title: Recovery of central and peripheral neuromuscular fatigue after exercise.

abstract: Sustained physical exercise leads to a reduced capacity to produce voluntary force that typically outlasts the exercise bout. This "fatigue" can be due both to impaired muscle function, termed "peripheral fatigue," and a reduction in the capacity of the central nervous system to activate muscles, termed "central fatigue." In this review we consider the factors that determine the recovery of voluntary force generating capacity after various types of exercise. After brief, high-intensity exercise there is typically a rapid restitution of force that is due to recovery of central fatigue (typically within 2 min) and aspects of peripheral fatigue associated with excitation-contraction coupling and reperfusion of muscles (typically within 3-5 min). Complete recovery of muscle function may be incomplete for some hours, however, due to prolonged impairment in intracellular Ca2+ release or sensitivity. After low-intensity exercise of long duration, voluntary force typically shows rapid, partial, recovery within the first few minutes, due largely to recovery of the central, neural component. However, the ability to voluntarily activate muscles may not recover completely within 30 min after exercise. Recovery of peripheral fatigue contributes comparatively little to the fast initial force restitution and is typically incomplete for at least 20-30 min. Work remains to identify what factors underlie the prolonged central fatigue that usually accompanies long-duration single joint and locomotor exercise and to document how the time course of neuromuscular recovery is affected by exercise intensity and duration in locomotor exercise. Such information could be useful to enhance rehabilitation and sports performance.


title: Investigating the Importance of Trust on Adopting an Autonomous Vehicle

abstract: The objective of this study is to examine the user’s adoption aspects of autonomous vehicle, as well as to investigate what factors drive people to trust an autonomous vehicle. A model explaining the impact of different factors on autonomous vehicles’ intention is developed based on the technology acceptance model and trust theory. A survey of 552 drivers was conducted and the results were analyzed using partial least squares. The results demonstrated that perceived usefulness and trust are major important determinants of intention to use autonomous vehicles. The results also show that three constructs—system transparency, technical competence, and situation management—have a positive effect on trust. The study identified that trust has a negative effect on perceived risk. Among the driving-related personality traits, locus of control has significant effects on behavioral intention, whereas sensation seeking did not. This study investigated that the developed model explains the factors that influence the ...


title: Proximal Medial Gastrocnemius Release in the Treatment of Recalcitrant Plantar Fasciitis

abstract: BACKGROUND ::: Isolated gastrocnemius contracture has been implicated as the cause of a number of foot and ankle conditions. Plantar fasciitis (PF) is one such condition that can be secondary to altered foot biomechanics as a result of gastrocnemius contracture. In this paper, we report our results with an isolated release of the proximal medial head of gastrocnemius for recalcitrant PF. ::: ::: ::: METHODS ::: We prospectively followed a consecutive series of 21 heels in 17 patients following a Proximal Medial Gastrocnemius Release (PMGR). PF was diagnosed clinically and confirmed radiologically in all cases. To be included, at least 1~year of conservative treatment must have been tried and an isolated gastrocnemius contracture confirmed clinically using Silfverskiold's test preoperatively. Outcome measures included a 5-point Likert scale as well as subjective and objective calf weakness assessments. Final followup was on average 24 (range, 8 to 36) months after the surgery. ::: ::: ::: RESULTS ::: Seventeen of the 21 heels (81%) reported total or significant pain relief following the surgery and none reported worsening of their symptoms. The majority did not have subjective or objective evidence of calf weakness. There were no `major' complications and only one case that suffered a `minor' complication. ::: ::: ::: CONCLUSION ::: We believe a PMGR is a simple way of treating a patient with PF who has failed to respond to conservative management. In our series, the results were favorable, the recovery fast and the morbidity low.


title: System Characteristics, Satisfaction and E-Learning Usage: A Structural Equation Model (SEM)

abstract: With the advent of the Internet, more and more public universities in Malaysia are putting in effort to introduce e-learning in their respective universities. Using a structured questionnaire derived from the literature, data was collected from 250 undergraduate students from a public university in Penang, Malaysia. Data was analyzed using AMOS version 16. The results of the structural equation model indicated that service quality (β = 0.20, p < 0.01), information quality (β = 0.37, p < 0.01) and system quality (β = 0.20, p < 0.01) were positively related to user satisfaction explaining a total of 45% variance. The second regression analysis was to examine the impact of user satisfaction on continuance intention. The results showed that satisfaction (β = 0.31, p < 0.01), system quality (β = 0.18, p < 0.01) and service quality (β = 0.30, p < 0.01) were positively related to continuance intention explaining 44% of the variance. Implications from these findings to e-learning system developers and implementers were further elaborated.


title: Towards security defect prediction with AI

abstract: In this study, we investigate the limits of the current state of the art AI system for detecting buffer overflows and compare it with current static analysis tools. To do so, we developed a code generator, s-bAbI, capable of producing an arbitrarily large number of code samples of controlled complexity. We found that the static analysis engines we examined have good precision, but poor recall on this dataset, except for a sound static analyzer that has good precision and recall. We found that the state of the art AI system, a memory network modeled after Choi et al. [1], can achieve similar performance to the static analysis engines, but requires an exhaustive amount of training data in order to do so. Our work points towards future approaches that may solve these problems; namely, using representations of code that can capture appropriate scope information and using deep learning methods that are able to perform arithmetic operations.


title: Real-time hand tracking using synergistic inverse kinematics

abstract: We present a method for real-time bare hand tracking that utilizes natural hand synergies to reduce the complexity and improve the plausibility of the hand posture estimation. The hand pose and posture are estimated by fitting a virtual hand model to the 3D point cloud obtained from a Kinect camera using an inverse kinematics approach. We use real human hand movements captured with a Vicon motion tracking system as the ground truth for deriving natural hand synergies based on principal component analysis. These synergies are integrated in the tracking scheme by optimizing the posture in a reduced parameter space. Tracking in this reduced space combined with joint limit avoidance constrains the posture estimation to natural hand articulations. The information loss associated with dimension reduction can be dealt with by employing a hierarchical optimization scheme. We show that our synergistic hand tracking approach improves runtime performance and increases the quality of the posture estimation.


title: Multiple Causal Inference with Latent Confounding

abstract: Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assumptions have focused on estimation of effects for a single treatment. In this work, we construct techniques for estimation with multiple treatments in the presence of unobserved confounding. We develop two assumptions based on shared confounding between treatments and independence of treatments given the confounder. Together, these assumptions lead to a confounder estimator regularized by mutual information. For this estimator, we develop a tractable lower bound. To recover treatment effects, we use the residual information in the treatments independent of the confounder. We validate on simulations and an example from clinical medicine.


title: Stress Analysis of Automotive Engine Cover under Static and Vibration Load

abstract: Structural analysis of automotive engine cover under vibration excitation is performed by finite element analysis (FEA) in order to identify the critical area of the structure. Assembly load due to the tightening of the bolts as well as the vibration excitation were considered to describe the actual loading condition. Natural frequencies of the system were extracted considering the damping effect of the structure. Dynamic analysis was performed based on the extracted natural frequency of the system. Experimental modal analysis (EMA) and measurement of strains were performed to verify the results of the analysis. Analysis results correlated closely with the experimental results. Analysis and experiments showed that contribution of the assembly load should not be ignored to predict the structural failure of the engine cover.


title: Feature Location in Source Code: A Taxonomy and Survey

abstract: SUMMARY ::: Feature location is the activity of identifying an initial location in the source code that implements functionality in a software system. Many feature location techniques have been introduced that automate some or all of this process, and a comprehensive overview of this large body of work would be beneficial to researchers and practitioners. This paper presents a systematic literature survey of feature location techniques. Eighty-nine articles from 25 venues have been reviewed and classified within the taxonomy in order to organize and structure existing work in the field of feature location. The paper also discusses open issues and defines future directions in the field of feature location. Copyright © 2011 John Wiley & Sons, Ltd.


title: Web-Based Live Speech-Driven Lip-Sync

abstract: Virtual characters are an integral part of many games and virtual worlds. The ability to accurately synchronize lip movement to audio speech is an important aspect in the believability of the character. In this paper we propose a simple rule-based lip-syncing algorithm for virtual agents using the web browser. It works in real-time with live input, unlike most current lip-syncing proposals, which may require considerable amounts of computation, expertise and time to set up. Our method generates reliable speech animation based on live speech using three blend shapes and no training, and it only needs manual adjustment of three parameters for each speaker (sensitivity, smoothness and vocal tract length). Our proposal is based on the limited real-time audio processing functions of the client web browser (thus, the algorithm needs to be simple), but this facilitates the use of web based embodied conversational agents.


title: “An Odd Kind of Pleasure”: Differentiating Emotional Challenge in Digital Games

abstract: Recent work introduced the notion of emotional challenge as a means to afford more unique and diverse gaming experiences. However, players' experience of emotional challenge has received little empirical attention. It remains unclear whether players enjoy it and what exactly constitutes the challenge thereof. We surveyed 171 players about a challenging or an emotionally challenging experience, and analyzed their responses with regards to what made the experience challenging, their emotional response, and the relation to core player experience constructs. We found that emotional challenge manifested itself in different ways, by confronting players with difficult themes or decisions, as well as having them deal with intense emotions. In contrast to more'conventional' challenge, emotional challenge evoked a wider range of negative emotions and was appreciated significantly more by players. Our findings showcase the appeal of uncomfortable gaming experiences, and extend current conceptualizations of challenge in games.


title: Measurement model of relationship between knowledge management enablers and knowledge management performance in public sector

abstract: This study investigates the relationship between KM enablers and the KM performance in the Malaysian public sector. Five main independent variables of KM enablers; organizational culture, organizational structure, technology, people and political influence were tested against KM performance using structural equation modeling (SEM). Five hypotheses were tested on the relationship between KM enablers and KM performance. The sampling population of this study was Ministry of Natural Resources and Environment (NRE), Malaysia. The result confirmed that organizational culture, organizational structure, technology and people are the distinct constructs for KM enablers. The hypothesis testing indicates that there is a significant relationship between KM enablers and KM performance. The result indicates that Malaysian public sector should cultivate on KM enablers in order to implement effective KM.


outputs Semantic patterns for sentiment analysis of Twitter Conference Item


title: Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms

abstract: One of the main causes of death the world over are cardiovascular diseases, of which coronary artery disease (CAD) is a major type. This disease occurs when the diameter narrowing of one of the left anterior descending, left circumflex, or right coronary arteries is equal to or greater than 50 percent. Angiography is the principal diagnostic modality for the stenos is of heart vessels, however, because of its complications and costs, researchers are looking for alternative methods such as data mining. This study conducts data mining algorithms on the Z-Alizadeh Sani dataset which has been collected from 303 random visitors to Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Center. In this paper, the reason of effectiveness of a preprocessing algorithm on the dataset is investigated. This algorithm which has been merely introduced in our previous works, extracts three new features from the dataset. These features are then used to enrich the primary dataset in order to achieve more accurate results. Moreover, despite the fact that misclassification of diseased patients has more side effects than that of healthy ones, to the best of our knowledge cost-sensitive algorithms have yet to be used in this field. Therefore, in this paper 10-fold cross validation on cost-sensitive algorithms along with base classifiers of Naive Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 were employed. As a result, the SMO algorithm has yield to very high sensitivity (97.22%) and accuracy (92.09%) rates, the likes of which have not been reported simultaneously in the existing literature.


title: Investigating socio-cultural behavior of users reflected in different social channels on K-pop

abstract: In this paper we investigated the socio-cultural behavior of users reflected in the two different social media channels, YouTube and Twitter. We conducted the comparative analysis of the networks generated from the two channels. The relationship we set for each network is the relatedness on YouTube and the co-links on Twitter. From the results, we revealed that the social media influenced the distinct socio-cultural behaviors of their users. Specifically, Twitter network better showed the actual consumption of contents in the field of the k-pop culture than YouTube. From this study, we contributed to offer a novel approach for exploring the socio-cultural behavior of users on the social media.


title: Breaking 104 bit WEP in less than 60 seconds

abstract: We demonstrate an active attack on the WEP protocol that is able to recover a 104-bit WEP key using less than 40,000 frames with a success probability of 50%. In order to succeed in 95% of all cases, 85,000 packets are needed. The IV of these packets can be randomly chosen. This is an improvement in the number of required frames by more than an order of magnitude over the best known key-recovery attacks for WEP. On a IEEE 802.11g network, the number of frames required can be obtained by re-injection in less than a minute. The required computational effort is approximately 220 RC4 key setups, which on current desktop and laptop CPUs is negligible.


Software Architecture Evaluation: A Systematic Mapping Study


title: Web-crawling reliability

abstract: In this article, I investigate the reliability, in the social science sense, of collecting informetric data about the World Wide Web by Web crawling. The investigation includes a critical examination of the practice of Web crawling and contrasts the results of content crawling with the results of link crawling. It is shown that Web crawling by search engines is intentionally biased and selective. I also report the results of a large-scale experimental simulation of Web crawling that illustrates the effects of different crawling policies on data collection. It is concluded that the reliability of Web crawling as a data collection technique is improved by fuller reporting of relevant crawling policies.


title: Physiology of Small-Sided Games Training in Football

abstract: Small-sided games (SSGs) are played on reduced pitch areas, often using modified rules and involving a smaller number of players than traditional football. These games are less structured than traditional fitness training methods but are very popular training drills for players of all ages and levels. At present, there is relatively little information regarding how SSGs can best be used to improve physical capacities and technical or tactical skills in footballers. However, many prescriptive variables controlled by the coach can influence the exercise intensity during SSGs. Coaches usually attempt to change the training stimulus in SSGs through altering the pitch area, player number, coach encouragement, training regimen (continuous vs interval training), rules and the use of goalkeepers. In general, it appears that SSG exercise intensity is increased with the concurrent reduction in player number and increase in relative pitch area per player. However, the inverse relationship between the number of players in each SSG and exercise intensity does not apply to the time-motion characteristics. Consistent coach encouragement can also increase training intensity, but most rule changes do not appear to strongly affect exercise intensity. The variation of exercise intensity measures are lower in smaller game formats (e.g. three vs three) and have acceptable reproducibility when the same game is repeated between different training sessions or within the same session. The variation in exercise intensity during SSGs can also be improved with consistent coach encouragement but it is still more variable than traditional generic training methods. Other studies have also shown that SSGs containing fewer players can exceed match intensity and elicit similar intensities to both long- and short-duration high-intensity interval running. It also appears that fitness and football-specific performance can be improved equally with SSG and generic training drills. Future research is required to examine the optimal periodization strategies of SSGs training for the long-term development of physiological capacity, technical skill and tactical proficiency.


title: Political Socialization and the Future of Politics

abstract: It is clear that generational replacement is one of the main driving forces for social and political change in liberal democracies. While there is considerable consensus about the observation that the political attitudes and behaviors of young people differ significantly from those of earlier cohorts, there is disagreement about how this observation should be interpreted. Some authors worry about the decline of participation and trust among young people; others herald their tolerance and critical attitudes. In this introduction, it is argued that, because of this generational replacement mechanism, youth studies offer us a glimpse of the future evolution of western political systems. If the differences that are currently being observed persist into later parts of the life cycle, this implies that political systems will have to find a way to adapt to a more critical and distant citizenry.


A Methodical Review on Image Stitching and Video Stitching Techniques


title: Speeded-Up Robust Features (SURF)

abstract: This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision.


title: BigDataBench: a Big Data Benchmark Suite from Internet Services

abstract: As architecture, systems, and data management communities pay greater attention to innovative big data systems and architecture, the pressure of benchmarking and evaluating these systems rises. However, the complexity, diversity, frequently changed workloads, and rapid evolution of big data systems raise great challenges in big data benchmarking. Considering the broad use of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads, which is the prerequisite for evaluating big data systems and architecture. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above.


title: Active Lezi: An incremental parsing algorithm for sequential prediction

abstract: Prediction is an important component in a variety of domains in Artificial Intelligence and Machine Learning, in order that Intelligent Systems may make more informed and reliable decisions. Certain domains require that prediction be performed on sequences of events that can typically be modeled as stochastic processes. This work presents Active LeZi (ALZ), a sequential prediction algorithm that is founded on an Information Theoretic approach, and is based on the acclaimed LZ78 family of data compression algorithms. The efficacy of this algorithm in a typical Smart Environment – the Smart Home, is demonstrated by employing this algorithm to predict device usage in the home. The performance of this algorithm is tested on synthetic data sets that are representative of typical interactions between a Smart Home and the inhabitant. In addition, for the Smart Home environment, we introduce a method of learning a measure of the relative time between actions using ALZ, and demonstrate the efficacy of this approach on synthetic Smart Home data.


title: 3D Wavelet Subbands Mixing for Image Denoising

abstract: A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the Brain Web simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented.


Semantifying OpenStreetMap


title: Meta-cognitive extreme learning machine for regression problems

abstract: In this paper, we present an efficient fast learning algorithm for regression problems using meta-cognitive extreme learning machine(McELM). The proposed algorithm has two components, namely the cognitive component and meta-cognitive component. The cognitive component is an extreme learning machine (ELM) while the meta-cognitive component which controls the cognitive component employs a self-regulating learning mechanism to decide what to learn, when to learn and how to learn. The meta-cognitive component chooses suitable learning method based on the samples presented namely, delete sample, reserve sample and network update. The use of ELM improves the network speed and reduces computational cost. Unlike traditional ELM, the number of hidden layers is not fixed priori in McELM, instead, the network is built during the learning phase. This algorithm is evaluated on a set of benchmark regression and approximation problems and also on a real-world wind force and moment coefficient prediction problem. Performance results in this study highlight that McELM can achieve better results compared with conventional ELM, support vector regression (SVR).


title: A crack in the Swedish welfare façade? A review of assessing social impacts in transport infrastructure planning

abstract: A comparison of social impact categorisation in strategic planning across European Union Member States shows that Sweden neither categorises nor breaks down categories of social impact in areas such as transport infrastructure development. This is surprising because Sweden is known as a country concerned about social issues and having a high standard of welfare. This article accordingly studies how social issues are handled during transport infrastructure planning. An analysis of different source materials will answer four research questions: 1) To what extent are social impacts integrated into environmental impact assessment (EIA) reports? 2) Are social impacts sufficiently integrated and/or does this treatment simply amount to ‘good practice’? 3) Can any trend be detected over time in terms of addressing social issues in impact assessments? 4) What key measures could increase the influence of social impact issues on transport infrastructure planning practice? The study involved a content analysis of six EIA handbooks and EIA statements (EISs) for 18 large transport infrastructure projects. The concepts searched for in these documents largely apply to issues of vulnerability, health, social problems, perceived safety, and alienation. Our data were interpreted through the theoretical lens of institutional interplay. We found that though social aspects are not new considerations in EIA research, they are included in only a small proportion of the 18 Swedish EISs, mostly in connection with health and accessibility. We believe that this does not suffice. We also found that the more recent documents allotted less space to social issues. It is unlikely that most individuals in the organisations that order EISs, or the consultancies that write them, are unaware of the broader interpretation of ‘human beings’ which includes social aspects. Based on increasing interest in social issues in planning and due to the lack of national goals and guidelines in this area, some municipalities and consultants have begun to create their own methods of measuring and assessing social impacts. This has resulted in multiple local-level practitioners who want to develop social issues within impact assessment, and possibly also to introduce a social impact assessment framework, but with no management or coordination among them. The conclusion is that in the absence of a government initiative to clarify how social impacts can be addressed in transport infrastructure planning, there is a need for an external network for organisations involved in transport infrastructure EISs. (Less)


title: Scale Adaptive Dictionary Learning

abstract: Dictionary learning has been widely used in many image processing tasks. In most of these methods, the number of basis vectors is either set by experience or coarsely evaluated empirically. In this paper, we propose a new scale adaptive dictionary learning framework, which jointly estimates suitable scales and corresponding atoms in an adaptive fashion according to the training data, without the need of prior information. We design an atom counting function and develop a reliable numerical scheme to solve the challenging optimization problem. Extensive experiments on texture and video data sets demonstrate quantitatively and visually that our method can estimate the scale, without damaging the sparse reconstruction ability.


title: Static Hand Gesture Recognition Using Artificial Neural Network

abstract: Computers are widely used in all fields. However, the interaction between human and machine is done mainly through the traditional input devices like mouse, keyboard etc. To satisfy the requirements of users, computers need other ways to interact more conveniently, such as using speech or body language (e.g. gestures, posture). In this paper, we propose a new method supporting hand gesture recognition in the static form, using artificial neural network. The proposed solution has been tested with high accuracy (98%) and is promising. 


title: Towards Binary-Valued Gates for Robust LSTM Training

abstract: Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. It aims to use gates to control information flow (e.g., whether to skip some information or not) in the recurrent computations, although its practical implementation based on soft gates only partially achieves this goal. In this paper, we propose a new way for LSTM training, which pushes the output values of the gates towards 0 or 1. By doing so, we can better control the information flow: the gates are mostly open or closed, instead of in a middle state, which makes the results more interpretable. Empirical studies show that (1) Although it seems that we restrict the model capacity, there is no performance drop: we achieve better or comparable performances due to its better generalization ability; (2) The outputs of gates are not sensitive to their inputs: we can easily compress the LSTM unit in multiple ways, e.g., low-rank approximation and low-precision approximation. The compressed models are even better than the baseline models without compression.


title: Wearable health monitoring system (WHMS) using wireless sensor network

abstract: The development in technologies has taken place in many sectors such as the Wearable Health Monitoring System (WHMS). It has been a remarkable revolution by the emerging of the Wireless Sensor Network (WSN) in performing the monitoring and recording of healthcare. Thus, the wearable wireless sensor based systems are intended to provide better personal health management, ubiquitous monitoring health condition, better quality treatment and care, along with to allow efficient circuit integrations, reliable vital signal measurements, low operation costs and optimum power usage. This paper therefore attempts to comprehensively review the basic concepts of Wearable Health Monitoring System (WHMS) using Wireless Sensor Network (WSN). A comparative study was carried out in various healthcare monitoring systems, which adapted different wireless technology. In addition, the challenges and limitations of WHMS systems are identified. The implication of this study is to minimize the challenges and limitations in the design, development and application of a wearable wireless sensor based systems. It is expected in the future that the information shared in this paper can serve similar scientific field and improve the system features.


title: Empirically Evaluating the Quality of Automatically Generated and Manually Written Test Suites

abstract: The creation, execution, and maintenance of tests are some of the most expensive tasks in software development. To help reduce the cost, automated test generation tools can be used to assist and guide developers in creating test cases. Yet, the tests that automated tools produce range from simple skeletons to fully executable test suites, hence their complexity and quality vary. This paper compares the complexity and quality of test suites created by sophisticated automated test generation tools to that of developer-written test suites. The empirical study in this paper examines ten real-world programs with existing test suites and applies two state-of-the-art automated test generation tools. The study measures the resulting test suite quality in terms of code coverage and fault-finding capability. On average, manual tests covered 31.5% of the branches while the automated tools covered 31.8% of the branches. In terms of mutation score, the tests generated by automated tools had an average mutation score of 39.8% compared to the average mutation score of 42.1% for manually written tests. Even though automatically created tests often contain more lines of source code than those written by developers, this paper's empirical results reveal that test generation tools can provide value by creating high quality test suites while reducing the cost and effort needed for testing.


title: A Medical Claim Fraud / Abuse Detection System based on Data Mining : A Case Study in Chile

abstract: This paper describes an effective medical claim fraud/abuse detection system based on data mining used by a Chilean private health insurance company. Fraud and abuse in medical claims have become a major concern within health insurance companies in Chile the last years due to the increasing losses in revenues. Processing medical claims is an exhausting manual task carried out by a few medical experts who have the responsibility of approving, modifying or rejecting the subsidies requested within a limited period from their reception. The proposed detection system uses one committee of multilayer perceptron neural networks (MLP) for each one of the entities involved in the fraud/abuse problem: medical claims, affiliates, medical professionals and employers. Results of the fraud detection system show a detection rate of approximately 75 fraudulent and abusive cases per month, making the detection 6.6 months earlier than without the system. The application of data mining to a real industrial problem through the implementation of an automatic fraud detection system changed the original non-standard medical claims checking process to a standardized process helping to fight against new, unusual and known fraudulent/abusive behaviors.


title: Issues and mitigation of high voltage isolation in solid state Marx generator

abstract: A three stage Marx generator is implemented in this paper with pulse width 5 μs and repetitive frequency of output pulse is 66 Hz. The circuit is verified with simulation and hardware results with an input of 90 V. In the present work, the circuit is tested with resistive load. This paper discusses issues related with isolation of high voltage Marx generator and various methods of isolation. A high performance driver circuit for three stage Marx using optical fiber is implemented and the circuit is tested. It is found that the rise time of the output pulse of Marx circuit is improved by this method of pulse transmission by 40 ns.


title: Event BAGGING: A novel event summarization approach in multiview surveillance videos

abstract: Due to advancement in digital multimedia technologies, huge amount of video contents in such arenas as sports, surveillance, news, etc., is becoming widely available. Due to factors like presence of inactive frames, inter-view dependencies and frequent illumination changes, whole video content may not be important. Moreover, users may not have the adequate time to access/ store the entire video in real time. We propose a machine learning ensemble method to summarize the events in multiview videos, as a solution to the aforementioned issues. In order to make the accurate decision on keyframes, we trained our ensembles using meta approach where the inter-dependency and illumination changes of views are considered in the training phase. Experimental results on two benchmark datasets show that our model outperforms the state-of-the-art models with the best Recall and F-measure. The computing analysis of the model also indicates that it meets all requirements of real-time applications.


title: Value creation drivers in a secondary buyout – the acquisition of Brenntag by BC Partners

abstract: Purpose - – The purpose of this paper is to identify specific drivers of value creation in secondary buyouts. While this type of private equity deal has risen in importance in recent years, it is not yet well understood. Through an in-depth analysis of the acquisition of Brenntag by BC Partners, we develop propositions on the value creation profile of secondary buyouts. Design/methodology/approach - – We use a single case study design to explore the information-rich context of a secondary buyout. The Brenntag case epitomizes the development of a company from forming part of a large conglomerate to being private-equity owned after the primary and secondary buyout, to its final disposition of public listing. Our analysis is based on ten semi-structured interviews with key protagonists and observers, as well as analysis of primary company data and additional secondary data sources. Findings - – We propose that even if the investment management and monitoring skills of the primary and secondary private equity group are similar, there is still potential to realize operational improvements in a secondary buyout, due to either early exit of the primary private equity group or measures that further enhance management incentives. In addition, the Brenntag case shows that low information asymmetries can lead to higher leverage and that opportunities for multiple expansions are limited in secondary buyouts. Originality/value - – While a secondary buyout has become a common exit route in recent years, we are the first to undertake an in-depth case analysis of a secondary buyout. Our study helps researchers and practitioners enhance their understanding of drivers behind the value creation profile of secondary buyouts.


title: “My Invisalign experience”: content, metrics and comment sentiment analysis of the most popular patient testimonials on YouTube

abstract: BackgroundThe aim of the study was to investigate the popularity, content of Invisalign patient testimonials on YouTube, as well as the sentiment of the related comments.MethodsUsing the term “Invisalign experience,” the top 100 results on YouTube by view count were screened for English spoken patient videos that attracted comments. Video information (time since video upload, sponsorship), engagement metrics (comments, likes, dislikes, subscriptions), and views were collected. Videos were rated for information completeness (ICS), and comments were classified by origin and content. The emotional loading of the comments was measured using automated sentiment analysis.ResultsThe 40 reviewed testimonials scored an average ICS of 3.78 (SD 0.97). ICS, time since upload, and video duration did not appear to significantly influence the number of views, subscriptions, likes, dislikes, and comments. There was a statistically significant difference (P = 0.03) between mean positive (2.01, SD 0.95) and negative sentiment scores (− 1.90, SD 1.14). Commenter’s status and overall comment on video were significantly associated with positive sentiment scores. There was a significant association between sponsorship, commenter’s status, overall comment on video, focus of concern, perceived Invisalign’s disadvantages, and increased negative sentiment scores.ConclusionsEngagement of audience and views of the most popular Invisalign patient testimonials were not significantly influenced by completeness of information, video duration, and lifespan. The sentiment of viewers’ comments about Invisalign treatment was significantly more positive and was significantly associated with their status, content, and sponsorship of videos. Orthodontic trends on YouTube need to be cautiously monitored for planning interventions that improve patients’ knowledge about orthodontics.


title: AI-assisted analysis of player strategy across level progressions in a puzzle game

abstract: Presenting levels commensurate with players' current understanding of game mechanics and level design is a significant challenge in designing games. Often game designers create levels by hand intending for the levels to increase in difficulty over the the course of the game while relying on their intuition or extensive user feedback, reiteration, and testing. Instead, this study starts from a number of procedurally generated levels originally generated by parameters expected to encourage a good difficulty progression and then presented to players during playtests. A number of AI-bots with different characteristics were then designed to assess the difficulty of each level. These findings are then compared with player data. Our findings show that bots encapsulating idealized player strategies can help us create a richer model of level difficulty that then reveals useful information about player struggles and learning across level progressions.


title: Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model

abstract: Previous work on statistical language modeling has shown that it is possible to train a feedforward neural network to approximate probabilities over sequences of words, resulting in significant error reduction when compared to standard baseline models based on n-grams. However, training the neural network model with the maximum-likelihood criterion requires computations proportional to the number of words in the vocabulary. In this paper, we introduce adaptive importance sampling as a way to accelerate training of the model. The idea is to use an adaptive n-gram model to track the conditional distributions produced by the neural network. We show that a very significant speedup can be obtained on standard problems.


title: Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems

abstract: Systems do not simply pop into existence. They progress through lifecycle phases of creation, production, operations, and disposal. The issues leading to undesirable and unpredicted emergent behavior are set in place during the phases of creation and production and realized during the operational phase, with many of those problematic issues due to human interaction. We propose that the idea of the Digital Twin, which links the physical system with its virtual equivalent can mitigate these problematic issues. We describe the Digital Twin concept and its development, show how it applies across the product lifecycle in defining and understanding system behavior, and define tests to evaluate how we are progressing. We discuss how the Digital Twin relates to Systems Engineering and how it can address the human interactions that lead to “normal accidents.” We address both Digital Twin obstacles and opportunities, such as system replication and front running. We finish with NASA’s current work with the Digital Twin.


title: COMpliant huMANoid COMAN: Optimal joint stiffness tuning for modal frequency control

abstract: The incorporation of passive compliance in robotic systems could improve their performance during interactions and impacts, for energy storage and efficiency, and for general safety for both the robots and humans. This paper presents the recently developed COMpliant huMANoid COMAN. COMAN is actuated by passive compliance actuators based on the series elastic actuation principle (SEA). The design and implementation of the overall body of the robot is discussed including the realization of the different body segments and the tuning of the joint distributed passive elasticity. This joint stiffness tuning is a critical parameter in the performance of compliant systems. A novel systematic method to optimally tune the joint elasticity of multi-dof SEA robots based on resonance analysis and energy storage maximization criteria forms one of the key contributions of this work. The paper will show this method being applied to the selection of the passive elasticity of COMAN legs. The first completed robot prototype is presented accompanied by experimental walking trials to demonstrate its operation.


title: Improved asynchronous parallel optimization analysis for stochastic incremental methods

abstract: As datasets continue to increase in size and multi-core computer architectures are developed, asynchronous parallel optimization algorithms become more and more essential to the field of Machine Learning. Unfortunately, conducting the theoretical analysis asynchronous methods is difficult, notably due to the introduction of delay and inconsistency in inherently sequential algorithms. Handling these issues often requires resorting to simplifying but unrealistic assumptions. Through a novel perspective, we revisit and clarify a subtle but important technical issue present in a large fraction of the recent convergence rate proofs for asynchronous parallel optimization algorithms, and propose a simplification of the recently introduced"perturbed iterate"framework that resolves it. We demonstrate the usefulness of our new framework by analyzing three distinct asynchronous parallel incremental optimization algorithms: Hogwild (asynchronous SGD), KROMAGNON (asynchronous SVRG) and ASAGA, a novel asynchronous parallel version of the incremental gradient algorithm SAGA that enjoys fast linear convergence rates. We are able to both remove problematic assumptions and obtain better theoretical results. Notably, we prove that ASAGA and KROMAGNON can obtain a theoretical linear speedup on multi-core systems even without sparsity assumptions. We present results of an implementation on a 40-core architecture illustrating the practical speedups as well as the hardware overhead. Finally, we investigate the overlap constant, an ill-understood but central quantity for the theoretical analysis of asynchronous parallel algorithms. We find that it encompasses much more complexity than suggested in previous work, and often is order-of-magnitude bigger than traditionally thought.


title: A survey of trust management systems for online social communities - Trust modeling, trust inference and attacks

abstract: Trust can help participants in online social communities to make decisions; however, it is a challenge for systems to map trust into computational models because of its subjective properties. Also, many online social communities are sparsely connected. Therefore, it is necessary to introduce mechanisms which can infer indirect trust among participants who are not directly connected. We provide a survey of existing trust management systems for online social communities. We also list four types of attacks, and analyze existing systems' vulnerabilities. Compared with previous surveys, our survey takes trust modeling, trust inference, and attacks into account. Although there are several survey papers about global trust/reputation related attacks, the main contribution of this paper is that we consider trust inference and potential local trust related attacks.


title: Machine-to-Machine Communication in LTE-A

abstract: Wireless equipped machines are increasing greatly in recent years, among which the cellular network based machine-to-machine communication (M2M) has shown the advantages of better coverage and lower network deployment cost. However, the current cellular network is designed for human-to-human communication, targeting the voice/media transmission with low access delay and high throughput, so the machines in GSM and UMTS are designed like a screenless phone. To extend the market to machine type communication, it is envisaged that the specific optimizations for M2M will be introduced in LTE-A, especially when M2M communication accounts for the considerable part in the total networking activities. In this paper, some of the considerations of the network side improvements are presented, from the physical layer, MAC to core network.


title: Natural Language Generation for dialogue: system

abstract: Many natural language dialogue systems make use of `canned text' for output generation. This approach may be su±cient for dialogues in restricted domains where system utterances are short and simple and use fixed expressions (e.g., slot filling dialogues in the ticket reservation or travel information domain); but for more sophisticated dialogues (e.g., tutoring dialogues) a more advanced generation method is required. In such dialogues, the system utterances should be produced in a context-sensitive fashion, for instance by pronominalising anaphoric references, and by using more or less elaborate wording depending on the state of the dialogue, the expertise of the user, etc. In the case of spoken dialogues, it is very useful if the natural language generation component can provide information that is relevant for determining the prosody of the speech output. Similarly, for use in embodied agents it is useful if the generation component can provide information about the facial and body movements that should accompany the language being produced by the agent. Clearly, it will be extremely di±cult to achieve all this using simple string manipulation, so a more flexible and context-sensitive generation method is required. This report discusses some of the possibilities for the sophisticated generation of system utterances in a dialogue system. The basic assumption is that this task is performed by a separate language generation module, which takes as its input a message specification produced by a dialogue planner and transforms this message into an expression in natural language. Part I of this report provides a general discussion of different methods for performing this task, and outlines some requirements on language generation systems that might be used for this purpose. Part II gives an overview of publicly available language generation systems, and discusses to what extent they meet the previously stated requirements.


title: An Ontology-based Summarization System for Arabic Documents (OSSAD)

abstract: With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper an Ontology-based Summarization System for Arabic Documents, OSSAD, is introduced. Domain knowledge is extracted from an Arabic corpus and represented by topic related concepts/keywords and the lexical relations among them. The user’s query is first expanded by using the Arabic WordNet and then by adding the domain-specific knowledge base to the expansion. For summarization, decision tree algorithm (C4.5) is used, which was trained by a set of features extracted from the original documents. For the testing dataset, Essex Arabic Summaries Corpus (EASC) was used. Recall Oriented Understudy for Gisting Evaluation (ROUGE) was used to compare OSSAD summaries with the human summaries along with other automatic summarization systems, showing that the proposed approach demonstrated promising results.


title: Deep convolutional neural network for decoding motor imagery based brain computer interface

abstract: Deep convolutional neural networks (CNNs) have made tremendous development in the field of image recognition and natural language processing. However, there is still a lack of knowledge of using CNN models to decode motor imagery based Brain Computer Interface (BCI). This paper presents a method applying CNN to analyze the EEG signals, which are produced by left and right hand motor imagery tasks. EEG signals are transferred into time-frequency images using Short-Time Fourier Transform (STFT) and then those images are fed as input of the network for classification. A comparison is made to verify the performance of three different activation functions during the network's learning procedure. They are rectified linear unit (ReLU), exponential linear unit (ELU) and the newly proposed scaled exponential linear unit (SELU). The results show that our method using CNN model can achieve better accuracy than the conventional method and SELU function shows superior ability for the network to convergence.


title: Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution

abstract: Recently, deep neural networks have achieved impressive performance in terms of both reconstruction accuracy and efficiency for single image super-resolution (SISR). However, the network model of these methods is a fully convolutional neural network, which is limit to exploit contextual information over the global region of the input image. In this paper, we discuss a new SR architecture where features are extracted in the low-resolution (LR) space, and then we use a fully connected layer which learns an array of upsampling weights to reconstruct the desired high-resolution (HR) image from the final LR features. By doing so, we effectively exploit global context information over the input image region, whilst maintaining the low computational complexity for the overall SR operation. In addition, we introduce an edge difference constraint into our loss function to pre-serve edges and texture structures. Extensive experiments validate that our meth-od outperforms the existing state-of-the-art methods


title: RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals

abstract: We consider the detection of activities from non-cooperating individuals with features obtained on the radio frequency channel. Since environmental changes impact the transmission channel between devices, the detection of this alteration can be used to classify environmental situations. We identify relevant features to detect activities of non-actively transmitting subjects. In particular, we distinguish with high accuracy an empty environment or a walking, lying, crawling or standing person, in case-studies of an active, device-free activity recognition system with software defined radios. We distinguish between two cases in which the transmitter is either under the control of the system or ambient. For activity detection the application of one-stage and two-stage classifiers is considered. Apart from the discrimination of the above activities, we can show that a detected activity can also be localized simultaneously within an area of less than 1 meter radius.


title: A conceptual model of client-driven agile requirements prioritization: results of a case study

abstract: Requirements (re)prioritization is an essential mechanism of agile development approaches to maximize the value for the clients and to accommodate changing requirements. Yet, in the agile Requirements Engineering (RE) literature, very little is known about how agile (re)prioritization happens in practice. Conceptual models about this process are missing, which, in turn, makes it difficult for both practitioners and researchers to reason about requirements decision-making at inter-iteration time. We did a multiple case study on agile requirements prioritization methods to yield a conceptual model for understanding the inter-iteration prioritization process. The model is derived by using interview data from practitioners in 8 development organizations. Such a model makes explicit the concepts that are used tacitly in the agile requirements prioritization practice and can be used for structuring future empirical investigations about this topic, and for analyzing, supporting, and improving the process in real-life projects.


title: Grasping force control of multi-fingered robot hand based on slip detection using tactile sensor

abstract: To achieve a human like grasping with a multi- fingered robot hand, the grasping force should be controlled without using information from the grasped object such as its weight and friction coefficient. In this study, we propose a method for detecting the slip of a grasped object using the force output of Center of Pressure (CoP) tactile sensors. CoP sensors can measure the center position of a distributed load and the total load applied on the surface of the sensor, within 1 ms. These sensors are arranged on the fingers of the robot hand, and their effectiveness as slip detecting sensors is confirmed in tests of slip detection during grasping. Finally, we propose a method for controlling grasping force to resist tangential force applied to the grasped object using a feedback control system with the CoP sensor force output.


title: Ambient Assisted Living Healthcare Frameworks, Platforms, Standards, and Quality Attributes

abstract: Ambient Assisted Living (AAL) is an emerging multi-disciplinary field aiming at exploiting information and communication technologies in personal healthcare and telehealth systems for countering the effects of growing elderly population. AAL systems are developed for personalized, adaptive, and anticipatory requirements, necessitating high quality-of-service to achieve interoperability, usability, security, and accuracy. The aim of this paper is to provide a comprehensive review of the AAL field with a focus on healthcare frameworks, platforms, standards, and quality attributes. To achieve this, we conducted a literature survey of state-of-the-art AAL frameworks, systems and platforms to identify the essential aspects of AAL systems and investigate the critical issues from the design, technology, quality-of-service, and user experience perspectives. In addition, we conducted an email-based survey for collecting usage data and current status of contemporary AAL systems. We found that most AAL systems are confined to a limited set of features ignoring many of the essential AAL system aspects. Standards and technologies are used in a limited and isolated manner, while quality attributes are often addressed insufficiently. In conclusion, we found that more inter-organizational collaboration, user-centered studies, increased standardization efforts, and a focus on open systems is needed to achieve more interoperable and synergetic AAL solutions.


title: BotHunter: Detecting Malware Infection Through IDSDriven Dialog Correlation

abstract: We present a new kind of network perimeter monitoring strategy, which focuses on recognizing the infection and coordination dialog that occurs during a successful malware infection. BotHunter is an application designed to track the two-way communication flows between internal assets and external entities, developing an evidence trail of data exchanges that match a state-based infection sequence model. BotHunter consists of a correlation engine that is driven by three malware-focused network packet sensors, each charged with detecting specific stages of the malware infection process, including inbound scanning, exploit usage, egg downloading, outbound bot coordination dialog, and outbound attack propagation. The BotHunter correlator then ties together the dialog trail of inbound intrusion alarms with those outbound communication patterns that are highly indicative of successful local host infection. When a sequence of evidence is found to match BotHunter's infection dialog model, a consolidated report is produced to capture all the relevant events and event sources that played a role during the infection process. We refer to this analytical strategy of matching the dialog flows between internal assets and the broader Internet as dialog-based correlation, and contrast this strategy to other intrusion detection and alert correlation methods. We present our experimental results using BotHunter in both virtual and live testing environments, and discuss our Internet release of the BotHunter prototype. BotHunter is made available both for operational use and to help stimulate research in understanding the life cycle of malware infections.


title: Efficient Models for Reverberation and Distance Rendering in Computer Music and Virtual Audio Reality

abstract: This paper discusses efficient digital signal processing algorithms for real-time synthesis of dynamically controllable, natural-sounding artificial reverberation. A general modular framework is proposed for configuring a spatial sound processing and mixing system according to the reproduction format or setup and the listening conditions, over loudspeakers or headphones. In conclusion, the implementation and applications of a spatial sound processing software are described, and approaches to control interface design and effective distance effects are reviewed.


title: Enhancing motivation for change in problem drinking: A controlled comparison of two therapist styles.

abstract: To investigate the impact of counselor style, a 2-session motivational checkup was offered to 42 problem drinkers (18 women and 24 men) who were randomly assigned to 3 groups: (a) immediate checkup with directive-confrontational counseling, (b) immediate checkup with client-centered counseling, or (c) delayed checkup (waiting-list control). Overall, the intervention resulted in a 57% reduction in drinking within 6 weeks, which was maintained at 1 year. Clients receiving immediate checkup showed significant reduction in drinking relative to controls. The 2 counseling styles were discriminable on therapist behaviors coded from audiotapes. The directive-confrontational style yielded significantly more resistance from clients, which in turn predicted poorer outcomes at 1 year. Therapist styles did not differ in overall impact on drinking, but a single therapist behavior was predictive (r = .65) of 1-year outcome such that the more the therapist confronted, the more the client drank.


title: Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

abstract: Cardiotocography (CTG) is used as a technique of measuring fetal well-being.The classification performances of different classification methods are examined.The research showed us there is no big difference in accuracies of the classifiers.The accuracy achieved by k-NN is 98.4% (minimum) and by RF is 99.18% (maximum). The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians' to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.


abstract: The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material. Uncertainty Quantification: Theory, Implementation, and Applications includes a large number of definitions and examples that use a suite of relatively simple models to illustrate concepts; numerous references to current and open research issues; and exercises that illustrate basic concepts and guide readers through the numerical implementation of algorithms for prototypical problems. It also features a wide range of applications, including weather and climate models, subsurface hydrology and geology models, nuclear power plant design, and models for biological phenomena, along with recent advances and topics that have appeared in the research literature within the last 15 years, including aspects of Bayesian model calibration, surrogate model development, parameter selection techniques, and global sensitivity analysis. Audience: The text is intended for advanced undergraduates, graduate students, and researchers in mathematics, statistics, operations research, computer science, biology, science, and engineering. It can be used as a textbook for one- or two-semester courses on uncertainty quantification or as a resource for researchers in a wide array of disciplines. A basic knowledge of probability, linear algebra, ordinary and partial differential equations, and introductory numerical analysis techniques is assumed. Contents: Chapter 1: Introduction; Chapter 2: Large-Scale Applications; Chapter 3: Prototypical Models; Chapter 4: Fundamentals of Probability, Random Processes, and Statistics; Chapter 5: Representation of Random Inputs; Chapter 6: Parameter Selection Techniques; Chapter 7: Frequentist Techniques for Parameter Estimation; Chapter 8: Bayesian Techniques for Parameter Estimation; Chapter 9: Uncertainty Propagation in Models; Chapter 10: Stochastic Spectral Methods; Chapter 11: Sparse Grid Quadrature and Interpolation Techniques; Chapter 12: Prediction in the Presence of Model Discrepancy; Chapter 13: Surrogate Models; Chapter 14: Local Sensitivity Analysis; Chapter 15: Global Sensitivity Analysis; Appendix A: Concepts from Functional Analysis; Bibliography; Index


title: Recent Advances in Video Based Document Processing: A Review

abstract: Extraction and recognition of text present in video has become a very popular research area in the last decade. Generally, text present in video frames is of different size, orientation, style, etc. with complex backgrounds, noise, low resolution and contrast. These factors make the automatic text extraction and recognition in video frames a challenging task. A large number of techniques have been proposed by various researchers in the recent past to address the problem. This paper presents a review of various state-of-the-art techniques proposed towards different stages (e.g. detection, localization, extraction, etc.) of text information processing in video frames. Looking at the growing popularity and the recent developments in the processing of text in video frames, this review imparts details of current trends and potential directions for further research activities to assist researchers.


title: IRIS Explorer Software for Radial-Depth Cueing Reovirus Particles and Other Macromolecular Structures Determined by Cryoelectron Microscopy and Image Reconstruction

abstract: Abstract Structures of biological macromolecules determined by transmission cryoelectron microscopy (cryo-TEM) and three-dimensional image reconstruction are often displayed as surface-shaded representations with depth cueing along the viewed direction (Z cueing) Depth cueing to indicate distance from the center of virus particles (radial-depth cueing, or R cueing) has also been used. We have found that a style of R cueing in which color is applied in smooth or discontinuous gradients using the IRIS Explorer software is an informative technique for displaying the structures of virus particles solved by cryo-TEM and image reconstruction. To develop and test these methods, we used existing cryo-TEM reconstructions of mammalian reovirus particles. The newly applied visualization techniques allowed us to discern several new structural features, including sites in the inner capsid through which the viral mRNAs may be extruded after they are synthesized by the reovirus transcriptase complexes. To demonstrate the broad utility of the methods, we also applied them to cryo-TEM reconstructions of human rhinovirus, native and swollen forms of cowpea chlorotic mottle virus, truncated core of pyruvate dehydrogenase complex from Saccharomyces cerevisiae, and flagellar filament of Salmonella typhimurium. We conclude that R cueing with color gradients is a useful tool for displaying virus particles and other macromolecules analyzed by cryo-TEM and image reconstruction.


title: OrientStream: A Framework for Dynamic Resource Allocation in Distributed Data Stream Management Systems

abstract: Distributed data stream management systems (DDSMS) are usually composed of upper layer relational query systems (RQS) and lower layer stream processing systems (SPS). When users submit new queries to RQS, a query planner needs to be converted into a directed acyclic graph (DAG) consisting of tasks which are running on SPS. Based on different query requests and data stream properties, SPS need to configure different deployments strategies. However, how to dynamically predict deployment configurations of SPS to ensure the processing throughput and low resource usage is a great challenge. This article presents OrientStream, a framework for dynamic resource allocation in DDSMS using incremental machine learning techniques. By introducing the data-level, query plan-level, operator-level and cluster-level's four-level feature extraction mechanism, we firstly use the different query workloads as training sets to predict the resource usage of DDSMS and then select the optimal resource configuration from candidate settings based on the current query requests and stream properties. Finally, we validate our approach on the open source SPS--Storm. Experiments show that OrientStream can reduce CPU usage of 8%-15% and memory usage of 38%-48% respectively.


title: Robust universal neural vocoding

abstract: This paper introduces a robust universal neural vocoder trained with 74 speakers (comprised of both genders) coming from 17 languages. This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker, style or recording condition seen during training or from an out-of-domain scenario. Together with the system, we present a full text-to-speech analysis of robustness of a number of implemented systems. The complexity of systems tested range from a convolutional neural networks-based system conditioned on linguistics to a recurrent neural networks-based system conditioned on mel-spectrograms. The analysis shows that convolutional neural networks-based systems are prone to occasional instabilities, while the recurrent approaches are significantly more stable and capable of providing universalizing robustness.


title: The Price of Privacy in Collaborative Learning

abstract: Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have enough data to train a reasonably accurate model. For such organizations, a realistic solution is to train machine learning models based on a joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player.


title: Dimensional synthesis of three-fingered robot hands for maximal precision manipulation workspace

abstract: This paper applies dimensional synthesis to explore the geometric design of dexterous three-fingered robotic hands for maximizing precision manipulation workspace, in which the hand stably moves an object with respect to the palm of the hand, with contacts only on the fingertips. We focus primarily on the tripod grasp, which is the most commonly used grasp for precision manipulation. We systematically explore the space of design parameters, with two main objectives: maximize the workspace of a fully actuated hand and explore how under-actuation modifies it. We use a mathematical framework that models the hand-plus-object system and examine how the workspace varies with changes in nine hand and object parameters such as link length and finger arrangement on the palm. ::: ::: Results show that to achieve the largest workspaces the palm radius should be approximately half of a finger length larger than the target object radius, that the distal link of the two-link fingers should be around 1-1.2 times the length of the proximal link, and that fingers should be arranged symmetrically about the palm with object contacts also symmetric. Furthermore, a proper parameter design for an under-actuated hand can achieve up to 50% of the workspace of a fully actuated hand. When compared to the system parameters of existing popular hand designs, larger palms and longer distal links are needed to maximize the manipulation workspace of the studied design.


title: Zensei: Embedded, Multi-electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition

abstract: Interactions and connectivity is increasingly expanding to shared objects and environments, such as furniture, vehicles, lighting, and entertainment systems. For transparent personalization in such contexts, we see an opportunity for embedded recognition, to complement traditional, explicit authentication. We introduce Zensei, an implicit sensing system that leverages bio-sensing, signal processing and machine learning to classify uninstrumented users by their body's electrical properties. Zensei could allow many objects to recognize users. E.g., phones that unlock when held, cars that automatically adjust mirrors and seats, or power tools that restore user settings. We introduce wide-spectrum bioimpedance hardware that measures both amplitude and phase. It extends previous approaches through multi-electrode sensing and high-speed wireless data collection for embedded devices. We implement the sensing in devices and furniture, where unique electrode configurations generate characteristic profiles based on user's unique electrical properties. Finally, we discuss results from a comprehensive longitudinal 22-day data collection experiment with 46 subjects. Our analysis shows promising classification accuracy and low false acceptance rate.


title: Guided tone mapping

abstract: In this paper, we propose a simple but effective tone mapping operator, which can achieve impressive results with low complexity in both the formulation and computation. The key point of our operator is to calculate a scalar matrix by which the input HDR image can be mapped into a displayable LDR image. From the graphical perspective, we can regard the scalar matrix as a guided image, as shown in Figure 1(b). Experimental results show that our algorithm can achieve real-time perceptually pleasing result.


Sentiment Classification of Arabic Tweets: A Supervised Approach


title: Evaluating machine learning algorithms for anomaly detection in clouds

abstract: Critical services in the field of Network Function Virtualization require elaborate reliability and high availability mechanisms to meet the high service quality requirements. Traditional monitoring systems detect overload situations and outages in order to automatically scale out services or mask faults. However, faults are often preceded by anomalies and subtle misbehaviors of the services, which are overlooked when detecting only outages. We propose to exploit machine learning techniques to detect abnormal behavior of services and hosts by analysing metrics collected from all layers and components of the cloud infrastructure. Various algorithms are able to compute models of a hosts normal behavior that can be used for anomaly detection at runtime. An offline evaluation of data collected from anomaly injection experiments shows that the models are able to achieve very high precision and recall values.


title: Assessing the effect of self-assembly ports in evolutionary swarm robotics

abstract: Self-assembly in swarm robotics is essential for a group of robots in achieving a common goal that is not possible to achieve by a single robot. Self-assembly also provides several advantages to swarm robotics. Some of these include versatility, scalability, re-configurability, cost-effectiveness, extended reliability, and capability for emergent phenomena. This work investigates the effect of self-assembly in evolutionary swarm robotics. Because of the lack of research literature within this paradigm, there are few comparisons of the different implementations of self-assembly mechanisms. This paper reports the influence of connection port configuration on evolutionary self-assembling swarm robots. The port configuration consists of the number and the relative positioning of the connection ports on each of the robot. Experimental results suggest that configuration of the connection ports can significantly impact the emergence of selfassembly in evolutionary swarm robotics.


title: Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet

abstract: A worldwide movement in advanced manufacturing countries is seeking to reinvigorate (and revolutionize) the industrial and manufacturing core competencies with the use of the latest advances in information and communications technology. Visual computing plays an important role as the "glue factor" in complete solutions. This article positions visual computing in its intrinsic crucial role for Industrie 4.0 and provides a general, broad overview and points out specific directions and scenarios for future research.


title: On accelerating pair-HMM computations in programmable hardware

abstract: This paper explores hardware acceleration to significantly improve the runtime of computing the forward algorithm on Pair-HMM models, a crucial step in analyzing mutations in sequenced genomes. We describe 1) the design and evaluation of a novel accelerator architecture that can efficiently process real sequence data without performing wasteful work; and 2) aggressive memoization techniques that can significantly reduce the number of invocations of, and the amount of data transferred to the accelerator. We describe our demonstration of the design on a Xilinx Virtex 7 FPGA in an IBM Power8 system. Our design achieves a 14.85× higher throughput than an 8-core CPU baseline (that uses SIMD and multi-threading) and a 147.49 × improvement in throughput per unit of energy expended on the NA12878 sample.


title: Factor mixture analysis of DSM-IV symptoms of major depression in a treatment seeking clinical population.

abstract: Abstract Background There is a paucity of empirical studies examining the latent structure of depression symptoms within clinical populations. Objective The current study aimed to evaluate the latent structure of DSM-IV major depression utilising dimensional, categorical, and hybrid models of dimensional and categorical latent variables in a large treatment-seeking population. Methods Latent class models, latent factor models, and factor mixture models were fit to data from 1165 patients currently undergoing online treatment for depression. Results Model fit statistics indicated that a two-factor model fit the data the best when compared to a one-factor model, latent class models, and factor mixture models. Conclusions The current study suggests that the structure of depression consists of two underlying dimensions of depression severity when compared to categorical or a mixture of both categorical and dimensional structures. For clinical samples, the two latent factors represent psychological and somatic symptoms.


title: Speed control of permanent magnet synchronous motor based on direct torque control method

abstract: As technology improves, studies on permanent magnet synchronous motors have increased and became to major on field oriented control and direct torque control methods. The main topics of permanent magnet motor control are mathematical modelling, voltage feed inverter model, space vector modulation and determination of voltage vectors. In this study, the structure and direct torque process of the PMSM (permanent magnet synchronous motor) is explained and speed control system simulation is realized for low voltage-high power PMSM.


title: Learning Fast Approximations of Sparse Coding

abstract: In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a given input, SC minimizes a quadratic reconstruction error with an L1 penalty term on the code. The process is often too slow for applications such as real-time pattern recognition. We proposed two versions of a very fast algorithm that produces approximate estimates of the sparse code that can be used to compute good visual features, or to initialize exact iterative algorithms. The main idea is to train a non-linear, feed-forward predictor with a specific architecture and a fixed depth to produce the best possible approximation of the sparse code. A version of the method, which can be seen as a trainable version of Li and Osher's coordinate descent method, is shown to produce approximate solutions with 10 times less computation than Li and Os-her's for the same approximation error. Unlike previous proposals for sparse code predictors, the system allows a kind of approximate "explaining away" to take place during inference. The resulting predictor is differentiable and can be included into globally-trained recognition systems.


title: Designing with traces

abstract: This paper draws on new materialist perspectives to introduce the analytic category of "material traces" to the field of human-computer interaction (HCI). Material traces reveal the dynamic and evocative nature of form by concretizing a unique location in time and space. Traces of skill, use, and time, for example, are valued for their emotional resonance in addition to the pragmatic goals in which they are embedded. Using this category, we develop a framework for design pedagogy that offers the lenses of attributes, entanglements, and trajectories as tools for gaining critical purchase on the objects produced. Mobilizing this framework within a classroom, design students envision poignant relationships to the non-human, engaged physics learning, and opportunities for reflection around breakage and repair. These design examples reveal how the category of material traces comes alive in practice and pedagogy. We end by discussing how this study of traces points to new opportunities for critical reflection in HCI.


title: Sleep Wrinkles: Facial Aging and Facial Distortion During Sleep

abstract: Wrinkles are just one indicator of facial aging, but an indicator that is of prime importance in our world of facial aesthetics. Wrinkles occur where fault lines develop in aging skin. Those fault lines may be due to skin distortion resulting from facial expression or may be due to skin distortion from mechanical compression during sleep. Expression wrinkles and sleep wrinkles differ in etiology, location, and anatomical pattern. Compression, shear, and stress forces act on the face in lateral or prone sleep positions. We review the literature relating to the development of wrinkles and the biomechanical changes that occur in response to intrinsic and extrinsic influences. We explore the possibility that compression during sleep not only results in wrinkles but may also contribute to facial skin expansion.


title: Image Alignment and Stitching

abstract: Stitching multiple images together to create beautiful high-resolution panoramas is one of the most popular consumer applications of image registration and blending. In this chapter, I review the motion models (geometric transformations) that underlie panoramic image stitching, discuss direct intensity-based and feature-based registration algorithms, and present global and local alignment techniques needed to establish high-accuracy correspondences between overlapping images. I then discuss various compositing options, including multi band and gradient-domain blending, as well as techniques for removing blur and ghosted images. The resulting techniques can be used to create high-quality panoramas for static or interactive viewing.


title: Misunderstandings between experimentalists and observationalists about causal inference

abstract: Summary. We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization and matching after assignment of treatment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions to understand better each other’s inferential problems and attempted solutions.


title: TIME OF CONSCIOUS INTENTION TO ACT IN RELATION TO ONSET OF CEREBRAL ACTIVITY (READINESS-POTENTIAL)

abstract: The recordable cerebral activity (readiness-potential, RP) that precedes a freely voluntary, fully endogenous motor act was directly compared with the reportable time (W) for appearance of the subjective experience of ‘wanting’ or intending to act. The onset of cerebral activity clearly preceded by at least several hundred milliseconds the reported time of conscious intention to act. This relationship held even for those series (with ‘type II’ RPs) in which subjects reported that all of the 40 self-initiated movements in the series appeared ‘spontaneously’ and capriciously.


title: Malware Classification Using Euclidean Distance and Artificial Neural Networks

abstract: Most of the samples discovered are variations of known malicious programs and thus have similar structures, however, there is no method of malware classification that is completely effective. To address this issue, the approach proposed in this paper represents a malware in terms of a vector, in which each feature consists of the amount of APIs called from a Dynamic Link Library (DLL). To determine if this approach is useful to classify malware variants into the correct families, we employ Euclidean Distance and a Multilayer Perceptron with several learning algorithms. The experimental results are analyzed to determine which method works best with the approach. The experiments were conducted with a database that contains real samples of worms and trojans and show that is possible to classify malware variants using the number of functions imported per library. However, the accuracy varies depending on the method used for the classification.


title: Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks

abstract: Understanding the evolution of research topics is crucial to detect emerging trends in science. This paper proposes a new approach and a framework to discover the evolution of topics based on dynamic co-word networks and communities within them. The NEViewer software was developed according to this approach and framework, as compared to the existing studies and science mapping software tools, our work is innovative in three aspects: (a) the design of a longitudinal framework based on the dynamics of co-word communities; (b) it proposes a community labelling algorithm and community evolution verification algorithms; (c) and visualizes the evolution of topics at the macro and micro level respectively using alluvial diagrams and coloring networks. A case study in computer science and a careful assessment was implemented and demonstrating that the new method and the software NEViewer is feasible and effective.


title: Hexpo: A vanishing-proof activation function

abstract: This article proposes “Hexpo”, an activation function that has ability to scale the gradient and hence overcome the vanishing gradient problem. Unlike rectified linear units family which produces identity mapping on positive inputs, Hexpo has scalable limits on both positive and negative values. With parametrization, the active domain of Hexpo and the output it maps to is flexible. Thus it can alleviate the vanishing gradient problem from both gradient flow and local gradient aspect, while preserving the upper and lower bound of output. Parametrization also offers Hexpo the ability to produce the output that is close to zero. On experiments involving MNIST hand digit recognition dataset, Hexpo outperforms rectified linear units family(rectified linear unit and exponential linear unit) by the accuracy and speed of learning. On the experiments involving CIFAR-10 tiny image recognition dataset and convolutional layers, Hexpo outperforms rectified linear unit and performs similar in comparison with exponential linear unit.


Techniques and Programs Used in Bidding and Playing phases of Imperfect Information Game – an Overview


title: MOVIES IN EFL CLASSROOMS: WITH OR WITHOUT SUBTITLES

abstract: The present study attempted to investigate the possible effect of the use of English movie subtitles on the improvement of listening comprehension in EFL intermediate students at Golestan University, Iran. To achieve this goal, 45 Iranian intermediate learners from two classes were assigned into two groups of Bimodal-Subtitles and No-Subtitles. The material consisted of an authentic English movie with 10 episodes. Each episode was about 15 minutes. The participants in each group were exposed to only one of the two treatment conditions, English subtitles and no subtitles. After ten sessions a post-test was given to the learners in order to evaluate their improvement in listening comprehension. The results showed that the English subtitle group performed better than the no subtitle group on the listening test.


title: Positive Emotions Trigger Upward Spirals Toward Emotional Well-Being

abstract: The broaden-and-build theory of positive emotions pre- dicts that positive emotions broaden the scopes of attention and cogni- tion, and, by consequence, initiate upward spirals toward increasing emotional well-being. The present study assessed this prediction by testing whether positive affect and broad-minded coping reciprocally and prospectively predict one another. One hundred thirty-eight col- lege students completed self-report measures of affect and coping at two assessment periods 5 weeks apart. As hypothesized, regression analyses showed that initial positive affect, but not negative affect, predicted improved broad-minded coping, and initial broad-minded coping predicted increased positive affect, but not reductions in nega- tive affect. Further mediational analyses showed that positive affect and broad-minded coping serially enhanced one another. These find- ings provide prospective evidence to support the prediction that posi- tive emotions initiate upward spirals toward enhanced emotional well- being. Implications for clinical practice and health promotion are dis- cussed. Positive emotions feel good. Plus, the balance of people's positive and negative emotions contributes to judgments of life satisfaction (Diener & Larsen, 1993). Are these the only reasons people should care about positive emotions? We think not. We propose that positive emotions not only feel good in the present, but also increase the likeli- hood that one will feel good in the future. That is, we suggest that pos- itive emotions trigger upward spirals toward enhanced emotional well- being. This prediction stems from a new perspective on positive emotions offered within Fredrickson's (1998, 2001) broaden-and-build theory . This model posits that, unlike negative emotions, which narrow people's thought-action repertoires (e.g., fight or flight), positive emo- tions broaden people's thought-action repertoires, encouraging them to discover novel lines of thought or action. Joy, for instance, creates the urge to play, interest creates the urge to explore, and so on. A key, incidental outcome of these broadened mind-sets is an increase in personal resources: As individuals discover new ideas and actions, they build their physical, intellectual, social, and psychological re- sources. Play, for instance, builds physical, socioemotional, and in- tellectual skills, and fuels brain development. Similarly, exploration increases knowledge and psychological complexity (Fredrickson, 1998, 2000).


title: Using machine learning for software aging detection in Android system

abstract: Software aging is a common experience in Android operating system, as the gradual performance degradation is usually complained by the users. However, the mathematical modelling and detection of such experience is still an emerging issue due to the complexity and relatively young age of Android. This paper applies and compares three machine learning algorithms, namely decision tree, Support Vector Machine (SVM), and Deep Belief Network (DBn), for the detection of software aging in Android. In addition to the traditional aging indicator of launch time (LT), this paper also investigates the effectiveness of page fault (PF) and multiple labels (i.e., the combination of LT and PF). Experimental results show that the accuracy of DBN is comparable to decision tree and SVM when the data volume increases to 5000, which means DBN and other similar algorithms suitable for high dimensional and large data may also play a role in software aging. The results also reveal that PF is a little more stable than LT in terms of variance of accuracy, and it can also be used a good indicator for aging of Android. However, the performance of multiple labels is not improved in our experiments. The observations of this paper are supposed to assist further analysis of software aging in Android and the design of corresponding rejuvenation methods.


title: The Brachistochrone Curve: The Problem of Quickest Descent

abstract: This article presents the problem of quickest descent, or the Brachistochrone curve, that may be solved by the calculus of variations and the Euler-Lagrang ee quation. The cycloid is the quickest curve and also has the property of isochronism by which Huygens improved on Galileo's pendulum.


title: MATHEMATICAL MODEL FOR MALARIA TRANSMISSION DYNAMICS IN HUMAN AND MOSQUITO POPULATIONS WITH NONLINEAR FORCES OF INFECTION

abstract: This paper presents a seven-dimensional ordinary differential equa- tion modelling the transmission of Plasmodium falciparum malaria between hu- mans and mosquitoes with non-linear forces of infection in form of saturated in- cidence rates. These incidence rates produce antibodies in response to the pres- ence of parasite-causing malaria in both human and mosquito populations.The existence of region where the model is epidemiologically feasible is established. Stability analysis of the disease-free equilibrium is investigated via the thresh- old parameter (reproduction number R0) obtained using the next generation matrix technique. The model results show that the disease-free equilibrium is asymptotically stable at threshold parameter less than unity and unstable at threshold parameter greater than unity. The existence of the unique endemic equilibrium is also determined under certain conditions. Numerical simulations are carried out to confirm the analytic results and explore the possible behavior of the formulated model.


Learned D-AMP: A Principled CNN-based Compressive Image Recovery Algorithm.