title: Monetizing freemium communities: Does paying for premium increase social engagement?

abstract: Making sustainable profits from a baseline zero price and motivating free consumers to convert to premium subscribers is a continuing challenge for all freemium communities. Prior research has causally established that social engagement (Oestreicher-Singer and Zalmanson 2013) and peer influence (Bapna and Umyarov 2015) are two important drivers of users converting to premium subscribers in such communities. In this paper, we flip the perspective of prior research and ask whether the decision to pay for a premium subscription causes users to become more socially engaged. In the context of the Last.fm music listening freemium social community, we establish, using a novel 41-month-long panel dataset, a look-ahead propensity score matching (LA-PSM) procedure coupled with a difference-in-difference estimator of the treatment effect, that payment for premium leads to more social engagement. Specifically, we find that paying for premium leads to an increase in both content-related and community-related social engagement. Free users who convert to premium listen to 287.2% more songs, create 1.92% more playlists, exhibit a 2.01% increase in the number of forum posts made, and gain 15.77% more friends. Thus, premium subscribers create value not only for themselves by consuming more content, but also for the community and site by organizing more content and adding more friends, who are subsequently engaged by the social diffusion emerging from the focal user’s activities.


title: Management of chronic posttraumatic headache: a multidisciplinary approach.

abstract: Although the definitions and possible treatments of posttraumatic headache have been discussed in the literature, treatment of this condition is not standardized. In addition, though various reviews and case studies have supported the use of spinal manipulation in the treatment of tension-type headaches and migraines, few describe the use of osteopathic manipulative treatment for patients with posttraumatic headache. In describing a 38-year-old woman with posttraumatic headache, we illustrate a multidisciplinary approach-including the use of osteopathic principles and practice-to treating patients with posttraumatic headache.


title: Predicting Named Entity Location Using Twitter

abstract: A knowledge base contains a set of concepts, entities, attributes, and relations. Knowledge bases are increasingly critical to a wide variety of applications in both industry and academia. Yet despite all that, knowledge bases are greatly incomplete. As the world evolves, new entities are generated. Enriching existing knowledge bases with new entities and new location attribute values for them becomes more and more important. Twitter is one of the most popular micro-blogging platforms. Named entities are mentioned frequently in the huge collection of tweets which contain abundant geographical location knowledge. Given a named entity and a set of tweets where the entity appears, we are interested in predicting the entity city-level location using the knowledge embedded in tweets. This task is helpful for many applications such as knowledge base enrichment, tweet location prediction, and entity search. In this paper we propose NELPT, the first unsupervised framework for Named Entity city-level Location Prediction by leveraging the geographical location knowledge from Twitter. This framework leverages a Linear Neural Network model as the predictive model combining two categories of information: (1) local count information; (2) global distributional information. A learning algorithm based on the expectation-maximization (EM) method is proposed to automatically learn the parameters of the Linear Neural Network predictive model without requiring any training data. The experimental results on a real world Twitter data set show that our framework significantly outperforms the baselines in terms of accuracy, and scales very well.


title: Area Attention

abstract: Existing attention mechanisms are trained to attend to individual items in a collection (the memory) with a predefined, fixed granularity, e.g., a word token or an image grid. We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e.g., spatially for a 2D memory such as images, or temporally for a 1D memory such as natural language sentences. Importantly, the shape and the size of an area are dynamically determined via learning, which enables a model to attend to information with varying granularity. Area attention can easily work with existing model architectures such as multi-head attention for simultaneously attending to multiple areas in the memory. We evaluate area attention on two tasks: neural machine translation (both character and token-level) and image captioning, and improve upon strong (state-of-the-art) baselines in all the cases. These improvements are obtainable with a basic form of area attention that is parameter free.


title: Network text sentiment analysis method combining LDA text representation and GRU-CNN

abstract: In order to improve the performance of internet public sentiment analysis, a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. First, the review texts are collected from the network for preprocessing. Then, using the LDA topic model to train the latent semantic space representation (topic distribution) of the short text, and the short text feature vector representation based on the topic distribution is constructed. Finally, the CNN with gated recurrent unit (GRU) is used as a classifier. According to the input feature matrix, the GRU-CNN strengthens the relationship between words and words, text and text, so as to achieve high accurate text classification. The simulation results show that this method can effectively improve the accuracy of text sentiment classification.


title: Phase locked loop using delay compensation techniques

abstract: This paper illustrates a modification to a basic phase locked loop (PLL) topology aimed at compensating the effects of loop latency. The technique, developed to address the performance degradation observed in high latency PLL topologies as is the case of Viterbi based decision directed read channel timing recovery, is readily applicable to digital baseband communication receivers.


title: Audience Ratings Prediction of TV Dramas Based on the Cast and Their Popularity

abstract: Television viewership ratings represent the popularity of TV programs and are an important indicator of the revenue generated by broadcasters from advertisements as well as other sources. Although higher ratings for a given program are beneficial for both broadcasters and advertisers, little is known about the factors that make programs more attractive to viewers. In this paper, we propose a method to predict the ratings of TV dramas before they are broadcasted by considering the cast and the staff involved with them. In order to consider the popularity of actors, we consider the number of hits received by their Wikipedia pages, and tweets related to them on Twitter. We tested our proposed method using a collection of 678 TV dramas. The experimental results showed that audience ratings can be predicted with a correlation coefficient of r = 0.845. The effect of each factor was also investigated to reveal the components significant to audience ratings.


title: Low temperature synthesis of ITO nanoparticles with cubic structure by calcining a mixture of indium tin hydroxide and sodium chloride

abstract: The indium tin oxide (ITO) nanoparticles with cubic structure were synthesized by heating a mixture of indium tin hydroxide and sodium chloride at low temperature of 300 °C for 2 h and washing with water. The effects of sodium chloride addition on the ITO phase and morphology were investigated using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and transmission electron microscopy (TEM). It is found that sodium chloride addition helps the formation of cubic structure of ITO nanoparticles. The particle size of the resulted ITO powder is about 3 nm.


title: Toward Computational Fact-Checking

abstract: Our news are saturated with claims of "facts" made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim "cherry-picking"? This paper proposes a framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate practical fact-checking tasks---reverse-engineering (often intentionally) vague claims, and countering questionable claims---as computational problems. Along with the modeling framework, we develop an algorithmic framework that enables efficient instantiations of "meta" algorithms by supplying appropriate algorithmic building blocks. We present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.


title: Multimodal Content Representation and Similarity Ranking of Movies

abstract: In this paper we examine the existence of correlation between movie similarity and low level features from respective movie content. In particular, we demonstrate the extraction of multi-modal representation models of movies based on subtitles, audio and metadata mining. We emphasize our research in topic modeling of movies based on their subtitles. In order to demonstrate the proposed content representation approach, we have built a small dataset of 160 widely known movies. We assert movie similarities, as propagated by the singular modalities and fusion models, in the form of recommendation rankings. We showcase a novel topic model browser for movies that allows for exploration of the different aspects of similarities between movies and an information retrieval system for movie similarity based on multi-modal content.


title: Soft-tissue simulation using the radial elements method

abstract: This paper introduces the Radial Elements Method - REM for the simulation of deformable objects. The REM was conceived for the real time, dynamic simulation of deformable objects. The method uses a combination of static and dynamic approaches to simulate deformations and dynamics of highly deformable objects. The real time performance of the method and its intrinsic properties of volume conservation, modeling based in material properties and simple meshing make it particularly attractive for soft tissue modeling and surgery simulation.


Hydrologic Analysis For River Nyando Using SWAT


title: Automated Classification of Stance in Student Essays: An Approach Using Stance Target Information and the Wikipedia Link-Based Measure

abstract: We present a new approach to the automated classification of document-level argument stance, a relatively under-researched sub-task of Sentiment Analysis. In place of the noisy online debate data currently used in stance classification research, a corpus of student essays annotated for essay-level stance is constructed for use in a series of classification experiments. A novel set of features designed to capture the stance, stance targets, and topical relationships between the essay prompt and the student's essay is described. Models trained on this feature set showed significant increases in accuracy relative to two high baselines.


title: Feature Learning for Activity Recognition in Ubiquitous Computing

abstract: Feature extraction for activity recognition in context-aware ubiquitous computing applications is usually a heuristic process, informed by underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to generalize across different application domains. We investigate the potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition. We also describe an alternative data representation based on the empirical cumulative distribution function of the raw data, which effectively abstracts from absolute values. Experiments on accelerometer data from four publicly available activity recognition datasets demonstrate the significant potential of our approach to address both contemporary activity recognition tasks and next generation problems such as skill assessment and the detection of novel activities.


Appearance of an electrocardiogram tracing on a bispectral index monitor with plausible BIS values


title: Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing

abstract: Due to the undeniable advantage of prediction and proactivity, many research areas and industrial applications are accelerating the pace to keep up with data science and predictive analytics. However and due to three well-known facts, the reactive Complex Event Processing (CEP) technology might lag behind when prediction becomes a requirement. 1st fact: The one and only inference mechanism in this domain is totally guided by CEP rules. 2nd fact: The only way to define a CEP rule is by writing it manually with the help of a human expert. 3rd fact: Experts tend to write reactive CEP rules, because and regardless of the level of expertise, it is nearly impossible to manually write predictive CEP rules. Combining these facts together, the CEP is---and will stay--- a reactive computing technique. Therefore in this article, we present a novel data mining-based approach that automatically learns predictive CEP rules. The approach proposes a new learning algorithm where complex patterns from multivariate time series are learned. Then at run-time, a seamless transformation into the CEP world takes place. The result is a ready-to-use CEP engine with enrolled predictive CEP rules. Many experiments on publicly-available data sets demonstrate the effectiveness of our approach.


title: MIS Problems and failures a socio-technical perspective part II: the application of socio-technical theory

abstract: This article provides, by means of a hypothetical example, the procedures to be followed in applying the Socio- Technical-Systems Approach to information systems design. The need for this approach was presented in the first part of this article which appeared in the September Issue of the MIS Quarterly. Here the authors provide an overview of the Socio- Technical-Systems procedure and show how it could be used in redesigning an information system used by the circulation department of a large newspaper. The step by step approach is intended to Illustrate the process to those practitioners feeling that more needs to be done in the area of using computer-based systems to improve the quality of working life of the system users.


title: An median graphs: properties, algorithms, and applications

abstract: In object prototype learning and similar tasks, median computation is an important technique for capturing the essential information of a given set of patterns. We extend the median concept to the domain of graphs. In terms of graph distance, we introduce the novel concepts of set median and generalized median of a set of graphs. We study properties of both types of median graphs. For the more complex task of computing generalized median graphs, a genetic search algorithm is developed. Experiments conducted on randomly generated graphs demonstrate the advantage of generalized median graphs compared to set median graphs and the ability of our genetic algorithm to find approximate generalized median graphs in reasonable time. Application examples with both synthetic and nonsynthetic data are shown to illustrate the practical usefulness of the concept of median graphs.


title: Provable security for block Ciphers by decorrelation

abstract: In this presentation we investigate a new way of protecting block ciphers against classes of attacks (including differential and linear crypt-analysis) which is based on the notion of decorrelation which is fairly connected to Carter-Wegman's notion of universal functions. This defines a simple and friendly combinatorial measurement which enables to quantify the security. We show that we can mix provable protections and heuristic protections. We finally propose two new block ciphers family we call COCONUT and PEANUT, which implement these ideas and achieve quite reasonable performances for real-life applications.


title: Analysis, classification and detection methods of attacks via wireless sensor networks in SCADA systems

abstract: Effectiveness of information security of automated process control systems, as well as of SCADA, depends on data transmissions protection technologies applied on transport environments components. This article investigates the problem of detecting attacks on WSN (wireless sensor networks) of SCADA systems. As the result of analytical studies the authors developed the detailed classification of external attacks on sensor networks and brought the detailed description of attacking impacts on components of SCADA systems in accordance with selected directions of attacks. Reviewed the methods of intrusion detection in wireless sensor networks of SCADA systems and functions of WIDS (wireless intrusion detection systems). Noticed the role of anthropogenic factors in internal security threats.


title: Learning mechanisms underlying threat absence and threat relief: Influences of trait anxiety

abstract: Abstract Impaired safety learning has been proposed as a risk factor for anxiety disorders, but safety can be indicated by either threat absence or threat termination (i.e., relief). Here, we investigated the role of trait anxiety for both kinds of safety learning. Ninety-one participants underwent an acquisition phase during which one shape ( threat CS) predicted a painful electric shock (unconditioned stimulus, US), one shape ( relief CS) followed the US, and one shape ( absence CS) became never associated with the US. In a following extinction phase, the three cues were presented again plus a control shape ( control CS). We found successful threat conditioning as threat CS was rated as more aversive (negative, arousing, anxiogenic and associated with US) than the other cues, and it elicited startle potentiation as well as a larger skin conductance response (SCR). Safety cues were rated equally positive and (non-)anxiogenic, but still lower than control CS, whereas physiologically relief CS elicited stronger appetitive responses (startle attenuation and low SCR) than absence CS. Interestingly, an increase in trait anxiety was associated with a decrease in the differences between absence CS and threat CS responses reflected in contingency ratings during extinction as well as stronger fear-startle responses to absence CS. In sum, physiological responses but not ratings triggered by a relief signal compared to a threat-absence signal, indicated that the former is more appetitive than the latter. Strikingly, trait anxiety specifically mediated learning of threat absence, but not of threat termination, indicating that high trait anxious individuals experience relief normally, but have deficits in identifying signals of threat absence.


title: Speaker segmentation using deep speaker vectors for fast speaker change scenarios

abstract: A novel speaker segmentation approach based on deep neural network is proposed and investigated. This approach uses deep speaker vectors (d-vectors) to represent speaker characteristics and to find speaker change points. The d-vector is a kind of frame-level speaker discriminative feature, whose discriminative training process corresponds to the goal of discriminating a speaker change point from a single speaker speech segment in a short time window. Following the traditional metric-based segmentation, each analysis window contains two sub-windows and is shifting along the audio stream to detect speaker change points, where the speaker characteristics are represented by the means of deep speaker vectors for all frames in each window. Experimental investigations conducted in fast speaker change scenarios show that the proposed method can detect speaker change points more quickly and more effectively than the commonly used segmentation methods.


title: Taxonomy discovery for personalized recommendation

abstract: Personalized recommender systems based on latent factor models are widely used to increase sales in e-commerce. Such systems use the past behavior of users to recommend new items that are likely to be of interest to them. However, latent factor model suffer from sparse user-item interaction in online shopping data: for a large portion of items that do not have sufficient purchase records, their latent factors cannot be estimated accurately. In this paper, we propose a novel approach that automatically discovers the taxonomies from online shopping data and jointly learns a taxonomy-based recommendation system. Out model is non-parametric and can learn the taxonomy structure automatically from the data. Since the taxonomy allows purchase data to be shared between items, it effectively improves the accuracy of recommending tail items by sharing strength with the more frequent items. Experiments on a large-scale online shopping dataset confirm that our proposed model improves significantly over state-of-the-art latent factor models. Moreover, our model generates high-quality and human readable taxonomies. Finally, using the algorithm-generated taxonomy, our model even outperforms latent factor models based on the human-induced taxonomy, thus alleviating the need for costly manual taxonomy generation.


title: Security Requirements Engineering: A Framework for Representation and Analysis

abstract: This paper presents a framework for security requirements elicitation and analysis. The framework is based on constructing a context for the system, representing security requirements as constraints, and developing satisfaction arguments for the security requirements. The system context is described using a problem-oriented notation, then is validated against the security requirements through construction of a satisfaction argument. The satisfaction argument consists of two parts: a formal argument that the system can meet its security requirements and a structured informal argument supporting the assumptions expressed in the formal argument. The construction of the satisfaction argument may fail, revealing either that the security requirement cannot be satisfied in the context or that the context does not contain sufficient information to develop the argument. In this case, designers and architects are asked to provide additional design information to resolve the problems. We evaluate the framework by applying it to a security requirements analysis within an air traffic control technology evaluation project.


title: An Adaptive Eigenshape Model

abstract: There has been a great deal of recent interest in statistical models of 2D landmark data for generating compact deformable models of a given object. This paper extends this work to a class of parametrised shapes where there are no landmarks available. A rigorous statistical framework for the eigenshape model is introduced, which is an extension to the conventional Linear Point Distribution Model. One of the problems associated with landmark free methods is that a large degree of variability in any shape descriptor may be due to the choice of parametrisation. An automated training method is described which utilises an iterative feedback method to overcome this problem. The result is an automatically generated compact linear shape model. The model has been successfully applied to a problem of tracking the outline of a walking pedestrian in real time.


title: Coherence and Coreference Revisited

abstract: For more than three decades, research into the psycholinguistics of pronoun interpretation has argued that hearers use various interpretation ‘preferences’ or ‘strategies’ that are associated with specific linguistic properties of antecedent expressions. This focus is a departure from the type of approach outlined in Hobbs (1979), who argues that the mechanisms supporting pronoun interpretation are driven predominantly by semantics, world knowledge and inference, with particular attention to how these are used to establish the coherence of a discourse. On the basis of three new experimental studies, we evaluate a coherence-driven analysis with respect to four previously proposed interpretation biases—based on grammatical role parallelism, thematic roles, implicit causality, and subjecthood—and argue that the coherence-driven analysis can explain the underlying source of the biases and predict in what contexts evidence for each will surface. The results further suggest that pronoun interpretation is incrementally influenced by probabilistic expectations that hearers have regarding what coherence relations are likely to ensue, together with their expectations about what entities will be mentioned next, which, crucially, are conditioned on those coherence relations.


title: Investigations of power analysis attacks on smartcards

abstract: This paper presents actual results from monitoring smartcard power signals and introduces techniques that help maximize such side-channel information. Adversaries will obviously choose attacks that maximize side-channel information, so it is very important that the strongest attacks be considered when designing defensive strategies. In this paper, power analysis techniques used to attack DES are reviewed and analyzed. The noise characteristics of the power signals are examined and an approach to model the signal to noise ratio is proposed. Test results from monitoring power signals are provided. Next, approaches to maximize the information content of the power signals are developed and tested. These results provide guidance for designing smartcard solutions that are secure against power analysis attacks.


title: An Earthworm-Like Robotic Endoscope System for Human Intestine: Design, Analysis, and Experiment

abstract: The existing endoscope brings too much discomfort to patients because its slim and rigid rod is difficult to pass through alpha, gamma loop of the human intestine. A robotic endoscope, as a novel solution, is expected to replace the current endoscope in clinic. A microrobotic endoscope based on wireless power supply was developed in this paper. This robot is mainly composed of a locomotion mechanism, a wireless power supply subsystem, and a communication subsystem. The locomotion mechanism is composed of three liner-driving cells connected with each other through a two-freedom universal joint. The wireless power supply subsystem is composed of a resonance transmit coil to transmit an alternating magnetic field, and a secondary coil to receive the power. Wireless communication system could transmit the image to the monitor, or send the control commands to the robot. The whole robot was packaged in the waterproof bellows. Activating the three driving cells under some rhythm, the robot could creep forward or backward as a worm. A mathematic model is built to express the energy coupling efficiency. Some experiments are performed to test the efficiency and the capability of energy transferring. The results show the wireless energy supply has enough power capacity. The velocity and the navigation ability in a pig intestine were measured in in vitro experiments. The results demonstrated this robot can navigate the intestine easily. In general, the wireless power supply and the wireless communication remove the need of a connecting wire and improve the motion flexibility. Meanwhile, the presented locomotion mechanism and principle have a high reliability and a good adaptability to the in vitro intestine. This research has laid a good foundation for the real application of the robotic endoscope in the future.


title: Recent advances in fault diagnosis by Park's vector approach

abstract: This paper addresses recent advances in fault diagnosis by Park's Vector Approach, with special emphasis being given to the most recent developments concerning power converter fault diagnosis in variable speed ac drives. With the aim to demonstrate the suitability of such approach for power converters self-diagnostic, a detailed comparison of four algorithms intended for real-time diagnosis is also included. Their performance evaluation is accomplished by means of experimental results, analyzing some key features such as effectiveness, robustness against false alarms, detection speed, implementation effort, tuning effort and computational burden.


title: Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension

abstract: While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates. This problem can be even worse in open-domain scenarios, where candidates from multiple passages should be combined to answer a single question. In this paper, we formulate reading comprehension as an extract-then-select two-stage procedure. We first extract answer candidates from passages, then select the final answer by combining information from all the candidates. Furthermore, we regard candidate extraction as a latent variable and train the two-stage process jointly with reinforcement learning. As a result, our approach has improved the state-of-the-art performance significantly on two challenging open-domain reading comprehension datasets. Further analysis demonstrates the effectiveness of our model components, especially the information fusion of all the candidates and the joint training of the extract-then-select procedure.


Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction.


Development of Energy Balance of Light Weight Electric Vehicle in Motion for Energy Conservation


title: Z-source inverter

abstract: This paper presents an impedance-source (or impedance-fed) power converter (abbreviated as Z-source converter) and its control method for implementing DC-to-AC, AC-to-DC, AC-to-AC, and DC-to-DC power conversion. The Z-source converter employs a unique impedance network (or circuit) to couple the converter main circuit to the power source, thus providing unique features that cannot be obtained in the traditional voltage-source (or voltage-fed) and current-source (or current-fed) converters where a capacitor and inductor are used respectively. The Z-source converter overcomes the conceptual and theoretical barriers and limitations of the traditional voltage-source converter (abbreviated as V-source converter) and current-source converter (abbreviated as I-source converter) and provides a novel power conversion concept. The Z-source concept can be applied to all DC-to-AC, AC-to-DC, AC-to-AC, and DC-to-DC power conversion. To describe the operating principle and control, this paper focuses on an example: a Z-source inverter for DC-AC power conversion needed in fuel cell applications. Simulation and experimental results are presented to demonstrate the new features.


title: Neural personalized response generation as domain adaptation

abstract: One of the most crucial problem on training personalized response generation models for conversational robots is the lack of large scale personal conversation data. To address the problem, we propose a two-phase approach, namely initialization then adaptation, to first pre-train an optimized RNN encoder-decoder model (LTS model) in a large scale conversational data for general response generation and then fine-tune the model in a small scale personal conversation data to generate personalized responses. For evaluation, we propose a novel human aided method, which can be seen as a quasi-Turing test, to evaluate the performance of the personalized response generation models. Experimental results show that the proposed personalized response generation model outperforms the state-of-the-art approaches to language model personalization and persona-based neural conversation generation on the automatic evaluation, offline human judgment and the quasi-Turing test.


title: Prospective Investigation of Video Game Use in Children and Subsequent Conduct Disorder and Depression Using Data from the Avon Longitudinal Study of Parents and Children

abstract: There is increasing public and scientific concern regarding the long-term behavioural effects of video game use in children, but currently little consensus as to the nature of any such relationships. We investigated the relationship between video game use in children, degree of violence in games, and measures of depression and a 6-level banded measure of conduct disorder. Data from the Avon Longitudinal Study of Parents and Children were used. A 3-level measure of game use at age 8/9 years was developed, taking into account degree of violence based on game genre. Associations with conduct disorder and depression, measured at age 15, were investigated using ordinal logistic regression, adjusted for a number of potential confounders. Shoot-em-up games were associated with conduct disorder bands, and with a binary measure of conduct disorder, although the strength of evidence for these associations was weak. A sensitivity analysis comparing those who play competitive games to those who play shoot-em-ups found weak evidence supporting the hypothesis that it is violence rather than competitiveness that is associated with conduct disorder. However this analysis was underpowered, and we cannot rule out the possibility that increasing levels of competition in games may be just as likely to account for the observed associations as violent content. Overall game exposure as indicated by number of games in a household was not related to conduct disorder, nor was any association found between shoot-em-up video game use and depression.


title: Workspace control of a wheel-legged mobile robot for gyrating locomotion with movable leg

abstract: We discuss motion generation problems of a wheel-legged mobile robot (WLMR) with movable legs. We consider and solve two problems of inverse kinematics of WLMRs. The first problem occurs in the generation of steering commands for WLMRs, which is due to the mechanism of the structure of the wheels. In general, inverse kinematics for WLMRs are applied in the velocity level. However, in our case, it is not possible to generate the proper steering motion. To solve this problem, we show that for WLMRs, the application of inverse kinematics in the acceleration level is effective. The second problem addressed is a singularity problem that occurs when controlling the contact point of legs during the wheeled locomotion of the WLMR. We show that this singularity problem can be avoided by utilizing the redundancy present in the wheel-leg mechanism. Finally, the effectiveness of our motion generation schemes is validated by conducting precise, three-dimensional simulations.


title: Adaptive Robust Control of DC Motors With Extended State Observer

abstract: Structured and unstructured uncertainties always exist in physical servo systems and degrade their tracking accuracy. In this paper, a practical method named adaptive robust control with extended state observer (ESO) is synthesized for high-accuracy motion control of a dc motor. The proposed controller accounts for not only the structured uncertainties (i.e., parametric uncertainties) but also the unstructured uncertainties (i.e., nonlinear friction, external disturbances, and/or unmodeled dynamics). Adaptive control for the structured uncertainty and ESO for the unstructured uncertainty are designed for compensating them respectively and integrated together via a feedforward cancellation technique. The global robustness of the controller is guaranteed by a feedback robust law. Furthermore, the controller theoretically guarantees a prescribed tracking performance in the presence of various uncertainties, which is very important for high-accuracy control of motion systems. Extensive comparative experimental results are obtained to verify the high-performance nature of the proposed control strategy.


title: A Unified Approach to Word Sense Representation and Disambiguation

abstract: The lexical ambiguity of words has been successfully clarified by representing words at a sense level instead of a word level. This is known as word sense representation (WSR). However, WSR models are typically trained in an unsupervised fashion without any guidance from sense inventories. Therefore, the number of sense vectors assigned to a word varies from model to model. This implies that some senses are missed or unnecessarily added. Moreover, to utilize their sense vectors in natural language processing tasks, we must determine which sense of a word to choose. In this paper, we introduce a unified neural model that incorporates WSR into word sense disambiguation (WSD), thereby leveraging the sense inventory. We use bidirectional long short-term memory networks to capture the sequential information of contexts effectively. To overcome the limitation of size with the labeled dataset, we train our model in a semi-supervised fashion to scale up the size of the dataset by leveraging a large-scale unlabeled dataset. We evaluate our proposed model on both WSR and WSD tasks. The experimental results demonstrate that our model outperforms state-of-the-art on WSR task by 0.27%, while, on WSD task, by 1.4% in terms of Spearman's correlation and F'l-score, respectively.


title: CrawlDroid: Effective Model-based GUI Testing of Android Apps

abstract: This paper presents an effective model-based GUI testing technique for Android apps. To avoid local and repetitive exploration, our approach groups equivalent widgets in a state and designs a novel feedback-based exploration strategy, which dynamically adjusts the priority of actions based on the execution result of those already triggered ones, and tends to select actions that can reach news states of apps. We implemented our technique in a tool, called CrawlDroid, and conducted empirical experiments. Our results show that the proposed technique is effective, and covers more code within a fixed testing budget.


title: Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO

abstract: The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability. The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO. This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI). This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment. The system utilizes external cameras, besides the robot's own visual system. Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot. This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session.


An Entity Graph Based Recommender System


title: Ketogenic Diet for Obesity: Friend or Foe?

abstract: Obesity is reaching epidemic proportions and is a strong risk factor for a number of cardiovascular and metabolic disorders such as hypertension, type 2 diabetes, dyslipidemia, atherosclerosis, and also certain types of cancers. Despite the constant recommendations of health care organizations regarding the importance of weight control, this goal often fails. Genetic predisposition in combination with inactive lifestyles and high caloric intake leads to excessive weight gain. Even though there may be agreement about the concept that lifestyle changes affecting dietary habits and physical activity are essential to promote weight loss and weight control, the ideal amount and type of exercise and also the ideal diet are still under debate. For many years, nutritional intervention studies have been focused on reducing dietary fat with little positive results over the long-term. One of the most studied strategies in the recent years for weight loss is the ketogenic diet. Many studies have shown that this kind of nutritional approach has a solid physiological and biochemical basis and is able to induce effective weight loss along with improvement in several cardiovascular risk parameters. This review discusses the physiological basis of ketogenic diets and the rationale for their use in obesity, discussing the strengths and the weaknesses of these diets together with cautions that should be used in obese patients.


title: Behavior Rule Specification-Based Intrusion Detection for Safety Critical Medical Cyber Physical Systems

abstract: We propose and analyze a behavior-rule specification-based technique for intrusion detection of medical devices embedded in a medical cyber physical system (MCPS) in which the patient’s safety is of the utmost importance. We propose a methodology to transform behavior rules to a state machine, so that a device that is being monitored for its behavior can easily be checked against the transformed state machine for deviation from its behavior specification. Using vital sign monitor medical devices as an example, we demonstrate that our intrusion detection technique can effectively trade false positives off for a high detection probability to cope with more sophisticated and hidden attackers to support ultra safe and secure MCPS applications. Moreover, through a comparative analysis, we demonstrate that our behavior-rule specification-based IDS technique outperforms two existing anomaly-based techniques for detecting abnormal patient behaviors in pervasive healthcare applications.


Image-Question-Linguistic Co-Attention for Visual Question Answering


title: Exoskeletons and robotic prosthetics: a review of recent developments

abstract: – The purpose of this paper is to review recent developments in exoskeletons and robotic prosthetics., – This paper first describes a number of recently developed exoskeletons for military, civil and medical applications. It then discusses robotic prosthetics and concludes with a brief consideration of progress in brain‐computer interface (BCI) technology., – Robotic exoskeletons are the topic of a major research effort, much being funded by the US military, and aims to impart superhuman strength to the wearer. Japanese research is also well advanced and concerns a range of non‐military applications, including strength enhancement and medical rehabilitation. Some products have recently been commercialised. There has also been significant progress in the development of robotic prosthetic limbs, a topic which is also attracting support from the US military. A key aim is the development of thought‐controlled prosthetics which will arise from advances in BCI technology., – This paper provides a detailed review of the latest developments in exoskeletons and robotic prosthetics.


title: Data-Driven Soft Sensor Modeling for Algal Blooms Monitoring

abstract: Currently, salvage is considered as an effective way for protecting ecosystems of inland water from toxin-producing algal blooms. Yet, the magnitude of algal blooms, which is the essential information required for dispatching salvage boats, cannot be estimated accurately with low cost in real time. In this paper, a data-driven soft sensor is proposed for algal blooms monitoring, which estimates the magnitude of algal blooms using data collected by inexpensive water quality sensors as input. The modeling of the soft sensor consists of two steps: 1) magnitude calculation and 2) regression model training. In the first step, we propose an active learning strategy to construct high-accuracy image classification model with ~50 % less labeled data. Based on this model, we design a new algorithm that recognizes algal blooms and calculates the magnitude using water surface pictures. In the second step, we propose to use Gaussian process to train the regression model that maps the multiparameter water quality sensor data to the calculated magnitude of algal blooms and learn the parameters of the model automatically from the training data. We conduct extensive experiments to evaluate our modeling method, AlgaeSense, based on over 200 000 heterogeneous sensor data records collected in four months from our field-deployed sensor system. The results indicate that the soft sensor can accurately estimate the magnitude of algal blooms in real time using data collected by just three kinds of inexpensive water quality sensors.


title: A Longitudinal Study of Predictors of Suicide Attempts Among Lesbian, Gay, Bisexual, and Transgender Youth

abstract: This short-term prospective study examined general and lesbian, gay, bisexual, and transgender (LGBT)-specific risk and protective factors for suicide attempts in an ethnically diverse sample of LGBT youth (N = 237, 47.7 % male). A structured psychiatric interview assessed clinical depression and conduct disorder symptoms, as well as past and prospective suicide attempts over a 1-year follow-up period (91 % retention). Participants completed questionnaires measuring general risk factors for suicide attempts, including hopelessness, impulsiveness, and perceived social support. They also completed measures of LGBT-specific suicide risk factors, including gender nonconformity, age of first same-sex attraction, and LGBT victimization. Correlation and multivariate regression analyses were conducted to examine the relations between predictors and suicide attempt, and to identify mediators. Of nine variables examined, seven were related to lifetime history of attempted suicide: hopelessness, depression symptoms, conduct disorder symptoms, impulsivity, victimization, age of first same-sex attraction, and low family support. Depressive symptoms and hopelessness mediated the relation between multiple risk and resilience factors and suicide attempts. Suicide attempt history was the strongest predictor of prospective suicide attempts. Participants who previously attempted suicide (31.6 % of the sample) had more than 10 times greater odds of making another attempt in the 1-year follow-up period than were those who had made no previous attempt. These results highlight the need for suicide prevention programs for LGBT youth and suggest the importance of addressing depression and hopelessness as proximal determinants and family support and victimization, which have more distal effects.


title: A Semantic Autonomous Video Surveillance System for Dense Camera Networks in Smart Cities

abstract: This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.


title: Multidisciplinary approach to brain metastasis from melanoma; local therapies for central nervous system metastases.

abstract: The overall treatment paradigm for melanoma brain metastases continues to evolve and reflects the relative radioresistance of this histology, as well as the effect of emerging systemic therapies with central nervous system (CNS) activity. Local therapies, including surgery, whole brain radiotherapy (WBRT), and stereotactic radiosurgery (SRS), play an important role in the multidisciplinary management of melanoma brain metastases. Treatment selection for local therapies must consider many factors: (1) size, number, and location of lesions, (2) presence or absence of neurological symptoms, (3) extracranial disease status, expected survival, age, and performance status, (4) prior treatment history, (5) expected treatment toxicities, and (6) predicted response to systemic therapies. The choice of treatment modalities for brain metastases is among the most controversial areas in oncology. There has been a trend toward reduced use of WBRT and increased reliance on SRS and surgery for melanoma brain metastases. Although no prospective randomized data exist comparing local therapies for melanoma brain metastases, several large retrospective studies suggest aggressive local treatment with modalities including surgery and SRS are associated with favorable outcomes in select patients. Multidisciplinary collaboration is required to facilitate a treatment plan that balances reduction in risk of neurological death and symptomatic progression against the risk of treatment-related toxicity.


The role of positivity and connectivity in the performance of business teams: A nonlinear dynamics model


title: Mobility Prediction in Mobile Ad Hoc Networks Using Extreme Learning Machines

abstract: Abstract Recent advances in wireless technology and computing have paved the way to the unprecedented rapid growth in de- mand and availability of mobile networking and services coupled with diverse system/network applications. Such advances triggered the emergence of future generation wireless networks and services to address the increasingly strin- gent requirements of quality-of-service (QoS) at various levels. The expected growth in wireless network activity and the number of wireless users will enable similar growth in bandwidth-crunching wireless applications to meet the QoS requirements. Mobility prediction of wireless users and units plays a major role in efficient planning and manage- ment of the bandwidth resources available in wireless networks. In return, this efficiency will allow better planning and improved overall QoS in terms of continuous service availability and efficient power management. In this paper, we propose extreme learning machines (ELMs), known for universal approximation, to model and predict mobility of arbitrary nodes in a mobile ad hoc network (MANET). MANETs use mobility prediction in location-aided routing and mobility aware topology control protocols. In these protocols, each mobile node is assumed to know its current mobility information (position, speed and movement direction angle). In this way, future node positions are predicted along with future distances between neighboring nodes. Unlike multilayer perceptrons (MLPs), ELMs capture better the existing interaction/correlation between the cartesian coordinates of the arbitrary nodes leading to more realistic and accurate mobility prediction based on several standard mobility models. Simulation results using standard mobility models illustrate how the proposed prediction method can lead to a significant improvement over conventional methods based on MLPs. Moreover, the proposed solution circumvents the prediction accuracy limitations in current algorithms when predicting future distances between neighboring nodes. The latter prediction is required by some applications like mobility aware topology control protocols.


title: A Generic Statistical Approach for Spam Detection in Online Social Networks

abstract: Abstract In this paper, we present a generic statistical approach to identify spam profiles on Online Social Networks (OSNs). Our study is based on real datasets containing both normal and spam profiles crawled from Facebook and Twitter networks. We have identified a set of 14 generic statistical features to identify spam profiles. The identified features are common to both Facebook and Twitter networks. For classification task, we have used three different classification algorithms – na i ve Bayes , Jrip , and J48 , and evaluated them on both individual and combined datasets to establish the discriminative property of the identified features. The results obtained on a combined dataset has detection rate (DR) as 0.957 and false positive rate (FPR) as 0.048, whereas on Facebook dataset the DR and FPR values are 0.964 and 0.089, respectively, and that on Twitter dataset the DR and FPR values are 0.976 and 0.075, respectively. We have also analyzed the contribution of each individual feature towards the detection accuracy of spam profiles. Thereafter, we have considered 7 most discriminative features and proposed a clustering-based approach to identify spam campaigns on Facebook and Twitter networks.


title: Research on the Relationship between Classic Denavit-Hartenberg and Modified Denavit-Hartenberg

abstract: Currently, homogeneous transformation method has been widely used for its advantages in robot. Its more commonly used modeling methods are classic Denavit-Hartenberg method and modified Denavit-Hartenberg method, but the relationship between the two is very few devoted. Then, the use area is limited. Therefore, the relationship between classic Denavit-Hartenberg method and modified Denavit-Hartenberg method for robotics research has important practical significance. Based on this, the geometric representations of classic Denavit-Hartenberg method and modified Denavit-Hartenberg method are described respectively, the models of link parameters are built by the two, and the corresponding transformation matrix is deduced. The relationship between classic Denavit-Hartenberg method and modified Denavit-Hartenberg method is researched deeply, and the relational expression of classic Denavit-Hartenberg method and modified Denavit-Hartenberg method is obtained. The research results of this paper are useful for the robot kinematics, dynamics and control algorithms.


title: Deeply Coupled Auto-encoder Networks for Cross-view Classification

abstract: The comparison of heterogeneous samples extensively exists in many applications, especially in the task of image classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers. In DCAN, each deep structure is developed via stacking multiple discriminative coupled auto-encoders, a denoising auto-encoder trained with maximum margin criterion consisting of intra-class compactness and inter-class penalty. This single layer component makes our model simultaneously preserve the local consistency and enhance its discriminative capability. With increasing number of layers, the coupled networks can gradually narrow the gap between the two views. Extensive experiments on cross-view image classification tasks demonstrate the superiority of our method over state-of-the-art methods.


title: A Survey on Replica Server Placement Algorithms for Content Delivery Networks

abstract: Content Delivery Networks (CDNs) have gained immense popularity over the years. Replica server placement is a key design issue in CDNs. It entails placing replica servers at meticulous locations, such that cost is minimized and Quality of Service (QoS) of end-users is satisfied. Many replica server placement models have been proposed in the literature of traditional CDN. As the CDN architecture is evolving through the adoption of emerging paradigms, such as, cloud computing and Network Functions Virtualization (NFV), new algorithms are being proposed. In this paper, we present a comprehensive survey of replica server placement algorithms in traditional and emerging paradigm based CDNs. We categorize the algorithms and provide a summary of their characteristics. Besides, we identify requirements for an efficient replica server placement algorithm and perform a comparison in the light of the requirements. Finally, we discuss potential avenues for further research in replica server placement in CDNs.


title: Fast Linear SVM Validation Based on Early Stopping in Iterative Learning

abstract: Classification is an important field in machine learning and pattern recognition. Amongst various types of classifiers such as nearest neighbor, neural network and Bayesian classifiers, support vector machine (SVM) is known as a very powerful classifier. One of the advantages of SVM in comparison with the other methods, is its efficient and adjustable generalization capability. The performance of SVM classifier depends on its parameters, specially regularization parameter C, that is usually selected by cross-validation. Despite its generalization, SVM suffers from some limitations such as its considerable low speed training phase. Cross-validation is a very time consuming part of training phase, because for any candidate value of the parameter C, the entire process of training and validating must be repeated completely. In this paper, we propose a novel approach for early stopping of the SVM learning algorithm. The proposed early stopping occurs by integrating the validation part into the optimization part of the SVM training without losing any generality or degrading performance of the classifier. Moreover, this method can be considered in conjunction with the other available accelerator methods since there is not any dependency between our proposed method and the other accelerator ones, thus no redundancy will happen. Our method was tested and verified on various UCI repository datasets and the results indicate that this method speeds up the learning phase of SVM without losing any generality or affecting the final model of classifier.


title: Identifying interesting Twitter contents using topical analysis

abstract: Social media platforms such as Twitter are becoming increasingly mainstream which provides valuable user-generated information by publishing and sharing contents. Identifying interesting and useful contents from large text-streams is a crucial issue in social media because many users struggle with information overload. Retweeting as a forwarding function plays an important role in information propagation where the retweet counts simply reflect a tweet's popularity. However, the main reason for retweets may be limited to personal interests and satisfactions. In this paper, we use a topic identification as a proxy to understand a large number of tweets and to score the interestingness of an individual tweet based on its latent topics. Our assumption is that fascinating topics generate contents that may be of potential interest to a wide audience. We propose a novel topic model called Trend Sensitive-Latent Dirichlet Allocation (TS-LDA) that can efficiently extract latent topics from contents by modeling temporal trends on Twitter over time. The experimental results on real world data from Twitter demonstrate that our proposed method outperforms several other baseline methods.


title: Topical Clustering of Tweets

abstract: In the emerging field of micro-blogging and social communication services, users post millions of short messages every day. Keeping track of all the messages posted by your friends and the conversation as a whole can become tedious or even impossible. In this paper, we presented a study on automatically clustering and classifying Twitter messages, also known as “tweets”, into different categories, inspired by the approaches taken by news aggregating services like Google News. Our results suggest that the clusters produced by traditional unsupervised methods can often be incoherent from a topical perspective, but utilizing a supervised methodology that utilize the hash-tags as indicators of topics produce surprisingly good results. We also offer a discussion on temporal effects of our methodology and training set size considerations. Lastly, we describe a simple method of finding the most representative tweet in a cluster, and provide an analysis of the results.


Gesture and speech in interaction : An overview


title: “My Roomba Is Rambo”: Intimate Home Appliances

abstract: Robots have entered our domestic lives, but yet, little is known about their impact on the home. This paper takes steps towards addressing this omission, by reporting results from an empirical study of iRobot's Roomba™, a vacuuming robot. Our findings suggest that, by developing intimacy to the robot, our participants were able to derive increased pleasure from cleaning, and expended effort to fit Roomba into their homes, and shared it with others. These findings lead us to propose four design implications that we argue could increase people's enthusiasm for smart home technologies.


title: Tracking by Sampling Trackers

abstract: We propose a novel tracking framework called visual tracker sampler that tracks a target robustly by searching for the appropriate trackers in each frame. Since the real-world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation. To do this, our method obtains several samples of not only the states of the target but also the trackers themselves during the sampling process. The trackers are efficiently sampled using the Markov Chain Monte Carlo method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the basic important components of visual trackers. Then, the sampled trackers run in parallel and interact with each other while covering various target variations efficiently. The experiment demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments and outperforms the state-of-the-art tracking methods.


title: Averaging Weights Leads to Wider Optima and Better Generalization

abstract: Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. We also show that this Stochastic Weight Averaging (SWA) procedure finds much broader optima than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model. Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet. In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead.


title: A review of methods for leakage management in pipe networks

abstract: Leakage in water distribution systems is an important issue which is affecting water companies and their customers worldwide. It is therefore no surprise that it has attracted a lot of attention by both practitioners and researchers over the past years. Most of the leakage management related methods developed so far can be broadly classified as follows: (1) leakage assessment methods which are focusing on quantifying the amount of water lost; (2) leakage detection methods which are primarily concerned with the detection of leakage hotspots and (3) leakage control models which are focused on the effective control of current and future leakage levels. This paper provides a comprehensive review of the above methods with the objective to identify the current state-of-the-art in the field and to then make recommendations for future work. The review ends with the main conclusion that despite all the advancements made in the past, there is still a lot of scope and need for further work, especially in area of rea...


title: Novel miniature motors with lateral stator for a wide torque and speed range

abstract: For various drilling applications where the space in the tool head is limited, two motor topologies with the stator outsourced from the head are proposed, allowing for compact design and direct drive. In order to meet the high torque requirements at low speed and operate at high speed up to 200 000 rpm a finite element analysis (FEA) is carried out and the machine design is optimized for maximum torque while considering the space limitations and the loss constraints at the critical operating points. As an example, for the specifications of a high-speed micro-machining spindle covering a wide application range, FEA results are presented. Moreover, a torque ripple compensation method based on phase current profiles obtained from FEA is proposed to eliminate the torque ripple caused by the stator asymmetry and slotting.


Perak, the Abode of Grace: A Study of an Eighteenth-Century Malay State


title: The fundamentals of standardizing global marketing strategy

abstract: Purpose – To examine issues of standardization and adaptation in global marketing strategy and to explain the dynamics of standardization.Design/methodology/approach – This is a conceptual research paper that has been developed based on gaps in prior frameworks of standardization/adaptation. A three‐factor model of standardization/adaptation of global marketing strategy was developed. The three factors include homogeneity of customer response to the marketing mix, transferability of competitive advantage, and similarities in the degree of economic freedom.Findings – The model through the use of feedback effects explains the dynamics of standardization.Research limitations/implications – Future research needs to empirically test the model. To enable empirical validation, reliable and valid measures of the three factors proposed in the model need to be developed. Additionally, the model may be used in future research to delineate the impact a variable may have on the ability of a firm to follow a standardiz...


title: Frontal fibrosing alopecia: a clinical review of 36 patients

abstract: Summary ::: Background Frontal fibrosing alopecia (FFA) is a primary lymphocytic cicatricial alopecia with a distinctive clinical pattern of progressive frontotemporal hairline recession. Currently, there are no evidence-based studies to guide treatment for patients with FFA; thus, treatment options vary among clinicians. ::: ::: Objectives We report clinical findings and treatment outcomes of 36 patients with FFA, the largest cohort to date. Further, we report the first evidence-based study of the efficacy of hydroxychloroquine in FFA using a quantitative clinical score, the Lichen Planopilaris Activity Index (LPPAI). ::: ::: Methods A retrospective case note review was performed of 36 adult patients with FFA. Data were collected on demographics and clinical findings. Treatment responses to hydroxychloroquine, doxycycline and mycophenolate mofetil were assessed using the LPPAI. Adverse events were monitored. ::: ::: Results Most patients in our cohort were female (97%), white (92%) and postmenopausal (83%). Apart from hairline recession, 75% also reported eyebrow loss. Scalp pruritus (67%) and perifollicular erythema (86%) were the most common presenting symptom and sign, respectively. A statistically significant reduction in signs and symptoms in subjects treated with hydroxychloroquine (P < 0·05) was found at both 6- and 12-month follow up. ::: ::: Conclusions In FFA, hairline recession, scalp pruritus, perifollicular erythema and eyebrow loss are common at presentation. Despite the limitations of a retrospective review, our data reveal that hydroxychloroquine is significantly effective in reducing signs and symptoms of FFA after both 6 and 12 months of treatment. However, the lack of a significant reduction in signs and symptoms between 6 and 12 months indicates that the maximal benefits of hydroxychloroquine are evident within the first 6 months of use.


title: A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks

abstract: Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use mobile anchor nodes (MANs), which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. A considerable body of research has addressed the mobile anchor node assisted localization (MANAL) problem. However, to the best of our knowledge, no updated surveys on MAAL reflecting recent advances in the field have been presented in the past few years. This survey presents a review of the most successful MANAL algorithms, focusing on the achievements made in the past decade, and aims to become a starting point for researchers who are initiating their endeavors in MANAL research field. In addition, we seek to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful advances in this research field.


title: Novel Square-Wave Signal Injection Method Using Zero-Sequence Voltage for Sensorless Control of PMSM Drives

abstract: In this paper, a novel square-wave-type carrier signal injection method using zero-sequence voltage is proposed for sensorless control of permanent-magnet (PM) synchronous machine drives. Different from the conventional square-wave-type injection methods employed in the stationary reference frame and estimated synchronous reference frame, the proposed square-wave injection is performed on the estimated reference frame, which rotates anticlockwise at twice estimated rotor electrical angular speed. Compared to the conventional square-wave methods with carrier current sensing, the proposed strategy using zero-sequence voltage has two main advantages, 1) the amplitude of the resultant carrier response is not related to the injection frequency, and 2) the carrier response does not require differentiation calculation for rotor position estimation. Therefore, the proposed method is more robust and also much simpler for signal demodulation, which can combine the synergies of zero-sequence method and square-wave injection (i.e., increased accuracy, bandwidths, and stability, etc.). Finally, all the theoretical analyses are validated by experiments on a laboratory PM machine.


title: Real-Time Localization and Tracking Using Visible Light Communication

abstract: In this paper an optical indoor positioning system is proposed, which utilizes modulated LED lights as anchor points, deployed in known positions. The sensor, deployed on the tracked object (e.g. autonomous vehicle), is an ordinary camera facing upwards. The system is able to estimate the position and orientation of the moving object in real-time, based on the camera's image stream, containing images of the anchor points. The paper contains comprehensive analysis on the possible error sources and their effect on the positioning accuracy. Real measurement tests show that the accuracy of the system is in the low centimeter range even if the tracked camera moves with a speed of 1 m/s.


Implementation Challenges Of Health Management Information Systems In Uganda : A Review


title: AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents

abstract: Two minimal requirements for a satisfactory multiagent learning algorithm are that it 1. learns to play optimally against stationary opponents and 2. converges to a Nash equilibrium in self-play. The previous algorithm that has come closest, WoLF-IGA, has been proven to have these two properties in 2-player 2-action (repeated) games--assuming that the opponent's mixed strategy is observable. Another algorithm, ReDVaLeR (which was introduced after the algorithm described in this paper), achieves the two properties in games with arbitrary numbers of actions and players, but still requires that the opponents' mixed strategies are observable. In this paper we present AWESOME, the first algorithm that is guaranteed to have the two properties in games with arbitrary numbers of actions and players. It is still the only algorithm that does so while only relying on observing the other players' actual actions (not their mixed strategies). It also learns to play optimally against opponents that eventually become stationary. The basic idea behind AWESOME (Adapt When Everybody is Stationary, Otherwise Move to Equilibrium) is to try to adapt to the others' strategies when they appear stationary, but otherwise to retreat to a precomputed equilibrium strategy. We provide experimental results that suggest that AWESOME converges fast in practice. The techniques used to prove the properties of AWESOME are fundamentally different from those used for previous algorithms, and may help in analyzing future multiagent learning algorithms as well.


title: Improving the accuracy of co-citation clustering using full text

abstract: Historically, co‐citation models have been based only on bibliographic information. Full‐text analysis offers the opportunity to significantly improve the quality of the signals upon which these co‐citation models are based. In this work we study the effect of reference proximity on the accuracy of co‐citation clusters. Using a corpus of 270,521 full text documents from 2007, we compare the results of traditional co‐citation clustering using only the bibliographic information to results from co‐citation clustering where proximity between reference pairs is factored into the pairwise relationships. We find that accounting for reference proximity from full text can increase the textual coherence (a measure of accuracy) of a co‐citation cluster solution by up to 30% over the traditional approach based on bibliographic information.


title: Accelerating Harris Algorithm with GPU for Corner Detection

abstract: Harris algorithm is a corner detection method based on gray scale, which detects corner points by calculating the gradient changes of pixels in horizontal and vertical direction. Due to the usage of Gaussian filtering, Harris algorithm performs well on aspect of robustness, accuracy and stability. A number of improved Harris algorithms have been proposed to enhance the accuracy and robustness during recent decades. However, the execution speed is limited by a large quantity of calculations especially for Gaussian smoothing, which has remained to be a bottleneck in real-time vision processing. Graphics processing unit (GPU) has special parallel computing resources in hardware. Single Instruction and Multiple Data (SIMD) architecture of shader processor array is suitable and fast for mass data calculations in parallel. Therefore, it provides us with an alternative implementation on GPU which can solve such a bottleneck problem. The paper proposes an implementation of Harris corner detection algorithm on GPU by using open graphics library (OpenGL) and graphics library shading language (GLSL) for portability. The experiment results show that the full execution performance of the implementation on GPU is over 73 times speedup of that completely on CPU at most, and can meet the requirements of real time, accuracy and robustness.


title: A Coarse-to-fine Pyramidal Model for Person Re-identification via Multi-Loss Dynamic Training

abstract: Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other. However, due to the inevitable challenging scenarios, current detection models often output inaccurate bounding boxes yet, which inevitably worsen the performance of these Re-ID algorithms. In this paper, to relax the requirement, we propose a novel coarse-to-fine pyramid model that not only incorporates local and global information, but also integrates the gradual cues between them. The pyramid model is able to match the cues at different scales and then search for the correct image of the same identity even when the image pair are not aligned. In addition, in order to learn discriminative identity representation, we explore a dynamic training scheme to seamlessly unify two losses and extract appropriate shared information between them. Experimental results clearly demonstrate that the proposed method achieves the state-of-the-art results on three datasets and it is worth noting that our approach exceeds the current best method by 9.5% on the most challenging dataset CUHK03.


Advantages of strong brands on customer reach and customer engagement on social media marketing


title: DEVELOPMENT AND EVALUATION OF Ni-Cu-P-PTFE COMPOSITE COATINGS TO MINIMIZE MICROBIAL ADHESION

abstract: In this paper a cost-effective Ni-Cu-P-PTFE composite coating with corrosion-resistant properties was developed by an auto-catalytic plating technique to minimize bacterial adhesion. The experimental results showed that the surface free energy of the Ni-Cu-P-PTFE coatings, which was altered by changing the PTFE content in the coatings, had a significant influence on bacterial adhesion. The Ni-Cu-P-PTFE coatings with surface free energy in the range 21-25.3 mJ/m 2 reduced the adhesion of E. coli Rosetta by over 96 - 98%, compared with stainless steel or titanium surfaces. The interaction energies between E. coli and the coatings were calculated with the extended DLVO theory. It was found that there was a strong correlation between the bacterial adhesion and the interaction energies or surface free energies of the coatings.


title: A circuit designer's guide to 5G mm-wave

abstract: The fourth generation mobile phone standards (4G) in widespread use include Long Term Evolution (LTE) and LTE-A (Advanced), which support up to 44 bands internationally, or an aggregate bandwidth of about 1 GHz in TDD and FDD modes. Techniques such as carrier aggregation allow the mobile operator to maximize bandwidth and deliver high data rate to users. As demand for wireless connectivity continues to grow exponentially, a fifth generation (5G) standard is envisioned, with the requirement to deliver higher throughputs, more spectrum-particularly in the mm-wave bands-higher capacity through spatial diversity, and lower latency. The projected deployment date of 5G is in 2019, and various proposals are under consideration. This paper will highlight important implications for the design of transceivers for 5G, particularly those targeting the mm-wave bands.


title: Effects of Dance on Movement Control in Parkinson’s Disease: A Comparison of Argentine Tango and American Ballroom

abstract: Objective—The basal ganglia may be selectively activated during rhythmic, metered movement like tango dancing, which may improve motor control in individuals with Parkinson disease (PD). Other partner dances may be suitable and preferable for those with PD. The purpose of this study was to compare the effects of tango, waltz/foxtrot and no intervention on functional motor control in individuals with PD. Design—This study employed a randomised, between-subject, prospective, repeated measures design. Subjects/Patients—Fifty-eight people with mild-moderate PD participated. Methods—Participants were randomly assigned to Tango, Waltz/Foxtrot or no intervention (Control). Those in the dance groups attended 1-hour classes 2 times per week, completing 20 lessons within thirteen weeks. Balance, functional mobility, forward and backward walking were evaluated before and after the intervention. Results—Both dance groups improved more than the Control group, which did not improve. Tango and Waltz/Foxtrot significantly improved on the Berg Balance Scale, six minute walk distance, and backward stride length. Tango improved as much or more than those in Waltz/Foxtrot on several measures. Conclusions—Tango may target deficits associated with PD more than Waltz/Foxtrot, but both dances may benefit balance and locomotion.


title: Two-dimensional PCA: a new approach to appearance-based face representation and recognition

abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.


title: Remote lab implementation on an embedded web server

abstract: The design, implementation and evaluation in setting up a remote laboratory for programming microcontrollers on an embedded web server residing over an ARM (Advanced RISC Machine) microprocessor based embedded system is proposed in this paper. The hex files or c codes generated with the help of various client machine IDE's (Integrated Development Environment) can be transferred to the server through the TCP (Transmission Control Protocol) port 80 which in turn programs the microcontroller attached to beaglebone with help of various software tools like avrdude, mspware etc. The system is designed by setting up an Apache web-server over the Beaglebone Black single board computer. Client machines can access the server through the static IP address assigned. The evaluation of the remote lab is done by programming Atmel AT89s52 microcontroller remotely and by performing a webcam interface to beaglebone thereby achieving video streaming, the real time video result can be viewed at the client end.


title: Prevalence of Mental Health Problems in Children and Its Associated Socio-Familial Factors in Urban Population of Semnan, Iran (2012)

abstract: Background: The World Health Organization (WHO) defines mental health as “a state of well-being in which every individual realizes own potential, can cope with the normal pressures of life, is able to work effectively, and can make a contribution to community”. Objectives: Mental Health Problems (MHP) is a great concern for all societies in terms of its burden and impact. This survey screened MHP and its impact in an Iranian urban population aged 6 - 12 years old, and explored its associated socio-familial factors. Patients and Methods: The survey was conducted in the elementary schools of Semnan, using random cluster sampling. Collection and analysis of data was performed using the parent version of the “Strengths and Difficulties Questionnaire (SDQ)” and survey commands of Stata-nine, taking into account cluster effect and population weights. Associations were assessed by fitting simple and multiple logistic regression models. P < 0.05 was considered significant. Results: With regard to the SDQ total score, 19.3% (95% CI: 8.6, 30.1) scored above the normal threshold (9.6% abnormal, 9.7% borderline). The frequency of problems ranged between 16.1% (peer problems) and 8.4% (emotional symptoms), and in all subscales boys were affected more than girls. The impact score was abnormal in 68.4% of all children, and was greater in girls than in boys. “A previously diagnosed mental health disorder” (OR = 11.11, 95% CI: 5.55, 25.00), “male gender” (OR = 1.43, 95% CI: 1.10, 1.87 and “less time spent with the child by father” (OR = 1.61, 95% CI: 1.20, 2.17) were significantly associated with an abnormal SDQ. Conclusions: The high rate of MHP in 6 - 12 year-old children and the lack of any significant correlation with their age, underpins the importance of early screening for MHP in schools, with particular focus on high risk groups.


title: A probabilistic approach to liquid level detection in cups using an RGB-D camera

abstract: Robotic assistants have the potential to greatly improve our quality of life by supporting us in our daily activities. A service robot acting autonomously in an indoor environment is faced with very complex tasks. Consider the problem of pouring a liquid into a cup, the robot should first determine if the cup is empty or partially filled. RGB-D cameras provide noisy depth measurements which depend on the opaqueness and refraction index of the liquid. In this paper, we present a novel probabilistic approach for estimating the fill-level of a liquid in a cup using an RGB-D camera. Our approach does not make any assumptions about the properties of the liquid like its opaqueness or its refraction index. We develop a probabilistic model using features extracted from RGB and depth data. Our experiments demonstrate the robustness of our method and an improvement over the state of the art.


title: Semi-supervised FusedGAN for Conditional Image Generation

abstract: We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controllability can be achieved by disentangling the generation process into various stages. In contrast to stacked GANs, where multiple stages of GANs are trained separately with full supervision of labeled intermediate images, the FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike existing methods, which requires full supervision with paired conditions and images, the FusedGAN can effectively leverage more abundant images without corresponding conditions in training, to produce more diverse samples with high fidelity. We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation. We demonstrate the efficacy of the FusedGAN in fine grained image generation tasks such as text-to-image, and attribute-to-face generation.


title: Sparse Bayesian Learning and the Relevance Vector Machine

abstract: This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the 'relevance vector machine' (RVM), a model of identical functional form to the popular and state-of-the-art 'support vector machine' (SVM). We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages. These include the benefits of probabilistic predictions, automatic estimation of 'nuisance' parameters, and the facility to utilise arbitrary basis functions (e.g. non-'Mercer' kernels). We detail the Bayesian framework and associated learning algorithm for the RVM, and give some illustrative examples of its application along with some comparative benchmarks. We offer some explanation for the exceptional degree of sparsity obtained, and discuss and demonstrate some of the advantageous features, and potential extensions, of Bayesian relevance learning.


title: On the Usefulness of Compression Models for Authorship Verification

abstract: Compression models represent an interesting approach for different classification tasks and have been used widely across many research fields. We adapt compression models to the field of authorship verification (AV), a branch of digital text forensics. The task in AV is to verify if a questioned document and a reference document of a known author are written by the same person. We propose an intrinsic AV method, which yields competitive results compared to a number of current state-of-the-art approaches, based on support vector machines or neural networks. However, in contrast to these approaches our method does not make use of machine learning algorithms, natural language processing techniques, feature engineering, hyperparameter optimization or external documents (a common strategy to transform AV from a one-class to a multi-class classification problem). Instead, the only three key components of our method are a compressing algorithm, a dissimilarity measure and a threshold, needed to accept or reject the authorship of the questioned document. Due to its compactness, our method performs very fast and can be reimplemented with minimal effort. In addition, the method can handle complicated AV cases where both, the questioned and the reference document, are not related to each other in terms of topic or genre. We evaluated our approach against publicly available datasets, which were used in three international AV competitions. Furthermore, we constructed our own corpora, where we evaluated our method against state-of-the-art approaches and achieved, in both cases, promising results.


title: On how AI & Law can help autonomous systems obey the law: a position paper

abstract: In this position paper I discuss to what extent current and past AI & law research is relevant for research on autonomous intelligent systems that exhibit legally relevant behaviour. After a brief review of the history of AI & law, I will compare the problems faced by autonomous intelligent systems with the problems faced by lawyers in traditional legal settings. This should give insights into the extent to which AI & law models of legal problem solving and decision support can be applied in the design of legally well-behaving autonomous systems.


title: Natural Language Generation in Interactive Systems

abstract: An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide serves to introduce graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on the research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software and other resources for pursuing research on natural language generation and interactive systems, including dialog systems, multimodal interfaces and assistive technologies. It is an ideal resource for students and researchers in computational linguistics, natural language processing and related fields.


title: Abstract Stronger Password Authentication Using Browser Extensions ∗

abstract: We describe a browser extension, PwdHash, that transparently produces a different password for each site, improving web password security and defending against password phishing and other attacks. Since the browser extension applies a cryptographic hash function to a combination of the plaintext password entered by the user, data associated with the web site, and (optionally) a private salt stored on the client machine, theft of the password received at one site will not yield a password that is useful at another site. While the scheme requires no changes on the server side, implementing this password method securely and transparently in a web browser extension turns out to be quite difficult. We describe the challenges we faced in implementing PwdHash and some techniques that may be useful to anyone facing similar security issues in a browser environment.


title: Demystifying the Characteristics of 3D-Stacked Memories: A Case Study for Hybrid Memory Cube

abstract: Three-dimensional (3D)-stacking technology, which enables the integration of DRAM and logic dies, offers high bandwidth and low energy consumption. This technology also empowers new memory designs for executing tasks not traditionally associated with memories. A practical 3D-stacked memory is Hybrid Memory Cube (HMC), which provides significant access bandwidth and low power consumption in a small area. Although several studies have taken advantage of the novel architecture of HMC, its characteristics in terms of latency and bandwidth or their correlation with temperature and power consumption have not been fully explored. This paper is the first, to the best of our knowledge, to characterize the thermal behavior of HMC in a real environment using the AC-510 accelerator and to identify temperature as a new limitation for this state-of-the-art design space. Moreover, besides bandwidth studies, we deconstruct factors that contribute to latency and reveal their sources for high- and low-load accesses. The results of this paper demonstrates essential behaviors and performance bottlenecks for future explorations of packet-switched and 3D-stacked memories.


title: Adaptive UKF-SLAM based on magnetic gradient inversion method for underwater navigation

abstract: Consider the two characteristics: (1) Simultaneous localization and mapping (SLAM) is a popular algorithm for autonomous underwater vehicle, but visual SLAM is significantly influenced by weak illumination. (2)Geomagnetism-aided navigation and gravity-aided navigation are equally important methods in the field of vehicle navigation, but both are affected heavily by time-varying noises and terrain fluctuations. However, magnetic gradient vector can avoid the influence of time-varying noises, and is less affected by terrain fluctuations. To this end, we propose an adaptive SLAM-based magnetic gradient aided navigation with the following advantages: (1) Adaptive SLAM is an efficient way to deal with uncertainty of the measurement model. (2) Magnetic gradient inversion equation is a good alternative to be used as measurement equation in visual SLAM-denied environment. Experimental results show that our proposed method is an effective solution, combining magnetic gradient information with SLAM.


title: Lightweight Authentication Scheme with Dynamic Group Members in IoT Environments

abstract: In IoT environments, the user may have many devices to connect each other and share the data. Also, the device will not have the powerful computation and storage ability. Many studies have focused on the lightweight authentication between the cloud server and the client in this environment. They can use the cloud server to help sensors or proxies to finish the authentication. But in the client side, how to create the group session key without the cloud capability is the most important issue in IoT environments. The most popular application network of IoT environments is the wireless body area network (WBAN). In WBAN, the proxy usually needs to control and monitor user's health data transmitted from the sensors. In this situation, the group authentication and group session key generation is needed. In this paper, in order to provide an efficient and robust group authentication and group session key generation in the client side of IoT environments, we propose a lightweight authentication scheme with dynamic group members in IoT environments. Our proposed scheme can satisfy the properties including the flexible generation of shared group keys, the dynamic participation, the active revocation, the low communication and computation cost, and no time synchronization problem. Also our scheme can achieve the security requirements including the mutual authentication, the group session key agreement, and prevent all various well-known attacks.


www.mdpi.com/journal/ijms Droplets Formation and Merging in Two-Phase Flow


title: Finding compiler bugs via live code mutation

abstract: Validating optimizing compilers is challenging because it is hard to generate valid test programs (i.e., those that do not expose any undefined behavior). Equivalence Modulo Inputs (EMI) is an effective, promising methodology to tackle this problem. Given a test program with some inputs, EMI mutates the program to derive variants that are semantically equivalent w.r.t. these inputs. The state-of-the-art instantiations of EMI are Orion and Athena, both of which rely on deleting code from or inserting code into code regions that are not executed under the inputs. Although both have demonstrated their ability in finding many bugs in GCC and LLVM, they are still limited due to their mutation strategies that operate only on dead code regions. This paper presents a novel EMI technique that allows mutation in the entire program (i.e., both live and dead regions). By removing the restriction of mutating only the dead regions, our technique significantly increases the EMI variant space. It also helps to more thoroughly stress test compilers as compilers must optimize mutated live code, whereas mutated dead code might be eliminated. Finally, our technique also makes compiler bugs more noticeable as miscompilations on mutated dead code may not be observable. We have realized the proposed technique in Hermes. The evaluation demonstrates Hermes’s effectiveness. In 13 months, Hermes found 168 confirmed, valid bugs in GCC and LLVM, of which 132 have already been fixed.