title: Examining Location-Based Services Usage from the Perspectives of Unified Theory of Acceptance and Use of Technology and Privacy Risk

abstract: ABSTRACT As an emerging service, location-based services (LBS) have not received wide adoption among users. Drawing on both perspectives of Unified Theory of Acceptance and Use of Technology (UTAUT) and privacy risk, this research examined user adoption of LBS. We conducted data analysis with structural equation modeling. The results indicated that usage intention is affected by both enablers such as performance expectancy and inhibitors such as perceived risk. This implies that service providers need to concern both perspectives of technological perceptions and privacy risk in order to facilitate user adoption of LBS. Keywords: location-based services; UTAUT; privacy concern; trust 1. Introduction Mobile internet has been developing rapidly around the world. Especially, the application of third generation (3G) communication technologies triggers mobile internet development. According to a report issued by China Internet Network Information Center (CNNIC) in January 2012, the number of mobile internet users in China has exceeded 356 million, accounting for 69% of its internet population (513 million) [CNNIC 2012]. Attracted by the great market, service providers have released a variety of mobile services, such as mobile instant messaging (IM), location-based services (LBS), mobile games and mobile payment. Among them, a few services such as mobile IM have been widely adopted by users. However, many services such as LBS have not received wide adoption among users. Mobile service providers need to understand the factors affecting user behavior. Then they can take measures to facilitate user adoption and usage of these services. LBS mean that mobile service providers present context-related information to users based on their location and preferences [Dhar and Varshney 2011]. This personalized service delivers a better experience to users. Due to this advantage, LBS are called the killer application of mobile business [Junglas and Watson 2008]. Typical LBS include location-based advertisement, navigation, emergency evacuation and location check-in services. LBS can bring great value to users and this may promote their usage behavior. However, LBS need to utilize users' location information. This may incur users' privacy concern and increase their perceived risk. Users feel that they are tracked by service providers. Their concern on privacy risk will negatively affect their usage of LBS. The objective of this research is to identify the factors affecting user adoption of LBS from both perspectives of technological perceptions and privacy risk. On one hand, LBS represent a new technological application. Thus we may apply information technology theories such as the unified theory of acceptance and use of technology (UTAUT) to explain user behavior. UTAUT is mainly concerned with the effect of enablers such as performance expectancy and effort expectancy on usage behavior. On the other hand, LBS user behavior may be also affected by inhibitors such as privacy risk. If users have high privacy concern, they may perceive great risk and have low trust in service providers. Thus it is necessary to integrate both perspectives of enablers such as performance expectancy and inhibitors such as privacy risk to examine LBS user behavior. Extant research has focused on the single effect of privacy risk on user adoption of LBS [Junglas et al. 2008b; Xu et al. 2011], and has seldom combined both perspectives of enablers and inhibitors to explain user behavior. This research tries to fill the gap. The results support our hypotheses. The rest of this paper is organized as follows. We review related literature in the next section. Then we develop research model and hypotheses in section three. Section four reports instrument development and data collection. Section five presents results and section six discusses these results. We present theoretical and managerial implications in section seven. …


title: On the purity of training and testing data for learning: The case of pedestrian detection

abstract: Abstract The training and the evaluation of learning algorithms depend critically on the quality of data samples. We denote as pure the samples that identify clearly and without any ambiguity the class of objects of interest. For instance, in pedestrian detection algorithms, we consider as pure samples the ones containing persons who are fully visible and are imaged at a good resolution (larger than the detector window in size). The exclusive use of pure samples entails two kinds of problems. In training, it biases the detector to neglect slightly occluded and small sized samples (which we denote as impure ), thus reducing its detection rate in a real world application. In testing, it leads to the unfair evaluation and comparison of different detectors since slightly impure samples, when detected, can be accounted for as false positives. In this paper we study how a sensible use of impure samples can benefit both the training and the evaluation of pedestrian detection algorithms. We improve the labelling of one of the most widely used pedestrian data sets (INRIA) taking into account the degree of sample impurity. We observe that including partially occluded pedestrians in the training improves performance, not only on partially visible examples, but also on the fully visible ones. Furthermore, we found that including pedestrians imaged at low resolutions is beneficial for detecting pedestrians in the same range of heights, leaving the performance on pure samples unchanged. However, including samples with too high a grade of impurity degrades the performance, thus a careful balance must be found. The proposed labelling will allow further studies on the role of impure samples in training pedestrian detectors and on devising fairer comparison metrics between different algorithms.


title: The Effect of Online Violent Video Games on Levels of Aggression

abstract: BACKGROUND: In recent years the video game industry has surpassed both the music and video industries in sales. Currently violent video games are among the most popular video games played by consumers, most specifically First-Person Shooters (FPS). Technological advancements in game play experience including the ability to play online has accounted for this increase in popularity. Previous research, utilising the General Aggression Model (GAM), has identified that violent video games increase levels of aggression. Little is known, however, as to the effect of playing a violent video game online. METHODS/PRINCIPAL FINDINGS: Participants (N = 101) were randomly assigned to one of four experimental conditions; neutral video game-offline, neutral video game-online, violent video game-offline and violent video game-online. Following this they completed questionnaires to assess their attitudes towards the game and engaged in a chilli sauce paradigm to measure behavioural aggression. The results identified that participants who played a violent video game exhibited more aggression than those who played a neutral video game. Furthermore, this main effect was not particularly pronounced when the game was played online. CONCLUSIONS/SIGNIFICANCE: These findings suggest that both playing violent video games online and offline compared to playing neutral video games increases aggression. Language: en


Frontal fibrosing alopecia – A Case report * Alopecia frontal fibrosante-Relato de caso


title: TADAM: Task dependent adaptive metric for improved few-shot learning

abstract: Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. Our code is publicly available at this https URL.


title: Embedding-based Query Expansion for Weighted Sequential Dependence Retrieval Model

abstract: Although information retrieval models based on Markov Random Fields (MRF), such as Sequential Dependence Model and Weighted Sequential Dependence Model (WSDM), have been shown to outperform bag-of-words probabilistic and language modeling retrieval models by taking into account term dependencies, it is not known how to effectively account for term dependencies in query expansion methods based on pseudo-relevance feedback (PRF) for retrieval models of this type. In this paper, we propose Semantic Weighted Dependence Model (SWDM), a PRF based query expansion method for WSDM, which utilizes distributed low-dimensional word representations (i.e., word embeddings). Our method finds the closest unigrams to each query term in the embedding space and top retrieved documents and directly incorporates them into the retrieval function of WSDM. Experiments on TREC datasets indicate statistically significant improvement of SWDM over state-of-the-art MRF retrieval models, PRF methods for MRF retrieval models and embedding based query expansion methods for bag-of-words retrieval models.


title: Looking past yesterday's tomorrow: using futures studies methods to extend the research horizon

abstract: Doing research is, in part, an act of foresight. Even though it is not explicit in many projects, we especially value research that is still relevant five, ten or more years after it is completed. However, published research in the field of interactive computing (and technology research in general) often lacks evidence of systematic thinking about the long-term impacts of current trends. For example, trends on an exponential curve change much more rapidly than intuition predicts. As a result, research may accidentally emphasize near-term thinking. When thinking about the future is approached systematically, we can critically examine multiple potential futures, expand the set of externalities under consideration, and address both negative and positive forecasts of the future. The field of Futures Studies provides methods that can support analysis of long-term trends, support the identification of new research areas and guide design and evaluation. We survey methods for futuristic thinking and discuss their relationship to Human Computer Interaction. Using the sustainability domain an example, we present a case study of a Futures Studies approach - the Delphi Method. We show how Futures Studies can be incorporated into Human Computer Interaction and highlight future work such as rethinking the role of externalities in the validation process.


title: Equivalent circuit model of cross and circular ring FSS using vector fitting

abstract: In this paper, a rational function approximation approach called vector-fitting is adopted for developing equivalent circuit model of cross and circular ring frequency selective surfaces. The simulations of microstructure are performed with CST Microwave Studio on single - substrate for different physical parameters, angle of incidences and polarizations. Then vector-fitting is used to extract rational function from simulated data which led to direct conversion to an equivalent circuit for SPICE (Simulation Program with Integrated Circuit Emphasis) simulation for both time and frequency analyses. ADS SPICE generator is used for validating the proposed circuit models by comparing the results against the EM simulation and traditional circuit models. The developed models are within 1% of average deviation against reference data.


title: Identification of GXXXXG motif in Chrysophsin-1 and its implication in the design of analogs with cell-selective antimicrobial and anti-endotoxin activities

abstract: Marine fish antimicrobial peptide, chrysophsin-1 possesses versatile biological activities but its non-selective nature restricts its therapeutic possibilities. Often small alterations in structural motifs result in significant changes in the properties of concerned proteins/peptides. We have identified GXXXXG motif in chrysophsin-1. Glycine residue(s) of this motif in Chrysophsin-1 was/were replaced with alanine, valine and proline residue(s). Of these, proline-substituted Chrysophsin-1 analogs exhibited significantly reduced cytotoxicity towards mammalian cells. Further, these analogs showed broad-spectrum activity against Gram-positive, Gram-negative bacteria, Methicillin-resistant Staphylococcus aureus strains and fungi and also retained antibacterial activity in presence of physiological salts, serum and at elevated temperatures indicative of their therapeutic potential. These Chrysophsin-1 analogs also inhibited lipopolysaccharide (LPS) induced pro-inflammatory responses in THP-1 cells and in murine primary macrophages. One of these single proline-substituted Chrysophsin-1 analogs inhibited LPS-stimulated pro-inflammatory cytokine production in BALB/c mice and elicited appreciable survival of mice administered with a lethal dose of LPS in a model of severe sepsis. The data for the first time showed the implication of GXXXXG motifs in functional and biological properties of an antimicrobial peptide and could be useful to design novel anti-microbial and anti-endotoxin peptides by employing this motif.


title: Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data

abstract: Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.


title: Memristive devices for computing: Beyond CMOS and beyond von Neumann

abstract: Traditional CMOS technology and its continuous down-scaling have been the driving force to improve performance of existing computer architectures. Today, however, both technology and computer architectures are facing challenges that make them incapable of delivering the growing computing performance requirement at pre-defined constraints. This forces the exploration of both novel architectures and technologies; not only to maintain the economic profit of technology scaling, but also to enable the computing architecture solutions for big-data and data-intensive applications. This paper discusses the emerging memristive device as a complement (or an alternative) to CMOS devices and shows how such devices enable novel computing paradigms that will solve the challenges of today's architectures for certain applications. The paper covers not only the potential of memristor devices in enabling novel memory technologies, logic design styles, and arithmetic operations, but also their potential in enabling in-memory computing and neuromorphic computing.


Why Python Rocks for Research


title: Network Characteristics of Successful Performance in Association Football. A Study on the UEFA Champions League

abstract: The synergistic interaction between teammates in association football has properties that can be captured by Social Network Analysis (SNA). The analysis of networks formed by team players passing a ball in a match shows that team success is correlated with high network density and clustering coefficient, as well as with reduced network centralization. However, oversimplification needs to be avoided, as network metrics events associated with success should not be considered equally to those that are not. In the present study, we investigated whether network density, clustering coefficient and centralization can predict successful or unsuccessful team performance. We analyzed 12 games of the Group Stage of UEFA Champions League 2015/2016 Group C by using public records from TV broadcasts. Notational analyses were performed to categorize attacking sequences as successful or unsuccessful, and to collect data on the ball-passing networks. The network metrics were then computed. A hierarchical logistic-regression model was used to predict the successfulness of the offensive plays from network density, clustering coefficient and centralization, after controlling for the effect of total passes on successfulness of offensive plays. Results confirmed the independent effect of network metrics. Density, but not clustering coefficient or centralization, was a significant predictor of the successfulness of offensive plays. We found a negative relation between density and successfulness of offensive plays. However, reduced density was associated with a higher number of offensive plays, albeit mostly unsuccessful. Conversely, high density was associated with a lower number of successful offensive plays, but also with overall fewer offensive plays and “ball possession losses” before the attacking team entered the finishing zone. Independent SNA of team performance is important to minimize the limitations of oversimplifying effective team synergies.


title: Adversary Resistant Deep Neural Networks with an Application to Malware Detection

abstract: Outside the highly publicized victories in the game of Go, there have been numerous successful applications of deep learning in the fields of information retrieval, computer vision, and speech recognition. In cybersecurity, an increasing number of companies have begun exploring the use of deep learning (DL) in a variety of security tasks with malware detection among the more popular. These companies claim that deep neural networks (DNNs) could help turn the tide in the war against malware infection. However, DNNs are vulnerable to adversarial samples, a shortcoming that plagues most, if not all, statistical and machine learning models. Recent research has demonstrated that those with malicious intent can easily circumvent deep learning-powered malware detection by exploiting this weakness. To address this problem, previous work developed defense mechanisms that are based on augmenting training data or enhancing model complexity. However, after analyzing DNN susceptibility to adversarial samples, we discover that the current defense mechanisms are limited and, more importantly, cannot provide theoretical guarantees of robustness against adversarial sampled-based attacks. As such, we propose a new adversary resistant technique that obstructs attackers from constructing impactful adversarial samples by randomly nullifying features within data vectors. Our proposed technique is evaluated on a real world dataset with 14,679 malware variants and 17,399 benign programs. We theoretically validate the robustness of our technique, and empirically show that our technique significantly boosts DNN robustness to adversarial samples while maintaining high accuracy in classification. To demonstrate the general applicability of our proposed method, we also conduct experiments using the MNIST and CIFAR-10 datasets, widely used in image recognition research.


title: Please hold on: Unobtrusive user authentication using smartphone's built-in sensors

abstract: Smartphones provide anytime-anywhere communications and are being increasingly used for a variety of purposes, e.g, sending email, performing online transactions, connecting with friends and acquaintances over social networks. As a result, a considerable amount of sensitive personal information is often generated and stored on smartphones. Thus, smartphone users may face financial as well as sentimental consequences if such information fall in the wrong hands. To address this problem all smartphones provide some form of user authentication, that is the process of verifying the user's identity. Existing authentication mechanisms, such as using 4-digit passcodes or graphical patterns, suffer from multiple limitations - they are neither highly secure nor easy to input. As a results, recent studies found that most smartphone's users do not use any authentication mechanism at all. In this paper, we present a fully unobtrusive user authentication scheme based on micro-movements of the user's hand(s) after the user unlocks her smartphone. The proposed scheme collects data from multiple 3-dimensional smartphone sensors in the background for a specific period of time and profiles a user based on the collected hand(s) movement patterns. Subsequently, the system matches the query pattern with the pre-stored patterns to authenticate the smartphone owner. Our system achieved a True Acceptance Rate (TAR) of 96% at an Equal Error Rate (EER) of 4%, on a dataset of 31 qualified volunteers (53, in total), using Random Forest (RF) classifier. Our scheme can be used as a primary authentication mechanism or can be used as a secondary authentication scheme in conjunction with any of the existing authentication schemes, e.g., passcodes, to improve their security.


title: A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security

abstract: Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to define the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. In this paper, we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. The high-level strategy uses the search performance to control the selection of which low-level heuristic should be used to generate a new SVM configuration. The low-level heuristics each use different rules to effectively explore the SVM configuration search space. To address bi-objective optimization, the proposed framework adaptively integrates the strengths of decomposition- and Pareto-based approaches to approximate the Pareto set of SVM configurations. The effectiveness of the proposed framework has been evaluated on two cyber security problems: Microsoft malware big data classification and anomaly intrusion detection. The obtained results demonstrate that the proposed framework is very effective, if not superior, compared with its counterparts and other algorithms.


title: Job Embeddedness : A Theoretical Foundation for Developing a Comprehensive Nurse Retention Plan

abstract: OBJECTIVE ::: Using a new construct, job embeddedness, from the business management literature, this study first examines its value in predicting employee retention in a healthcare setting and second, assesses whether the factors that influence the retention of nurses are systematically different from those influencing other healthcare workers. ::: ::: ::: BACKGROUND ::: The shortage of skilled healthcare workers makes it imperative that healthcare providers develop effective recruitment and retention plans. With nursing turnover averaging more than 20% a year and competition to hire new nurses fierce, many administrators rightly question whether they should develop specialized plans to recruit and retain nurses. ::: ::: ::: METHODS ::: A longitudinal research design was employed to assess the predictive validity of the job embeddedness concept. At time 1, surveys were mailed to a random sample of 500 employees of a community-based hospital in the Northwest region of the United States. The survey assessed personal characteristics, job satisfaction, organizational commitment, job embeddedness, job search, perceived alternatives, and intent to leave. One year later (time 2) the organization provided data regarding voluntary leavers from the hospital. ::: ::: ::: RESULTS ::: Hospital employees returned 232 surveys, yielding a response rate of 46.4 %. The results indicate that job embeddedness predicted turnover over and beyond a combination of perceived desirability of movement measures (job satisfaction, organizational commitment) and perceived ease of movement measures (job alternatives, job search). Thus, job embeddedness assesses new and meaningful variance in turnover in excess of that predicted by the major variables included in almost all the major models of turnover. ::: ::: ::: CONCLUSIONS ::: The findings suggest that job embeddedness is a valuable lens through which to evaluate employee retention in healthcare organizations. Further, the levers for influencing retention are substantially similar for nurses and other healthcare workers. Implications of these findings and recommendations for recruitment and retention policy development are presented.


title: An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning

abstract: Abstract : Learning behaviors in a multiagent environment are crucial for developing and adapting multiagent systems. Reinforcement learning techniques have addressed this problem for a single agent acting in a stationary environment, which is modeled as a Markov decision process (MDP). But, multiagent environments are inherently non-stationary since the other agents are free to change their behavior as they also learn and adapt. Stochastic games, first studied in the game theory community, are a natural extension of MDPs to include multiple agents. In this paper we contribute a comprehensive presentation of the relevant techniques for solving stochastic games from both the game theory community and reinforcement learning communities. We examine the assumptions and limitations of these algorithms, and identify similarities between these algorithms, single agent reinforcement learners, and basic game theory techniques.


title: A self-oscillating class D audio amplifier with dual voltage and current feedback

abstract: We propose a self-oscillating PWM Class D audio amplifier with dual voltage and current feedback. The voltage feedback is taken after the output filter to mitigate any negative effects of the output filter. The current feedback is employed to mitigate the switching ripple voltage and its associated power dissipation. Simulation results using discrete components show that the proposed amplifier achieves the lowest THD+N of 0.04 % at 25 mW output power, and a maximum power efficiency of 94 % at 1.7 W output power with a THD+N of 1.18 %.


title: An Efficient Micropayment System Based on Probabilistic Polling

abstract: Existing software proposals for electronic payments can be divided into “on-line” schemes that require participation of a trusted party (the bank) in every transaction and are secure against overspending, and the “off-line” schemes that do not require a third party and guarantee only that overspending is detected when vendors submit their transaction records to the bank (usually at the end of the day). We propose a new hybrid scheme that combines the advantages of both of the above traditional design strategies. It allows for control of overspending at a cost of only a modest increase in communication compared to the off-line schemes. Our protocol is based on probabilistic polling. During each transaction, with some small probability, the vendor forwards information about this transaction to the bank. This enables the bank to maintain an accurate approximation of a customer's spending. The frequency of polling messages is related to the monetary value of transactions and the amount of overspending the bank is willing to risk.


title: Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

abstract: The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and econometric techniques for crude oil price prediction might demonstrate demotions to the prediction performance. Machine Learning and Computational Intelligence approach through combination of historical quantitative data with qualitative data from experts' view and news is a remedy proposed to predict this. This paper will discuss the first part of the research, focusing on to (i) the development of Hierarchical Conceptual (HC) model and (ii) the development of Artificial Neural Networks-Quantitative (ANN-Q) model.


title: Comparison of iris recognition algorithms

abstract: In this paper, we have studied various well known algorithms for iris recognition. Four algorithms due to Sanchez-Avila et al. (2001), Li Ma et al. (2002), Tisse et al. (2002) and Daugman (2001) are implemented and compared on the CASIA iris image database. The results show that the Daugman's algorithm gave the highest accuracy of 99.9%.


title: Optical Flow Motion Detection on Raspberry Pi

abstract: This paper presents the implementation of Optical Flow Motion Detection algorithm on Raspberry Pi. The Lucas-Kanade method was chosen for the implementation. The algorithm works by comparing two successive image frames. To find out a displaced object, the algorithm tries to guess the direction of displaced object rather than scanning the second image for the matching pixel. This can be done by solving for the optical flow vector by assuming that the vector will be similar to a small neighbourhood surrounding the pixel. The algorithm was simulated using Python OpenCV. The implementation of Lucas-Kanade algorithm was successfully done on Raspberry Pi.


title: On Using Tree Visualisation Techniques to Support Source Code Comprehension

abstract: This paper presents a design study that investigates the use of compact tree visualisations to provide software developers with an overview of the static structure of a source code document within a code editor in order to facilitate source code understanding and navigation. A prototype is presented which utilises an icicle tree visualisation to encode the control structure hierarchy of a source code document, as well as a circular treemap visualisation to encode the scope hierarchy of a source code document. An overview of the prototype and its functionality is given as well as a detailed discussion on the design rationale behind the tool. Possible applications and future work plans are also discussed.


title: Multi-level predictive maintenance for multi-component systems

abstract: In this paper, a novel predictive maintenance policy with multi-level decision-making is proposed for multi-component system with complex structure. The main idea is to propose a decision-making process considered on two levels: system level and component one. The goal of the decision rules at the system level is to address if preventive maintenance actions are needed regarding the predictive reliability of the system. At component level the decision rules aim at identifying optimally a group of several components to be preventively maintained when preventive maintenance is trigged due to the system level decision. Selecting optimal components is based on a cost-based group improvement factor taking into account the predictive reliability of the components, the economic dependencies as well as the location of the components in the system. Moreover, a cost model is developed to find the optimal maintenance decision variables. A 14-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance policy. Different sensitivity analysis are also investigated and discussed. Indeed, the proposed policy provides more flexibility in maintenance decision-making for complex structure systems, hence leading to significant profits in terms of maintenance cost when compared with existing policies.


title: Young smokers’ views of genetic susceptibility testing for lung cancer risk: minding unintended consequences

abstract: Assessment of smokers’ responses to individualized feedback of genetic susceptibility has shown little or no influence on smoking cessation outcomes. One explanation is that smokers may be having unintended responses that undermine the feedback’s motivational impact (e.g., fatalism or downplaying risk). In preparation for a large randomized trial with college smokers, we conducted a qualitative pilot study to explore smokers’ motives for genetic testing and how these motives might influence interpretation of genetic risk feedback. Prior to reviewing informational materials describing a test for the glutathione S-transferase M1 gene, 33 college smokers (18 to 21 years) participated in a 30-min, semistructured, open-ended interview regarding their attitudes on health risks, genetic testing in general, genetic testing for lung cancer risk, and informational needs regarding genetics and genetic testing for lung cancer risk. Two central themes emerged from an analysis of the interviews: general impressions of genetic testing and perceived value of genetic testing. Prominent in the second theme was the finding that genetic risk feedback may be unsuccessful in motivating quitting (a) due to skepticism about genetic tests, (b) participants dismissing genetic feedback as personally irrelevant, and (c) participants receiving low-risk results justifying continued smoking in light of public health messages that “it’s never too late to quit.” These findings require careful consideration among health professionals looking to genetic risk feedback as a vehicle to motivate disease prevention or behavior change.


title: Automated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy Images

abstract: Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.


title: Development of Image Synthesis Algorithm with Multi-Camera

abstract: AVM system is recently being adopted to the automotive industries to show area around the vehicle. This system combines images from four cameras and provides the driver with a synthesized image. This study proposes a camera parameter optimization algorithm to establish highly accurate image of the surrounding using the correspondence between the overlapping cameras. And we propose a mathematical multi-camera model to design projection models. As a result, by using these models, we can design various view modes.


title: A Hexagonal Coverage LED-ID Indoor Positioning Based on TDOA with Extended Kalman Filter

abstract: In this paper we propose a positioning approach based on the time different of arrival (TDOA) algorithm from light emitting diodes to localize mobile targets in indoor environments. The hexagonal lattice alignment of LED transmitters is proposed to reduce the coverage holes and the areas of overlapping radiation. The accuracy of the position estimation is compared to the one in a typical rectangular grid alignment. In addition, an extended Kalman filter is adopted together with the TDOA method to enhance the position estimation performance.


title: Deep Expander Networks: Efficient Deep Networks from Graph Theory

abstract: Deep Neural Networks, while being unreasonably effective for several vision tasks, have their usage limited by the computational and memory requirements, both during training and inference stages. Analyzing and improving the connectivity patterns between layers of a network has resulted in several compact architectures like GoogleNet, ResNet and DenseNet-BC. In this work, we utilize results from graph theory to develop an efficient connection pattern between consecutive layers. Specifically, we use {\it expander graphs} that have excellent connectivity properties to develop a sparse network architecture, the deep expander network (X-Net). The X-Nets are shown to have high connectivity for a given level of sparsity. We also develop highly efficient training and inference algorithms for such networks. Experimental results show that we can achieve the similar or better accuracy as DenseNet-BC with two-thirds the number of parameters and FLOPs on several image classification benchmarks. We hope that this work motivates other approaches to utilize results from graph theory to develop efficient network architectures.


title: Development of an Accelerometer-Linked Online Intervention System to Promote Physical Activity in Adolescents

abstract: Most adolescents do not achieve the recommended levels of moderate-to-vigorous physical activity (MVPA), placing them at increased risk for a diverse array of chronic diseases in adulthood. There is a great need for scalable and effective interventions that can increase MVPA in adolescents. Here we report the results of a measurement validation study and a preliminary proof-of-concept experiment testing the impact of Zamzee, an accelerometer-linked online intervention system that combines proximal performance feedback and incentive motivation features to promote MVPA. In a calibration study that parametrically varied levels of physical activity in 31 12-14 year-old children, the Zamzee activity meter was shown to provide a valid measure of MVPA (sensitivity in detecting MVPA = 85.9%, specificity = 97.5%, and r = .94 correspondence with the benchmark RT3 accelerometer system; all p < .0001). In a subsequent randomized controlled multi-site experiment involving 182 middle school-aged children assessed for MVPA over 6 wks, intent-to-treat analyses found that those who received access to the Zamzee intervention had average MVPA levels 54% greater than those of a passive control group (p < 0.0001) and 68% greater than those of an active control group that received access to a commercially available active videogame (p < .0001). Zamzee’s effects on MVPA did not diminish significantly over the course of the 6-wk study period, and were statistically significant in both females and males, and in normal- vs. high-BMI subgroups. These results provide promising initial indications that combining the Zamzee activity meter with online proximal performance feedback and incentive motivation features can positively impact MVPA levels in adolescents.


title: Self-oscillating half bridge series resonant converter controlled by a small saturable core

abstract: A self-oscillating half bridge series resonant converter control is proposed. The turn-on and turn-off of the switch are zero-voltage-switching with resonant transition. A saturable core is used to achieve the self-excitation and the zero-voltage-switched resonant transition. Although the system of the self-oscillating command presents a simple working safety, it presents the necessity of determination of the magnetic parameters of the saturable core. To consider the regulation of the output current in an ulterior stage, we propose to inject a continuous current through the saturable core and to determine the influence thereafter on the command. Theoretical analysis and experimental result on the proposed converter are provided.


title: Deep Learning and Wavelets for High-Frequency Price Forecasting

abstract: This paper presents improvements in financial time series prediction using a Deep Neural Network (DNN) in conjunction with a Discrete Wavelet Transform (DWT). When comparing our model to other three alternatives, including ARIMA and other deep learning topologies, ours has a better performance. All of the experiments were conducted on High-Frequency Data (HFD). Given the fact that DWT decomposes signals in terms of frequency and time, we expect this transformation will make a better representation of the sequential behavior of high-frequency data. The input data for every experiment consists of 27 variables: The last 3 one-minute pseudo-log-returns and last 3 one-minute compressed tick-by-tick wavelet vectors, each vector is a product of compressing the tick-by-tick transactions inside a particular minute using a DWT with length 8. Furthermore, the DNN predicts the next one-minute pseudo-log-return that can be transformed into the next predicted one-minute average price. For testing purposes, we use tick-by-tick data of 19 companies in the Dow Jones Industrial Average Index (DJIA), from January 2015 to July 2017. The proposed DNN’s Directional Accuracy (DA) presents a remarkable forecasting performance ranging from 64% to 72%.


title: Constant Current Power Transmission Line-Based Power Delivery Network for Single-Ended Signaling

abstract: The performance of a system depends heavily on the communication speed between integrated circuits. One of the most important bottlenecks that limit the communication speed is simultaneous switching noise (SSN). A major contribution to SSN is return path discontinuities, which are caused by the change or disruption in return currents due to via transitions, aperture effects, etc. In this paper, a new concept based on power transmission line (PTL) is proposed to supply power, improve signal and power integrity, and enhance chip-to-chip communication speed. The first demonstration of constant current PTL (CCPTL)-based single-ended signaling scheme is implemented and measured. The results show that the CCPTL scheme improves the quality of the received signal in terms of voltage and timing margin.


title: Gossip algorithms: design, analysis and applications

abstract: Motivated by applications to sensor, peer-to-peer and ad hoc networks, we study distributed asynchronous algorithms, also known as gossip algorithms, for computation and information exchange in an arbitrarily connected network of nodes. Nodes in such networks operate under limited computational, communication and energy resources. These constraints naturally give rise to "gossip" algorithms: schemes which distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for arbitrary network, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly stochastic matrix characterizing the algorithm. Using recent results of Boyd, Diaconis and Xiao (2003), we show that minimizing this quantity to design the fastest averaging algorithm on the network is a semi-definite program (SDP). In general, SDPs cannot be solved distributedly; however, exploiting problem structure, we propose a subgradient method that distributedly solves the optimization problem over the network. The relation of averaging time to the second largest eigenvalue naturally relates it to the mixing time of a random walk with transition probabilities that are derived from the gossip algorithm. We use this connection to study the performance of gossip algorithm on two popular networks: wireless sensor networks, which are modeled as geometric random graphs, and the Internet graph under the so-called preferential connectivity model.


title: Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks

abstract: Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive. We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regularization term. This potential is however not the original loss function in general. So SGD does perform variational inference, but for a different loss than the one used to compute the gradients. Even more surprisingly, SGD does not even converge in the classical sense: we show that the most likely trajectories of SGD for deep networks do not behave like Brownian motion around critical points. Instead, they resemble closed loops with deterministic components. We prove that such"out-of-equilibrium"behavior is a consequence of highly non-isotropic gradient noise in SGD; the covariance matrix of mini-batch gradients for deep networks has a rank as small as 1% of its dimension. We provide extensive empirical validation of these claims, proven in the appendix.


title: 01ER25509. Autotuning Sparse Matrix-Vector Multiplication for Multicore

abstract: Sparse matrix-vector multiplication (SpMV) is an important kernel in scientific and engineering computing. Straightforward parallel implementations of SpMV often perform poorly, and with the increasing variety of architectural features in multicore processors, it is getting more difficult to determine the sparse matrix data structure and corresponding SpMV implementation that optimize performance. In this paper we present pOSKI, an autotuning system for SpMV that automatically searches over a large set of possible data structures and implementations to optimize SpMV performance on multicore platforms. pOSKI explores a design space that depends on both the nonzero pattern of the sparse matrix, typically not known until run-time, and the architecture, which is explored off-line as much as possible, in order to reduce tuning time. We demonstrate significant performance improvements compared to previous serial and parallel implementations, and compare performance to upper bounds based on architectural models. General Terms: Design, Experimentation, Performance Additional


title: Influence Factors of Understanding Business Process Models

abstract: The increasing utilization of business process models both in business analysis and information systems development raises several issues regarding quality measures. In this context, this paper discusses understandability as a particular quality aspect and its connection with personal, model, and content related factors. We use an online survey to explore the ability of the model reader to draw correct conclusions from a set of process models. For the first group of the participants we used models with abstract activity labels (e.g. A, B, C) while the second group received the same models with illustrative labels such as “check credit limit”. The results suggest that all three categories indeed have an impact on the understandability.


A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling


title: Depth enhancement via non-local means filter

abstract: The depth image captured by a RGB-D camera is noisy and usually misses the values at some pixels, especially around the object boundaries. There are many methods take advantage of the corresponding color images to enhance the depth images. Most of them bring the texture of the color image into the depth image. In this paper, an adaptive double non-local means (ADNLM) method of depth enhancement is proposed. First, ADNLM pre-inpaint the depth image via a color-guided non-local means method; second, a depth-based non-local means method is used to denoise the pre-inpainted depth image. Experiments on the public benchmarks show that, ADNLM can avoid the color texture, and effectively enhance the depth image, especially when there are several large missing regions in the depth image. Also, the performance in terms of PSNR of ADNLM is slightly better than that of the state of the arts.


title: Open Access Database www.i-techonline.com Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

abstract: There is a trend in the scientific community to model and solve complex optimization problems by employing natural metaphors. This is mainly due to inefficiency of classical optimization algorithms in solving larger scale combinatorial and/or highly non-linear problems. The situation is not much different if integer and/or discrete decision variables are required in most of the linear optimization models as well. One of the main characteristics of the classical optimization algorithms is their inflexibility to adapt the solution algorithm to a given problem. Generally a given problem is modelled in such a way that a classical algorithm like simplex algorithm can handle it. This generally requires making several assumptions which might not be easy to validate in many situations. In order to overcome these limitations more flexible and adaptable general purpose algorithms are needed. It should be easy to tailor these algorithms to model a given problem as close as to reality. Based on this motivation many nature inspired algorithms were developed in the literature like genetic algorithms, simulated annealing and tabu search. It has also been shown that these algorithms can provide far better solutions in comparison to classical algorithms. A branch of nature inspired algorithms which are known as swarm intelligence is focused on insect behaviour in order to develop some meta-heuristics which can mimic insect's problem solution abilities. Ant colony optimization, particle swarm optimization, wasp nets etc. are some of the well known algorithms that mimic insect behaviour in problem modelling and solution. Artificial Bee Colony (ABC) is a relatively new member of swarm intelligence. ABC tries to model natural behaviour of real honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new intelligent search algorithms. In this chapter an extensive review of work on artificial bee algorithms is given. Afterwards, development of an ABC algorithm for solving generalized assignment problem which is known as NP-hard problem is presented in detail along with some comparisons. It is a well known fact that classical optimization techniques impose several limitations on solving mathematical programming and operational research models. This is mainly due to inherent solution mechanisms of these techniques. Solution strategies of classical optimization algorithms are generally depended on the type of objective and constraint


title: Neural circuits as computational dynamical systems

abstract: Many recent studies of neurons recorded from cortex reveal complex temporal dynamics. How such dynamics embody the computations that ultimately lead to behavior remains a mystery. Approaching this issue requires developing plausible hypotheses couched in terms of neural dynamics. A tool ideally suited to aid in this question is the recurrent neural network (RNN). RNNs straddle the fields of nonlinear dynamical systems and machine learning and have recently seen great advances in both theory and application. I summarize recent theoretical and technological advances and highlight an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex.


title: The perceptions towards mobile services: an empirical analysis of the role of use facilitators

abstract: The adoption patterns for new technology such as mobile services are essential to understand for developing them further. Hence, it is important to study how willing people are to try new mobile services and do variables, which facilitate the use of the services have an effect on the consumers' perceptions of the services and their adoption readiness. This study analyses how the familiarity of mobile devices, the time the services are initially used and users' technology skills affect the initial mobile service usage experience and the likelihood of continuous usage. The results show that the duration of the use does not effect consumers' perceptions about mobile services, but familiarity of the device and user skills have an impact on the perceptions of the services.


title: Interactive Full Image Segmentation

abstract: We address the task of interactive full image annotation, where the goal is to produce accurate segmentations for all object and stuff regions in an image. To this end we propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions. This enables the annotator to focus on the largest errors made by the machine across the whole image, and to share corrections across nearby regions. Furthermore, we adapt Mask-RCNN into a fast interactive segmentation framework and introduce a new instance-aware loss measured at the pixel-level in the full image canvas, which lets predictions for nearby regions properly compete. Finally, we compare to interactive single object segmentation on the on the COCO panoptic dataset. We demonstrate that, at a budget of four extreme clicks and four corrective scribbles per region, our interactive full image segmentation approach leads to a 5% IoU gain, reaching 90% IoU.


Designing coupling beams and joints in concrete buildings for improved earthquake resistance


title: Future Trends in Software Engineering Research for Mobile Apps

abstract: There has been tremendous growth in the use of mobile devices over the last few years. This growth has fueled the development of millions of software applications for these mobile devices often called as 'apps'. Current estimates indicate that there are hundreds of thousands of mobile app developers. As a result, in recent years, there has been an increasing amount of software engineering research conducted on mobile apps to help such mobile app developers. In this paper, we discuss current and future research trends within the framework of the various stages in the software development life-cycle: requirements (including non-functional), design and development, testing, and maintenance. While there are several non-functional requirements, we focus on the topics of energy and security in our paper, since mobile apps are not necessarily built by large companies that can afford to get experts for solving these two topics. For the same reason we also discuss the monetizing aspects of a mobile app at the end of the paper. For each topic of interest, we first present the recent advances done in these stages and then we present the challenges present in current work, followed by the future opportunities and the risks present in pursuing such research.


title: Social interaction in pedestrian evacuation: a cellular discrete event simulation approach

abstract: This paper presents a cellular simulation model for pedestrian emergency evacuation, which takes into account social factors such as individual vision, environmental familiarity, communication and conduction, and velocity adjustment. Variables are introduced to describe the relationship between the evacuees and their environment, as well as the interaction with each other. RESTful CD++ has been used as a middleware to run simulations remotely. The results obtained confirm the role of social interactions in emergency evacuation. This work, on the one hand, shows the advantages and advancement of model-driven theory and DEVS formalism to address social science issues; on the other hand, it helps to better integrate evacuation planning and public safety management in the design of urban architectures.


title: How developers search for code: a case study

abstract: With the advent of large code repositories and sophisticated search capabilities, code search is increasingly becoming a key software development activity. In this work we shed some light into how developers search for code through a case study performed at Google, using a combination of survey and log-analysis methodologies. Our study provides insights into what developers are doing and trying to learn when per- forming a search, search scope, query properties, and what a search session under different contexts usually entails. Our results indicate that programmers search for code very frequently, conducting an average of five search sessions with 12 total queries each workday. The search queries are often targeted at a particular code location and programmers are typically looking for code with which they are somewhat familiar. Further, programmers are generally seeking answers to questions about how to use an API, what code does, why something is failing, or where code is located.


title: Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network

abstract: The next generation wireless networks are expected to operate in fully automated fashion to meet the burgeoning capacity demand and to serve users with superior quality of experience. Mobile wireless networks can leverage spatio-temporal information about user and network condition to embed the system with end-to-end visibility and intelligence. Big data analytics has emerged as a promising approach to unearth meaningful insights and to build artificially intelligent models with assistance of machine learning tools. Utilizing aforementioned tools and techniques, this paper contributes in two ways. First, we utilize mobile network data (Big Data)—call detail record—to analyze anomalous behavior of mobile wireless network. For anomaly detection purposes, we use unsupervised clustering techniques namely k-means clustering and hierarchical clustering. We compare the detected anomalies with ground truth information to verify their correctness. From the comparative analysis, we observe that when the network experiences abruptly high (unusual) traffic demand at any location and time, it identifies that as anomaly. This helps in identifying regions of interest in the network for special action such as resource allocation, fault avoidance solution, etc. Second, we train a neural-network-based prediction model with anomalous and anomaly-free data to highlight the effect of anomalies in data while training/building intelligent models. In this phase, we transform our anomalous data to anomaly-free and we observe that the error in prediction, while training the model with anomaly-free data has largely decreased as compared to the case when the model was trained with anomalous data.


title: Bistatic Synthetic Aperture Radar Imaging for Arbitrary Flight Trajectories

abstract: This paper presents an approximate analytic inversion method for bistatic synthetic aperture radar. A scene of interest is illuminated by electromagnetic waves that are transmitted from positions along an arbitrary, but known, flight trajectory and the scattered waves are measured from positions along a different flight trajectory which is also arbitrary, but known. We assume a single-scattering model for the radar data, and we assume that the ground topography is known but not necessarily flat. We use microlocal analysis to develop a filtered-backprojection-type reconstruction method.


title: Transverse modes suppression and loss reduction for buried electrodes SAW devices

abstract: High coupling substrates using overcoat layers of silicon oxide for temperature compensation exhibit a strong affinity toward multi-mode behavior. Apodization, which is the standard technique to suppress the transverse modes, suffers from drawbacks such as reduction of the transduction efficiency and additional losses. By changing the transducer layout, the guiding can be improved and a “piston mode” can be obtained. This approach is demonstrated on resonators and DMS test devices manufactured on lithium niobate with an orientation close to YX128 deg.


title: Advanced Active Gate Drive for Switching Performance Improvement and Overvoltage Protection of High-Power IGBTs

abstract: This paper presents a new active gate drive (AGD) for switching performance improvement and overvoltage protection of high-power insulated gate bipolar transistors (IGBTs). In addition to the conventional gate drive (CGD) based on fixed voltage sources and fixed gate drive resistors, the proposed AGD has a complementary current source to provide extra gate drive current into the gate. Specific transient switching stages of the IGBT can be therefore accelerated, leading to higher switching speed and lower switching loss of the IGBT. Additionally, the turn-off voltage overshoot of the IGBT can be controlled at a preset reference value with a fast closed-loop overvoltage protection circuit. Moreover, the switching speed of the IGBT, including the turn-on/off delay times and the turn-on/off voltage slopes, can be effectively regulated with an adaptive switching speed control method. Accordingly, the gate drive is capable of operating the IGBT at specified delay times and fixed voltage slopes when varying the switching conditions (e.g., temperature, load current). The operation principle of the proposed AGD and control concept are presented. By comparing with the CGD, the proposed method is experimentally verified on a 3.3 kV/1.5 kA IGBT module in both double-pulse and multipulse tests.


Efficient Multiple Instance Convolutional Neural Networks for Gigapixel Resolution Image Classification.


title: Low-level components of analytic activity in information visualization

abstract: Existing system level taxonomies of visualization tasks are geared more towards the design of particular representations than the facilitation of user analytic activity. We present a set of ten low level analysis tasks that largely capture people's activities while employing information visualization tools for understanding data. To help develop these tasks, we collected nearly 200 sample questions from students about how they would analyze five particular data sets from different domains. The questions, while not being totally comprehensive, illustrated the sheer variety of analytic questions typically posed by users when employing information visualization systems. We hope that the presented set of tasks is useful for information visualization system designers as a kind of common substrate to discuss the relative analytic capabilities of the systems. Further, the tasks may provide a form of checklist for system designers.


title: A Test of the ARCC© Model Improves Implementation of Evidence‐Based Practice, Healthcare Culture, and Patient Outcomes

abstract: BACKGROUND ::: Although several models of evidence-based practice (EBP) exist, there is a paucity of studies that have been conducted to evaluate their implementation in healthcare settings. ::: ::: ::: AIM ::: The purpose of this study was to examine the impact of the Advancing Research and Clinical practice through close Collaboration (ARCC) Model on organizational culture, clinicians' EBP beliefs and EBP implementation, and patient outcomes at one healthcare system in the western United States. ::: ::: ::: DESIGN ::: A pre-test, post-test longitudinal pre-experimental study was conducted with follow-up immediately following full implementation of the ARCC Model. ::: ::: ::: SETTING AND SAMPLE ::: The study was conducted at a 341-bed acute care hospital in the western region of the United States. The sample consisted of 58 interprofessional healthcare professionals. ::: ::: ::: METHODS ::: The ARCC Model was implemented in a sequential format over 12 months with the key strategy of preparing a critical mass of EBP mentors for the healthcare system. Healthcare professionals' EBP beliefs, EBP implementation, and organizational culture were measured with valid and reliable instruments. Patient outcomes were collected in aggregate from the hospital's medical records. ::: ::: ::: RESULTS ::: Findings indicated significant increases in clinicians' EBP beliefs and EBP implementation along with positive movement toward an organizational EBP culture. Study findings also indicated substantial improvements in several patient outcomes. ::: ::: ::: LINKING EVIDENCE TO ACTION ::: Implementation of the ARCC Model in healthcare systems can enhance clinicians' beliefs and implementation of evidence-based care, improve patient outcomes, and move organizational culture toward EBP.


title: Automated Progress Monitoring Based on Photogrammetric Point Clouds and Precedence Relationship Graphs

abstract: Construction progress monitoring is an essential but time-consuming work on all construction sites. This research introduces a method to facilitate the asplanned versus as-built comparison through image based monitoring. A dense point cloud is reconstructed from the images that is compared to an existing 4D building information model (BIM). However, due to the numerous obstructions found on a construction site, only a minority of building elements can be detected directly. In this paper, we discuss how the detection results are significantly refined and enriched by using additional spatial and temporal information gained from the 4D BIM. In this regard, a precedence relationship graph is derived which helps to identify occluded elements and enhance the detection algorithm.


title: Similarity measures for binary and numerical data: a survey

abstract: Similarity measures aim at quantifying the extent to which objects resemble each other. Many techniques in data mining, data analysis or information retrieval require a similarity measure, and selecting an appropriate measure for a given problem is a difficult task. In this paper, the diverse forms similarity measures can take are examined, as well as their relationships and respective properties. Their semantic differences are highlighted and numerical tools to quantify these differences are proposed, considering several points of view and including global and local comparisons, order-based and value-based comparisons, and mathematical properties such as derivability. The paper studies similarity measures for two types of data: binary and numerical data, i.e., set data represented by the presence or absence of characteristics and data represented by real vectors.


title: Noncontact ultrasonic transportation of small objects over long distances in air using a bending vibrator and a reflector

abstract: Ultrasonic manipulation of small particles, including liquid droplets, over long distances is discussed. It is well known that particles can be trapped at the nodal points of an acoustic standing wave if the particles are much smaller than the wavelength of the standing wave. We used an experimental setup consisting of a 3-mm-thick, 605-mm-long duralumin bending vibrating plate and a reflector. A bolt-clamped Langevin transducer with horn was attached to each end of the vibrating plate to generate flexural vibrations along the plate. A plane reflector with the same dimensions as the vibrating plate was installed parallel to the plate at a distance of approximately 17 mm to generate an ultrasonic standing wave between them and to trap the small particles at the nodal lines. The acoustic field and acoustic radiation force between the vibrator and reflector were calculated by finite element analysis to predict the positions of the trapped particles. The sound pressure distribution was measured experimentally using a scanning laser Doppler vibrometer. By controlling the driving phase difference between the two transducers, a flexural traveling wave can be generated along the vibrating plate, and the vertical nodal lines of the standing wave and the trapped particles can be moved. The flexural wave was excited along the vibrator at 22.5 kHz. A lattice standing wave with a wavelength of 35 mm in the length direction could be excited between the vibrator and the reflector, and polystyrene spheres with diameters of several millimeters could be trapped at the nodal lines of the standing wave. The experimental and calculated results showed good agreement for the relationship between the driving phase difference and the positions of the trapped particles. Noncontact transportation of the trapped particles over long distances could be achieved by changing the driving phase difference. The position of the trapped particles could be controlled to an accuracy of 0.046 mm/deg. An ethanol droplet could also be trapped and moved.


title: The BORG: Nanoprobing Binaries for Buffer Overreads

abstract: Automated program testing tools typically try to explore, and cover, as much of a tested program as possible, while attempting to trigger and detect bugs. An alternative and complementary approach can be to first select a specific part of a program that may be subject to a specific class of bug, and then narrowly focus exploration towards program paths that could trigger such a bug. In this work, we introduce the BORG (Buffer Over-Read Guard), a testing tool that uses static and dynamic program analysis, taint propagation and symbolic execution to detect buffer overread bugs in real-world programs. BORG works by first selecting buffer accesses that could lead to an overread and then guiding symbolic execution towards those accesses along program paths that could actually lead to an overread. BORG operates on binaries and does not require source code. To demonstrate BORG's effectiveness, we use it to detect overreads in six complex server applications and libraries, including lighttpd, FFmpeg and ClamAV.


title: Detecting Conflicts in ABAC Policies with Rule-Reduction and Binary-Search Techniques

abstract: Attribute-based access control (ABAC) policies are effective and flexible in governing the access to information and resources in open distributed computing environments. However, ABAC policy rules are often complex making them prone to conflicts. This paper proposes an optimized method to detect the conflicts between statistically conflicting rules in an ABAC policy. This method includes two optimization techniques: rule reduction and binary-search. The first technique reduces the rules into a set of compact, semantically equivalent rules through removing redundant information among the rules. The binary-search technique is then applied to discover the conflicts among them.


title: Drivers of consumer–brand identification

abstract: Abstract The concept of consumer–brand identification (CBI) is central to our understanding of how, when, and why brands help consumers articulate their identities. This paper proposes and tests an integrative theoretical framework of the antecedents of CBI. Six drivers of CBI, a moderator, and two consequences are posited and tested with survey data from a large sample of German household consumers. The results confirm the influence of five of the six drivers, namely, brand–self similarity, brand distinctiveness, brand social benefits, brand warmth, and memorable brand experiences. Further, we find that all five of these antecedents have stronger causal relationships with CBI when consumers have higher involvement with the brand's product category. Finally, CBI is tied to two important pro-company consequences, brand loyalty and brand advocacy. Theoretical and managerial significance of the findings are discussed.


title: A comparative analysis of Hindi word sense disambiguation and its approaches

abstract: Hindi language is written and spoken by majority of people in India. Like other natural languages, Hindi is also an ambiguous language which creates obstacle in usage of information technology properly. To use Hindi language efficiently and effectively on web, we require a tool to remove ambiguity from a single word, or from all words, called word sense disambiguation (WSD). In this paper we introduce the reader to the brief introduction of task, resources required for disambiguation, applications, brief review of previous works. We also compare and investigate different techniques and algorithms under these approaches which can be used for Hindi Word Sense Disambiguation (HWSD) task and finally, some open problems, conclusion and future directions are discussed on the basis of these comparisons.


title: Deep Android Malware Detection

abstract: In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing the need for hand-engineered malware features. The training pipeline of our proposed system is much simpler than existing n-gram based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification, thus removing the need to explicitly enumerate millions of n-grams during training. The network design also allows the use of long n-gram like features, not computationally feasible with existing methods. Once trained, the network can be efficiently executed on a GPU, allowing a very large number of files to be scanned quickly.


title: A Survey of Collaborative Filtering Techniques

abstract: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.


title: Treatment and posttreatment dentoalveolar changes following intrusion of mandibular molars with application of a skeletal anchorage system (SAS) for open bite correction.

abstract: The skeletal anchorage system (SAS) consists of titanium anchor plates and monocortical screws that are temporarily implanted in either the maxilla or the mandible as absolute orthodontic anchorage. With SAS, anterior open bite can be improved by the counterclockwise rotation of the mandible, accompanied by the intrusion of molars. The present study was designed to evaluate treatment and posttreatment dentoalveolar changes following the intrusion of mandibular molars. Nine adult open bite patients (7 women and 2 men) successfully treated with SAS were included in the following study. The amount of intrusion, relapse, and dentoalveolar changes were measured on cephalometric radiographs, panoramic radiographs, and dental casts. The results of this study were as follows: (1) the average amount of intrusion of the mandibular first and second molars was 1.7 mm and 2.8 mm, respectively; (2) the average relapse rates were 27.2% at the first molars and 30.3% at the second molars; (3) there were no significant changes in crestal bone heights, clinical crown length, or root length; and (4) counterclockwise rotation of the mandible and decrease of anterior facial height were observed during treatment. Thus, it was concluded that SAS would be a valid modality to intrude mandibular molars for correction of open bite.


Constructing Simplicial Complexes over Topological Spaces


title: Parallel analytics as a service

abstract: Recently, massively parallel processing relational database systems (MPPDBs) have gained much momentum in the big data analytic market. With the advent of hosted cloud computing, we envision that the offering of MPPDB-as-a-Service (MPPDBaaS) will become attractive for companies having analytical tasks on only hundreds gigabytes to some ten terabytes of data because they can enjoy high-end parallel analytics at a cheap cost. This paper presents Thrifty, a prototype implementation of MPPDB-as-a-service. The major research issue is how to achieve a lower total cost of ownership by consolidating thousands of MPPDB tenants on to a shared hardware infrastructure, with a performance SLA that guarantees the tenants can obtain the query results as if they are executing their queries on dedicated machines. Thrifty achieves the goal by using a tenant-driven design that includes (1) a cluster design that carefully arranges the nodes in the cluster into groups and creates an MPPDB for each group of nodes, (2) a tenant placement that assigns each tenant to several MPPDBs (for high availability service through replication), and (3) a query routing algorithm that routes a tenant's query to the proper MPPDB at run-time. Experiments show that in a MPPDBaaS with 5000 tenants, where each tenant requests 2 to 32 nodes MPPDB to query against 200GB to 3.2TB of data, Thrifty can serve all the tenants with a 99.9% performance SLA guarantee and a high availability replication factor of 3, using only 18.7% of the nodes requested by the tenants.


title: Trot pattern generation for quadruped robot based on the ZMP stability margin

abstract: Quadruped robot is expected to serve in complex conditions such as mountain road, grassland, etc., therefore we desire a walking pattern generation that can guarantee both the speed and the stability of the quadruped robot. In order to solve this problem, this paper focuses on the stability for the tort pattern and proposes trot pattern generation for quadruped robot on the basis of ZMP stability margin. The foot trajectory is first designed based on the work space limitation. Then the ZMP and stability margin is computed to achieve the optimal trajectory of the midpoint of the hip joint of the robot. The angles of each joint are finally obtained through the inverse kinematics calculation. Finally, the effectiveness of the proposed method is demonstrated by the results from the simulation and the experiment on the quadruped robot in BIT.


title: A Study of the Influence of Gaming Behavior on Academic Performance of IT College Students

abstract: Video-game playing is popular among college students. Cognitive and negative consequences have been studied frequently. However, little is known about the influence of gaming behavior on IT college students’ academic performance. An increasing number of college students take online courses, use social network websites for social interactions, and play video games online. To analyze the relationship between college students’ gaming behavior and their academic performance, a research model is proposed and a survey study is conducted. The study result of a multiple regression analysis shows that self-control capability, social interaction using face-to-face or phone communications, and playing video games using a personal computer make statistically significant contributions to the IT college students’ academic performance measured by GPA.


title: The mix estimation algorithm for battery State-of-Charge estimator- Analysis of the sensitivity to measurement errors.

abstract: The problem considered in this paper is the analysis of a battery State-of-Charge estimation algorithm: in particular the mix estimation algorithm. This algorithm provides the estimation mixing two estimation approaches: namely the Coulomb-Counting and the Model-Based. The mix algorithm is qualitatively able to provide a more robust and accurate estimation with respect to the estimation provided by the approaches the algorithm mixes together. The aim of this paper is to analyze the differences between the three algorithms and the advantages produced by the mixing procedure. In particular the paper presents the comparison of the mix algorithm behavior with the Coulomb-Counting and the Model-Based behaviors in case of measure errors.


Capturing Argument Relationship for Chinese Semantic Role Labeling


title: What's decidable about hybrid automata?

abstract: Hybrid automata model systems with both digital and analog components, such as embedded control programs. Many verification tasks for such programs can be expressed as reachability problems for hybrid automata. By improving on previous decidability and undecidability results, we identify the precise boundary between decidability and undecidability of the reachability problem for hybrid automata. On the positive side, we give an (optimal) PSPACE reachability algorithm for the case of initialized rectangular automata, where all analog variables follow trajectories within piecewise-linear envelopes and are reinitialized whenever the envelope changes. Our algorithm is based on the construction of a timed automaton that contains all reachability information about a given initialized rectangular automaton. The translation has practical significance for verification, because it guarantees the termination of symbolic procedures for the reachability analysis of initialized rectangular automata. The translation also preserves the $\omega$-languages of initialized rectangular automata with bounded nondeterminism. On the negative side, we show that several slight generalizations of initialized rectangular automata lead to an undecidable reachability problem. In particular, we prove that the reachability problem is undecidable for timed automata augmented with a single stopwatch.


title: Smartphone Forensics Analysis: A Case Study

abstract: Abstract—Smartphone forensics is a sub-set of digital forensics, and refers to the investigation and acquisition of artefacts in mobile phones. New threats to mobile phones made forensic science a challenging endeavour in the last couple of years. Number of mobile users is increasing worldwide and createstremendous problems and challenges. The literature relevant to Smartphone forensics, as explored in this paper, focuses on the architecture of Smartphone operating systems and anti-forensics techniques. It also addresses the digital evidence of Smartphone applications. In this paper, through a consideration of types of crimes involving Smartphone’s, a real case study from the Sultanate of Oman is presented. This case study undertakes practical experiments to identified sources for evidence that can later be used in the judiciary system.


title: Kalis — A System for Knowledge-Driven Adaptable Intrusion Detection for the Internet of Things

abstract: In this paper, we introduce Kalis, a self-adapting, knowledge-driven expert Intrusion Detection System able to detect attacks in real time across a wide range of IoT systems. Kalis does not require changes to existing IoT software, can monitor a wide variety of protocols, has no performance impact on applications on IoT devices, and enables collaborative security scenarios. Kalis is the first comprehensive approach to intrusion detection for IoT that does not target individual protocols or applications, and adapts the detection strategy to the specific network features. Extensive evaluation shows that Kalis is effective and efficient in detecting attacks to IoT systems.


title: The Structural Biology of CRISPR-Cas Systems

abstract: Prokaryotic CRISPR-Cas genomic loci encode RNA-mediated adaptive immune systems that bear some functional similarities with eukaryotic RNA interference. Acquired and heritable immunity against bacteriophage and plasmids begins with integration of ∼30 base pair foreign DNA sequences into the host genome. CRISPR-derived transcripts assemble with CRISPR-associated (Cas) proteins to target complementary nucleic acids for degradation. Here we review recent advances in the structural biology of these targeting complexes, with a focus on structural studies of the multisubunit Type I CRISPR RNA-guided surveillance and the Cas9 DNA endonuclease found in Type II CRISPR-Cas systems. These complexes have distinct structures that are each capable of site-specific double-stranded DNA binding and local helix unwinding.


title: Design and Analysis of 239 GHz CMOS Push-Push Transformer-Based VCO With High Efficiency and Wide Tuning Range

abstract: A push-push transformer-based voltage-controlled oscillator (VCO) is proposed and analyzed to achieve high efficiency and a wide tuning range at sub-terahertz (THz) frequencies. Analyses show that the coupling factor of the transformer to obtain high output power $({\bf P}_{\rm OUT})$ has to be carefully chosen by consideration of gate-to-drain voltage gain as well as matching impedances seen from the drain and to the gate of a VCO transistor at the 2nd harmonic frequency $(2f_{o})$ . Analysis also shows that the transformer-based resonator allows a wide tuning range. In addition, it has been shown that the introduction of a parallel inductor to a varactor leads to high ${\bf P}_{\rm OUT}$ and low phase noise. The proposed 239 GHz VCO with a 65 nm CMOS process demonstrates the high efficiency of 1.45%, the ${\bf P}_{\rm OUT}$ of $-$ 4.8 dBm, and the wide tuning range of 12.5% with a supply voltage of 1.2 V.


title: Real-Time Barcode Detection in the Wild

abstract: The linear 1D barcode is the main tagging system for billions of products sold each day. Barcodes have many advantages but require a laser scanner for fast and robust scanning. Solutions exist to read barcodes from cell phones but they assume a carefully framed image within the field of view. This undermines the true potential of barcodes in a wide range of scenarios. In this paper we present a real time technique to detect barcodes in the wild from video streams. Our technique outperforms the state-of-the-art passive techniques both in accuracy and speed. Potential commercial applications enabled by such passive scanning system are also discussed in this paper.


title: GOP primary season on twitter: "popular" political sentiment in social media

abstract: As mainstream news media and political campaigns start to pay attention to the political discourse online, a systematic analysis of political speech in social media becomes more critical. What exactly do people say on these sites, and how useful is this data in estimating political popularity? In this study we examine Twitter discussions surrounding seven US Republican politicians who were running for the US Presidential nomination in 2011. We show this largely negative rhetoric to be laced with sarcasm and humor and dominated by a small portion of users. Furthermore, we show that using out-of-the-box classification tools results in a poor performance, and instead develop a highly optimized multi-stage approach designed for general-purpose political sentiment classification. Finally, we compare the change in sentiment detected in our dataset before and after 19 Republican debates, concluding that, at least in this case, the Twitter political chatter is not indicative of national political polls.


title: Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

abstract: Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).


title: Motion Detection and Face Recognition for CCTV Surveillance System

abstract: Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used for motion detection, and Haar Classifiers Cascade used for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video . The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a f ace detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time.


title: Steps toward Building of a Good AI for Complex Wargame-Type Simulation Games

abstract: ABSTRACT One of the key areas for the application of Artificial Intelligence to the game domain is in the design of challenging artificial opponents for human players. Complex simulations such as historical wargames can be seen as natural extensions of classical games where AI techniques such as planning or learning have already proved powerful. Yet the parallel nature of more recent games introduce new levels of complexity which can be tackled at various levels. This paper focuses on the question of finding good representations for the AI design, which implies finding relevant granularities for the various tasks involved, for a popular historical wargame. This work is based on the partially automated use of the rules of the game, as well as some common sense and historical military knowledge, to design relevant heuristics. The resulting gain in representation complexity will help the application of techniques such as Reinforcement Learning. INTRODUCTION A type of computer games that has been gaining significant popularity over the years lets 2 or more opponents confront each other via the manipulation of a number of units on a given terrain. Sounds familiar? Of course, this description is so general that it encompasses age old games such as chess. What we are interested in here are strategy games which range from real time action-oriented games such as Age of Empires (Microsoft) to intricate historical simulations and wargames such as Sid Meier’s Gettysburg (Firaxis) or Talonsoft Battleground series. The innovation in this new type of games, from the point of view of AI and complexity, is both quantitative as well as qualitative. Quantitatively, they show an increased complexity by letting players manipulate high numbers of units (typically in the hundreds, if not thousands). Additionally, these units have open to them a high number of possible actions, depending on their characteristics, which fall in various categories such as movement, combat, or building activities for some of them. Moreover, the physical space on which they move is much larger. While chess has 64 positions, and backgammon 24, the new games we are interested in involve 2-dimensional grids which extend over hundreds of squares (or hexagons) in each direction, so the total number of positions is in the tens of thousands. Despite the huge quantitative scale-up required to use existing AI techniques on these new problems, the main source of complexity is actually elsewhere. While traditional games usually let the player select one (or a very small number of) unit(s), and then select an action for it, large modern simulations replicate the parallel nature of the system they simulate: each turn, all of the units can be moved (or take other actions) simultaneously. Therefore, while the branching factor of most traditional games increases linearly with the number of units, its increase is exponential in our games. In practical terms, that means that the standard versions of popular AI techniques (such as planning or learning) [Newell & Simon, 1965, Samuel, 1959, Lenat 1983, Meyer et. al., 1997, Sutton & Barto, 1998] are rendered irrelevant because of the complexity involved here. In this paper, we investigate how a careful examination of a game, good choices of representation (as well as possible innovations in the algorithms themselves), can help to circumvent these limitations. In the remaining of this paper we will focus on one specific commercial game series named Battleground (Talonsoft®, designer John Tiller). It is a turn-based game considered as one of the best simulations of Napoleonic battles. On this application, we will expose a number of research directions under investigation, all aiming at dealing with the complexity of the game so as to make it amenable to efficient AI techniques. This paper focuses mainly on the issue of finding good representations, using available sources of knowledge about the problem, Its intended impact is therefore on the initial stage of AI design. It can be seen as a complement to other active research directions in the field of machine learning which work on the learning algorithms themselves to deal with higher complexities, e.g. work on learning within a hierarchy of goals [Dietterich,


title: Summarizing Software Artifacts: A Literature Review

abstract: This paper presents a literature review in the field of summarizing software artifacts, focusing on bug reports, source code, mailing lists and developer discussions artifacts. From Jan. 2010 to Apr. 2016, numerous summarization techniques, approaches, and tools have been proposed to satisfy the ongoing demand of improving software performance and quality and facilitating developers in understanding the problems at hand. Since aforementioned artifacts contain both structured and unstructured data at the same time, researchers have applied different machine learning and data mining techniques to generate summaries. Therefore, this paper first intends to provide a general perspective on the state of the art, describing the type of artifacts, approaches for summarization, as well as the common portions of experimental procedures shared among these artifacts. Moreover, we discuss the applications of summarization, i.e., what tasks at hand have been achieved through summarization. Next, this paper presents tools that are generated for summarization tasks or employed during summarization tasks. In addition, we present different summarization evaluation methods employed in selected studies as well as other important factors that are used for the evaluation of generated summaries such as adequacy and quality. Moreover, we briefly present modern communication channels and complementarities with commonalities among different software artifacts. Finally, some thoughts about the challenges applicable to the existing studies in general as well as future research directions are also discussed. The survey of existing studies will allow future researchers to have a wide and useful background knowledge on the main and important aspects of this research field.