unknown, (λ) = (X T X + nλI)−1 X T y. We study the method of generalized cross-validation (GCV) for choosing a good value for λ from the data. The estimate is the minimizer of V(λ) given by where A(λ) = X(X T X + nλI)−1 X T . This estimate is a rotation-invariant version of Allen's PRESS, or ordinary cross-validation. This estimate behaves like a risk improvement estimator, but does not require an estimate of σ2, so can be used when n − p is small, or even if p ≥ 2 n in certain cases. The GCV method can also be used in subset selection and singular value truncation methods for regression, and even to choose from among mixtures of these methods.


title: Ambient occlusion fields

abstract: We present a novel real-time technique for computing inter-object ambient occlusion. For each occluding object, we precompute a field in the surrounding space that encodes an approximation of the occlusion caused by the object. This volumetric information is then used at run-time in a fragment program for quickly determining the shadow cast on the receiving objects. According to our results, both the computational and storage requirements are low enough for the technique to be directly applicable to computer games running on the current graphics hardware.


title: Zika virus genome biology and molecular pathogenesis

abstract: Zika virus (ZIKV) is an emerging RNA virus in the widespread Flavivirus genus. Recently, ZIKV has rapidly spread around the world and has been implicated in human disease, including neurological disorders, triggering public and scientific attention. Understanding how ZIKV causes disease is the highest priority, yet little is known about this virus. Here we examine the currently published data from ZIKV studies to provide the latest understanding of ZIKV genome biology and molecular pathogenesis. The ZIKV genome evolved rapidly from the Flavivirus genus and diverged from the members of this genus, even within the dengue virus cluster to which ZIKV belongs. Genome variations and divergences also exist among ZIKV strains/isolates. These genome divergences might account for the uniqueness of Zika disease. ZIKV infection activates not only the antiviral immune response but also the pro-inflammatory responses associated with disease symptoms. Strikingly, ZIKV activates protein complexes that are functionally associated with disease process, such as glial cell activation and proliferation (for example, Toll-like receptors), apoptosis and cell death, and inflammation. The activation of these complexes may critically contribute to Zika disease. The novel insights into ZIKV genome divergence and disease mechanisms summarized in this review will help accelerate the development of anti-ZIKV strategies.


title: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs

abstract: Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion's share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics. For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor's experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest "administrative factories" in The Netherlands.


Zone out no more: Mitigating mind wandering during computerized reading


Standing Guard: Protecting Foreign Capital in the Nineteenth and Twentieth Centuries


title: Cortical Thinning of the Attention and Executive Function Networks in Adults with Attention-Deficit / Hyperactivity Disorder

abstract: Attention-deficit/hyperactivity disorder (ADHD) has been associated with structural alterations in brain networks influencing cognitive and motor behaviors. Volumetric studies in children identify abnormalities in cortical, striatal, callosal, and cerebellar regions. In a prior volumetric study, we found that ADHD adults had significantly smaller overall cortical gray matter, prefrontal, and anterior cingulate volumes than matched controls. Thickness and surface area are additional indicators of integrity of cytoarchitecture in the cortex. To expand upon our earlier results and further refine the regions of structural abnormality, we carried out a structural magnetic resonance imaging study of cortical thickness in the same sample of adults with ADHD (n 5 24) and controls (n 5 18), hypothesizing that the cortical networks underlying attention and executive function (EF) would be most affected. Compared with healthy adults, adults with ADHD showed selective thinning of cerebral cortex in the networks that subserve attention and EF. In the present study, we found significant cortical thinning in ADHD in a distinct cortical network supporting attention especially in the right hemisphere involving the inferior parietal lobule, the dorsolateral prefrontal, and the anterior cingulate cortices. This is the first documentation that ADHD in adults is associated with thinner cortex in the cortical networks that modulate attention and EF.


Traffic Light Detection


title: CoGrec: A Community-Oriented Group Recommendation Framework

abstract: Recently, group recommendation becomes substantially significant when it frequently happens that a group of users need to determine which item (e.g. movie, music, restaurant, etc.) to choose. In this paper we employ the information of friend network to propose a Community-Oriented Group Recommendation framework (CoGrec) consisting of non-negative matrix factorization based user profile generation, community detection based group identification, and overlapping community membership based group decision. Along with four inherent aggregation and allocation strategies, our proposed framework is evaluated through extensive experiments on real-world datasets. The experimental results show that the proposed framework is promising and more accurate when the given friend network is much denser, which is suitable for modern review and rating systems.


title: Class-Splitting Generative Adversarial Networks

abstract: Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserstein GAN formulation which stabilized adversarial training and allows considering high capacity network architectures such as ResNet. In this work we show how to boost conditional GAN by augmenting available class labels. The new classes come from clustering in the representation space learned by the same GAN model. The proposed strategy is also feasible when no class information is available, i.e. in the unsupervised setup. Our generated samples reach state-of-the-art Inception scores for CIFAR-10 and STL-10 datasets in both supervised and unsupervised setup.


title: Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation

abstract: Massive graphs, such as online social networks and communication networks, have become common today. To efficiently analyze such large graphs, many distributed graph computing systems have been developed. These systems employ the "think like a vertex" programming paradigm, where a program proceeds in iterations and at each iteration, vertices exchange messages with each other. However, using Pregel's simple message passing mechanism, some vertices may send/receive significantly more messages than others due to either the high degree of these vertices or the logic of the algorithm used. This forms the communication bottleneck and leads to imbalanced workload among machines in the cluster. In this paper, we propose two effective message reduction techniques: (1)vertex mirroring with message combining, and (2)an additional request-respond API. These techniques not only reduce the total number of messages exchanged through the network, but also bound the number of messages sent/received by any single vertex. We theoretically analyze the effectiveness of our techniques, and implement them on top of our open-source Pregel implementation called Pregel+. Our experiments on various large real graphs demonstrate that our message reduction techniques significantly improve the performance of distributed graph computation.


A Survey of the Robotics Ethical Landscape


title: MuJoCo HAPTIX: A virtual reality system for hand manipulation

abstract: Data-driven methods have lead to advances in multiple fields including robotics. These methods however have had limited impact on dexterous hand manipulation, partly due to lack of rich and physically-consistent dataset as well as technology able to collect them. To fill this gap, we developed a virtual reality system combining real-time motion capture, physics simulation and stereoscopic visualization. The system enables a user wearing a CyberGlove to "reach-in" the simulation, and manipulate virtual objects through contacts with a tele-operated virtual hand. The system is evaluated on a subset of tasks in the Southampton Hand Assessment Procedure — which is a clinically validated test of hand function. The system is also being used by performer teams in the DARPA Hand Proprioception & Touch Interfaces program to develop neural control interfaces in simulation. The software is freely available at www.mujoco.org.


title: A Logic For Programming Database Transactions

abstract: We propose an extension of classical predicate calculus, called Transaction Logic, which provides a logical foundation for the phenomenon of state changes in logic programs and databases. Transaction Logic comes with a natural model theory and a sound and complete proof theory. The proof theory not only verifies programs, but also executes them, which makes this logic an ideal tool for declarative programming of database transactions and state-modifying logic programs. The semantics of Transaction Logic leads naturally to features whose amalgamation in a single logic has proved elusive in the past. These features include hypothetical and committed updates, dynamic constraints on transaction execution, non-determinism, and bulk updates. Finally, Transaction Logic holds promise as a logical model of hitherto non-logical phenomena, including so-called procedural knowledge in AI, and the behavior of object-oriented databases, especially methods with side effects. This paper presents the semantics of Transaction Logic and a sound and complete SLD-style proof theory for a Horn-like subset of the logic.


title: Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics

abstract: Fig. 1. The Progressive Insights system features progressive visual analytics, and supports user-driven exploration of in-progress analytics. Partial results from the progressive analytics enhance the scatterplot, list, and tree visualizations without interfering with users' cognitive workflow. Abstract— As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-launching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records. Index Terms—Progressive visual analytics, information visualization, interactive machine learning, electronic medical records.


title: Effects of Acute Smoked Marijuana on Complex Cognitive Performance

abstract: Although the ability to perform complex cognitive operations is assumed to be impaired following acute marijuana smoking, complex cognitive performance after acute marijuana use has not been adequately assessed under experimental conditions. In the present study, we used a within-participant double-blind design to evaluate the effects acute marijuana smoking on complex cognitive performance in experienced marijuana smokers. Eighteen healthy research volunteers (8 females, 10 males), averaging 24 marijuana cigarettes per week, completed this three-session outpatient study; sessions were separated by at least 72-hrs. During sessions, participants completed baseline computerized cognitive tasks, smoked a single marijuana cigarette (0%, 1.8%, or 3.9% Δ9-THC w/w), and completed additional cognitive tasks. Blood pressure, heart rate, and subjective effects were also assessed throughout sessions. Marijuana cigarettes were administered in a double-blind fashion and the sequence of Δ9-THC concentration order was balanced across participants. Although marijuana significantly increased the number of premature responses and the time participants required to complete several tasks, it had no effect on accuracy on measures of cognitive flexibility, mental calculation, and reasoning. Additionally, heart rate and several subjective-effect ratings (e.g., “Good Drug Effect,” “High,” “Mellow”) were significantly increased in a Δ9-THC concentration-dependent manner. These data demonstrate that acute marijuana smoking produced minimal effects on complex cognitive task performance in experienced marijuana users.


title: Recent trends in drug abuse in China

abstract: Drug abuse has spread quickly since reemerging as a national problem in China in the late 1980s. The number of registered drug abusers increased from 70,000 in 1990 to more than one million by the end of 2004. In addition to opioids, abuse of 'new' types of drugs including 3,4-methylenedioxymethamphetamine (MDMA) and ketamine has spread since 1997. Illicit drug trafficking and production have swept most of southern China, and throughout the country drug abuse has caused many problems for both abusers and the community. One major drug-related problem is the spread of HIV, which has caused major social and economic damage in China. In response, the Chinese government has begun an anti-drug campaign, including legislative measures to control drug abuse. However, changing the public's attitudes toward drug abusers and breaking the link between drug use and HIV spread are equally important.


title: A shape memory hydrogel induced by the interactions between metal ions and phosphate

abstract: A novel ferric-phosphate induced shape memory (SM) hydrogel is prepared by the one-step copolymerization of isopropenyl phosphonic acid (IPPA) and acrylamide (AM) in the presence of a cross-linker polyethylene glycol diacrylate (PEGDA). Different from the traditional SM hydrogels, our SM hydrogel can be processed into various shapes as needed and recovers to its original form in ‘multi-conditions’ such as in the presence of a reducing agent or in the presence of a competitive complexing agent. This unique feature is attributed to the fact that the oxidized ferric ions show a high complexation ability with phosphate groups of IPPA, which acts as a physical crosslinker to form the secondary networks within the hydrogels to induce the shape memory effect. The memory behavior was totally reversible, owing to Fe3+ that can be reduced to Fe2+ and extracted by the complexing agent. Particularly, the SM hydrogels exhibit controllable and good mechanical characteristics by introduction of the ferric ions, i.e., the elastic modulus can increase from 2 kPa to 70 kPa dramatically. Learning from biological systems, phosphate-metal ion based hydrogels could become an attractive candidate for various biomedical and environmental applications.


title: Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition

abstract: Recently, there has been a lot of interest in automatically generating descriptions for an image. Most existing language-model based approaches for this task learn to generate an image description word by word in its original word order. However, for humans, it is more natural to locate the objects and their relationships first, and then elaborate on each object, describing notable attributes. We present a coarse-to-fine method that decomposes the original image description into a skeleton sentence and its attributes, and generates the skeleton sentence and attribute phrases separately. By this decomposition, our method can generate more accurate and novel descriptions than the previous state-of-the-art. Experimental results on the MS-COCO and a larger scale Stock3M datasets show that our algorithm yields consistent improvements across different evaluation metrics, especially on the SPICE metric, which has much higher correlation with human ratings than the conventional metrics. Furthermore, our algorithm can generate descriptions with varied length, benefiting from the separate control of the skeleton and attributes. This enables image description generation that better accommodates user preferences.


title: Predicting stocks returns correlations based on unstructured data sources

abstract: The recent outbreak of information demand for financial investment management forces to look for novel ways of quantitative data analysis. Relying only on traditional data sources means to loose the edge over the competence and become irrelevant in the future. On the other hand, the Big Data and Data Analytics trends are getting traction in financial domain and are being sought as highly beneficial in the long term. This paper presents an approach for forecasting stock correlations based on big volumes of unstructured and noisy data. We evaluate the prediction model and demonstrate its viability for certain industrial sectors.


title: Theories of addiction

abstract: Addiction is currently dee ned as a behaviour over which an individual has impaired control with harmful consequences (Cottler, 1993; Rounsaville et al., 1993). Thus, individuals who recognize that the behaviour is harming them or those whom they care about e nd themselves unable to stop engaging in the behaviour when they try to do so (Heather, 1998). The severity of the medical, psychological and social harm that can be caused by addiction, together with the fact that it violates the individual’s freedom of choice, means that it is appropriate to consider it to be a form of psychiatric disorder: a disorder of motivation. The main challenges for theories of addiction are to explain how this happens both at an individual and a societal level, and what mechanisms and societal changes underlie prevention and recovery. Addiction typically involves initial exposure to a stimulus followed by behaviours seeking to repeat the experience. After a number of repetitions of the behaviour‐stimulus sequence, the addiction becomes established. The character and severity of the addiction may change over time, and it may be punctuated by attempts by the sufferer to abstain or regain control. In some cases, sufferers will achieve recovery for a sustained period or even permanently. There are inevitable questions about what constitutes a theory in the e eld of addiction. The Oxford English Dictionary dee nes a theory as ‘a scheme or system of ideas or statements held as an explanation or account of a group of facts or phenomena’. Much of what is proposed as theory may be considered by some to be too specie c or descriptive to warrant the epithet. Often the terms ‘theory’ and ‘model’ are used interchangeably. Strictly speaking, a ‘model’ is better construed as a coherent representation of key elements of a structure or system and is thus more descriptive than explanatory, but in practice the dividing line between ‘model’ and ‘theory’ is e ne and open to differing interpretations. This article adopts a fairly pragmatic approach, including models and theories which attempt to make some kind of statement about mechanisms and processes which go beyond the immediate observation. A search of Medline and the Science and Social Science Citation Indexes using the keywords ‘theory’ or ‘model’ together with ‘addiction’, ‘dependence’, ‘alcohol’, ‘drug’ or ‘nicotine’ yielded a host of articles. Reviews of theories of addiction can also be found in Glass (1991) and more recently in a special issue of Addiction


title: Labor market risks of industry 4.0, digitization, robots and AI

abstract: Digitization changes our world. Industry 4.0, the digital transformation of manufacturing changes the labor market. The impacts of rapid technology development of the fourth industrial revolution present huge challenges for the society and for policy makers. Are we facing reduction of employment by automation rendering human work force uncompetitive with machines? Can creation of new fields of employment, new types of jobs compensate for the loss of traditional labor market requirements?


title: Are deep neural networks the best choice for modeling source code?

abstract: Current statistical language modeling techniques, including deep-learning based models, have proven to be quite effective for source code. We argue here that the special properties of source code can be exploited for further improvements. In this work, we enhance established language modeling approaches to handle the special challenges of modeling source code, such as: frequent changes, larger, changing vocabularies, deeply nested scopes, etc. We present a fast, nested language modeling toolkit specifically designed for software, with the ability to add & remove text, and mix & swap out many models. Specifically, we improve upon prior cache-modeling work and present a model with a much more expansive, multi-level notion of locality that we show to be well-suited for modeling software. We present results on varying corpora in comparison with traditional N-gram, as well as RNN, and LSTM deep-learning language models, and release all our source code for public use. Our evaluations suggest that carefully adapting N-gram models for source code can yield performance that surpasses even RNN and LSTM based deep-learning models.


title: Characterization of the Traditional Chinese Medicine Yuhuanglian by HPLC-ESI-MS

abstract: As increasing numbers of people worldwide are using traditional Chinese medicines, their characterization has become increasingly critical. This study describes the development of a facile and reproducible high-performance liquid chromatography-based method to obtain the fingerprint of the traditional Chinese medicine Yuhuanglian. First, similarity analysis and Principal Component Analysis (PCA) were investigated to interpret differences in the chromatograms of samples from different groups. Seven fingerprint peaks were identified by electrospray ionization tandem mass spectrometry (ESI-MS/MS), including magnolforine, coptisine, berberin, epiberberin, dehydroevodiamine, 12α-hydroxylimonin, and 12α-hydroxyevodol. The established fingerprint method was then applied to the analysis of Evodiae Fructus, Coptis Rhizoma, Yuhuanglian, and analogs of Yuhuanglian, which reflected their chemical constituent properties. The proposed method provides a technical platform for the characterization of Yuhuanglian, which w...


title: Adverse reaction after hyaluronan injection for minimally invasive papilla volume augmentation. A report on two cases

abstract: OBJECTIVES ::: To report two cases of adverse reaction after mucosal hyaluronan (HY) injection around implant-supported crowns, with the aim to augment the missing interdental papilla. ::: ::: ::: MATERIAL AND METHODS ::: Two patients with single, non-neighbouring, implants in the anterior maxilla, who were treated within the frames of a randomized controlled clinical trial testing the effectiveness of HY gel injection to reconstruct missing papilla volume at single implants, presented an adverse reaction. Injection of HY was performed bilaterally using a 3-step technique: (i) creation of a reservoir in the mucosa directly above the mucogingival junction, (ii) injection into the attached gingiva/mucosa below the missing papilla, and (iii) injection 2-3 mm apically to the papilla tip. The whole-injection session was repeated once after approximately 4 weeks. ::: ::: ::: RESULTS ::: Both patients presented with swelling and extreme tenderness with a burning sensation on the lip next to the injection area, after the second injection session. In one of the cases, a net-like skin discoloration (livedo reticularis) was also noted. The symptoms lasted for up to 7 days, and in both cases, symptoms resolved without any signs of skin or mucosal necrosis or any permanent damage. ::: ::: ::: CONCLUSION ::: Most likely, water attraction over time by the highly hygroscopic HY, exerted progressively an external vascular compression and at least partial occlusion of neighbouring blood vessels. An infection or an allergic reaction seems unlikely, since all symptoms gradually disappeared within a week irrespective use of antimicrobials, while an allergic reaction most likely would not have been restricted to one side.


Simultaneous Perception and Path Generation Using Fully Convolutional Neural Networks.


A-life, Organism and Body: the semiotics of emergent levels


title: Where does gender come from? Evidence from a complex inflectional system

abstract: Although inflectional morphology has been the focus of considerable debate in recent years, most research has focused on English, which has a much simpler inflectional system than in many other languages. We have been studying Serbian, which has a complex inflectional system that encodes gender, number, and case. The present study investigated the representation of gender. In standard theories of language production, gender is treated as an abstract syntactic feature segregated from semantic and phonological factors. However, we describe corpus analyses and computational models which indicate that gender is correlated with semantic and phonological information, consistent with other cross-linguistic studies. The research supports the idea that gender representations emerge in the course of learning to map from an intended message to a phonological representation. Implications for models of speech production are discussed.


title: Community-based greedy algorithm for mining top-K influential nodes in mobile social networks

abstract: With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and influence in the form of "word-of-mouth". It is a fundamental issue to find a subset of influential individuals in a mobile social network such that targeting them initially (e.g. to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). The problem of finding the most influential nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation; However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large mobile network. In this paper we propose a new algorithm called Community-based Greedy algorithm for mining top-K influential nodes. The proposed algorithm encompasses two components: 1) an algorithm for detecting communities in a social network by taking into account information diffusion; and 2) a dynamic programming algorithm for selecting communities to find influential nodes. We also provide provable approximation guarantees for our algorithm. Empirical studies on a large real-world mobile social network show that our algorithm is more than an order of magnitudes faster than the state-of-the-art Greedy algorithm for finding top-K influential nodes and the error of our approximate algorithm is small.


title: Malware detection using assembly code and control flow graph optimization

abstract: Malware detection is a crucial aspect of software security. A malware detector is a system that attempts to determine whether a program has malicious intent. Current malware detectors work by checking for signatures, which attempt to capture the syntactic characteristics of the machine level byte sequence of the malware. This syntactic approach makes current detectors vulnerable to code obfuscations, increasingly used by malware writers that alter the syntactic properties of the malware byte sequence without significantly affecting their execution behavior. This paper derives from the idea that the key to malware identification lies in their syntactic as well as semantic features. It explains an approach using control flow graphs (CFG) for malware detectors. We present an architecture for detecting malicious patterns in executables that is resilient to common obfuscation transformations.


title: MACAW: a media access protocol for wireless LAN's

abstract: In recent years, a wide variety of mobile computing devices has emerged, including portables, palmtops, and personal digital assistants. Providing adequate network connectivity for these devices will require a new generation of wireless LAN technology. In this paper we study media access protocols for a single channel wireless LAN being developed at Xerox Corporation's Palo Alto Research Center. We start with the MACA media access protocol first proposed by Karn [9] and later refined by Biba [3] which uses an RTS-CTS-DATA packet exchange and binary exponential back-off. Using packet-level simulations, we examine various performance and design issues in such protocols. Our analysis leads to a new protocol, MACAW, which uses an RTS-CTS-DS-DATA-ACK message exchange and includes a significantly different backoff algorithm.


title: Social and technical challenges in parenting teens' social media use

abstract: With millions of teenagers on the Internet, millions of parents are trying to understand what their teens are doing and why. Understanding how technology use impacts teens' learning, growth, and social development is critical for their health and wellbeing and for the welfare of the family. Yet, balancing parent authority with teen privacy and autonomy is difficult. We conducted an interview study with 16 parents to examine challenges in "technoparenting" - parenting teens' technology use. Parents said they wanted more transparency in their teens' use of cell phones and the Internet and they struggled with their own unfamiliarity with technology. Technoparenting is a distributed problem and, surprisingly, parents wanted support and collaboration from the broader community. We conclude with design implications for a socially translucent "digital window".


title: Energy-Efficient Wide-Range Voltage Level Shifters Reaching 4.2 fJ/Transition

abstract: This letter proposes a new level-shifting structure that can convert extremely low levels of input voltages to high output voltages while maintaining excellent delay and power dissipation. In order to reduce contention and voltage swing in the internal nodes, the proposed circuit uses a diode-connected level shifter between gate terminals of the output inverter. Using a control circuit, only during the high-to-low transitions of the output, a current is forced into the diode-connected device. Measurement results demonstrate that the proposed circuit can consume as small energy as 4.2 fJ/transition with ${V} _{\text {DDL}}$ and ${V} _{\text {DDH}}$ of 0.35 V and 1.1 V, respectively, when implemented in a 40-nm CMOS technology. Furthermore, when fabricated in a 180-nm technology, the level-shifting circuit can convert ${V} _{\text {DDL}}$ as small as 80 mV to 1.8 V without using low-threshold devices.


title: Automatic Sensor Data Stream Segmentation for Real-time Activity Prediction in Smart Spaces

abstract: Recently, human activity recognition and prediction have become important functionalities in ambient-assisted living. Activity inference algorithms detect what task a human undertakes, by analyzing the data stream pattern generated from various Internet of Things (IoT) devices. However, determining how the data stream should be segmented in real-time, referred to as data segmentation, remains as one of the most difficult challenges. In this paper, we propose an automatic data segmentation approach for real-time activity prediction by employing the Jaro-Winkler Distance measurement. Our approach selects a breakpoint of a stream by comparing the Jaro-Winkler distance between the training dataset and the data stream and finding a peak among the variations. The resultant segment also becomes new training data after being tagged; this removes the need to segment the stream data manually for humans. From the experiment based on MIT's smart home dataset collected from a real living environment, our approach shows reasonable performance of 76% accuracy even though the dataset size is relatively diminutive.


title: Multilevel converters for large electric drives

abstract: Traditional two-level high-frequency pulse width modulation (PWM) inverters for motor drives have several problems associated with their high frequency switching which produces common-mode voltage and high voltage change (dV/dt) rates to the motor windings. Multilevel inverters solve these problems because their devices can switch at a much lower frequency. Two different multilevel topologies are identified for use as a power converter for electric drives: a cascade inverter with separate DC sources; and a back-to-back diode clamped converter. The cascade inverter is a natural fit for large automotive all-electric drives because of the high VA ratings possible and because it uses several levels of DC voltage sources which would be available from batteries or fuel cells. The back-to-back diode clamped converter is ideal where a source of AC voltage is available such as a hybrid electric vehicle. Simulation and experimental results show the superiority of these two power converters over PWM-based drives.


title: Basal Ganglia Control of Substantia Nigra Dopaminergic Neurons

abstract: Although substantia nigra dopaminergic neurons are spontaneously active both in vivo and in vitro, this activity does not depend on afferent input as these neurons express an endogenous calcium-dependent oscillatory mechanism sufficient to drive action potential generation. However, afferents to these neurons, a large proportion of them GABAergic and arising from other nuclei in the basal ganglia, play a crucial role in modulating the activity of dopaminergic neurons. In the absence of afferent activity or when in brain slices, dopaminergic neurons fire in a very regular, pacemaker-like mode. Phasic activity in GABAergic, glutamatergic, and cholinergic inputs modulates the pacemaker activity into two other modes. The most common is a random firing pattern in which interspike intervals assume a Poisson-like distribution, and a less common pattern, often in response to a conditioned stimulus or a reward in which the neurons fire bursts of 2-8 spikes time-locked to the stimulus. Typically in vivo, all three firing patterns are observed, intermixed, in single nigrostriatal neurons varying over time. Although the precise mechanism(s) underlying the burst are currently the focus of intensive study, it is obvious that bursting must be triggered by afferent inputs. Most of the afferents to substantia nigra pars compacta dopaminergic neurons comprise monosynaptic inputs from GABAergic projection neurons in the ipsilateral neostriatum, the globus pallidus, and the substantia nigra pars reticulata. A smaller fraction of the basal ganglia inputs, something less than 30%, are glutamatergic and arise principally from the ipsilateral subthalamic nucleus and pedunculopontine nucleus. The pedunculopontine nucleus also sends a cholinergic input to nigral dopaminergic neurons. The GABAergic pars reticulata projection neurons also receive inputs from all of these sources, in some cases relaying them disynaptically to the dopaminergic neurons, thereby playing a particularly significant role in setting and/or modulating the firing pattern of the nigrostriatal neurons.


title: Riemannian Approaches in Brain-Computer Interfaces: A Review

abstract: Although promising from numerous applications, current brain-computer interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of electroencephalographic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.


International Journal of Innovative Research in Computer and Communication Engineering


title: A system for graph-based visualization of the evolution of software

abstract: We describe G EVOL , a system that visualizes the evolution of software using a novel graph drawing technique for visualization of large graphs with a temporal component. G EVOL extracts information about a Java program stored within a CVS version control system and displays it using a temporal graph visualizer. This information can be used by programmers to understand the evolution of a legacy program: Why is the program structured the way it is? Which programmers were responsible for which parts of the program during which time periods? Which parts of the program appear unstable over long periods of time and may need to be rewritten? This type of information will complement that produced by more static tools such as source code browsers, slicers, and static analyzers.


title: Understanding the Effects of Mobile Gamification on Learning Performance

abstract: With the development of mobile technology, mobile learning, which is not bound by time and space, has become one of the most essential learning methods for modern people. However, previous studies have suggested that improvements are still needed in mobile learning performance. One of the means to achieve this goal is situated learning, which enhances learners’ autonomous motivation and make them more enthusiastic in learning. Nonetheless, few studies have attempted to increase learning performance through situated learning. This study attempts to explore the development of a productive learning atmosphere in the context of mobile situated learning. An experiment is conducted with university-level students having homogenous background and coursework by applying heterogeneous pedagogies, including textual pedagogy, video pedagogy, collaborative pedagogy, and gamification pedagogy. Moreover, the uses and gratification theory and the cognitive load theory were adopted to identify the cognitive factors that influence the learning performance in situated learning. It is hoped that education service providers can benefit from the insights discovered from this study and implement more effective learning strategies in mobile education.


title: Minutiae Extraction from Fingerprint Images - a Review

abstract: Fingerprints are the oldest and most widely used form of biometric identification. Everyone is known to have unique, immutable fingerprints. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is very important. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction. A critical step in automatic fingerprint matching is to reliably extract minutiae from the input fingerprint images. This paper presents a review of a large number of techniques present in the literature for extracting fingerprint minutiae. The techniques are broadly classified as those working on binarized images and those that work on gray scale images directly.


On the Theory of Sampling from Finite Populations


title: Developing the Models of Service Quality Gaps: A Critical Discussion

abstract: High service quality is imperative and important for competitiveness of service industry. In order to provide much quality service, a deeper research on service quality models is necessary. There are plenty of service quality models which enable managers and practitioners to identify quality problems and improve the efficiency and profitability of overall performance. One of the most influential models in the service quality literature is the model of service quality gaps. In this paper, the model of service quality gaps has been critically reviewed and developed in order to make it more comprehensive. The developed model has been verified based using a survey on 16 experts. Compared to the traditional models, the proposed model involves five additional components and eight additional gaps.


title: Human activity recognition with smartphone sensors using deep learning neural networks

abstract: This paper proposes a deep convolutional neural network for HAR using smartphone sensors.Experiments show that the proposed method derives relevant and more complex features.The method achieved an almost perfect classification on moving activities.It outperforms other state-of-the-art data mining techniques in HAR. Human activities are inherently translation invariant and hierarchical. Human activity recognition (HAR), a field that has garnered a lot of attention in recent years due to its high demand in various application domains, makes use of time-series sensor data to infer activities. In this paper, a deep convolutional neural network (convnet) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics of activities and 1D time-series signals, at the same time providing a way to automatically and data-adaptively extract robust features from raw data. Experiments show that convnets indeed derive relevant and more complex features with every additional layer, although difference of feature complexity level decreases with every additional layer. A wider time span of temporal local correlation can be exploited (1?9-1?14) and a low pooling size (1?2-1?3) is shown to be beneficial. Convnets also achieved an almost perfect classification on moving activities, especially very similar ones which were previously perceived to be very difficult to classify. Lastly, convnets outperform other state-of-the-art data mining techniques in HAR for the benchmark dataset collected from 30 volunteer subjects, achieving an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of the HAR data set.


title: Improving Generalization for Temporal Difference Learning: The Successor Representation

abstract: Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes particular constraints on good function approximators or representations. Appropriate generalization between states is determined by how similar their successors are, and representations should follow suit. This paper shows how TD machinery can be used to learn such representations, and illustrates, using a navigation task, the appropriately distributed nature of the result.


title: Guidelines of Business Process Modeling

abstract: Process modeling becomes more and more an important task not only for the purpose of software engineering, but also for many other purposes besides the development of software. Therefore it is necessary to evaluate the quality of process models from different viewpoints. This is even more important as the increasing number of different end users, different purposes and the availability of different modeling techniques and modeling tools leads to a higher complexity of information models. In this paper the Guidelines of Modeling (GoM)1, a framework to structure factors for the evaluation of process models, is presented. Exemplary, Guidelines of Modeling for workflow management and simulation are presented. Moreover, six general techniques for adjusting models to the perspectives of different types of user and purposes will be explained.


title: Development of Anthropomorphic Robot Hand with Tactile Sensor : SKKU Hand II

abstract: In this paper, an anthropomorphic robot hand called SKKU Hand II is presented, which has a miniaturized fingertip tactile sensor. The thumb is designed as one part of the palm and provides the mobility of the palm. The fingertip tactile sensor, based on polyvinylidene fluoride (PVDF) and pressure variable resistor ink, is physically flexible enough to be deformed into any three-dimensional geometry. In order to detect incipient slip, a PVDF strip is arranged along the direction normal to the surface of the finger of the robot hand. Also, a thin flexible sensor to sense the static force as well as the contact location is fabricated into an arrayed type using pressure variable resistor ink. The driving circuits for the SKKU Hand II are embedded in the hand, and each driving circuit communicates with others using CAN protocol. In addition, a tactile sensing system is developed with miniaturized electronic hardwares such as charge amplifier, signal processing unit etc., and it is integrated into the robot hand. The SKKU Hand II is manufactured and feasibility of the hand and the fingertip tactile sensor is validated through preliminary experiments


title: Towards Osmotic Computing: Analyzing Overlay Network Solutions to Optimize the Deployment of Container-Based Microservices in Fog, Edge and IoT Environments

abstract: In recent years, the rapid growth of new Cloud technologies acted as an enabling factor for the adoption of microservices based architecture that leverages container virtualization in order to build modular and robust systems. As the number of containers running on hosts increases, it becomes essential to have tools to manage them in a simple, straightforward manner and with a high level of abstraction. Osmotic Computing is an emerging research field that studies the migration, deployment and optimization of microservices from the Cloud to Fog, Edge, and Internet of Things (IoT) environments. However, in order to achieve Osmotic Computing environments, connectivity issues have to be addressed. This paper investigates these connectivity issues leveraging different network overlays. In particular, we analyze the performance of four network overlays that are OVN, Calico, Weave, and Flannel. Our results give a concrete overview in terms of overhead and performances for each proposed overlay solution, helping us to understand which the best overlay solution is. Specifically, we deployed CoAP and FTP microservices which helped us to carry out these benchmarks and collect the results in terms of transfer times.


Fraud Detection in Banking Sector using Data mining


title: Community Detection in Temporal Multilayer Networks, with an Application to Correlation Networks

abstract: Networks are a convenient way to represent complex systems of interacting entities. Many networks contain “communities” of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilayer networks. As a focal example, we study time-dependent financial-asset correlation networks. We first argue that the use of the “modularity” quality function---which is defined by comparing edge weights in an observed network to expected edge weights in a “null network''---is application-dependent. We differentiate between “null networks” and “null models” in our discussion of modularity maximization, and we highlight that the same null network can correspond to different null models. We then investigate a multilayer modularity-maximization problem to identify communities in temporal networks. Our multilayer analysis depends only on the form of the maximization problem and not on the specific quality function that one chooses. We introduce a diagnostic to measure persistence of community structure in a multilayer network partition. We prove several results that describe how the multilayer maximization problem measures a trade-off between static community structure within layers and larger values of persistence across layers. We also discuss some computational issues that the popular “Louvain” heuristic faces with temporal multilayer networks and suggest ways to mitigate them.


HUMAN FACTORS IN AVIATION: CRM (CREW RESOURCE MANAGEMENT)


title: Blurred image restoration: A fast method of finding the motion length and angle

abstract: Motion blur in photographic images is a result of camera movement or shake. Methods such as Blind Deconvolution are used when information about the direction and size of blur is not known. Restoration methods, such as Lucy and Richardson or Wiener reconstruction use information about the direction and size of the blur in the deconvolution kernel (called Point Spread Function - PSF). Correct and fast determination of the direction and size of blur improves the quality of restoration and it can substantially reduce the computational time. In this article, a fast method for finding the direction and size of the blur automatically is presented. The method is based on computation of the power spectrum of the image gradient in the frequency domain. The method has achieved good results on both types of images: artificially blurred and naturally blurred (by the camera shake).


title: High-efficiency line-start interior permanent-magnet synchronous motors

abstract: This paper presents a successful design of a high-efficiency small but novel interior permanent-magnet motor using NdBFe magnets. It is designed to operate both at line and variable frequencies. Line start with high inertia load was a special consideration. Time-stepping finite-element analysis has been used to successfully predict the dynamic and transient performances of the prototype motors. It has been found that the proposed design has yielded successful simulation and experimental results. The maximum load inertia corresponding to the rotor-bar depth has been determined from the simulation results.


title: Cooking as a biological trait

abstract: Abstract No human foragers have been recorded as living without cooking, and people who choose a ‘raw-foodist’ life-style experience low energy and impaired reproductive function. This suggests that cooking may be obligatory for humans. The possibility that cooking is obligatory is supported by calculations suggesting that a diet of raw food could not supply sufficient calories for a normal hunter–gatherer lifestyle. In particular, many plant foods are too fiber-rich when raw, while most raw meat appears too tough to allow easy chewing. If cooking is indeed obligatory for humans but not for other apes, this means that human biology must have adapted to the ingestion of cooked food (i.e. food that is tender and low in fiber) in ways that no longer allow efficient processing of raw foods. Cooking has been practiced for ample time to allow the evolution of such adaptations. Digestive adaptations have not been investigated in detail but may include small teeth, small hind-guts, large small intestines, a fast gut passage rate, and possibly reduced ability to detoxify. The adoption of cooking can also be expected to have had far-reaching effects on such aspects of human biology as life-history, social behavior, and evolutionary psychology. Since dietary adaptations are central to understanding species evolution, cooking appears to have been a key feature of the environment of human evolutionary adaptedness. Further investigation is therefore needed of the ways in which human digestive physiology is constrained by the need for food of relatively high caloric density compared to other great apes.


title: Representations for Rigid Solids: Theory, Methods, and Systems

abstract: Computer-based systems for modehng the geometry of rigid solid objects are becoming increasingly important in mechanical and civil engineering, architecture, computer graphics, computer vision, and other fields that deal with spatial phenomena. At the heart of such systems are symbol structures (representations) designating "abstract solids" (subsets of Euclidean space) that model physical solids. Representations are the sources of data for procedures which compute useful properties of objects. The variety and uses of systems embodying representations of solids are growing rapidly, but so are the difficulties in assessing current designs, specifying the characteristics that future systems should exhibit, and designing systems t9 meet such specifications. This paper resolves many of these difficulties by providing a coherent view, based on sound theoretical principles, of what is presently known about the representation of solids. The paper is divided into three parts. The first introduces a simple mathematical framework for characterizing certain important aspects of representations, for example, their semantic (geometric) integrity. The second part uses the framework to describe and compare all of the major knownschemes fo~ representing solids. The third part briefly surveys extant geometric modeling systems and then applies the concepts developed in the paper to the high-level design of a multiple*representation geometric modeling system which exhibits a level of reliability and versatility supermr to that of systems currently used in industrial computer-aided design and manufacturing.


title: Cancer cell metabolism.

abstract: Although the generation of a distinctive metabolic profile is a well-known aspect of cancer, the significance of these adaptations and their potential for exploitation for anticancer therapy has not been fully appreciated until recently. Many oncogenic changes known to affect intracellular signaling pathways play an active role in mediating these metabolic changes, which, in turn, function to support cancer cell growth and replication. In this chapter, we discuss the most current findings in cancer cell metabolism in terms of their impact on tumor cell growth as well as their potential for identifying new targets for therapeutic intervention.


title: A Probabilistic Model for Traditional Chinese Architecture

abstract: Increasing numbers of 3D models provide a greatopportunity for data-driven shape modeling, analysis andsynthesis. The most critical core technique is to aggregateinformation from model collections to improve reasoningtheir properties and relationships. In this paper, we proposea probabilistic model for traditional Chinese architectures, which encodes the semantic type and hierarchicalrelationships for their basic components. Firstly aprobabilistic hierarchical graph is designed to represent thetypical component structure of Chinese ancient buildings. Secondly, a Bayesian Network is trained from a collection of3D models with consistent labels. Finally, the BayesianNetwork with structure and parameters learned from datacan be used to synthesis and recommend components inapplications. Experimental results show the effective of theproposed method.


title: Are emoticons good enough to train emotion classifiers of Arabic tweets?

abstract: Nowadays, the automatic detection of emotions is employed by many applications across different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media. In this study, we address the problem of emotion detection in Arabic tweets. We focus on the supervised approach for this problem where a classifier is trained on an already labeled dataset. Typically, such a training set is manually annotated, which is expensive and time consuming. We propose to use an automatic approach to annotate the training data based on using emojis, which are a new generation of emoticons. We show that such an approach produces classifiers that are more accurate than the ones trained on a manually annotated dataset. To achieve our goal, a dataset of emotional Arabic tweets is constructed, where the emotion classes under consideration are: anger, disgust, joy and sadness. Moreover, we consider two classifiers: Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The results of the tests show that the automatic labeling approaches using SVM and MNB outperform manual labeling approaches.


title: Automated conversation skills assistant

abstract: Conversational skills training are getting popular now a days but often very hard to get due to expense and lack of accessibility. In this paper, we present the idea of an automated conversational skills training assistant, which provides both realtime and post summary feedback while having a conversation with a virtual agent. Our exploratory effort shows the applicability of this system and significant improvement of our participant's conversational skills over a control group. We present our intended goals and the methodologies that we will use on our collected training data in order to automate this system in future.


title: Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents

abstract: Recognizing polarity requires a list of polar words and phrases. For the purpose of building such lexicon automatically, a lot of studies have investigated (semi-) unsupervised method of learning polarity of words and phrases. In this paper, we explore to use structural clues that can extract polar sentences from Japanese HTML documents, and build lexicon from the extracted polar sentences. The key idea is to develop the structural clues so that it achieves extremely high precision at the cost of recall. In order to compensate for the low recall, we used massive collection of HTML documents. Thus, we could prepare enough polar sentence corpus.


title: Multi-level preference regression for cold-start recommendations

abstract: Due to the absence of historical ratings of new users/items, cold-start recommendation remains a challenge for collaborative filtering. Many matrix factorization based methods are used to predict new user’s/item’s latent profile before predicting ratings. This kind of methods is usually non-convex. In this work, we design a new convex framework for cold-start recommendations, multi-level preference regression (MPR), directly to predict the ratings rather than latent profiles. We suppose that ratings are mainly affected by three components: (1) correlation between user’s attributes (such as age and gender) and item’s attributes (such as genre and producer); (2) each user’s preference on item’s attributes; (3) item’s popularity in a group of users with some attributes. Adjusting the impact of the three components, we can tackle three cold-start scenarios of user, item, and system. In the MPR framework, three different learning strategies are discussed: pointwise regression, pairwise regression, and large-margin learning. Experimental results on three datasets demonstrate that the proposed model can achieve the state of the art in the user cold-start scenario and the best performance in other scenarios.


title: Lidar-histogram for fast road and obstacle detection

abstract: Detection of traversable road regions, positive and negative obstacles, and water hazards is a fundamental task for autonomous driving vehicles, especially in off-road environment. This paper proposes an efficient method, called Lidar-histogram. It can be used to integrate the detection of traversable road regions, obstacles and water hazards into one single framework. The weak assumption of the Lidar-histogram is that a decent-sized area in front of the vehicle is flat. The Lidar-histogram is derived from an efficient organized map of Lidar point cloud, called Lidar-imagery, to index, describe and store Lidar data. The organized point-cloud map can be easily obtained by indexing the original unordered 3D point cloud to a Lidar-specific 2D coordinate system. In the Lidar-histogram representation, the 3D traversable road plane in front of vehicle can be projected as a straight line segment, and the positive and negative obstacles are projected above and below the line segment, respectively. In this way, the problem of detecting traversable road and obstacles is converted into a simple linear classification task in 2D space. Experiments have been conducted in different kinds of off-road and urban scenes, and we have obtained very promising results.


title: Incremental diagnostic benefit of resolution recovery software in patients with equivocal myocardial perfusion single-photon emission computed tomography (SPECT)

abstract: Background ::: Though myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) is an established diagnostic method, equivocal studies are commonly encountered. New software has been introduced that incorporates resolution recovery (RR) and noise regulation into the reconstruction algorithm and has been used to facilitate “half-dose” and “half-time” studies. Its utility with “full-time, full-dose” acquisition has not been well studied.


title: THE REPERTORY GRID TECHNIQUE : A METHOD FOR THE STUDY OF COGNITION IN INFORMATION SYSTEMS INTRODUCTION

abstract: Recent studies have confirmed the importance of understanding the cognition of users and information systems (IS) professionals. These works agree that organizational cognition is far too critical to be ignored as it can impact on IS outcomes. While cognition has been considered in a variety of IS contexts, no specific methodology has dominated. A theory and method suitable to the study of cognition-defined as personal constructs that individuals use to understand IT in organizations-is Kelly's (1955) personal construct theory and its cognitive mapping tool known as the repertory grid (RepGrid). This article expounds on the potential of this technique to IS researchers by considering the variety of ways the RepGrid may be employed. The flexibility of the RepGrid is illustrated by examining published studies in IS. The diagnostic qualities of the RepGrid and its mapping outcomes can be used for practical intervention at the individual and organizational levels.


title: Believe it or not: Credibility of blogs in tourism

abstract: This study examines the use of blogs as a means of tourism marketing communication. Using a scenario-based approach, an online experiment was conducted to test whether consumers perceive corporate and personal blogs to be credible sources of information and to compare the perceived credibility of blogs to that of traditional word-of-mouth. The findings suggest that while consumers do not generally equate the overall credibility of blogs with that of traditional word-of-mouth, some consumers do appear to attribute similar levels of authoritativeness, a dimension of credibility, to them.


title: A Layered Security Approach for Cloud Computing Infrastructure

abstract: This paper introduces a practical security model based on key security considerations by looking at a number of infrastructure aspects of Cloud Computing such as SaaS, Utility, Web, Platform and Managed Services, Service commerce platforms and Internet Integration which was introduced with a concise literature review. The purpose of this paper is to offer a macro level solution for identified common infrastructure security requirements. This model with a number of emerged patterns can be applied to infrastructure aspect of Cloud Computing as a proposed shared security approach in system development life cycle focusing on the plan-built-run scope.


title: Social Network Analysis techniques: implications for information and knowledge sharing in virtual learning communities

abstract: This work employs social network techniques to analyse patterns of interactions critical to information and knowledge sharing among learners in a virtual learning community. Drawing from the results of this analysis, fundamental variables which are more likely to affect information and knowledge sharing in virtual learning communities are explored and future research initiatives to pursue these issues is presented.


Addendum: Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs


title: A Deep Learning Based Method for Handling Imbalanced Problem in Network Traffic Classification

abstract: Network traffic classification is an important problem in network traffic analysis. It plays a vital role in many network tasks including quality of service, firewall enforcement and security. One of the challenging problems of classifying network traffic is the imbalanced property of network data. Usually, the amount of traffic in some classes is much higher than the amount of traffic in other classes. In this paper, we proposed an application of a deep learning approach to address imbalanced data problem in network traffic classification. We used a recent proposed deep network for unsupervised learning called Auxiliary Classifier Generative Adversarial Network to generate synthesized data samples for balancing between the minor and the major classes. We tested our method on a well-known network traffic dataset and the results showed that our proposed method achieved better performance compared to a recent proposed method for handling imbalanced problem in network traffic classification.


title: Borehole image correspondence and automated alignment

abstract: Borehole images are often misaligned due to a depth offset in the acquisition sensors of a wireline tool. We propose an algorithm that identifies matching feature points and aligns the image. One of the main challenges is that imaging pads have no azimuthal overlap. This has been solved by extrapolating the pixels on the boundary to create a synthetic overlap that facilitates feature matching. Feature matching is implemented in two stages: first by finding a local correspondence among feature points and second by performing a global minimization with additional regularization constraints on the estimated shifts. The method has been successfully used to align several imaging logs in less than 1 minute whereas a manual alignment would take 6 hours.


title: COPSS-lite: Lightweight ICN Based Pub/Sub for IoT Environments

abstract: Information Centric Networking is a new networking paradigm that treats content as first class entity. It provides content to users without regards to the current location of the content. The publish/subscribe systems have gained popularity in Internet. Pub/sub systems dismisses the need for users to request every content of their interest. Instead, the content is supplied to interested users (subscribers) as and when it is published. CCN/NDN are popular ICN proposals widely accepted in the ICN community however, they do not provide an efficient pub/sub mechanism. COPSS enhances CCN/NDN with an efficient pub/sub capability. Internet of Things (IoT) is a growing topic of interest in both Academia and Industry. The current designs for IoT relies on IP. However, the IoT devices are constrained in their available resources and IP is heavy for their operation.We observed that IoT's are information centric in nature and hence ICN is a more suitable candidate to support IoT environments. Although NDN and COPSS work well for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. CCN-lite is a light weight, inter-operable version of the CCNx protocol for supporting the IoT devices. However, CCN-lite like its ancestors lacks the support for an efficient pub/sub mechanism. In this paper, we developed COPSS-lite, an efficient and light weight implementation of pub/sub for IoT. COPSS-lite is developed to enhance CCN-lite and also support multi-hop connection by incorporating the famous RPL protocol for low power and lossy networks. We provide a preliminary evaluation to show proof of operability with real world sensor devices in IoT lab. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and we observe significant performance benefits with COPSS-lite in IoT environments.


title: Development of Robotic Gloves for Hand Rehabilitation Post-Stroke

abstract: This paper presents the hardware and software developments that have been achieved and implemented in the robotic gloves for hand rehabilitation post-stroke. The practical results are shown that prove the functionalities of the robotic gloves in common operating conditions. This work focused on development of a lightweight and low-cost robotic glove that patients can wear and use to recover hand functionality. We developed some structures for the robotic glove, as exoskeleton and as soft robotic glove. The paper presents a comparison of these structures.


title: Understanding and Enhancement of Internal Clustering Validation Measures

abstract: Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a study of 11 widely used internal clustering validation measures for crisp clustering. The results of this study indicate that these existing measures have certain limitations in different application scenarios. As an alternative choice, we propose a new internal clustering validation measure, named clustering validation index based on nearest neighbors (CVNN), which is based on the notion of nearest neighbors. This measure can dynamically select multiple objects as representatives for different clusters in different situations. Experimental results show that CVNN outperforms the existing measures on both synthetic data and real-world data in different application scenarios.


title: Mining Query Logs

abstract: Web Search Engines (WSEs) have stored in their query logs information about users since they started to operate. This information often serves many purposes. The primary focus of this tutorial is to introduce to the discipline of query log mining. We will show its foundations, by giving a unified view on the literature on query log analysis, and also present in detail the basic algorithms and techniques that could be used to extract useful knowledge from this (potentially) infinite source of information. Finally, we will discuss how the extracted knowledge can be exploited to improve different quality features of a WSE system, mainly its effectiveness and efficiency.


title: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization

abstract: Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.


title: MicroLib: A Case for the Quantitative Comparison of Micro-Architecture Mechanisms

abstract: While most research papers on computer architectures include some performance measurements, these performance numbers tend to be distrusted. Up to the point that, after so many research articles on data cache architectures, for instance, few researchers have a clear view of what are the best data cache mechanisms. To illustrate the usefulness of a fair quantitative comparison, we have picked a target architecture component for which lots of optimizations have been proposed (data caches), and we have implemented most of the hardware data cache optimizations of the past 4 years in top conferences. Then we have ranked the different mechanisms, or more precisely, we have examined the impact of benchmark selection, process model precision,. . . on ranking, and obtained some surprising results. This study is part of a broader effort, called MicroLib, aimed at promoting the disclosure and sharing of simulator models.


title: On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition

abstract: We investigate the problem of readability assessment using a range of lexical and syntactic features and study their impact on predicting the grade level of texts. As empirical basis, we combined two web-based text sources, Weekly Reader and BBC Bitesize, targeting different age groups, to cover a broad range of school grades. On the conceptual side, we explore the use of lexical and syntactic measures originally designed to measure language development in the production of second language learners. We show that the developmental measures from Second Language Acquisition (SLA) research when combined with traditional readability features such as word length and sentence length provide a good indication of text readability across different grades. The resulting classifiers significantly outperform the previous approaches on readability classification, reaching a classification accuracy of 93.3%.


title: Inter-twin contact in a case of monochorionic diamniotic twins with acrania of one twin fetus at 10–13 weeks’ gestation

abstract: To the Editor, Anencephaly can be reliably diagnosed using ultrasound late in the first trimester of pregnancy [1]. The prevalence of anencephaly in twins is higher than that in singleton pregnancies, and the prevalence of discordance in anencephaly in monochorionic twins is higher than that in dichorionic twins [2]. There have been only two reports on conventional twodimensional (2D) sonographic assessment of fetal behavior in twins discordant because of anencephaly after 20 weeks of gestation [3, 4]. However, there has been no report on four-dimensional (4D) sonographic assessment of intertwin contact in twins discordant because of anencephaly in utero. To the best of our knowledge, this is the first report on 4D sonographic assessment of inter-twin contact in a case of monochorionic diamniotic (MD) twins with acrania of one twin fetus late in the first trimester. A 28-year-old Japanese woman, gravida 3, para 1, visited our hospital because of secondary amenorrhea, and MD twin pregnancy at 7 weeks and 4 days was diagnosed. At 11 weeks and 4 days, twin pregnancy with acrania of one twin fetus was diagnosed (Fig. 1). The parents were informed about the lethality of the affected twin fetus, but they elected to continue the pregnancy. At 25 weeks and 1 day, she was admitted to our hospital because of threatened premature labor (short cervix and irritable uterine contractions). At 38 weeks and 4 days, the first of two female infants (anencephalic twin), weighing 1,948 g, and the other infant (second twin), weighing 2,372 g (Apgar score 7 at 1 min and 9 at 5 min; umbilical artery blood pH 7.089), were delivered by elective cesarean section because of two previous cesarean sections. The anencephalic twin died soon after delivery. Permission to conduct an autopsy was not granted by the parents. The second twin is doing well. Detailed descriptions of the data-collecting methods and measurement procedures used in this patient have been presented in a previous publication [5]. In brief, examinations were performed for 30 min with transabdominal 4D sonography at 11 weeks and 4 days and 13 weeks and 1 day of pregnancy, respectively. She was asked whether she would agree to a 30-min observation of fetal movements and inter-twin contact after undergoing routine sonographic examinations. This study was approved by the local ethics committee of Kagawa University School of Medicine, and standardized written informed consent was obtained from the patient. All 4D examinations were performed using Voluson 730 Expert (GE Medical Systems, Milwaukee, WI) with a transabdominal 2–5-MHz transducer. Ten types of inter-twin contact (head to head, head to arm, head to trunk, head to leg, arm to arm, arm to trunk, arm to leg, trunk to trunk, trunk to leg, and leg to leg) were analyzed during playback of the video recordings. The total number of all inter-twin contacts was determined by a single experienced observer (M.S.) and compared to the quartile range obtained from normal MD twins [5]. The frequencies of ten types of inter-twin contact were also compared to the quartile ranges obtained from normal MD twins [6]. The total number of inter-twin contacts in this patient was low compared to those of normal MD twin fetuses at 10–11 and 12–13 weeks’ gestation (Fig. 2). The frequencies of 10 types of inter-twin contact at 10–11 weeks were almost within quartile ranges (Fig. 3), T. Hata (&) K. Kanenishi U. Hanaoka M. Sasaki T. Yanagihara Department of Perinatology and Gynecology, Kagawa University School of Medicine, 1750-1 Ikenobe, Miki, Kagawa 761-0793, Japan e-mail: toshi28@med.kagawa-u.ac.jp