title: A Framework for Clustering Uncertain Data Streams

text: In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.


title: Weakly Supervised Affordance Detection

text: Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convolutional neural network for multilabel affordance segmentation. We also propose an approach to train the network from very few keypoint annotations. Our approach achieves a higher affordance detection accuracy than other weakly supervised methods that also rely on keypoint annotations or image annotations as weak supervision.


title: A Framework for Evaluating Mobile App Repackaging Detection Algorithms

text: Because it is not hard to reverse engineer the Dalvik bytecode used in the Dalvik virtual machine, Android application repackaging has become a serious problem. With repackaging, a plagiarist can simply steal others’ code violating the intellectual property of the developers. More seriously, after repackaging, popular apps can become the carriers of malware, adware or spy-ware for wide spreading. To maintain a healthy app market, several detection algorithms have been proposed recently, which can catch some types of repackaged apps in various markets efficiently. However, they are generally lack of valid analysis on their effectiveness. After analyzing these approaches, we find simple obfuscation techniques can potentially cause false negatives, because they change the main characteristics or features of the apps that are used for similarity detections. In practice, more sophisticated obfuscation techniques can be adopted (or have already been performed) in the context of mobile apps. We envision this obfuscation based repackaging will become a phenomenon due to the arms race between repackaging and its detection. To this end, we propose a framework to evaluate the obfuscation resilience of repackaging detection algorithms comprehensively. Our evaluation framework is able to perform a set of obfuscation algorithms in various forms on the Dalvik bytecode. Our results provide insights to help gauge both broadness and depth of algorithms’ obfuscation resilience. We applied our framework to conduct a comprehensive case study on AndroGuard, an Android repackaging detector proposed in Black-hat 2011. Our experimental results have demonstrated the effectiveness and stability of our framework.


title: Mining Personal Data Using Smartphones and Wearable Devices: A Survey

text: The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.


title: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

text: The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.


title: Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge.

text: Functional magnetic resonance imaging (fMRI) of the human brain was used to study whether the amygdala is activated in response to emotional stimuli, even in the absence of explicit knowledge that such stimuli were presented. Pictures of human faces bearing fearful or happy expressions were presented to 10 normal, healthy subjects by using a backward masking procedure that resulted in 8 of 10 subjects reporting that they had not seen these facial expressions. The backward masking procedure consisted of 33 msec presentations of fearful or happy facial expressions, their offset coincident with the onset of 167 msec presentations of neutral facial expressions. Although subjects reported seeing only neutral faces, blood oxygen level-dependent (BOLD) fMRI signal in the amygdala was significantly higher during viewing of masked fearful faces than during the viewing of masked happy faces. This difference was composed of significant signal increases in the amygdala to masked fearful faces as well as significant signal decreases to masked happy faces, consistent with the notion that the level of amygdala activation is affected differentially by the emotional valence of external stimuli. In addition, these facial expressions activated the sublenticular substantia innominata (SI), where signal increases were observed to both fearful and happy faces--suggesting a spatial dissociation of territories that respond to emotional valence versus salience or arousal value. This study, using fMRI in conjunction with masked stimulus presentations, represents an initial step toward determining the role of the amygdala in nonconscious processing.


title: A cognitive developmental approach to morality: investigating the psychopath

text: Various social animal species have been noted to inhibit aggressive attacks when a conspecific displays submission cues. Blair (1993) has suggested that humans possess a functionally similar mechanism which mediates the suppression of aggression in the context of distress cues. He has suggested that this mechanism is a prerequisite for the development of the moral/conventional distinction; the consistently observed distinction in subject's judgments between moral and conventional transgressions. Psychopaths may lack this violence inhibitor. A causal model is developed showing how the lack of this mechanism would explain the core behavioural symptoms associated with the psychopathic disorder. A prediction of such a causal model would be that psychopaths should fail to make the moral/conventional distinction. This prediction was confirmed. The implication of this finding for other theories of morality is discussed.


title: Fears, phobias, and preparedness: toward an evolved module of fear and fear learning.

text: An evolved module for fear elicitation and fear learning with 4 characteristics is proposed. (a) The fear module is preferentially activated in aversive contexts by stimuli that are fear relevant in an evolutionary perspective. (b) Its activation to such stimuli is automatic. (c) It is relatively impenetrable to cognitive control. (d) It originates in a dedicated neural circuitry, centered on the amygdala. Evidence supporting these propositions is reviewed from conditioning studies, both in humans and in monkeys; illusory correlation studies; studies using unreportable stimuli; and studies from animal neuroscience. The fear module is assumed to mediate an emotional level of fear learning that is relatively independent and dissociable from cognitive learning of stimulus relationships.


title: Approach and avoidance strength during goal attainment: regulatory focus and the "goal looms larger" effect.

text: Three studies tested the hypothesis that regulatory focus, both as a chronic person variable and as a manipulated situational variable, differentially affects the strength of participants' approach and avoidance strategic motivations as they get closer to the goal. In 2 studies, flexion and extension arm pressure were used as on-line measures of approach and avoidance intensity, respectively. As predicted, the approach gradient was steeper for participants with a promotion focus on aspirations and gains than for participants with a prevention focus on responsibilities and nonlosses, whereas the reverse was true for the avoidance gradient. In a third study, the same pattern of results was found on a persistence measure of motivational strength. Participants with a promotion focus worked longer on anagrams closer to the goal when they were approach means for goal attainment than when they were avoidance means, whereas the reverse was true for participants with a prevention focus.


title: Emotion in the criminal psychopath: startle reflex modulation.

text: Startle-elicited blinks were measured during presentation of affective slides to test hypotheses concerning emotional responding in psychopaths. Subjects were 54 incarcerated sexual offenders divided into nonpsychopathic, psychopathic, and mixed groups based on file and interview data. Consistent with findings for normal college students, nonpsychopaths and mixed subjects showed a significant linear relationship between slide valence and startle magnitude, with startle responses largest during unpleasant slides and smallest during pleasant slides. This effect was absent in psychopaths. Group differences in startle modulation were related to affective features of psychopathy, but not to antisocial behavior per se. Psychopathy had no effect on autonomic or self-report responses to slides. These results suggest an abnormality in the processing of emotional stimuli by psychopaths that manifests itself independently of affective report.


title: Cynefin : Uncertainty , Small Worlds and Scenarios

text: Uncertainty, its modelling and analysis have been discussed across many literatures including statistics and operational research, knowledge management and philosophy. Adherents to Bayesian approaches have usually argued that uncertainty should either be modelled by probabilities or resolved by discussion which clarifies meaning. Others have followed Knight in distinguishing between contexts of risk and of uncertainty: the former admitting modelling and analysis through probability; the latter not. There are also host of approaches in the literatures stemming from Zadeh’s concept of a fuzzy set. Theories of sense-making in the philosophy and management literatures see knowledge and uncertainty as opposite extremes of human understanding and discuss the resolution of uncertainty accordingly. Here we take a Bayesian stance, but a softer one than the conventional, which recognises the concerns in other approaches and, in particular, sets their concerns in the Cynefin framework of decision contexts to reflect on processes of modelling and analysis in statistical, risk and decision analysis. The approach builds on several recent strands of discussion which argue for a convergence of qualitative scenario planning ideas and more quantitative approaches to analysis I discuss how these suggestions and discussions relate to some earlier thinking on the methodology of modelling and, in particular, the concept of a ‘small world’ articulated by Savage.


title: Social Media as It Interfaces with Psychosocial Development and Mental Illness in Transitional Age Youth.

text: For transitional age individuals, social media (SM) is an integral component of connecting with others. There are 2 billion SM users worldwide. SM users may experience an increase in perceived social support and life satisfaction. Use of SM may facilitate forming connections among people with potentially stigmatizing mental disorders. However, epidemiologic studies suggest that increased SM use is associated with conditions such as depression, anxiety, and sleep disturbance. Future research should examine directionality of these associations and the role of contextual factors. It also will be useful to leverage SM to provide mental health care and surveillance of mental health concerns.


title: A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment.

text: Veterinary antibiotics (VAs) are widely used in many countries worldwide to treat disease and protect the health of animals. They are also incorporated into animal feed to improve growth rate and feed efficiency. As antibiotics are poorly adsorbed in the gut of the animals, the majority is excreted unchanged in faeces and urine. Given that land application of animal waste as a supplement to fertilizer is often a common practice in many countries, there is a growing international concern about the potential impact of antibiotic residues on the environment. Frequent use of antibiotics has also raised concerns about increased antibiotic resistance of microorganisms. We have attempted in this paper to summarize the latest information available in the literature on the use, sales, exposure pathways, environmental occurrence, fate and effects of veterinary antibiotics in animal agriculture. The review has focused on four important groups of antibiotics (tylosin, tetracycline, sulfonamides and, to a lesser extent, bacitracin) giving a background on their chemical nature, fate processes, occurrence, and effects on plants, soil organisms and bacterial community. Recognising the importance and the growing debate, the issue of antibiotic resistance due to the frequent use of antibiotics in food-producing animals is also briefly covered. The final section highlights some unresolved questions and presents a way forward on issues requiring urgent attention.


title: Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)

text: Supervised Machine Learning (SML) algorithms such as Gradient Boosting, Random Forest, and Neural Networks have become popular in recent years due to their increased predictive performance over traditional statistical methods. This is especially true with large data sets (millions or more observations and hundreds to thousands of predictors). However, the complexity of the SML models makes them opaque and hard to interpret without additional tools. There has been a lot of interest recently in developing global and local diagnostics for interpreting and explaining SML models. In this paper, we propose locally interpretable models and effects based on supervised partitioning (trees) referred to as LIME-SUP. This is in contrast with the KLIME approach that is based on clustering the predictor space. We describe LIME-SUP based on fitting trees to the fitted response (LIM-SUP-R) as well as the derivatives of the fitted response (LIME-SUP-D). We compare the results with KLIME and describe its advantages using simulation and real data.


title: A Review of the Design Issues and Techniques for Radial-Flux Brushless Surface and Internal Rare-Earth Permanent-Magnet Motors

text: This paper reviews many design issues and analysis techniques for the brushless permanent-magnet machine. It reviews the basic requirements for the use of both ac and dc machines and issues concerning the selection of pole number, winding layout, rotor topology, drive strategy, field weakening, and cooling. These are key issues in the design of a motor. Leading-edge design techniques are illustrated. This paper is aimed as a tutor for motor designers who may be unfamiliar with this particular type of machine.


title: The Most Dangerous Code in the Browser

text: Browser extensions are ubiquitous. Yet, in today’s browsers, extensions are the most dangerous code to user privacy. Extensions are third-party code, like web applications, but run with elevated privileges. Even worse, existing browser extension systems give users a false sense of security by considering extensions to be more trustworthy than web applications. This is because the user typically has to explicitly grant the extension a series of permissions it requests, e.g., to access the current tab or a particular website. Unfortunately, extensions developers do not request minimum privileges and users have become desensitized to install-time warnings. Furthermore, permissions offered by popular browsers are very broad and vague. For example, over 71% of the top-500 Chrome extensions can trivially leak the user’s data from any site. In this paper, we argue for new extension system design, based on mandatory access control, that protects the user’s privacy from malicious extensions. A system employing this design can enable a range of common extensions to be considered safe, i.e., they do not require user permissions and can be ensured to not leak information, while allowing the user to share information when desired. Importantly, such a design can make permission requests a rarity and thus more meaningful.


title: Modeling Survival Data: Extending the Cox Model

text: Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the modeling survival data extending the cox model book. You can open the device and get the book by on-line.


title: DjiNN and Tonic: DNN as a service and its implications for future warehouse scale computers

text:
As applications such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, web-service companies are adopting large deep neural networks (DNN) for machine learning challenges such as image processing, speech recognition, natural language processing, among others. A number of open questions arise as to the design of a server platform specialized for DNN and how modern warehouse scale computers (WSCs) should be outfitted to provide DNN as a service for these applications.
 In this paper, we present DjiNN, an open infrastructure for DNN as a service in WSCs, and Tonic Suite, a suite of 7 end-to-end applications that span image, speech, and language processing. We use DjiNN to design a high throughput DNN system based on massive GPU server designs and provide insights as to the varying characteristics across applications. After studying the throughput, bandwidth, and power properties of DjiNN and Tonic Suite, we investigate several design points for future WSC architectures. We investigate the total cost of ownership implications of having a WSC with a disaggregated GPU pool versus a WSC composed of homogeneous integrated GPU servers. We improve DNN throughput by over 120x for all but one application (40x for Facial Recognition) on an NVIDIA K40 GPU. On a GPU server composed of 8 NVIDIA K40s, we achieve near-linear scaling (around 1000x throughput improvement) for 3 of the 7 applications. Through our analysis, we also find that GPU-enabled WSCs improve total cost of ownership over CPU-only designs by 4-20x, depending on the composition of the workload


title: Design of cavity-backed spiral antennas

text: In this paper, the design procedure of the 2-18 GHz and the 18-40 GHz cavity-backed spiral antenna is described. By varying the antenna parameters the VSWR and the axial ratio characteristics of the antenna are investigated. For the 2-18 GHz spiral antenna, the solid absorber and the Marchand balun are applied. The measured results of the fabricated antenna are compared with the simulated ones. For the 18-40 GHz antenna, the honeycomb absorber having variable loss tangent is considered in simulation to investigate for improvement of the VSWR and the axial ratio characteristics.


title: Technology and Education : Computers , Software , and the Internet

text: Technology and Education: Computers, Software, and the Internet* A substantial amount of money is spent on technology by schools, families and policymakers with the hope of improving educational outcomes. This chapter explores the theoretical and empirical literature on the impacts of technology on educational outcomes. The literature focuses on two primary contexts in which technology may be used for educational purposes: i) classroom use in schools, and ii) home use by students. Theoretically, ICT investment and CAI use by schools and the use of computers at home have ambiguous implications for educational achievement: expenditures devoted to technology necessarily offset inputs that may be more or less efficient, and time allocated to using technology may displace traditional classroom instruction and educational activities at home. However, much of the evidence in the schooling literature is based on interventions that provide supplemental funding for technology or additional class time, and thus favor finding positive effects. Nonetheless, studies of ICT and CAI in schools produce mixed evidence with a pattern of null results. Notable exceptions to this pattern occur in studies of developing countries and CAI interventions that target math rather than language. In the context of home use, early studies based on multivariate and instrumental variables approaches tend to find large positive (and in a few cases negative) effects while recent studies based on randomized control experiments tend to find small or null effects. Early research focused on developed countries while more recently several experiments have been conducted in developing countries. JEL Classification: I2


title: Improved CCG Parsing with Semi-supervised Supertagging

text: Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings enables our model to better generalize from the labelled data, and allows us to accurately assign lexical categories without depending on a POS-tagger. Our approach leads to substantial improvements in dependency parsing results over the standard supervised CCG parser when evaluated on Wall Street Journal (0.8%), Wikipedia (1.8%) and biomedical (3.4%) text. We compare the performance of two recently proposed approaches for classification using a wide variety of word embeddings. We also give a detailed error analysis demonstrating where using embeddings outperforms traditional feature sets, and showing how including POS features can decrease accuracy.


title: What do students want?: towards an instrument for students' evaluation of quality of learning analytics services

text: Quality assurance in any organization is important for ensuring that service users are satisfied with the service offered. For higher education institutes, the use of service quality measures allows for ideological gaps to be both identified and resolved. The learning analytic community, however, has rarely addressed the concept of service quality. A potential outcome of this is the provision of a learning analytics service that only meets the expectations of certain stakeholders (e.g., managers), whilst overlooking those who are most important (e.g., students). In order to resolve this issue, we outline a framework and our current progress towards developing a scale to assess student expectations and perceptions of learning analytics as a service.


title: An improved one-time password authentication scheme

text: The rapid development of the Internet facilitates our lives in many aspects. More and more business will be done through Internet. Under such circumstances, enough attention must be given to the information security, of which the identity authentication is one important problem. In the traditional authentication scheme, the user provides the username and static password to service provider, but there are some inherent shortcomings of this method-static passwords maybe guessed, forgotten, and eavesdropped. One-Time Password (OTP) is considered as the strongest authentication scheme among all password-based solutions. In this paper, a novel two-factor authentication scheme based OTP is proposed. The scheme not only satisfies the mutual authentication between the user and service provider, but also presents higher security and lower computational cost than traditional schemes based OTP.


title: Mometasone furoate in the treatment of vulvar lichen sclerosus: could its formulation influence efficacy, tolerability and adherence to treatment?

text:
PURPOSE
To assess the effectiveness, tolerability, and convenience of the cream formulation of mometasone furoate 0.1% (MMF) in the treatment of active vulvar lichen sclerosus (VLS) and to compare the cream with the ointment formulation.

METHODS
The following efficacy parameters were assessed in 27 VLS patients treated with MMF cream for 12 weeks (group A): (i) response rate, (ii) percentage of patients achieving an improvement from baseline of ≥75% in subjective and objective scores, and (iii) mean reduction in subjective and objective scores. These efficacy assessments, as well as those regarding safety and adherence, were compared with the assessments recorded among 37 VLS patients treated with MMF ointment (group B).

RESULTS
59.3% (group A) and 78.4% (group B) of patients were considered responders; 44.4% and 40.7% of patients in group A and 54.1% and 45.9% in group B achieved an improvement of at least 75% in subjective and objective scores, respectively. MMF ointment obtained a significantly higher improvement in symptom scores in comparison with the cream formulation.

CONCLUSIONS
MMF in ointment formulation seems to be more effective in treating active VLS in comparison with MMF cream. Both formulations are well tolerated and there is no difference in patient adherence and satisfaction.


title: Genotyping by Sequencing for SNP-Based Linkage Analysis and Identification of QTLs Linked to Fruit Quality Traits in Japanese Plum (Prunus salicina Lindl.)

text: Marker-assisted selection (MAS) in stone fruit (Prunus species) breeding is currently difficult to achieve due to the polygenic nature of the most relevant agronomic traits linked to fruit quality. Genotyping by sequencing (GBS), however, provides a large quantity of useful data suitable for fine mapping using Single Nucleotide Polymorphisms (SNPs) from a reference genome. In this study, GBS was used to genotype 272 seedlings of three F1 Japanese plum (Prunus salicina Lindl) progenies derived from crossing "98-99" (as a common female parent) with "Angeleno," "September King," and "September Queen" as male parents. Raw sequences were aligned to the Peach genome v1, and 42,909 filtered SNPs were obtained after sequence alignment. In addition, 153 seedlings from the "98-99" × "Angeleno" cross were used to develop a genetic map for each parent. A total of 981 SNPs were mapped (479 for "98-99" and 502 for "Angeleno"), covering a genetic distance of 688.8 and 647.03 cM, respectively. Fifty five seedlings from this progeny were phenotyped for different fruit quality traits including ripening time, fruit weight, fruit shape, chlorophyll index, skin color, flesh color, over color, firmness, and soluble solids content in the years 2015 and 2016. Linkage-based QTL analysis allowed the identification of genomic regions significantly associated with ripening time (LG4 of both parents and both phenotyping years), fruit skin color (LG3 and LG4 of both parents and both years), chlorophyll degradation index (LG3 of both parents in 2015) and fruit weight (LG7 of both parents in 2016). These results represent a promising situation for GBS in the identification of SNP variants associated to fruit quality traits, potentially applicable in breeding programs through MAS, in a highly heterozygous crop species such as Japanese plum.


title: Distributed Representation-based Recommender Systems in E-commerce

text: Recommender system plays an important role in many e-commerce services, such as in Rakuten. In this paper, we focus on the item-to-item recommender and the user-to-item recommenders, which are two most widely used functions in online services for presenting relevant items given an item, or a particular user. We use a large amount of log data from one of Rakuten markets, and apply distributed representation method to that data for developing two types of recommender systems. The key idea of our approach is treating items as words, and users’ sessions as sentences, then training the Word2vec model and Doc2vec models based on those items and user’s information. Resulting item vectors from the Word2vec model can be used to calculate the cosine similarity between items, and find the similar items given an item. Similarly, Doc2vec model helps users find relevant items that might interest them using similarity between items and vectors. We also use the item vectors from both embedding models to build an additional user-to-item recommender, namely Item Vector-based system. The experiments show that our best system achieved a hit-rate of 24.17% for recommending items to users in testing data, which outperformed conventional approaches to a significant extent. Keyword Recommender System,Distributed Representation,Item Vector-based


title: Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters

text: We consider assortment optimization problems under the multinomial logit model, where the parameters of the choice model are random. The randomness in the choice model parameters is motivated by the fact that there are multiple customer segments, each with different preferences for the products, and the segment of each customer is unknown to the firm when the customer makes a purchase. This choice model is also called the mixture-of-logits model. The goal of the firm is to choose an assortment of products to offer that maximizes the expected revenue per customer, across all customer segments. We establish that the problem is NP-complete even when there are just two customer segments. Motivated by this complexity result, we focus on assortments consisting of products with the highest revenues, which we refer to as revenue-ordered assortments. We identify specially structured cases of the problem where revenue-ordered assortments are optimal. When the randomness in the choice model parameters does not follow a special structure, we derive tight approximation guarantees for revenue-ordered assortments. We extend our model to the multi-period capacity allocation problem, and prove that, when restricted to the revenue-ordered assortments, the mixture-of-logits model possesses the nesting-by-fare-order property. This result implies that revenue-ordered assortments can be incorporated into existing revenue management systems through nested protection levels. Numerical experiments show that revenue-ordered assortments perform remarkably well, generally yielding profits that are within a fraction of a percent of the optimal.


title: A location-based smart shopping system with IoT technology

text: Localization is one important part of Internet of Things(IoT) where the Location of Everything (LoE) system plays a important role to improve most services in IoT area. On the other hand, data mining techniques are essential analyses when we have big data from IoT platforms. Indeed, integration of location-based methods and data mining analysis process can make a smart system service for IoT scenarios and applications. For this purpose, we design a smart shopping platform including four components, location of everything component, data collection component, data filtering/analysing component and data mining component. Then a novel accurate localization scheme named “location orbital” is developed that estimates the current location of mobile objects (users or everything) based on both current and the previous locations. Finally, an implementation of the experiment in a shopping mall is conducted to practically examine performance evaluation of the location-based scheme. The experimental results show that the proposed scheme could achieve significant higher precision than other localization techniques.


title: Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

text:
Background
Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy.

Objective
Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style.

Method
We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined.

Result
Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010).

Conclusions
The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.


title: Adverse health effects of non-medical cannabis use

text: For over two decades, cannabis, commonly known as marijuana, has been the most widely used illicit drug by young people in high-income countries, and has recently become popular on a global scale. Epidemiological research during the past 10 years suggests that regular use of cannabis during adolescence and into adulthood can have adverse effects. Epidemiological, clinical, and laboratory studies have established an association between cannabis use and adverse outcomes. We focus on adverse health effects of greatest potential public health interest-that is, those that are most likely to occur and to affect a large number of cannabis users. The most probable adverse effects include a dependence syndrome, increased risk of motor vehicle crashes, impaired respiratory function, cardiovascular disease, and adverse effects of regular use on adolescent psychosocial development and mental health.


title: Enhancement of Sentence-Generation Based Summarization Method By Modelling Inter-Sentential Consequent-Relationships

text: In the text summarization field, based on how to create the summary, there are two main theoretic approaches (E. Lloret [22], K. Jezek and J. Steinberger [13]): the trend based on "extraction" in which the most important sentences in the source text will be chosen; the trend based on "abstraction" in which a new reduced grammatical and meaningful text will be created based on understanding the original text. In this paper, we focus on "abstraction" approach to summarize Vietnamese texts having only two sentences. We also restrict this research to consider verbs representing "actions" and "states", which appear in both two sentences of source text. Our method is based on modeling and processing Consequent-Relations between the verbs in the first sentence and the second sentence. The aim of this research is to generate new Vietnamese meaning-summarizing sentences which satisfy two requirements: (i) summarize the true meaning of the source pair of sentences; (ii) have natural expression for summarization. We also experiment and assess our approach by proposing a new quantitative evaluation method that suitable for the research approach.


title: Understanding Hand-Object Manipulation with Grasp Types and Object Attributes

text: Our goal is to automate the understanding of natural hand-object manipulation by developing computer visionbased techniques. Our hypothesis is that it is necessary to model the grasp types of hands and the attributes of manipulated objects in order to accurately recognize manipulation actions. Specifically, we focus on recognizing hand grasp types, object attributes and actions from a single image within an unified model. First, we explore the contextual relationship between grasp types and object attributes, and show how that context can be used to boost the recognition of both grasp types and object attributes. Second, we propose to model actions with grasp types and object attributes based on the hypothesis that grasp types and object attributes contain complementary information for characterizing different actions. Our proposed action model outperforms traditional appearance-based models which are not designed to take into account semantic constraints such as grasp types or object attributes. Experiment results on public egocentric activities datasets strongly support our hypothesis.


title: A Survey of Schema Matching Research using Database Schemas and Instances

text: Schema matching is considered as one of the essential phases of data integration in database systems. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. Many approaches based on instances have been proposed aiming at improving the accuracy of the matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. We survey and analyze the schema matching techniques applied in the literature by highlighting the strengths and the weaknesses of each technique. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in contemporary


title: Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm

text: Exoskeleton robots are promising assistive/rehabilitative devices that can help people with force deficits or allow the recovery of patients who have suffered from pathologies such as stroke. The key component that allows the user to control the exoskeleton is the human machine interface (HMI). Setting the HMI at the neuro-muscular level may lead to seamless integration and intuitive control of the exoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a model of the human muscle, the "myoprocessor," running in real-time and in parallel to the physiological muscle, that predicts joint torques as a function of the joint kinematics and neural activation levels. This paper presents the development of myoprocessors for the upper limb based on the Hill phenomenological muscle model. Genetic algorithms are used to optimize the internal parameters of the myoprocessors utilizing an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufficiently robust for further integration into the exoskeleton control system


title: Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience.

text: For over 25 years, personal assistant robots for severely disabled individuals have been in development. More recently, using robots to deliver rehabilitation therapy has been proposed. This paper summarizes the development and clinical testing of three mechatronic systems for post-stroke therapy conducted at the VA Palo Alto in collaboration with Stanford University. We describe the philosophy and experiences that guided their evolution. Unique to the Palo Alto approach is provision for bimanual, mirror-image, patient-controlled therapeutic exercise. Proof-of-concept was established with a 2-degree-of-freedom (DOF) elbow/forearm manipulator. Tests of a second-generation therapy robot producing planar forearm movements in 19 hemiplegic and control subjects confirmed the validity and reliability of interaction forces during mechanically assisted upper-limb movements. Clinical trials comparing 3-D robot-assisted therapy to traditional therapy in 21 chronic stroke subjects showed significant improvement in the Fugl-Meyer (FM) measure of motor recovery in the robot group, which exceeded improvements in the control group.


title: Robot-aided neurorehabilitation of the upper extremities

text: Task-oriented repetitive movements can improve muscle strength and movement co-ordination in patients with impairments due to neurological lesions. The application of robotics and automation technology can serve to assist, enhance, evaluate and document the rehabilitation of movements. The paper provides an overview of existing devices that can support movement therapy of the upper extremities in subjects with neurological pathologies. The devices are critically compared with respect to technical function, clinical applicability, and, if they exist, clinical outcomes.


title: A myosignal-based powered exoskeleton system

text: Integrating humans and robotic machines into one system offers multiple opportunities for creating assistive technologies that can be used in biomedical, industrial, and aerospace applications. The scope of the present research is to study the integration of a human arm with a powered exoskeleton (orthotic device) and its experimental implementation in an elbow joint, naturally controlled by the human. The Human–Machine interface was set at the neuromuscular level, by using the neuromuscular signal (EMG) as the primary command signal for the exoskeleton system. The EMG signal along with the joint kinematics were fed into a myoprocessor (Hill-based muscle model) which in turn predicted the muscle moments on the elbow joint. The moment-based control system integrated myoprocessor moment prediction with feedback moments measured at the human arm/exoskeleton and external load/exoskeleton interfaces. The exoskeleton structure under study was a two-link, two-joint mechanism, corresponding to the arm limbs and joints, which was mechanically linked (worn) by the human operator. In the present setup the shoulder joint was kept fixed at given positions and the actuator was mounted on the exoskeleton elbow joint. The operator manipulated an external weight, located at the exoskeleton tip, while feeling a scaled-down version of the load. The remaining external load on the joint was carried by the exoskeleton actuator. Four indices of performance were used to define the quality of the human/machine integration and to evaluate the operational envelope of the system. Experimental tests have shown that synthesizing the processed EMG signals as command signals with the external-load/human-arm moment feedback, significantly improved the mechanical gain of the system, while maintaining natural human control of the system, relative to other control algorithms that used only position or contact forces. The results indicated the feasibility of an EMG-based power exoskeleton system as an integrated human–machine system using highlevel neurological signals.


title: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.

text: This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.


title: High Performance Emulation of Quantum Circuits

text: As quantum computers of non-trivial size become available in the near future, it is imperative to develop tools to emulate small quantum computers. This allows for validation and debugging of algorithms as well as exploring hardware-software co-design to guide the development of quantum hardware and architectures. The simulation of quantum computers entails multiplications of sparse matrices with very large dense vectors of dimension 2n, where n denotes the number of qubits, making this a memory-bound and network bandwidth-limited application. We introduce the concept of a quantum computer emulator as a component of a software framework for quantum computing, enabling a significant performance advantage over simulators by emulating quantum algorithms at a high level rather than simulating individual gate operations. We describe various optimization approaches and present benchmarking results, establishing the superiority of quantum computer emulators in terms of performance.


title: Fire flame detection in video sequences using multi-stage pattern recognition techniques

text: In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempospatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate. & 2012 Elsevier Ltd. All rights reserved.


title: Prediction of click frauds in mobile advertising

text: Click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. This paper proposes a novel framework for prediction of click fraud in mobile advertising which consists of feature selection using Recursive Feature Elimination (RFE) and classification through Hellinger Distance Decision Tree (HDDT).RFE is chosen for the feature selection as it has given better results as compared to wrapper approach when evaluated using different classifiers. HDDT is also selected as classifier to deal with class imbalance issue present in the data set. The efficiency of proposed framework is investigated on the data set provided by Buzzcity and compared with J48, Rep Tree, logitboost, and random forest. Results show that accuracy achieved by proposed framework is 64.07 % which is best as compared to existing methods under study.


title: SlowFast Networks for Video Recognition

text: We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report 79.0% accuracy on the Kinetics dataset without using any pre-training, largely surpassing the previous best results of this kind. On AVA action detection we achieve a new stateof-the-art of 28.3 mAP. Code will be made publicly available.


title: Reducing the risk of query expansion via robust constrained optimization

text: We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a robust constrained optimization problem. This framework provides a powerful method for modeling and solving complex expansion problems, by allowing multiple sources of domain knowledge or evidence to be encoded as simultaneous optimization constraints. Our robust optimization approach provides a clean theoretical way to model not only expansion benefit, but also expansion risk, by optimizing over uncertainty sets for the data. In addition, we introduce risk-reward curves to visualize expansion algorithm performance and analyze parameter sensitivity. We show that a robust approach significantly reduces the number and magnitude of expansion failures for a strong baseline algorithm, with no loss in average gain. Our approach is implemented as a highly efficient post-processing step that assumes little about the baseline expansion method used as input, making it easy to apply to existing expansion methods. We provide analysis showing that this approach is a natural and effective way to do selective expansion, automatically reducing or avoiding expansion in risky scenarios, and successfully attenuating noise in poor baseline methods.


title: Reusing Scientific Data: How Earthquake Engineering Researchers Assess the Reusability of Colleagues’ Data

text: Investments in cyberinfrastructure and e-Science initiatives are motivated by the desire to accelerate scientific discovery. Always viewed as a foundation of science, data sharing is appropriately seen as critical to the success of such initiatives, but new technologies supporting increasingly data-intensive and collaborative science raise significant challenges and opportunities. Overcoming the technical and social challenges to broader data sharing is a common and important research objective, but increasing the supply and accessibility of scientific data is no guarantee data will be applied by scientists. Before reusing data created by others, scientists need to assess the data’s relevance, they seek confidence the data can be understood, and they must trust the data. Using interview data from earthquake engineering researchers affiliated with the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES), we examine how these scientists assess the reusability of colleagues’ experimental data for model validation.


title: The Multidimensional Knapsack Problem: Structure and Algorithms

text: Structure and Algorithms Jakob Puchinger NICTA Victoria Laboratory Department of Computer Science & Software Engineering University of Melbourne, Australia jakobp@csse.unimelb.edu.au Günther R. Raidl Institute of Computer Graphics and Algorithms Vienna University of Technology, Austria raidl@ads.tuwien.ac.at Ulrich Pferschy Department of Statistics and Operations Research University of Graz, Austria pferschy@uni-graz.at


title: State of the Art: Embedding Security in Vehicles

text: For new automotive applications and services, information technology (IT) has gained central importance. IT-related costs in car manufacturing are already high and they will increase dramatically in the future. Yet whereas safety and reliability have become a relatively well-established field, the protection of vehicular IT systems against systematic manipulation or intrusion has only recently started to emerge. Nevertheless, IT security is already the base of some vehicular applications such as immobilizers or digital tachographs. To securely enable future automotive applications and business models, IT security will be one of the central technologies for the next generation of vehicles. After a state-of-the-art overview of IT security in vehicles, we give a short introduction into cryptographic terminology and functionality. This contribution will then identify the need for automotive IT security while presenting typical attacks, resulting security objectives, and characteristic constraints within the automotive area. We will introduce core security technologies and relevant security mechanisms followed by a detailed description of critical vehicular applications, business models, and components relying on IT security. We conclude our contribution with a detailed statement about challenges and opportunities for the automotive IT community for embedding IT security in vehicles.


title: Culture as situated cognition : Cultural mindsets , cultural fluency , and meaning making

text: The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable


title: Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

text: Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.


title: A Joint Reconstruction and Segmentation Method for Limited-Angle X-Ray Tomography

text: Limited-angle computed tomography (CT) is common in industrial applications, where incomplete projection data can cause artifacts. For objects made from homogeneous materials, we propose a joint reconstruction and segmentation method that performs joint image reconstruction and segmentation directly on the projection data. We describe an alternating minimization algorithm to solve the resulting optimization problem, and we modify the primal-dual hybrid gradient algorithm for the non-convex piecewise constant Mumford-Shah model, which is a popular approximation model in biomedical image segmentation. The effectiveness of the proposed approach is validated by simulation and by application to actual micro-CT data sets.


title: Unsupervised document zone identification using probabilistic graphical models

text: Document zone identification aims to automatically classify sequences of text-spans (e.g. sentences) within a document into predefined zone categories. Current approaches to document zone identification mostly rely on supervised machine learning methods, which require a large amount of annotated data, which is often difficult and expensive to obtain. In order to overcome this bottleneck, we propose graphical models based on the popular Latent Dirichlet Allocation (LDA) model. The first model, which we call zoneLDA aims to cluster the sentences into zone classes using only unlabelled data. We also study an extension of zoneLDA called zoneLDAb, which makes distinction between common words and non-common words within the different zone types. We present results on two different domains: the scientific domain and the technical domain. For the latter one we propose a new document zone classification schema, which has been annotated over a collection of 689 documents, achieving a Kappa score of 85%. Overall our experiments show promising results for both of the domains, outperforming the baseline model. Furthermore, on the technical domain the performance of the models are comparable to the supervised approach using the same feature sets. We thus believe that graphical models are a promising avenue of research for automatic document zoning.


title: Violence during pregnancy in Jordan: its prevalence and associated risk and protective factors.

text: This study estimates the lifetime prevalence of physical violence during pregnancy and examines risk and protective factors among women (N = 390) attending reproductive health clinics in Jordan. Approximately 15% reported physical violence during pregnancy. The husband was the sole perpetrator in 83% of the cases. A high frequency of quarreling, the husband's use of alcohol, attitudes supportive of a woman's duty to obey her husband, infrequent communication between the respondent and her family, and exposure to violence as a child increased the risk of violence. Consanguinity (marriage to a blood relative) and higher education levels were protective against violence during pregnancy.


title: A study on lidar data forensics

text: 3D LiDAR (Light Imaging Detection and Ranging) data has recently been used in a wide range of applications such as vehicle automation and crime scene reconstruction. Decision making in such applications is highly dependent on LiDAR data. Thus, it becomes crucial to authenticate the data before using it. Though authentication of 2D digital images and video has been widely studied, the area of 3D data forensic is relatively unexplored. In this paper, we investigate and identify three possible attacks on the LiDAR data. We also propose two novel forensic approaches as a countermeasure for such attacks and study their effectiveness. The first forensic approach utilises the density consistency check while the second method leverages the occlusion effect for revealing the forgery. Experimental results demonstrate the effectiveness of the proposed forgery attacks and raise the awareness against unauthenticated use of LiDAR data. The performance analyses of the proposed forensic approaches indicate that the proposed methods are very efficient and provide the detection accuracy of more than 95% for certain kinds of forgery attacks. While the forensic approach is unable to handle all forgery attacks, the study motivates to explore more sophisticated forensic methods for LiDAR data.


title: Can we Evaluate the Quality of Generated Text?

text: Evaluating the output of NLG systems is notoriously difficult, and performin g assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality h ave been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propo se that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text qua lity.


title: Condensation methodologies for two-dimensional non-uniform constellations

text: Two-dimensional non-uniform constellations (2D-NUC) have been adopted for the most recent terrestrial broadcasting system called ATSC 3.0. They are known for being more efficient than one-dimensional non uniform constellations (1D-NUC) and uniform constellations (UC). They can be used in any communication system with the bit-interleaved coded modulation (BICM) structure. However, one of the main challenges of such constellations is their design for optimal behaviour in a wide range of cases and the design of a demapper that exploits their advantages. This paper presents two different condensation methodologies to design efficient 2D-NUCs simple in design and with limited demapping complexity. The proposal provides a complexity reduction of the design and demapping processes in the range from 13% to 94%. The demapping stage provides performance losses less than 0.1 dB if compared with standard 2D-NUC demapping.


title: 1 Text Mining Systems for Predicting Market Response to News : A Survey

text: Several prototypes for predicting the short-term market reaction to news based on text mining techniques have been developed. However, no detailed comparison of the systems and their performances is available thus far. This paper describes the main systems developed and presents a framework for comparing the approaches. The prototypes differ in the text mining methods applied and the data sets used for performance evaluation. Some (mostly implicit) assumptions of these evaluations are rather unrealistic with respect to properties of financial markets and the performance results cannot be achieved in reality. Furthermore, the adequacy of applying text mining techniques for predicting stock price movements in general and approaches for dealing with existing problems are discussed.


title: Fast routing in road networks with transit nodes.

text: When you drive to somewhere far away, you will leave your current location via one of only a few important traffic junctions. Starting from this informal observation, we developed an algorithmic approach, transit node routing, that allows us to reduce quickest path queries in road networks to a small number of table lookups. For road maps of Western Europe and the United States, our best query times improved over the best previously published figures by two orders of magnitude. This is also more than one million times faster than the best known algorithm for general networks.


title: Twin Networks: Using the Future as a Regularizer

text: Being able to model long-term dependencies in sequential data, such as text, has been among the long-standing challenges of recurrent neural networks (RNNs). This issue is strictly related to the absence of explicit planning in current RNN architectures, more explicitly, the network is trained to predict only the next token given previous ones. In this paper, we introduce a simple way of biasing the RNNs towards planning behavior. Particularly, we introduce an additional neural network which is trained to generate the sequence in reverse order, and we require closeness between the states of the forward RNN and backward RNN that predict the same token. At each step, the states of the forward RNN are required to match the future information contained in the backward states. We hypothesize that the approach eases modeling of long-term dependencies thus helping in generating more globally consistent samples. The model trained with conditional generation achieved 4% relative improvement (CER of 7.3 compared to a baseline of 7.6).


title: Design of a multi-DOF cable-driven mechanism of a miniature serial manipulator for robot-assisted minimally invasive surgery

text: While multi-fingered robotic hands have been developed for decades, none has been used for surgical operations. μAngelo is an anthropomorphic master-slave system for teleoperated robot-assisted surgery. As part of this system, this paper focuses on its slave instrument, a miniature three-digit hand. The design of the mechanism of such a manipulator poses a challenge due to the required miniaturization and the many active degrees of freedom. As the instrument has a human-centered design, its relation to the human hand is discussed. Two ways of routing its cable-driven mechanism are investigated and the method of deriving the input-output functions that drive the mechanism is presented.


title: German version of the Multidimensional Body-Self Relations Questionnaire - Appearance Scales (MBSRQ-AS): confirmatory factor analysis and validation.

text: The Multidimensional Body-Self Relations Questionnaire (MBSRQ) is a widely used questionnaire that measures body image as a multidimensional construct. The Appearance Scales (AS) of the MBSRQ (Appearance Evaluation, Appearance Orientation, Body Areas Satisfaction, Overweight Preoccupation and Self-Classified Weight) are subscales which facilitate a parsimonious assessment of appearance-related aspects of body image. The current study tested the psychometric properties and factor structure of a German translation of the MBSRQ-AS. Participants were n=230 female patients with the SCID diagnosis of an eating disorder and n=293 female healthy controls. In a confirmatory factor analysis, convincing goodness-of-fit indices emerged. The subscales of the questionnaire yielded good reliability and convergent and discriminant validity coefficients, with most items showing excellent characteristics. Like the English version, the German adaptation of the questionnaire can be recommended for a multidimensional assessment of appearance-related aspects of body image in both research and clinical practice.


title: “Anarchy is what states make of it: the social construction of power politics,”

text: Wendt recasts the realist/liberal debate as an argument over the determinants of state action in the international system: i.e., whether state action is influenced by “structure” (anarchy and relative power) or “process” (interaction and learning). He puts forth a “constructivist” argument rejecting the realist belief that the structure of the international system – anarchy and “self-help” – forces states to “play competitive power politics.” Rather, Wendt argues that self-help and power politics do not follow “either logically or causally” from anarchy; if they exist, it is due to process, not structure (394). Thus, Wendt argues that state identities and interests are shaped and transformed within the international system, rather than (as the realists believe) existing as exogenous variables.


title: Automatic scoring of pronunciation quality

text: We present a paradigm for the automatic assessment of pronunciation quality by machine. In this scoring paradigm, both native and nonnative speech data is collected and a database of human-expert ratings is created to enable the development of a variety of machine scores. We ®rst discuss issues related to the design of speech databases and the reliability of human ratings. We then address pronunciation evaluation as a prediction problem, trying to predict the grade a human expert would assign to a particular skill. Using the speech and the expert-ratings databases, we build statistical models and introduce dierent machine scores that can be used as predictor variables. We validate these machine scores on the Voice Interactive Language Training System (VILTS) corpus, evaluating the pronunciation of American speakers speaking French and we show that certain machine scores, like the log-posterior and the normalized duration, achieve a correlation with the targeted human grades that is comparable to the human-to-human correlation when a sucient amount of speech data is available. Ó 2000 Elsevier Science B.V. All rights reserved.


title: Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

text: Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.


title: A tactile sensor for the fingertips of the humanoid robot iCub

text: In order to successfully perform object manipulation, humanoid robots must be equipped with tactile sensors. However, the limited space that is available in robotic fingers imposes severe design constraints. In [1] we presented a small prototype fingertip which incorporates a capacitive pressure system. This paper shows an improved version, which has been integrated on the hand of the humanoid robot iCub. The fingertip is 14.5 mm long and 13 mm wide. The capacitive pressure sensor system has 12 sensitive zones and includes the electronics to send the 12 measurements over a serial bus with only 4 wires. Each synthetic fingertip is shaped approximately like a human fingertip. Furthermore, an integral part of the capacitive sensor is soft silicone foam, and therefore the fingertip is compliant. We describe the structure of the fingertip, their integration on the humanoid robot iCub and present test results to show the characteristics of the sensor.


title: Tactile sensing for mechatronics * a state of the art survey

text: In this paper we examine the state of the art in tactile sensing for mechatronics[ We de_ne a tactile sensor as a device or system that can measure a given property of an object or contact event through physical contact between the sensor and the object[ We consider any property that can be measured through contact\ including the shape of an object\ texture\ temperature\ hardness\ moisture content\ etc[ A comprehensive search of the literature revealed that there was a signi_cant increase in publications on tactile sensing from 0880 onwards[ Considerable e}ort in the 0879s was spent investigating transduction techniques and developing new sensors\ whilst emphasis in more recent research has focused on experiments using tactile sensors to perform a variety of tasks[ This paper reports on progress in tactile sensing in the following areas] cutaneous sensors\ sensing _ngers\ soft materials\ industrial robot grippers\ multi_ngered hands\ probes and whiskers\ analysis of sensing devices\ haptic perception\ processing sensory data and new application areas[ We conclude that the predominant choice of transduction method is piezoelectric\ with arrays using resistive or capacitive sensing[ We found that increased emphasis on understanding tactile sensing and perception issues has opened up potential for new application areas[ The predicted growth in applications in industrial automation has not eventuated[ New applications for tactile sensing including surgery\ rehabilitation and service robotics\ and food processing automation show considerable potential and are now receiving signi_cant levels of research attention[ Þ 0887 Elsevier Science Ltd[ All rights reserved[


title: The iCub humanoid robot: an open platform for research in embodied cognition

text: We report about the iCub, a humanoid robot for research in embodied cognition. At 104 cm tall, the iCub has the size of a three and half year old child. It will be able to crawl on all fours and sit up to manipulate objects. Its hands have been designed to support sophisticate manipulation skills. The iCub is distributed as Open Source following the GPL/FDL licenses. The entire design is available for download from the project homepage and repository (http://www.robotcub.org). In the following, we will concentrate on the description of the hardware and software systems. The scientific objectives of the project and its philosophical underpinning are described extensively elsewhere [1].


title: HAWK : Halting Anomaly with Weighted ChoKing to Rescue Well-Behaved TCP Sessions from Shrew DoS Attacks

text: 1 Manuscript submitted to ICDCS 2005 on October 8, 2004. All rights reserved by the authors. This research was supported by an NSF ITR Research Grant under contract number ACI-0325409. Corresponding Author: Kai Hwang, Email: kaihwang@usc.edu, Tel: 213-740-4470, Fax: 213-740-4418. Abstract—High availability in network services is crucial for effective large-scale distributed computing. While denial-ofservice (DoS) attacks through massive packet flooding have baffled researchers for years, a new type of even more detrimental attack—shrew attacks (periodic intensive packet bursts with low average rate)—has recently been identified. Shrew attacks can significantly degrade well-behaved TCP sessions and are very difficult to detect, not to mention defend against, due to its low average rate. We propose a new stateful adaptive queue management technique called HAWK (Halting Anomaly with Weighted choKing) which works by judiciously identifying malicious shrew packet flows using a small flow table and dropping such packets decisively to halt the attack such that well-behaved TCP sessions can re-gain their bandwidth shares. Our NS-2 based extensive performance results indicate that HAWK is highly agile in the sense that it can swiftly (e.g., within 5 seconds) identify and then halt shrew attacks with a flow table of tiny size (e.g., 600 bytes). Thus, HAWK is readily amenable to practical implementation in a real environment.


title: Centralities in Large Networks: Algorithms and Observations

text: Node centrality measures are important in a large number of graph applications, from search and ranking to social and biological network analysis. In this paper we study node centrality for very large graphs, up to billions of nodes and edges. Various definitions for centrality have been proposed, ranging from very simple (e.g., node degree) to more elaborate. However, measuring centrality in billion-scale graphs poses several challenges. Many of the “traditional” definitions such as closeness and betweenness were not designed with scalability in mind. Therefore, it is very difficult, if not impossible, to compute them both accurately and efficiently. In this paper, we propose centrality measures suitable for very large graphs, as well as scalable methods to effectively compute them. More specifically, we propose effective closeness and LINERANK which are designed for billion-scale graphs. We also develop algorithms to compute the proposed centrality measures in MAPREDUCE, a modern paradigm for large-scale, distributed data processing. We present extensive experimental results on both synthetic and real datasets, which demonstrate the scalability of our approach to very large graphs, as well as interesting findings and anomalies.


title: Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

text: This paper proposes a novel approach to voice conversion with non-parallel training data. The idea is to bridge between speakers by means of Phonetic PosteriorGrams (PPGs) obtained from a speaker-independent automatic speech recognition (SI-ASR) system. It is assumed that these PPGs can represent articulation of speech sounds in a speaker-normalized space and correspond to spoken content speaker-independently. The proposed approach first obtains PPGs of target speech. Then, a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Network (DBLSTM) structure is used to model the relationships between the PPGs and acoustic features of the target speech. To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments show that our approach performs equally well or better than state-of-the-art systems in both speech quality and speaker similarity.


title: Telos: Representing Knowledge About Information Systems

text:
We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge. Accordingly, the proposed language is founded on concepts from knowledge representations. Indeed, the language is appropriate for representing knowledge about a variety of worlds related to a particular information system, such as the subject world (application domain), the usage world (user models, environments), the system world (software requirements, design), and the development world (teams, metodologies).
We introduce the features of the language through examples, focusing on those provided for desribing metaconcepts that can then be used to describe knowledge relevant to a particular information system. Telos' fetures include an object-centered framework which supports aggregation, generalization, and classification; a novel treatment of attributes; an explicit representation of time; and facilities for specifying integrity constraints and deductive rules. We review actual applications of the language through further examples, and we sketch a formalization of the language.


title: Fighting Accounting Fraud Through Forensic Data Analytics

text: Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.


title: Mindfulness and emotion regulation in depression and anxiety: common and distinct mechanisms of action.

text:
BACKGROUND
The current study seeks to investigate the mechanisms through which mindfulness is related to mental health in a clinical sample of adults by examining (1) whether specific cognitive emotion regulation strategies (rumination, reappraisal, worry, and nonacceptance) mediate associations between mindfulness and depression and anxiety, respectively, and (2) whether these emotion regulation strategies operate uniquely or transdiagnostically in relation to depression and anxiety.

METHODS
Participants were 187 adults seeking treatment at a mood and anxiety disorders clinic in Connecticut. Participants completed a battery of self-report measures that included assessments of depression and anxiety (Mood and Anxiety Symptom Questionnaire), and emotion regulation (Ruminative Response Scale, Penn State Worry Questionnaire, Emotion Regulation Questionnaire, Difficulties in Emotion Regulation Scale).

RESULTS
Simple mediation analyses indicated that rumination and worry significantly mediated associations between mindfulness and anxiety symptoms, whereas rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms. Multiple mediation analyses showed that worry significantly mediated associations between mindfulness and anxiety symptoms and rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms.

CONCLUSIONS
Findings suggest that mindfulness operates through distinct and common mechanisms depending on clinical context.


title: The principles and practice of probabilistic programming

text: Probabilities describe degrees of belief, and probabilistic inference describes rational reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded onto the scene of modern artificial intelligence, cognitive science, and applied statistics: these are all sciences of inference under uncertainty. But as probabilistic models have become more sophisticated, the tools to formally describe them and to perform probabilistic inference have wrestled with new complexity. Just as programming beyond the simplest algorithms requires tools for abstraction and composition, complex probabilistic modeling requires new progress in model representation—probabilistic programming languages. These languages provide compositional means for describing complex probability distributions; implementations of these languages provide generic inference engines: tools for performing efficient probabilistic inference over an arbitrary program. In their simplest form, probabilistic programming languages extend a well-specified deterministic programming language with primitive constructs for random choice. This is a relatively old idea, with foundational work by Giry, Kozen, Jones, Moggi, SahebDjahromi, Plotkin, and others [see e.g. 7]. Yet it has seen a resurgence thanks to new tools for probabilistic inference and new complexity of probabilistic modeling applications. There are a number of recent probabilistic programming languages [e.g. 8, 9, 11–17], embodying different tradeoffs in expressivity, efficiency, and perspicuity. We will focus on the probabilistic programming language Church [6] for simplicity, but the design of probabilistic languages to best support complex model representation and efficient inference is an active and important topic. Church extends (the purely functional subset of) Scheme with elementary random primitives, such as flip (a bernoulli), multinomial, and gaussian. In addition, Church includes language constructs that simplify modeling. For instance, mem, a higher-order procedure that memoizes its input function, is useful for describing persistent random properties and lazy model construction. (Interestingly, memoization has a semantic effect in probabilistic languages.) If we view the semantics of the underlying deterministic language as a map from programs to executions of the program, the semantics of the probabilistic language will be a map from programs to distributions over executions. When the program halts


title: Shilling attacks against recommender systems: a comprehensive survey

text: Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions.


title: Modeling people flow: simulation analysis of international-departure passenger flows in an airport terminal

text: An entire airport terminal building is simulated to examine passenger flows, especially international departures. First, times needed for passengers to be processed in the terminal building are examined. It is found that the waiting time for check-in accounts for more than 80 percent of the total waiting time of passengers spent in the airport. A special-purpose data-generator is designed and developed to create experimental data for executing a simulation. It is found that the possible number of passengers missing their flights could be drastically reduced by adding supporting staff to and by making use of first-and business-class check-in counters for processing economy-and group-class passengers.


title: Enhancement of MRI human thigh muscle segmentation by template-based framework

text: Image segmentation of anatomic structures is often an essential step in medical image analysis. A variety of segmentation methods have been proposed, but none provides automatic segmentation of the thigh. In magnetic resonance images of the thigh, the segmentation is complicated by factors, such as artifacts (e.g. intensity inhomogeneity and echo) and inconsistency of soft and hard tissue compositions, especially in muscle from older people, where accumulation of intermuscular fat is greater than in young muscles. In this paper, the combination framework that leads to a segmentation enhancement method for region of interest segmentation are demonstrated. Appropriate methods of image pre-processing, thresholding, manual interaction of muscle border, template conversion and deformable contours in combination with image filters are applied. Prior geometrical information in an initial template image is used to automatically derive the muscle outlines by application of snake active contours, in serial images within a single MRI dataset. Our approach has an average segmented output accuracy of 93.34% by Jaccard Similarity Index, and reduced the processing time by 97.73% per image compared to manual segmentation.


title: Age and gender estimation using deep residual learning network

text: In this paper, we propose a deep residual learning model for age and gender estimation. Our method detects faces in input images, and then the age and gender of each face are estimated. The estimation method consists of three deep neural networks where we adopt residual learning methods. We train the model with IMDB-WIKI database [4]. However, since the database has only a small number of face images under the age of 20, we augment the set by collecting the images on the Internet. Experimental results show that the proposed model with residual learning yields improved performance.


title: Misinformation in Online Social Networks: Detect Them All with a Limited Budget

text:
Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors.
 In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its #P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.


title: The pains and pleasures of parenting: when, why, and how is parenthood associated with more or less well-being?

text: The relationship between parenthood and well-being has become a hot topic among scholars, media, and general public alike. The research, however, has been mixed-some studies indicate that parents are happier than nonparents, whereas others suggest the reverse. We suggest that the question of whether parents are more or less happy than their childless peers is not the most meaningful one. To reconcile the conflicting literature and expand understanding of the emotional experience of parenthood, we present a model of parents' well-being that describes why and how parents experience more or less happiness than nonparents (i.e., mediators of the link between parenthood and well-being). We then apply this model to explain when parents are more likely to experience more or less happiness (i.e., moderators of parents' well-being, such as parent age or child temperament). Supporting our model, we review 3 primary methodological approaches: studies comparing parents and nonparents, studies examining changes in well-being across the transition to parenthood, and studies comparing parents' experiences while with their children to their other daily activities. Our review suggests that the relationship between parenthood and well-being is highly complex. We propose that parents are unhappy to the extent that they encounter relatively greater negative emotions, magnified financial problems, more sleep disturbance, and troubled marriages. By contrast, when parents experience greater meaning in life, satisfaction of their basic needs, greater positive emotions, and enhanced social roles, they are met with happiness and joy.


title: Control Strategy on Doubly Fed Induction Generator Using PSIM

text: This paper deals with doubly fed induction generator control strategy. This paper includes the results performed in PSIM-9 software such as Performance comparison of output voltage under different loading conditions and output power. Keywords— Doubly Fed Induction Generator (DFIG), variable speed wind turbine


title: On Semantic Algebra: A Denotational Mathematics for Natural Language Comprehension and Cognitive Computing

text: Semantics is composed meaning of language expressions and perceptions at the levels of words, phrase, sentence, paragraph, and essay. The basic unit of formal semantics is a concept as a formal model of words in a natural language. Cognitive linguistics focuses on cognitive semantics and its interaction with underlying syntactic structures. A denotational mathematics known as semantic algebra is presented for rigorous semantics manipulations in cognitive linguistics. Semantic algebra deals with formal semantics of linguistics by a general mathematical model of semantics and a set of algebraic operators. On the basis of semantic algebra, language semantics may not only be deductively analyzed based on syntactic structures from the top down, but also be synthetically composed by the algebraic semantic operators from the bottom up at different levels of language units. Semantic algebra enables a wide range of applications in cognitive informatics, cognitive linguistics, computational linguistics, semantic computing, cognitive computing, machine learning, computing with words, as well as natural language analysis, synthesis, and comprehension.


title: Cross-Lingual Propagation for Deep Sentiment Analysis

text: Across the globe, people are voicing their opinion in social media and various other online fora. Given such data, modern deep learning-based sentiment analysis methods excel at determining the sentiment polarity of what is being said about companies, products, etc. Unfortunately, such deep methods require significant training data, while for many languages, resources and training data are scarce. In this work, we present a cross-lingual propagation algorithm that yields sentiment embedding vectors for numerous languages. We then rely on a dual-channel convolutional neural architecture to incorporate them into the network. This allows us to achieve gains in deep sentiment analysis across a range of languages and domains.


title: The Ring of Gyges : Using Smart Contracts for Crime

text: Thanks to their anonymity (pseudonymity) and lack of trusted intermediaries, cryptocurrencies such as Bitcoin have created or stimulated growth in many businesses and communities. Unfortunately, some are criminal, e.g., money laundering, marketplaces for illicit goods, and ransomware. Next-generation cryptocurrencies such as Ethereum will include rich scripting languages in support of smart contracts, programs that autonomously intermediate transactions. We illuminate the extent to which these new cryptocurrencies, by enabling criminal activities to be conducted anonymously and with minimal trust assumptions, may fuel new criminal ecosystems. Specifically, we show how what we call criminal smart contracts (CSCs) can facilitate leakage of confidential information, theft of cryptographic keys, and various realworld crimes (murder, arson, terrorism). We show significantly that CSCs for leakage of secrets are efficiently realizable in existing scripting languages such as that in Ethereum. We show that CSCs for key theft can be achieved using cryptographic primitives, such as Succinct Non-interactive ARguments of Knowledge (SNARKs), that are already expressible in these languages and for which efficient supporting language extensions are anticipated. We demonstrate similarly that authenticated data feeds, another anticipated feature of smart contract systems, can facilitate CSCs for real-world crimes. Our results illuminate the scope of possible abuses in nextgeneration cryptocurrencies. They highlight the urgency of creating policy and technical safeguards and thereby realizing the great promise of smart contracts for beneficial goals.