title: Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data.

text: Despite significant advances in adult clinical electrocardiography (ECG) signal processing techniques and the power of digital processors, the analysis of non-invasive foetal ECG (NI-FECG) is still in its infancy. The Physionet/Computing in Cardiology Challenge 2013 addresses some of these limitations by making a set of FECG data publicly available to the scientific community for evaluation of signal processing techniques.The abdominal ECG signals were first preprocessed with a band-pass filter in order to remove higher frequencies and baseline wander. A notch filter to remove power interferences at 50 Hz or 60 Hz was applied if required. The signals were then normalized before applying various source separation techniques to cancel the maternal ECG. These techniques included: template subtraction, principal/independent component analysis, extended Kalman filter and a combination of a subset of these methods (FUSE method). Foetal QRS detection was performed on all residuals using a Pan and Tompkins QRS detector and the residual channel with the smoothest foetal heart rate time series was selected.The FUSE algorithm performed better than all the individual methods on the training data set. On the validation and test sets, the best Challenge scores obtained were E1 = 179.44, E2 = 20.79, E3 = 153.07, E4 = 29.62 and E5 = 4.67 for events 1-5 respectively using the FUSE method. These were the best Challenge scores for E1 and E2 and third and second best Challenge scores for E3, E4 and E5 out of the 53 international teams that entered the Challenge. The results demonstrated that existing standard approaches for foetal heart rate estimation can be improved by fusing estimators together. We provide open source code to enable benchmarking for each of the standard approaches described.


title: Absolute Orientation for Word Embedding Alignment

text: We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith etal (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.


title: Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

text: With the recent advent of high-throughput technologies for both compound synthesis and biological screening, there is no shortage of publicly or commercially available data sets and databases that can be used for computational drug discovery applications (reviewed recently in Williams et al.). Rapid growth of large, publicly available databases (such as PubChem or ChemSpider containing more than 20 million molecular records each) enabled by experimental projects such as NIH’s Molecular Libraries and Imaging Initiative provides new opportunities for the development of cheminformatics methodologies and their application to knowledge discovery in molecular databases. A fundamental assumption of any cheminformatics study is the correctness of the input data generated by experimental scientists and available in various data sets. Nevertheless, a recent study showed that on average there are two errors per each medicinal chemistry publication with an overall error rate for compounds indexed in the WOMBAT database as high as 8%. In another recent study, the authors investigated several public and commercial databases to calculate their error rates: the latter were ranging from 0.1 to 3.4% depending on the database. How significant is the problem of accurate structure representation (given that the error rates in current databases may appear relatively low) since it concerns exploratory cheminformatics and molecular modeling research? Recent investigations by a large group of collaborators from six laboratories have clearly demonstrated that the type of chemical descriptors has much greater influence on the prediction performance of QSAR models than the nature of model optimization techniques. These findings suggest that having erroneous structures represented by erroneous descriptors should have a detrimental effect on model performance. Indeed, a recent seminal publication clearly pointed out the importance of chemical data curation in the context of QSAR modeling. The authors have discussed the error rates in several known databases and evaluated the consequences of both random and systematic errors with respect to the prediction performances of the derivative QSAR models. They also presented several illustrative examples of incorrect structures generated from either correct or incorrect SMILES. The main conclusions of the study were that small structural errors within a data set could lead to significant losses of predictive ability of QSAR models. The authors further demonstrated that manual curation of structural data leads to substantial increase in the model predictivity. This conclusion becomes especially important in light of the aforementioned study of Oprea et al. that cited a significant error rate in medicinal chemistry literature. Alarmed by these conclusions, we have examined several popular public databases of bioactive molecules to assess possible error rates in structure representation. For instance, the NCI AIDS Antiviral Screen (the paper describing this screen has been cited 57 times in PubMed Central only) comprises 42687 chemical records with their associated activity. Even quick analysis of this data sets revealed that 4202 (i.e., ca. 10%) compounds should be either treated with care or removed before any cheminformatics investigation: 3350 compounds were mixtures and salts, and we detected 741 pairs of exact duplicates and stereoisomers, possessing different or opposite reported activities. Similar observations can be made for several well-known public databases such as NCI Human Tumor Cell Line and PubChem, as well as for smaller data sets studied and reported in published literature. For instance, in a study already mentioned above, six research teams each specializing in QSAR modeling (including the authors of this paper!) collaborated on the analysis of the Tetrahymena pyriformis aquatic toxicity data set comprising 1093 compounds. Later this exact data set was used by the organizers of CADASTER toxicity challenge. However, our re-examination of this data set showed the presence of six pairs of duplicates among 1093 compounds because of the presence of different metal cations in salts (with different aquatic toxicities measured by pIGC50 ranging from ∼0.1 to 1 logarithmic unit. Furthermore, in the new external set compiled by the organizers of the CADASTER challenge to evaluate the comparative performance of competing models, eight out of 120 compounds were found to be structurally identical to modeling set compounds but with different toxicity values (∼0.3 pIGC50). Such disappointing observations may, at least in part, explain why QSAR models may sometimes fail, which is * To whom correspondence should be addressed. E-mail: alex_tropsha@ unc.edu. † University of North Carolina at Chapel Hill. ‡ A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine. J. Chem. Inf. Model. XXXX, xxx, 000 A


title: Cyberbullying and Primary-School Aged Children : The Psychological Literature and the Challenge for Sociology

text: Cyberbullying is an international issue for schools, young people and their families. Whilst many research domains have explored this phenomenon, and bullying more generally, the majority of reported studies appear in the psychological and educational literatures, where bullying, and more recently, cyberbullying has been examined primarily at the individual level: amongst adolescents and young people, with a focus on the definition, its prevalence, behaviours, and impact. There also is growing evidence that younger children are increasingly accessing technology and engaging with social media, yet there is limited research dedicated to this younger age group. The purpose of this paper is to report on a systematic literature review from the psychological and educational research domains related to this younger age group, to inform future research across the disciplines. Younger children require different methods of engagement. This review highlights the methodological challenges associated with this age group present in the psychological literature, and argues for a greater use of sociological, child-centred approaches to data collection. This review examined studies published in English, between 2009 and 2014, and conducted with children aged 5–12 years, about their experiences with cyberbullying. Searches were conducted on seven key databases using keywords associated with cyberbullying and age of children. A Google Scholar search also examined published and unpublished reports. A total of 966 articles and reports were retrieved. A random peer review process was employed to establish inter-rater reliability and veracity of the review. Findings revealed 38 studies reported specifically on children aged 5–12 years. The dominant focus of these articles was on prevalence of cyberbullying, established through survey methodology. Few studies noted OPEN ACCESS Societies 2015, 5 493 impacts, understanding and behaviours or engaged children’s independent voice. This review highlights current gaps in our knowledge about younger children’s experiences with this form of bullying, and the importance of employing cross-disciplinary and developmentally appropriate methodologies to inform future research.


title: Smooth contact-aware facial blendshapes transfer

text: In this paper, we propose a method to improve the quality of the facial blendshapes synthesized using deformation transfer by introducing the idea of contact and smoothness awareness. Simply applying deformation transfer for face models easily results in penetrations and separations which do not exist in the original data. As there is no concept of smoothness, crumpling artifacts appear where the mesh is extremely concentrated, e.g. eyelids and mouth corners. We solve these issues by first introducing the concept of virtual triangles and then adding a Laplacian energy term into the linear problem to be solved for the deformation transfer. Experimental results show that our system reduces the amount of artifacts caused by the original deformation transfer, improving the efficiency of the animation pipeline.


title: Educational Software Applied in Teaching Electrocardiogram: A Systematic Review

text:
Background
The electrocardiogram (ECG) is the most used diagnostic tool in medicine; in this sense, it is essential that medical undergraduates learn how to interpret it correctly while they are still on training. Naturally, they go through classic learning (e.g., lectures and speeches). However, they are not often efficiently trained in analyzing ECG results. In this regard, methodologies such as other educational support tools in medical practice, such as educational software, should be considered a valuable approach for medical training purposes.

Methods
We performed a literature review in six electronic databases, considering studies published before April 2017. The resulting set comprises 2,467 studies. From this collection, 12 studies have been selected, initially, whereby we carried out a snowballing process to identify other relevant studies through the reference lists of these studies, resulting in five relevant studies, making up a total of 17 articles that passed all stages and criteria.

Results
The results show that 52.9% of software types were tutorial and 58.8% were designed to be run locally on a computer. The subjects were discussed together with a greater focus on the teaching of electrophysiology and/or cardiac physiology, identifying patterns of ECG and/or arrhythmias.

Conclusions
We found positive results with the introduction of educational software for ECG teaching. However, there is a clear need for using higher quality research methodologies and the inclusion of appropriate controls, in order to obtain more precise conclusions about how beneficial the inclusion of such tools can be for the practices of ECG interpretation.


URLs: Self-disclosure on Facebook among female users and its relationship to feelings of loneliness Author(s): Al-Saggaf, Y.M. ; Nielsen, S.G. Computers in Human Behavior


title: Optimal dimensioning of active cell balancing architectures

text: This paper presents an approach to optimal dimensioning of active cell balancing architectures, which are of increasing relevance in Electrical Energy Storages (EESs) for Electric Vehicles (EVs) or stationary applications such as smart grids. Active cell balancing equalizes the state of charge of cells within a battery pack via charge transfers, increasing the effective capacity and lifetime. While optimization approaches have been introduced into the design process of several aspects of EESs, active cell balancing architectures have, until now, not been systematically optimized in terms of their components. Therefore, this paper analyzes existing architectures to develop design metrics for energy dissipation, installation volume, and balancing current. Based on these design metrics, a methodology to efficiently obtain Pareto-optimal configurations for a wide range of inductors and transistors at different balancing currents is developed. Our methodology is then applied to a case study, optimizing two state-of-the-art architectures using realistic balancing algorithms. The results give evidence of the applicability of systematic optimization in the domain of cell balancing, leading to higher energy efficiencies with minimized installation space.


title: Algorithms for Unevenly Spaced Time Series : Moving Averages and Other Rolling Operators

text: This paper describes algorithms for efficiently calculating certain rolling time series operators for unevenly spaced data. In particular, we show how to calculate simple moving averages (SMAs), exponential moving averages (EMAs), and related operators in linear time with respect to the number of observations in a time series. A web appendix provides an implementation of these algorithms in the programming language C and a package (forthcoming) for the statistical software R.


title: Effects of strength training with blood flow restriction on torque, muscle activation and local muscular endurance in healthy subjects

text: The present study aimed to analyse the effects of six weeks of strength training (ST), with and without blood flow restriction (BFR), on torque, muscle activation, and local muscular endurance (LME) of the knee extensors. Thirty-seven healthy young individuals were divided into four groups: high intensity (HI), low intensity with BFR (LI+BFR), high intensity and low intensity + BFR (COMB), and low intensity (LI). Torque, muscle activation and LME were evaluated before the test and at the 2nd, 4th and 6th weeks after exercise. All groups had increased torque, muscle activation and LME (p<0.05) after the intervention, but the effect size and magnitude were greater in the HI, LI+BFR and COMB groups. In conclusion, the groups with BFR (LI+BFR and COMB) produced magnitudes of muscle activation, torque and LME similar to those of the HI group.


title: Discovery-driven graph summarization

text: Large graph datasets are ubiquitous in many domains, including social networking and biology. Graph summarization techniques are crucial in such domains as they can assist in uncovering useful insights about the patterns hidden in the underlying data. One important type of graph summarization is to produce small and informative summaries based on user-selected node attributes and relationships, and allowing users to interactively drill-down or roll-up to navigate through summaries with different resolutions. However, two key components are missing from the previous work in this area that limit the use of this method in practice. First, the previous work only deals with categorical node attributes. Consequently, users have to manually bucketize numerical attributes based on domain knowledge, which is not always possible. Moreover, users often have to manually iterate through many resolutions of summaries to identify the most interesting ones. This paper addresses both these key issues to make the interactive graph summarization approach more useful in practice. We first present a method to automatically categorize numerical attributes values by exploiting the domain knowledge hidden inside the node attributes values and graph link structures. Furthermore, we propose an interestingness measure for graph summaries to point users to the potentially most insightful summaries. Using two real datasets, we demonstrate the effectiveness and efficiency of our techniques.


title: Wound microbiology and associated approaches to wound management.

text: The majority of dermal wounds are colonized with aerobic and anaerobic microorganisms that originate predominantly from mucosal surfaces such as those of the oral cavity and gut. The role and significance of microorganisms in wound healing has been debated for many years. While some experts consider the microbial density to be critical in predicting wound healing and infection, others consider the types of microorganisms to be of greater importance. However, these and other factors such as microbial synergy, the host immune response, and the quality of tissue must be considered collectively in assessing the probability of infection. Debate also exists regarding the value of wound sampling, the types of wounds that should be sampled, and the sampling technique required to generate the most meaningful data. In the laboratory, consideration must be given to the relevance of culturing polymicrobial specimens, the value in identifying one or more microorganisms, and the microorganisms that should be assayed for antibiotic susceptibility. Although appropriate systemic antibiotics are essential for the treatment of deteriorating, clinically infected wounds, debate exists regarding the relevance and use of antibiotics (systemic or topical) and antiseptics (topical) in the treatment of nonhealing wounds that have no clinical signs of infection. In providing a detailed analysis of wound microbiology, together with current opinion and controversies regarding wound assessment and treatment, this review has attempted to capture and address microbiological aspects that are critical to the successful management of microorganisms in wounds.


title: The Psychometric Properties of the Internet Addiction Test

text: There is growing concern about excessive Internet use and whether this can amount to an addiction. In researching this topic, a valid and reliable assessment instrument is essential. In her survey of Internet addiction, Young designed the Internet Addiction Test (IAT), which provides a basis for developments. The IAT has high face validity, but it has not been subjected to systematic psychometric testing. This study sought to replicate and expand Young's survey, and to examine the IAT more systematically. A questionnaire that existed as a Web page was devised, consisting of the IAT and 15 other questions regarding the respondents' demographic information and Internet usage. Participants were recruited through the Internet, yielding 86 valid responses (29 males and 57 females). Factor analysis of the IAT revealed six factors--salience, excessive use, neglecting work, anticipation, lack of control, and neglecting social life. These factors showed good internal consistency and concurrent validity, with salience being the most reliable. Younger and more recent users reported more problems, mainly concerning the neglect of work and social life. We expected interactive Internet functions to be more addictive; however, this was not found to be so. Overall, the IAT is a valid and reliable instrument that may be used in further research on Internet addiction.


title: Futures of elderly care in Iran: A protocol with scenario approach

text: Background: The number of people aged 60 and older is increasing faster than other age groups worldwide. Iran will experience a sharp aging population increase in the next decades, and this will pose new challenges to the healthcare system. Since providing high quality aged-care services would be the major concern of the policymakers, this question arises that what types of aged care services should be organized in the coming 10 years? This protocol has been designed to develop a set of scenarios for the future of elderly care in Iran. Methods: In this study, intuitive logics approach and Global Business Network (GBN) model were used to develop scenarios for elderly care in Iran. In terms of perspective, the scenarios in this approach are normative, qualitative with respect to methodology and deductive in constructing the process of scenarios. The three phases of GBN model are as follows: 1) Orientation: Identifying strategic levels, stakeholders, participants and time horizon; 2) Exploration: Identifying the driving forces and key uncertainties; 3) Synthesis: Defining the scenario logics and constructing scenario storyline. Results: Presently, two phases are completed and the results will be published in mid-2016. Conclusion: This study delivers a comprehensive framework for taking appropriate actions in providing care for the elderly in the future. Moreover, policy makers should specify and provide the full range of services for the elderly, and in doing so, the scenarios and key findings of this study could be of valuable help.


title: Adolescent self-compassion : Associations with narcissism , self-esteem , aggression , and internalizing symptoms in at-risk males

text: Self-compassion is an attitude toward oneself that involves perceiving one’s experiences as an opportunity for self-awareness and improvement, as well as limited self-judgment after failure. Self-compassion has not been extensively studied in adolescence, a time when self-perception and self-appraisals regarding success and failure take on notable importance. This study considered the connection between self-compassion, narcissism, self-esteem, aggression, and internalizing problems in a sample of 251 male adolescents, ages 16–18, attending a residential program. Self-compassion was negatively correlated with aggression and vulnerable narcissism and positively correlated with self-esteem. In general, selfcompassion did not exhibit the hypothesized protective effect on the relation between narcissism and aggression. Findings indicate that, as expected, self-compassion is indicative of a relatively secure, positive sense of self in adolescents. 2014 Elsevier Ltd. All rights reserved.


title: Megaspeed Drive Systems: Pushing Beyond 1 Million r/min

text: The latest research in mesoscale drive systems is targeting rotational speeds toward 1 million r/min for a power range of 1-1 kW. Emerging applications for megaspeed drives (MegaNdrives) are to be found in future turbo compressor systems for fuel cells and heat pumps, generators/starters for portable nanoscale gas turbines, printed circuit board drilling and machining spindles, and electric power generation from pressurized gas flow. The selection of the machine type and the challenges involved in designing a machine for megaspeed operation such as the winding concepts, a mechanical rotor design capable of 1 000 000 r/min, the selection of magnetic materials for the stator, and the optimization concerning high-frequency losses and torque density are presented. Furthermore, a review of the advantageous inverter topologies, taking into account the extremely low stator inductance and possible high-speed bearing types such as ball bearings, air bearings, foil bearings, and magnetic bearings, are given. Finally, prototypes and experimental results originating from MegaNdrive research at Swiss Federal Institute of Technology Zurich are discussed and extreme temperature operation and power microelectricalmechanical system are identified as targets for future research.


title: Potentials and limits of high-speed PM motors

text: This paper illustrates potentials and limits of high-speed permanent-magnet (PM) motors. The influence of materials chosen for PM, stator core, and retaining sleeve is highlighted. Slotted and slotless topologies are considered and compared, computing magnetic, electrical, mechanical, and thermal quantities by means of both analytical and finite-element approach. Thermal and PM demagnetization limits as well as rotor losses are evaluated. A criteria of optimization of the motor structure is described, with the diameter ratio and the iron flux density as main design variables.


title: An Integrated Flywheel Energy Storage System with a Homopolar Inductor Motor / Generator and High-Frequency Drive

text: The design, construction, and test of an integrated flywheel energy storage system with a homopolar inductor motor/generator and high-frequency drive is presented in this paper. The work is presented as an integrated design of flywheel system, motor, drive, and controller. The motor design features low rotor losses, a slotless stator, construction from robust and low cost materials, and a rotor that also serves as the energy storage rotor for the flywheel system. A high-frequency six-step drive scheme is used in place of pulsewidth modulation because of the high electrical frequencies. A speed-sensorless controller that works without state estimation is also described. A prototype of the flywheel system has been demonstrated at a power level of 9.4 kW, with an average system efficiency of 83% over a 30 000–60 000-r/min speed range.


title: Analytical and Experimental Investigation of a Low Torque, Ultra-High Speed Drive System

text: New application areas are demanding the development of ultra-high speed electrical machines. Significant challenges exist to produce a test bench that is capable of withstanding operating speeds exceeding 500,000 rpm and measuring very low torque values of mNm. This paper describes a purpose built test bench that is able to characterize the operation of an ultra-high speed drive system. Furthermore, the calculation of electromagnetic losses, air friction and critical speeds is presented and a comparison of analytical and experimental results is given. The ultra-high speed machine has an efficiency of 95%, however, in the upper speed ranges the frictional losses become dominant


title: Design of a 100 W, 500000 rpm permanent-magnet generator for mesoscale gas turbines

text: Mesoscale gas turbine generator systems are a promising solution for high energy and power density portable devices. This paper focuses on the design of a 100 W, 500000 rpm generator suitable for use with a gas turbine. The design procedure selects the suitable machine type and bearing technology, and determines the electromagnetic characteristics. The losses caused by the high frequency operation are minimized by optimizing the winding and the stator core material. The final design is a permanent-magnet machine with a volume of 3.5 cm/sup 3/ and experimental measurements from a test bench are presented.


Chapter 1 Feature Engineering for Twitter-based Applications


title: Discovering Reader's Emotions Triggered from News Articles

text: The study of sentiment analysis aims to explore the occurrences of opinion words in a document, and their orientation and strength. Most existing researches focus on writer's emotions, which are the feelings that authors were expressing. In contrast, news articles are usually described in objective terms, in which opinion words are rare. In this paper, we propose to discover the reader's feelings when they read news articles. First, different feature units are extracted such as unigrams, bigrams, and segmented words. We compare several feature selection methods including document frequency variation, information gain, mutual information, and chi-square test, to select the candidate features for sentiment classification. Then, the performance for multi-class classification and multiple binary classification are evaluated. The experimental results on real news articles show the effectiveness of the proposed method for discovering reader's emotions triggered from news articles. Further investigation is needed to validate the performance in larger scales.


title: Visual discrimination learning requires sleep after training

text: Performance on a visual discrimination task showed maximal improvement 48–96 hours after initial training, even without intervening practice. When subjects were deprived of sleep for 30 hours after training and then tested after two full nights of recovery sleep, they showed no significant improvement, despite normal levels of alertness. Together with previous findings that subjects show no improvement when retested the same day as training, this demonstrates that sleep within 30 hours of training is absolutely required for improved performance.


title: Automatic Simplification of Obfuscated JavaScript Code: A Semantics-Based Approach

text: JavaScript is a scripting language that is commonly used to create sophisticated interactive client-side web applications. However, JavaScript code can also be used to exploit vulnerabilities in the web browser and its extensions, and in recent years it has become a major mechanism for web-based malware delivery. In order to avoid detection, attackers often take advantage of the dynamic nature of JavaScript to create highly obfuscated code. This paper describes a semantics-based approach for automatic deobfuscation of JavaScript code. Experiments using a prototype implementation indicate that our approach is able to penetrate multiple layers of complex obfuscations and extract the core logic of the computation, which makes it easier to understand the behavior of the code.


title: Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

text: Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150m probe vs. 1m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.


title: CCR - A Content-Collaborative Reciprocal Recommender for Online Dating

text: We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. Evaluation results show that the success rate of the recommendations is 69.26% compared with a baseline of 35.19% for the top 10 ranked recommendations.


title: Patch-based Image Correlation with Rapid Filtering

text: This paper describes a patch-based approach for rapid image correlation or template matching. By representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Rectangle filters are applied to each image patch for fast filtering based on the integral image representation. A new method is developed for feature dimension reduction by detecting the "salient" image structures given a single image. Experiments on a variety images show the success of the method in dealing with different variations in the test images. In terms of computation time, the approach is faster than traditional methods by up to two orders of magnitude and is at least three times faster than a fast implementation of normalized cross correlation.


title: Music and Cognitive Abilities

text: Does music make you smarter? Music listening and music lessons have been claimed to confer intellectual advantages. Any association between music and intellectual functioning would be notable only if the benefits apply reliably to nonmusical abilities and if music is unique in producing the effects. The available evidence indicates that music listening leads to enhanced performance on a variety of cognitive tests, but that such effects are shortterm and stem from the impact of music on arousal level and mood, which, in turn, affect cognitive performance; experiences other than music listening have similar effects. Music lessons in childhood tell a different story. They are associated with small but general and long-lasting intellectual benefits that cannot be attributed to obvious confounding variables such as family income and parents’ education. The mechanisms underlying this association have yet to be determined. KEYWORDS—music cognition; intelligence; cognitive development People’s eagerness for quick fixes can be seen in the seemingly irrational appeal of crash diets, get-rich-quick schemes, and getsmart-fast programs. Is the claim of music as a route to enhanced intelligence yet another self-improvement fantasy? In the pages that follow, I outline the origins of the claim and evaluate the available evidence. Intellectual benefits of exposure to music would be noteworthy if (a) they extend to nonmusical abilities, (b) they are systematic and reliable, and (c) they are more likely to result from hearing or playing music than from other activities. Unlike in other cultures, where virtually everyone participates in music making, musical experiences in Western society typically involve listening and, only in some cases, lessons and performing. Music listening is ubiquitous, both purposefully (e.g., listening to the radio) and incidentally (e.g., background music in stores and restaurants). By contrast, relatively few individuals take music lessons for several years. The consequences of music listening are likely to differ quantitatively and qualitatively from those of music lessons.


title: A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface & Modified k-Means Clustering

text: Keywords: Eigenface, face Recognition, k-means clustering, principle component analysis (PCA)


title: Modified Caesar Cipher for Better Security Enhancement

text: Encryption is the process of scrambling a message so that only the intended recipient can read it. With the fast progression of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. Caesar cipher is a mono alphabetic cipher. It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter. In this paper, author modified the traditional Caesar cipher and fixed the key size as one. Another thing alphabet index is checked if the alphabet index is even then increase the value by one else alphabet index is odd decrease the key value by one. Encryption and scrambling of the letters in the Cipher Text.


title: Addressing Legal Requirements in Requirements Engineering

text: Legal texts, such as regulations and legislation, are playing an increasingly important role in requirements engineering and system development. Monitoring systems for requirements and policy compliance has been recognized in the requirements engineering community as a key area for research. Similarly, regulatory compliance is critical in systems that are governed by regulations and law, especially given that non-compliance can result in both financial and criminal penalties. Working with legal texts can be very challenging, however, because they contain numerous ambiguities, cross-references, domain-specific definitions, and acronyms, and are frequently amended via new regulations and case law. Requirements engineers and compliance auditors must be able to identify relevant regulations, extract requirements and other key concepts, and monitor compliance throughout the software lifecycle. This paper surveys research efforts over the past 50 years in handling legal texts for systems development. These efforts include the use of symbolic logic, logic programming, first-order temporal logic, deontic logic, defeasible logic, goal modeling, and semi-structured representations. This survey can aid requirements engineers and auditors to better specify, monitor, and test software systems for compliance.


title: Modeling the Clickstream : Implications for Web-Based Advertising Efforts

text: Advertising revenues have become a critical element in the business plans of most commercial Web sites. Despite extensive research on advertising in traditional media, managers and researchers face considerable uncertainty about its role in the online environment. The benefits offered by the medium notwithstanding, the lack of models to measure and predict advertising performance is a major deterrent to acceptance of the Web by mainstream advertisers. Presently, existing media models based on aggregate vehicle and advertising exposure are being adapted which underutilize the unique characteristics of the medium. What is required are methods that measure how consumers interact with advertising stimuli in ad-supported Web sites, going beyond mere counts of individual "eyeballs" attending to media. Given the enormous potential of this dynamic medium, academic investigation is sorely needed before cost implications and skepticism endanger the ability of the medium to generate and maintain advertiser support. This paper addresses advertiser and publisher need to understand and predict how consumers interact with advertising stimuli placed at Web sites. We do so by developing a framework to formally model the commercial “clickstream” at an advertiser supported Web site with mandatory visitor registration. Consumers visiting such Web sites are conceptualized as deriving utility from navigating through editorial and advertising content subject to time constraints. The clickstream represents a new source of consumer response data detailing the content and banner ads that consumers click on during the online navigation process. To our knowledge, this paper is the first to model the clickstream from an actual commercial Web site. Clickstream data allow us to investigate how consumers respond to advertising over time at an individual level. Such modeling is not possible in broadcast media because the data do not exist. Our results contrast dramatically from those typically found in traditional broadcast media. First, 3 the effect of repeated exposures to banner ads is U-shaped. This is in contrast with the inverted Ushaped response found in broadcast media. Second, the differential effects of each successive ad exposure is initially negative, but non-linear, and becomes positive later at higher levels of passive ad exposures. Third, the negative response to repeated banner ad exposures increases for consumers who visit the site more frequently. Fourth, in contrast to findings in traditional media the effect of exposure to competing ads is either insignificant or positive. However, carryover effects of past advertising exposures are similar to those proposed in broadcast media. Finally, heterogeneity in cumulative effects of advertising exposure and involvement across consumers captured by cumulative click behavior across visits and click behavior during the visit was found to significantly contribute to differences in click response. This has implications for dynamic ad placement based on past history of consumer exposure and interaction with advertising at the Web site. Response parameters of consumer visitor segments can be used by media buyers and advertisers to understand the benefits consumers seek from the advertising vehicle, and thus guide advertising media placement decisions. In sum, our modeling effort offers an important first look into how advertisers can use the Web medium to maximize their desired advertising outcomes.


title: Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.

text:
BACKGROUND
We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.

METHODS
We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach.

RESULTS
Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data.

CONCLUSION
Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.


title: Control of Flight Operation of a Quad rotor AR . Drone Using Depth Map from Microsoft Kinect Sensor

text: In recent years, many user-interface devices appear for managing variety the physical interactions. The Microsft Kinect camera is a revolutionary and useful depth camera giving new user experience of interactive gaming on the Xbox platform through gesture or motion detection. In this paper we present an approach for control the Quadrotor AR.Drone using the Microsft Kinect sensor.


title: Discriminant kernel and regularization parameter learning via semidefinite programming

text: Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection of kernels. In this paper, we consider the problem of learning an optimal kernel over a convex set of kernels. We show that the kernel learning problem can be formulated as a semidefinite program (SDP) in the binary-class case. We further extend the SDP formulation to the multi-class case. It is based on a key result established in this paper, that is, the multi-class kernel learning problem can be decomposed into a set of binary-class kernel learning problems. In addition, we propose an approximation scheme to reduce the computational complexity of the multi-class SDP formulation. The performance of RKDA also depends on the value of the regularization parameter. We show that this value can be learned automatically in the framework. Experimental results on benchmark data sets demonstrate the efficacy of the proposed SDP formulations.


title: To have or not to have Internet at home: Implications for online shopping

text: This paper analyzes the individual decision of online shopping, in terms of socioeconomic characteristics, internet related variables and location factors. Since online shopping is only observed for internet users, we use the two-step Heckmans model to correct sample selection. We argue that one of the relevant variables to explain online shopping, the existence of a home internet connection, can be endogenous. To account for this potential endogeneity, we jointly estimate the probability of online shopping and the probability of having internet at home. The dataset used in this paper comes from the Household Survey of ICT Equipment and Usage, conducted by the Spanish Statistical O¢ ce on an annual basis. Our analysis covers the period 2004-2009. Our results show that not accounting for the endogeneity of having internet at home, yields to overestimate its e¤ect on the probability of buying online. This nding can be important in the design of public policies aimed at enhancing e-commerce through providing households with internet connection at home. We also show that, compared to other variables that are also relevant for online shopping, the e¤ect of internet at home is quite small. JEL Classication: D12, C35, C25


title: Surprisingly Easy Hard-Attention for Sequence to Sequence Learning

text: In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation and two morphological inflection tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.


title: Parkinsons disease patients perspective on context aware wearable technology for auditive assistance

text: In this paper we present a wearable assistive technology for the freezing of gait (FOG) symptom in patients with Parkinsons disease (PD), with emphasis on subjective user appreciation. Patients with advanced PD often suffer from FOG, which is a sudden and transient inability to move. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non+pharmacologic treatments are of special interest.


title: Achieving IT-Business Strategic Alignment via Enterprise-Wide Implementation of Balanced Scorecards

text: Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.


title: Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings

text: A review and meta-analysis was performed of seventy-five articles concerned with innovation characteristics and their relationship to innovation adoption and implementation. One part of the analysis consisted of constructing a methodological profile of the existing studies, and contrasting this with a hypothetical optimal approach. A second part of the study employed meta-analytic statistical techniques to assess the generality and consistency of existing empirical findings. Three innovation characteristics (compatibility, relative advantage, and complexity) had the most consistent significant relationship to innovation adoption. Suggestions for future research in the area were made.


title: A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces

text: As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.


title: High frequency data in financial markets : Issues and applications

text: The development of high frequency data bases allows for empirical investigations of a wide range of issues in the financial markets. In this paper, we set out some of the many important issues connected with the use, analysis, and application of high-frequency data sets. These include the effects of market structure on the availability and interpretation of the data, methodological issues such as the treatment of time, the effects of intra-day seasonals, and the effects of time-varying volatility, and the information content of various market data. We also address using high frequency data to determine the linkages between markets and to determine the applicability of temporal trading rules. The paper concludes with a discussion of the issues for future research. © 1997 Elsevier Science B.V. JEL classification: Cl0; C50; F30; G14; GI5


title: Understanding the Metropolis-Hastings Algorithm

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/astata.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.


title: A Bayesian Approach to Filtering Junk E-Mail

text: In addressing the growing problem of junk E-mail on the Internet, we examine methods for the automated construction of filters to eliminate such unwanted messages from a user’s mail stream. By casting this problem in a decision theoretic framework, we are able to make use of probabilistic learning methods in conjunction with a notion of differential misclassification cost to produce filters Which are especially appropriate for the nuances of this task. While this may appear, at first, to be a straight-forward text classification problem, we show that by considering domain-specific features of this problem in addition to the raw text of E-mail messages, we can produce much more accurate filters. Finally, we show the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment.


title: Topic sentiment mixture: modeling facets and opinions in weblogs

text: In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.


title: Beating the Bookies : Predicting the Outcome of Soccer Games

text: Soccer is the most popular sport in the world. With an estimated 3.5 billion fans around the world, it is enjoyed by nearly half the world’s population. My project asks the following question: Is it possible to predict the outcome of soccer games with high accuracy automatically? This questions is not merely of interest to fans who must satisfy their curiosity, but it is also of significant economical relevance. A BBC article from 2013 (BBC) estimates that the soccer betting market is worth between 500 and 700 billion USD a year.


title: Exploiting Geographical Neighborhood Characteristics for Location Recommendation

text: Geographical characteristics derived from the historical check-in data have been reported effective in improving location recommendation accuracy. However, previous studies mainly exploit geographical characteristics from a user's perspective, via modeling the geographical distribution of each individual user's check-ins. In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location. The neighborhood is modeled at two levels: the instance-level neighborhood defined by a few nearest neighbors of the location, and the region-level neighborhood for the geographical region where the location exists. We propose a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region-level characteristics, i.e., locations in the same geographical region may share similar user preferences. In IRenMF, the two levels of geographical characteristics are naturally incorporated into the learning of latent features of users and locations, so that IRenMF predicts users' preferences on locations more accurately. Extensive experiments on the real data collected from Gowalla, a popular LBSN, demonstrate the effectiveness and advantages of our approach.


title: Towards a Universal Grammar for Natural Language Processing

text: Universal Dependencies is a recent initiative to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. In this talk, I outline the motivation behind the initiative and explain how the basic design principles follow from these requirements. I then discuss the different components of the annotation standard, including principles for word segmentation, morphological annotation, and syntactic annotation. I conclude with some thoughts on the challenges that lie ahead.


title: High voltage direct current transmission - A Review, Part II - Converter technologies

text: This paper is the 2nd part of the survey titled “High Voltage Direct Current Transmission - A Review, Part I”. The main converter technologies and HVDC systems' components will be discussed in this complementary paper.


title: Energy characterization of a tiled architecture processor with on-chip networks

text: Tiled architectures provide a paradigm for designers to turn silicon resources into processors with burgeoning quantities of programmable functional units and memories. The architecture has a dual responsibility: first, it must expose these resources in a way that is programmable. Second, it needs to manage the power associated with such resources.We present the power management facilities of the 16-tile Raw microprocessor. This design selectively turns on and off 48 SRAM macros, 96 functional unit clusters, 32 fetch units, and over 250 unique processor pipeline stages, all according to the needs of the computation and environment at hand.


title: The nature of feelings: evolutionary and neurobiological origins

text: Feelings are mental experiences of body states. They signify physiological need (for example, hunger), tissue injury (for example, pain), optimal function (for example, well-being), threats to the organism (for example, fear or anger) or specific social interactions (for example, compassion, gratitude or love). Feelings constitute a crucial component of the mechanisms of life regulation, from simple to complex. Their neural substrates can be found at all levels of the nervous system, from individual neurons to subcortical nuclei and cortical regions.


title: Neural Network training model for weather forecasting using Fireworks Algorithm

text: Weather forecasting is the application of science and technology in order to predict the weather conditions. It is important for agricultural and industrial sectors. Models of Artificial Neural Networks with supervised learning paradigm are suitable for weather forecasting in complexity atmosphere. Training algorithm is required for providing weight and bias values to the model. This research proposed a weather forecasting method using Artificial Neural Networks trained by Fireworks Algorithm. Fireworks Algorithm is a recently developed Swarm Intelligence Algorithm for optimization. The main objective of the method is to predict daily mean temperature based on various measured parameters gained from the Meteorological Station, located in Bangkok. The experimental results indicate that the proposed method is advantageous for weather forecasting.


title: Learning preferences for manipulation tasks from online coactive feedback

text: We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We implement our algorithm on two high-degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings, household chores and grocery store checkout, and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).


title: Authentication Using Mobile Phone as a Security Token

text: Today security concerns are on the rise in all areas industries such as banks, governmental applications, healthcare industry, militaryorganization, educational institutions etc, with one common weak link being “passwords”. Several proper strategies for using passwords have been proposed. Some of which are very difficult to use and others might not meet the company’s security concerns. The usage of passwords for authentication is no longer sufficient and stronger authentication schemes are necessary. Two factor authentication uses elements or devices such as tokens and ATM cards. We have proposed to resolve the password problem, which could be inconvenient for the user and costly for the service providers to combat otherwise. To avoid the usage of additional device, the mobile phone is adopted as security token. In this paper several different authentication solutions using the mobile as authentication token are discussed, where these solutions vary in complexity, strength, security and user friendliness. One of the authentication schemes (OTP solution) is implemented to verify their usability. Hence, a classification and evaluation of the different solutions is provided according to defined criteria. Keywords‐ Two-factor authentication, Security token, UPIF, IMSI, IMEI, one time password (OTP).


title: Survey on Performance of CMOS Sensors

text: This paper presents a survey on CMOS Image Sensors (CIS) based on active Pixel Sensors (APS). Due to its properties, the APS is used in low voltage, low power and its applications in portable imaging systems such as disposable cameras and on-chip autonomous security cameras are random access, faster readout and low noise with peripheral circuitry.


title: Fluid Vector Flow and Applications in Brain Tumor Segmentation

text: In this paper, we propose a new approach that we call the ldquofluid vector flowrdquo (FVF) active contour model to address problems of insufficient capture range and poor convergence for concavities. With the ability to capture a large range and extract concave shapes, FVF demonstrates improvements over techniques like gradient vector flow, boundary vector flow, and magnetostatic active contour on three sets of experiments: synthetic images, pediatric head MRI images, and brain tumor MRI images from the Internet brain segmentation repository.


title: Compressing deep neural networks using a rank-constrained topology

text: We present a general approach to reduce the size of feedforward deep neural networks (DNNs). We propose a rankconstrained topology, which factors the weights in the input layer of the DNN in terms of a low-rank representation: unlike previous work, our technique is applied at the level of the filters learned at individual hidden layer nodes, and exploits the natural two-dimensional time-frequency structure in the input. These techniques are applied on a small-footprint DNN-based keyword spotting task, where we find that we can reduce model size by 75% relative to the baseline, without any loss in performance. Furthermore, we find that the proposed approach is more effective at improving model performance compared to other popular dimensionality reduction techniques, when evaluated with a comparable number of parameters.


title: RMDL: Random Multimodel Deep Learning for Classification

text: The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.


title: Initial Beam Association in Millimeter Wave Cellular Systems: Analysis and Design Insights

text: Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. Prior work on coverage and rate of mmWave cellular networks focused on the case when basestations and mobile beamforming vectors are predesigned for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training, which may impact both the SINR coverage and rate of mmWave systems. This paper evaluates mmWave cellular network performance while accounting for the beam training/association overhead. First, a model for the initial beam association is developed based on beam sweeping and downlink control pilot reuse. To incorporate the impact of beam training, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective rate of mmWave cellular networks is derived for two special cases: near-orthogonal pilots and full pilot reuse. Analytical and simulation results provide insights into the answers of two important questions. First, what is the impact of beam association on mmWave network performance? Then, should orthogonal or reused pilots be employed? The results show that unless the employed beams are very wide, initial beam training with full pilot reuse is nearly as good as perfect beam alignment.


title: Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

text: This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.


title: Overview of the Photovoltaic System Topologies

text: — Due to increased interest for solar energy harvesting systems in recent years the number of developed system types is large. In order to choose the optimal one for future system development the analysis of common architectures being used for photovoltaic systems has been done. The paper contains the small description of different converter architectures and analysis of prototyped or simulated systems proposed in the references. Systems of different distribution level are observed from more integrated to more distributed topologies. Distribution level comes hand-to-hand to the overall system efficiency. Less distributed systems in case of low irradiation disturbance show better performance due to higher efficiency that could be achieved relatively easy. More distributed systems have better performance in cases of the frequent partial shading for example in building-integrated photovoltaic systems when there are relatively high objects (like trees, chimneys and other buildings) close to the PV-panel mounting place that can partially shade the PV. But this type of systems have large number of small-power converters that usually has smaller efficiency as the design of effective and small-power converters is hard technical task. The reason of better performance is that distributed PV-systems have larger number of maximum power point tracking converters and therefore increase the harvest of energy by keeping the photovoltaic power generation process optimal. This paper is made for choice of most suitable system topology for future PV-system implementation.


title: Semantic Parsing with Syntax- and Table-Aware SQL Generation

text: We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query wordby-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest questionSQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.


title: Deep Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Imagery

text: A semantic segmentation algorithm must assign a label to every pixel in an image. Recently, semantic segmentation of RGB imagery has advanced significantly due to deep learning. Because creating datasets for semantic segmentation is laborious, these datasets tend to be significantly smaller than object recognition datasets. This makes it difficult to directly train a deep neural network for semantic segmentation, because it will be prone to overfitting. To cope with this, deep learning models typically use convolutional neural networks pre-trained on large-scale image classification datasets, which are then fine-tuned for semantic segmentation. For non-RGB imagery, this is currently not possible because large-scale labeled non-RGB datasets do not exist. In this paper, we developed two deep neural networks for semantic segmentation of multispectral remote sensing imagery. Prior to training on the target dataset, we initialize the networks with large amounts of synthetic multispectral imagery. We show that this significantly improves results on real-world remote sensing imagery, and we establish a new state-of-the-art result on the challenging Hamlin Beach State Park Dataset.


title: ATRIUM: A Radar Target Simulator for Complex Traffic Scenarios

text: Future autonomous automobiles will require extremely reliable radar sensors to guarantee the safety of road users. This requires test environments to verify that automotive radars work properly even in complex traffic situations. Radar target simulators can generate virtual scattering centers in front of the radar sensor and are therefore a promising verification technique. However, currently available systems only offer a very basic approach to manipulating scattering centers. With the workflow presented in this publication it becomes possible to decompose high-level descriptions of traffic scenarios into parameters required for radar target simulation. The workflow can therefore be used to generate realistic traffic scenarios and thus increases the reliability of automotive radar sensor tests.


title: SentiWordNet for Indian Languages

text: The discipline where sentiment/ opinion/ emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found in sentiment lexicon literature deal mostly with English texts. In this article, we propose multiple computational techniques like, WordNet based, dictionary based, corpus based or generative approaches for generating SentiWordNet(s) for Indian languages. Currently, SentiWordNet(s) are being developed for three Indian languages: Bengali, Hindi and Telugu. An online intuitive game has been developed to create and validate the developed SentiWordNet(s) by involving Internet population. A number of automatic, semi-automatic and manual validations and evaluation methodologies have been adopted to measure the coverage and credibility of the developed SentiWordNet(s).


title: Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems

text: An efficient optimization method called ‘Teaching–Learning-Based Optimization (TLBO)’ is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods. 2011 Elsevier Inc. All rights reserved.


title: The Evolution of Accuracy and Bias in Social Judgment

text: Humans are an intensely social species and therefore it is essential for our interpersonal judgments to be valid enough to help us to avoid enemies, form useful alliances and find suitable mates; flawed judgments can literally be fatal. An evolutionary perspective implies that humans ought to have developed sufficient skills at solving problems of interpersonal judgment, including gauging the personalities of others, to be useful for the basic tasks of survival and reproduction. Yet, the view to be derived from the large and influential bias-and-error literature of social psychology is decidedly different—the social mind seems riddled with fundamental design flaws. We will argue in this paper that flawed design is probably the least plausible explanation for the existence of so many errors. We present an evolutionarily-based taxonomy of known bias effects that distinguishes between biases that are trivial or even artifactual and lead virtually nowhere, and those that have interesting implications and deserve further study. Finally, we present an evolutionary perspective that suggests that the ubiquity, automaticity, and success of interpersonal judgment, among other considerations, presents the possibility of a universal Personality Judgment Instinct. Archeological evidence and behavioral patterns observed in extant hunter-gatherer groups indicate that the human species has been intensely social for a long time (e.g., Chagnon, 1983, Tooby & Devore, 1987). Human offspring have a remarkably extended period of juvenile dependency, which both requires and provides the skills for surviving in a complex social world (Hrdy, 1999). Humans evolved language and universal emotional expressions which serve the social purpose of discerning and influencing the thoughts of others (e. and humans will infer social intentions on the basis of minimal cues, as Heider and Simmel (1944) demonstrated in their classic experiment involving chasing triangles and evading circles. Recent work has shown that children above age 4 and adults in disparate cultures (Germans and Amazonian Indians) can categorize intentions—chasing, fighting, following, playing, and courting (for adults)— from no more than the motion patterns of computerized v-shaped arrowheads (Barrett, Todd, Miller, & Blythe, in press). Most notably, humans have a deeply-felt need for social inclusion. Deprivation of social contact produces anxiety, loneliness, and depression (Baumeister & Leary, 1995); indeed, as William James (1890) observed: " Solitary confinement is by many regarded as a mode of torture Accuracy and Bias-3 too cruel and unnatural for civilised countries to adopt. " Participants in laboratory studies who are left out of a face-to-face …


title: Pre-Prosthetic Orthodontic Implant for Management of Congenitally Unerupted Lateral Incisors – A Case Report

text: The maxillary lateral incisor is one of the most common congenitally missing teeth of the permanent dentition. With the advent of implants in the field of restorative dentistry, a stable and predictable fixed prosthetic replacement has become a reality, especially for young adult patients who suffer from congenital absence of teeth. The dual goals of establishment of functional stability as well as enhancement of esthetic outcomes are made achievable by the placement of implants. A multidisciplinary team approach involving the triad of orthodontist, periodontist and restorative dentist will ensure the successful completion of the integrated treatment approach in these patients. The present case report achieved successful implant based oral rehabilitation in a patient diagnosed with congenital absence of bilateral maxillary lateral incisors utilizing a preprosthetic orthodontic implant site preparation for the purpose of space gain.


title: Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

text:
Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results.
 Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders of magnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results.
 As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial reduction in the space and running time. In our experiments, our technique was faster: (i) than distance browsing (a well-known method for solving the problem exactly) by several orders of magnitude, and (ii) than D-shift (an approximate approach with theoretical guarantees in low-dimensional space) by one order of magnitude, and at the same time, outputs better results.


title: Adversarial Examples for Generative Models

text: We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.


title: Exploring hacker assets in underground forums

text: Many large companies today face the risk of data breaches via malicious software, compromising their business. These types of attacks are usually executed using hacker assets. Researching hacker assets within underground communities can help identify the tools which may be used in a cyberattack, provide knowledge on how to implement and use such assets and assist in organizing tools in a manner conducive to ethical reuse and education. This study aims to understand the functions and characteristics of assets in hacker forums by applying classification and topic modeling techniques. This research contributes to hacker literature by gaining a deeper understanding of hacker assets in well-known forums and organizing them in a fashion conducive to educational reuse. Additionally, companies can apply our framework to forums of their choosing to extract their assets and appropriate functions.


title: Analysis of circulating tumor DNA to monitor metastatic breast cancer.

text:
BACKGROUND
The management of metastatic breast cancer requires monitoring of the tumor burden to determine the response to treatment, and improved biomarkers are needed. Biomarkers such as cancer antigen 15-3 (CA 15-3) and circulating tumor cells have been widely studied. However, circulating cell-free DNA carrying tumor-specific alterations (circulating tumor DNA) has not been extensively investigated or compared with other circulating biomarkers in breast cancer.

METHODS
We compared the radiographic imaging of tumors with the assay of circulating tumor DNA, CA 15-3, and circulating tumor cells in 30 women with metastatic breast cancer who were receiving systemic therapy. We used targeted or whole-genome sequencing to identify somatic genomic alterations and designed personalized assays to quantify circulating tumor DNA in serially collected plasma specimens. CA 15-3 levels and numbers of circulating tumor cells were measured at identical time points.

RESULTS
Circulating tumor DNA was successfully detected in 29 of the 30 women (97%) in whom somatic genomic alterations were identified; CA 15-3 and circulating tumor cells were detected in 21 of 27 women (78%) and 26 of 30 women (87%), respectively. Circulating tumor DNA levels showed a greater dynamic range, and greater correlation with changes in tumor burden, than did CA 15-3 or circulating tumor cells. Among the measures tested, circulating tumor DNA provided the earliest measure of treatment response in 10 of 19 women (53%).

CONCLUSIONS
This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.).


title: Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation

text: This paper addresses the problem of weakly supervised semantic image segmentation. Our goal is to label every pixel in a new image, given only image-level object labels associated with training images. Our problem statement differs from common semantic segmentation, where pixel-wise annotations are typically assumed available in training. We specify a novel deep architecture which fuses three distinct computation processes toward semantic segmentation – namely, (i) the bottom-up computation of neural activations in a CNN for the image-level prediction of object classes, (ii) the top-down estimation of conditional likelihoods of the CNNs activations given the predicted objects, resulting in probabilistic attention maps per object class, and (iii) the lateral attention-message passing from neighboring neurons at the same CNN layer. The fusion of (i)-(iii) is realized via a conditional random field as recurrent network aimed at generating a smooth and boundary-preserving segmentation. Unlike existing work, we formulate a unified end-to-end learning of all components of our deep architecture. Evaluation on the benchmark PASCAL VOC 2012 dataset demonstrates that we outperform reasonable weakly supervised baselines and state-of-the-art approaches.


title: Skipping Refinement

text: We introduce skipping refinement, a new notion of correctness for reasoning about optimized reactive systems. Reasoning about reactive systems using refinement involves defining an abstract, highlevel specification system and a concrete, low-level implementation system. One then shows that every behavior allowed by the implementation is also allowed by the specification. Due to the difference in abstraction levels, it is often the case that the implementation requires many steps to match one step of the specification, hence, it is quite useful for refinement to directly account for stuttering. Some optimized implementations, however, can actually take multiple specification steps at once. For example, a memory controller can buffer the commands to the memory and at a later time simultaneously update multiple memory locations, thereby skipping several observable states of the abstract specification, which only updates one memory location at a time. We introduce skipping simulation refinement and provide a sound and complete characterization consisting of “local” proof rules that are amenable to mechanization and automated verification. We present case studies that highlight the applicability of skipping refinement: a JVM-inspired stack machine, a simple memory controller and a scalar to vector compiler transformation. Our experimental results demonstrate that current model-checking and automated theorem proving tools have difficultly automatically analyzing these systems using existing notions of correctness, but they can analyze the systems if we use skipping refinement.


title: Fossil chrysophycean cyst flora of Racze Lake, Wolin Island (Poland) in relation to paleoenvironmental conditions

text: The study presents stratigraphic distribution of fossil chrysophycean cysts in the bottom sediments of Racze Lake (Poland). Thirty morphotypes are described, most of them for the first time. The description of the cyst includes SEM microphotographs. The long-term relationship between the lake's conditions and the occurrence of characteristic morphotypes of chrysophycean cysts is discussed.


title: Design and implementation of a 120A resonant inverter for induction furnace

text: This paper presents the design and implementation of a series resonant inverter operating at high frequency for induction heating applications. The main advantage of this work is focused in applying power electronics, particularly a series resonant inverter based on an IGBT's full bridge and a series RLC Load matched with a high frequency coupling transformer. The series resonant inverter is designed to deliver a current load up to 120A with a one phase AC source. Due to the inherent high speed operation and high efficiency of switching converters, other advantages of the induction furnace presented in this paper are that it require less time to start melting, and require less physical space than other traditional furnaces.