title: Extreme hypertrophy of the labia minora.

abstract: Hypertrophic labia minora are occasionally a concern of young adults or adolescent girls. The hypertrophy may cause local irritation and discomfort in walking or sitting, as well as problems of personal hygiene during menstruation. Labial hypertrophy is defined as a maximum distance of the labia from base to edge of 4 cm (1). Although most cases of subjectively reported hypertrophic labia minora require reassurance and no correction is necessary, some cases should be surgically corrected for several reasons. We have recently experienced a case of extreme protuberance of bilateral labia minora in a young Japanese woman without any appreciable causal factors.


title: Modeling and backstepping control of under-actuated spherical UAV

abstract: The main contribution of this paper is to derive a nonlinear mathematical model and design a controller for a single rotor under-actuated spherical UAV providing asymptotic stability and trajectory tracking for the closed-loop system. A quaternion representation is first used to cast the model in two cascaded subsystems. A backstepping controller is then designed based on the nonlinear model. The performance of the controller is shown in simulation results.


title: The psycholinguistic and affective structure of words conveying pain

abstract: Despite the flourishing research on the relationships between affect and language, the characteristics of pain-related words, a specific type of negative words, have never been systematically investigated from a psycholinguistic and emotional perspective, despite their psychological relevance. This study offers psycholinguistic, affective, and pain-related norms for words expressing physical and social pain. This may provide a useful tool for the selection of stimulus materials in future studies on negative emotions and/or pain. We explored the relationships between psycholinguistic, affective, and pain-related properties of 512 Italian words (nouns, adjectives, and verbs) conveying physical and social pain by asking 1020 Italian participants to provide ratings of Familiarity, Age of Acquisition, Imageability, Concreteness, Context Availability, Valence, Arousal, Pain-Relatedness, Intensity, and Unpleasantness. We also collected data concerning Length, Written Frequency (Subtlex-IT), N-Size, Orthographic Levenshtein Distance 20, Neighbor Mean Frequency, and Neighbor Maximum Frequency of each word. Interestingly, the words expressing social pain were rated as more negative, arousing, pain-related, and conveying more intense and unpleasant experiences than the words conveying physical pain.


title: Evaluating Sentiment Analysis in the Context of Securities Trading

abstract: There are numerous studies suggesting that published news stories have an important effect on the direction of the stock market, its volatility, the volume of trades, and the value of individual stocks mentioned in the news. There is even some published research suggesting that automated sentiment analysis of news documents, quarterly reports, blogs and/or twitter data can be productively used as part of a trading strategy. This paper presents just such a family of trading strategies, and then uses this application to re-examine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application. This discrepancy comes at a cost.


Getting started with SUSAS: a speech under simulated and actual stress database.


title: Refreshing Labioplasty Techniques for Plastic Surgeons

abstract: BACKGROUND ::: Patient requests for plastic surgery of the female genitalia have become more common. There are several technical options to manage hypertrophic labia minora, including direct excision, wedge resection, and deepithelialization. Labia majora hypertrophy and hypoplasia can also be managed surgically. The aim of this study was to summarize labioplasty techniques to date, and describe the senior author's technique for labia minora and majora labioplasty. ::: ::: ::: METHODS ::: Seventy-four patients underwent direct labia minora excision with or without clitoral hood molding and management of labia majora by the senior author from January 2009 to August 2011. ::: ::: ::: RESULTS ::: The average follow-up time was 6 months. The range of the patients' ages was wide with an average age of 35 years. One patient had an infection and one had wound dehiscence. Six patients had palpable fatty cysts in the labia majora after fat injections; the cysts of five of these patients reabsorbed before 6 months. In one patient a palpable mass persisted 8 months after surgery; however, it was not visible or painful and the patient was satisfied with the result. No cases of scar contracture, painful scar, or discomfort because of labial edge distortion were noted. The satisfaction rate was close to 100 %. ::: ::: ::: CONCLUSION ::: The direct excision of the excess labia minora is a good technique and does not produce scar-associated problems. ::: ::: ::: LEVEL OF EVIDENCE III ::: This journal requires that authors assign a level of evidence to each article.


title: Phase-Shifted Carrier-Based Synchronized Sinusoidal PWM Techniques for a Cascaded H-Bridge Multilevel Inverter

abstract: This paper analyzes synchronization strategy for cascaded H-bridge multilevel inverter (CHBMLI) topologies with a carrier-based sinusoidal phase-shifted pulse width modulation (PSPWM) technique. In the PSPWM technique, a separate carrier is used for each H-bridge (HB). The carriers are generally phase shifted from each other by π / x rad ( x = No. of HBs) for unipolar PWM. With the carrier frequency being an integer (odd/even) multiple of the fundamental frequency, it is observed that the positions of zero crossings of the carriers with respect to the zero crossings of voltage references play an important role for maintaining quarter-wave symmetry among multilevel inverter pole voltage waveforms. This paper analytically shows the conditions for half-wave symmetry and quarter-wave symmetry and experimentally verifies those conditions for the PSPWM technique with a five-level CHBMLI laboratory prototype.


title: Predicting Active Users' Personality Based on Micro-Blogging Behaviors

abstract: Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.


title: On Evaluating the Generalization of LSTM Models in Formal Languages

abstract: Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing. Yet, there still remains an uncertainty regarding their language learning capabilities. In this paper, we empirically evaluate the inductive learning capabilities of Long Short-Term Memory networks, a popular extension of simple RNNs, to learn simple formal languages, in particular $a^nb^n$, $a^nb^nc^n$, and $a^nb^nc^nd^n$. We investigate the influence of various aspects of learning, such as training data regimes and model capacity, on the generalization to unobserved samples. We find striking differences in model performances under different training settings and highlight the need for careful analysis and assessment when making claims about the learning capabilities of neural network models.


title: Environmental innovation: in search of a meaning

abstract: Purpose – The purpose of this paper is to propose a definition of the term “green/environmental innovation”, based on a systematic literature review.Design/methodology/approach – The literature review conducted in this research was based on papers published in ISI Web of Science and Scopus databases.Findings – Environmental innovations are organizational implementations and changes focusing on the environment, with implications for companies’ products, manufacturing processes and marketing, with different degrees of novelty. They can be merely incremental improvements that intensify the performance of something that already exists, or radical ones that promote something completely unprecedented, where the main objective is to reduce the company's environmental impacts. In addition, environmental innovation has a bilateral relationship with the level of proactive environmental management adopted by companies. Increasing of environmental innovation tends to come up against many barriers.Originality/value – ...


title: The Cruelest Cure? Ethical Issues in the Implementation of Exposure-Based Treatments

abstract: Numerous studies have provided supportive evidence for the efficacy of exposure-based treatments for many psychological disorders. However, surprisingly few therapists use exposure therapy in the clinical setting. Although the limited use of exposure-based treatments may be partially attributable to a shortage of suitably trained therapists, exposure therapy also suffers from a “public relations problem” predicated upon concerns that it is cruel and at odds with some ethical considerations (e.g., first do no harm). This article provides an overview of ethical issues and considerations relevant to the use of exposure therapy. It is argued that the degree to which ethical issues become problematic in implementing exposure-based treatments is largely dependent upon the therapist's ability to create an adequately safe and professional context. Specific strategies that may be employed for avoiding potential ethical conflicts in the use of exposure-based treatments are discussed.


title: An Empirical Study on Mutation, Statement and Branch Coverage Fault Revelation That Avoids the Unreliable Clean Program Assumption

abstract: Many studies suggest using coverage concepts, such as branch coverage, as the starting point of testing, while others as the most prominent test quality indicator. Yet the relationship between coverage and fault-revelation remains unknown, yielding uncertainty and controversy. Most previous studies rely on the Clean Program Assumption, that a test suite will obtain similar coverage for both faulty and fixed (`clean') program versions. This assumption may appear intuitive, especially for bugs that denote small semantic deviations. However, we present evidence that the Clean Program Assumption does not always hold, thereby raising a critical threat to the validity of previous results. We then conducted a study using a robust experimental methodology that avoids this threat to validity, from which our primary finding is that strong mutation testing has the highest fault revelation of four widely-used criteria. Our findings also revealed that fault revelation starts to increase significantly only once relatively high levels of coverage are attained.


title: Modelling and simulation of solar photovoltaic fed induction motor for water pumping application using perturb and observer MPPT algorithm

abstract: This work focuses on the photovoltaic array fed water pumping system utilizing induction motor with the model developed in Matlab/Simulink. PV system is designed to avail the solar energy by means of Photovoltaic cells. It consists of an arrangement of several components, including solar panels to absorb and convert sunlight into electricity. Maximum Power Point Tracking is a technique used to maximize power output from the Photovoltaic module and finally a 3 phase induction motor is driven through it. In this paper a Photovoltaic array is used to run a induction motor that drives the centrifugal pump. The simulink model of the PV array connected to induction motor where MPPT technique (perturb and observable) plays an important role that is developed in MATLAB and the outputs are observed.


title: Journal impact measures in bibliometric research

abstract: The Impact Factor introduced by Eugene Garfield is a fundamental citation-based measure for significance and performance of scientific journals. It is perhaps the most popular bibliometric product used in bibliometrics itself, as well as outside the scientific community. First, a concise review of the background and history of the ISI impact factor and the basic ideas underlying it are given. A cross-citation matrix is used to visualise the construction of the Impact Factor and several related journal citation measures.Both strengths and flaws of the impact factor are discussed. Several attempts made by different authors to introduce more sophisticated journal citation measures and the reasons why many indicators aiming at a correction of methodological limitations of the Impact Factor were not successful are described. The next section is devoted to the analysis of basic technical and methodological aspects. In this context, the most important sources of possible biases and distortions for calculation and use of journal citation measures are studied. Thereafter, main characteristics of application contexts are summarised. The last section is concerned with questions of statistical reliability of journal citation measures. It is shown that in contrast to a common misbelief statistical methods can be applied to discrete "skewed" distributions, and that the statistical reliability of these statistics can be used as a basis for application of journal impact measures in comparative analyses. Finally, the question of sufficiency or insufficiency of a single, howsoever complex measure for characterising the citation impact of scientific journals is discussed.


title: PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise

abstract: We present PEFAC, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios. The algorithm combines a normalization stage, to remove channel dependency and to attenuate strong noise components, with a harmonic summing filter applied in the log-frequency power spectral domain, the impulse response of which is chosen to sum the energy of the fundamental frequency harmonics while attenuating smoothly-varying noise components. Temporal continuity constraints are applied to the selected pitch candidates and a voiced speech probability is computed from the likelihood ratio of two classifiers, one for voiced speech and one for unvoiced speech/silence. We compare the performance of our algorithm with that of other widely used algorithms and demonstrate that it performs well in both high and low levels of additive noise.


title: ROBUST ADAPTIVE BEAMFORMER USING INTERPO- LATION TECHNIQUE FOR CONFORMAL ANTENNA ARRAY

abstract: A novel robust adaptive beamforming method for conformal array is proposed. By using interpolation technique, the cylindrical conformal array with directional antenna elements is transformed to a virtual uniform linear array with omni-directional elements. This method can compensate the amplitude and mutual coupling errors as well as desired signal point errors of the conformal array e-ciently. It is a universal method and can be applied to other curved conformal arrays. After the transformation, most of the existing adaptive beamforming algorithms can be applied to conformal array directly. The e-ciency of the proposed scheme is assessed through numerical simulations.


Connecting Education and Cognitive Neuroscience : Where will the journey take


title: Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance

abstract: Real-time fraud detection in credit card transactions is challenging due to the intrinsic properties of transaction data, namely data imbalance, noise, borderline entities and concept drift. The advent of mobile payment systems has further complicated the fraud detection process. This paper proposes a transaction window bagging (TWB) model, a parallel and incremental learning ensemble, as a solution to handle the issues in credit card transaction data. TWB model uses a parallelized bagging approach, incorporated with an incremental learning model, cost-sensitive base learner and a weighted voting-based combiner to effectively handle concept drift and data imbalance. Experiments were performed with Brazilian Bank data and University of California, San Diego (UCSD) data, and results were compared with state-of-the-art models. Comparisons on Brazilian Bank data indicates increased fraud detection levels between 18–38% and 1.3–2 times lower cost levels, which exhibits the enhanced performances of TWB. Comparisons on UCSD data indicate improved precision levels ranging between 8 and 25%, indicating the robustness of the TWB model. Future extensions of the proposed model will be on incorporating feature engineering to improve performances.


title: A Shared Task on Multimodal Machine Translation and Crosslingual Image Description

abstract: This paper introduces and summarises the findings of a new shared task at the intersection of Natural Language Processing and Computer Vision: the generation of image descriptions in a target language, given an image and/or one or more descriptions in a different (source) language. This challenge was organised along with the Conference on Machine Translation (WMT16), and called for system submissions for two task variants: (i) a translation task, in which a source language image description needs to be translated to a target language, (optionally) with additional cues from the corresponding image, and (ii) a description generation task, in which a target language description needs to be generated for an image, (optionally) with additional cues from source language descriptions of the same image. In this first edition of the shared task, 16 systems were submitted for the translation task and seven for the image description task, from a total of 10 teams.


title: TN-RSI: Trend-normalized RSI Indicator for Stock Trading Systems with Evolutionary Computation

abstract: Abstract RSI is a commonly used indicator preferred by stock traders. However, even though it works well when the market is trendless, during bull or bear market conditions (when there is a clear trend) its performance degrades. In this study, we developed a trading model using a modified RSI using trend-removed stock data. The model has several parameters including, the trend detection period, RSI buy-sell trigger levels and periods. These parameters are optimized using genetic algorithms; then the trading performance is compared against B&H and standard RSI indicator usage. 9 different ETFs are selected for evaluating trading performance. The results indicate there is a performance improvement both in profit and success rates using this new model. As future work, other indicators might be modelled in a similar fashion in order to see if it is possible to find one indicator that can work under any market condition.


title: Image sentiment analysis using deep convolutional neural networks with domain specific fine tuning

abstract: Images are the easiest medium through which people can express their emotions on social networking sites. Social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Significant progress has been made with this technology, however, there is little research focus on the picture sentiments. In this work, an image sentiment prediction framework is built with Convolutional Neural Networks (CNN). Specifically, this framework is pretrained on a large scale data for object recognition to further perform transfer learning. Extensive experiments were conducted on manually labeled Flickr image dataset. To make use of such labeled data, we employ a progressive strategy of domain specific fine tuning of the deep network. The results show that the proposed CNN training can achieve better performance in image sentiment analysis than competing networks.


title: Active Learning for Graph Embedding

abstract: Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph embedding algorithms assume the labelled nodes are given, which may not be always true in the real world. While manually label all training data is inapplicable, how to select the subset of training data to label so as to maximize the graph analysis task performance is of great importance. This motivates our proposed active graph embedding (AGE) framework, in which we design a general active learning query strategy for any semi-supervised graph embedding algorithm. AGE selects the most informative nodes as the training labelled nodes based on the graphical information (i.e., node centrality) as well as the learnt node embedding (i.e., node classification uncertainty and node embedding representativeness). Different query criteria are combined with the time-sensitive parameters which shift the focus from graph based query criteria to embedding based criteria as the learning progresses. Experiments have been conducted on three public data sets and the results verified the effectiveness of each component of our query strategy and the power of combining them using time-sensitive parameters. Our code is available online at: https://github.com/vwz/AGE.


title: Content to Node: Self-Translation Network Embedding

abstract: This paper concerns the problem of network embedding (NE), whose aim is to learn low-dimensional representations for nodes in networks. Such dense vector representations offer great promises for many network analysis problems. However, existing NE approaches are still faced with challenges posed by the characteristics of complex networks in real-world applications. First, for many real-world networks associated with rich content information, previous NE methods tend to learn separated content and structure representations for each node, which requires a post-processing of combination. The empirical and simple combination strategies often make the final vector suboptimal. Second, the existing NE methods preserve the structure information by considering short and fixed neighborhood scope, such as the first- and/or the second-order proximities. However, it is hard to decide the scope of the neighborhood when facing a complex problem. To this end, we propose a novel sequence-to-sequence model based NE framework which is referred to as Self-Translation Network Embedding (STNE) model. With the sequences generated by random walks on a network, STNE learns the mapping that translates each sequence itself from the content sequence to the node sequence. On the one hand, the bi-directional LSTM encoder of STNE fuses the content and structure information seamlessly from the raw input. On the other hand, high-order proximity can be flexibly learned with the memories of LSTM to capture long-range structural information. By such self-translation from content to node, the learned hidden representations can be adopted as node embeddings. Extensive experimental results based on three real-world datasets demonstrate that the proposed STNE outperforms the state-of-the-art NE approaches. To facilitate reproduction and further study, we provide Internet access to the code and datasets\footnotehttp://dm.nankai.edu.cn/code/STNE.rar.


title: Understanding Relations using Concepts and Semantics

abstract: The Financial Entity Identification and Information Integration (FEIII) task aims at the question of understanding relationships among financial entities and their roles using three sentences extracted from each financial contract containing the target word. FEIII task has two challenges - 1) data sparseness: small training sets (9% of test data) and 2) context sparseness: limited context (three sentences). Existing statistical approaches, such as Bayes and TF-IDF, cannot evaluate the imporatance of words unobservged in training data, which is vulnerable to the above challenges. We overcome each challenge by considering 1) the concepts of words from knowledge bases (Probase) in addition to the words themselves (conceptual feature) and 2) word semantics from distributed representations such as word2vec (semantic feature). We empirically evaluate the proposed classification model on the four-class classification (highly relevant, relevant, neutral, and irrelevant), and show that the proposed model increases 18% of F1-score compared to the statistical baselines.


title: Swing-up of the double pendulum on a cart by feedforward and feedback control with experimental validation

abstract: The swing-up maneuver of the double pendulum on a cart serves to demonstrate a new approach of inversion-based feedforward control design introduced recently. The concept treats the transition task as a nonlinear two-point boundary value problem of the internal dynamics by providing free parameters in the desired output trajectory for the cart position. A feedback control is designed with linear methods to stabilize the swing-up maneuver. The emphasis of the paper is on the experimental realization of the double pendulum swing-up, which reveals the accuracy of the feedforward/feedback control scheme.


Identifying Raga Similarity in Hindustani Classical Music through Distributed Representation of Raga Names


title: Targeting Regions of Chaotic Attractors Using Small Perturbation Control of Symbolic Dynamics.

abstract: Abstract : It has been shown both theoretically and experimentally that it is possible to control the symbolic dynamics of a chaotic system using small perturbations. We show a method of very rapidly targeting a region of width e near a desired state point on a one-dimensional map using control of symbolic dynamics; this method demonstrates that controlling symbolic dynamics provides an efficient algorithm for targeting.


title: Any-angle path planning for computer games

abstract: Path planning is a critical part of modern computer games; rare is the game where nothing moves and path planning is not necessary. A* is the workhorse for most path planning applications. Block A* is a state-of-the-art algorithm that is always faster than A* in experiments using game maps. Unlike other methods that improve upon A*'s performance, Block A* does not require knowledge of the map before the search and its paths are never longer than A* paths. In our experiments, Block A* is ideal for games with randomly generated maps, large maps, or games with a highly dynamic multi-agent environment. In the domain of grid-based any-angle path planning, we show that Block A* is an order of magnitude faster than the previous best any-angle path planning algorithm, Theta*. We empirically show our results using maps from DRAGON AGE: ORIGINS and STARCRAFT. Finally, we introduce "populated game maps" as a new testbed that is a better approximation of real game conditions than the standard testbeds of this field. The main contributions of this paper are a more rigorous set of experiments for Block A* and introduction of a new testbed (populated game maps).


title: Converter Volt-Ampere Requirements of the Switched Reluctance Motor Drive

abstract: An algebraic nonlinear analysis oftheswitched reluctance drivesystemisdescribed. Theanalysis isintended toprovide an understanding ofthefactors thatdetermine thekVArequirements ofthe electronic powerconverter andtodetermine thefundamental nature of thetorque/speed characteristics. Theeffect ofsaturation isgiven special attention. Itisshownthatsaturation hasthetwomaineffects of increasing themotorsize required foragiven torque, andatthesametime decreasing thekVAperhorsepower (i.e., increasing theeffective power factor byanalogy withanacmachine). ThekVAperhorsepower islower thanpredicted bysimple linear analysis thatneglects saturation. Neces- saryconditions arealso developed foraflat-topped current waveform by correctly determining themotorback-EMF. Thereason whyitisdesirable toallowthephasecurrent tocontinue (though withmuchreduced magnitude) evenafter thepoleshavepassed thealigned position is explained. Thetheory provides aformula fordetermining therequired commutation angle forthephasecurrent. Thebasis isprovided foran estimation ofthekVA requirements oftheswitched reluctance (SR) drive. Theserequirements havebeenmeasured andalsocalculated bya computer simulation program.


title: Look & touch: gaze-supported target acquisition

abstract: While eye tracking has a high potential for fast selection tasks, it is often regarded as error-prone and unnatural, especially for gaze-only interaction. To improve on that, we propose gaze-supported interaction as a more natural and effective way combining a user's gaze with touch input from a handheld device. In particular, we contribute a set of novel and practical gaze-supported selection techniques for distant displays. Designed according to the principle gaze suggests, touch confirms they include an enhanced gaze-directed cursor, local zoom lenses and more elaborated techniques utilizing manual fine positioning of the cursor via touch. In a comprehensive user study with 24 participants, we investigated the potential of these techniques for different target sizes and distances. All novel techniques outperformed a simple gaze-directed cursor and showed individual advantages. In particular those techniques using touch for fine cursor adjustments (MAGIC touch) and for cycling through a list of possible close-to-gaze targets (MAGIC tab) demonstrated a high overall performance and usability.


title: Overlapping community detection using Bayesian non-negative matrix factorization

abstract: Identifying overlapping communities in networks is a challenging task. In this work we present a probabilistic ::: approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract ::: overlapping modules from a network. The scheme has the advantage of soft-partitioning solutions, assignment ::: of node participation scores to modules, and an intuitive foundation. We present the performance of the method ::: against a variety of benchmark problems and compare and contrast it to several other algorithms for community ::: detection.


title: Exploring the synergy between entrepreneurship and innovation

abstract: Purpose – To contribute to an understanding of the complementary nature of entrepreneurship and innovation through an empirical study of various organisations; and to develop an integrative framework of the interaction between entrepreneurship and innovation.Design/methodology/approach – The study takes a qualitative approach in exploring the synergies between entrepreneurship and innovation and in analysing the factors that foster an interaction between the two. Case studies of six entrepreneurial and innovative organisations and in‐depth interviews with senior managers were conducted to complement a comprehensive literature review of entrepreneurship and innovation.Findings – The study has found that: entrepreneurship and innovation are positively related to each other and interact to help an organisation to flourish; entrepreneurship and innovation are complementary, and a combination of the two is vital to organisational success and sustainability in today's dynamic and changing environment; entrepren...


Solving Text Imputation Using Recurrent Neural Networks


title: An Analysis of Information Technology on Data Processing by using Cobit Framework

abstract: Information technology and processes is inter connected, directing and controlling the company in achieving corporate goals through value-added and balancing the risks and benefits of information technology. This study is aimed to analyze the level of maturity (maturity level) on the data process and produced information technology recommendations that can be made as regards the management of IT to support the academic performance of the service to be better. Maturity level calculation was done by analyzing questionnaires on the state of information technology. The results of this study obtainable that the governance of information technology in data processing in Mataram ASM currently quite good. Current maturity value for the data processing has the value 2.69. This means that the company / organization already has a pattern of repeatedly done in managing the activities related to data management processes. Based on the data analysis, there is an effect on the current conditions and expected conditions can be taken solution or corrective actions to improve IT governance in the process of data management at ASM Mataram gradually.


title: Outlier Detection in High Dimension Data Based On Multimodality And Neighbourhood Size Using KNN Method

abstract: Outlier detection in high-dimensional data presents various challenges resulting from the “curse of dimensionality.” A prevailing view is that distance concentration, i.e., the tendency of distances in high-dimensional data to become indiscernible, hinders the detection of outliers by making distance-based methods label all points as almost equally good outliers. In this paper, we provide evidence supporting the opinion that such a view is too simple, by demonstrating that distance-based methods can produce more contrasting outlier scores in high-dimensional settings. Furthermore, we show that high dimensionality can have a different impact, by reexamining the notion of reverse nearest neighbors in the unsupervised outlier-detection context. Namely, it was recently observed that the distribution of points’ reverse-neighbor counts becomes skewed in high dimensions, resulting in the phenomenon known as hubness. We provide insight into how some points (antihubs) appear very infrequently in k-NN lists of other points, and explain the connection between antihubs, outliers, and existing unsupervised outlier-detection methods. By evaluating the classic k-NN method, the angle-based technique designed for high-dimensional data, the density-based local outlier factor and influenced outlierness methods, and antihub-based methods on various synthetic and real-world data sets, we offer novel insight into the usefulness of reverse neighbor counts in unsupervised outlier detection.


title: Diffusion dynamics in small-world networks with heterogeneous consumers

abstract: Diffusions of new products and technologies through social networks can be formalized as spreading of infectious diseases. However, while epidemiological models describe infection in terms of transmissibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our agent-based model consumers' probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study how social processes affect diffusion dynamics and how the speed of the diffusion depends on the network structure and on consumer heterogeneity. First, we show that the speed of diffusion changes with the degree of randomness in the network. In markets with high social influence and in which consumers have a sufficiently large local network, the speed is low in regular networks, it increases in small-world networks and, contrarily to what epidemic models suggest, it becomes very low again in random networks. Second, we show that heterogeneity helps the diffusion. Ceteris paribus and varying the degree of heterogeneity in the population of agents simulation results show that the more heterogeneous the population, the faster the speed of the diffusion. These results can contribute to the development of marketing strategies for the launch and the dissemination of new products and technologies, especially in turbulent and fashionable markets.


title: A Minimax Approach to Supervised Learning

abstract: Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of probability distributions $\Gamma$ on $(X,Y)$, what is the optimal decision rule minimizing the worst-case expected loss over $\Gamma$? In this paper, we address this question by introducing a generalization of the principle of maximum entropy. Applying this principle to sets of distributions with marginal on $X$ constrained to be the empirical marginal from the data, we develop a general minimax approach for supervised learning problems. While for some loss functions such as squared-error and log loss, the minimax approach rederives well-knwon regression models, for the 0-1 loss it results in a new linear classifier which we call the maximum entropy machine. The maximum entropy machine minimizes the worst-case 0-1 loss over the structured set of distribution, and by our numerical experiments can outperform other well-known linear classifiers such as SVM. We also prove a bound on the generalization worst-case error in the minimax approach.


title: Globus: a Metacomputing Infrastructure Toolkit

abstract: The Globus system is intended to achieve a vertically integrated treatment of application, middleware, and net work. A low-level toolkit provides basic mechanisms such as communication, authentication, network information, and data access. These mechanisms are used to con struct various higher level metacomputing services, such as parallel programming tools and schedulers. The long- term goal is to build an adaptive wide area resource environment AWARE, an integrated set of higher level services that enable applications to adapt to heteroge neous and dynamically changing metacomputing environ ments. Preliminary versions of Globus components were deployed successfully as part of the I-WAY networking experiment.


title: Model checking csp revisited: Introducing a process analysis toolkit

abstract: FDR, initially introduced decades ago, is the de facto analyzer for Communicating Sequential Processes (CSP). Model checking techniques have been evolved rapidly since then. This paper describes Pat, i.e., a process analysis toolkit which complements FDR in several aspects. Pat is designed to analyze event-based compositional system models specified using CSP as well as shared variables and asynchronous message passing. It supports automated refinement checking, model checking of LTL extended with events, etc. In this paper, we highlight how partial order reduction is applied to improve refinement checking in Pat. Experiment results show that Pat outperforms FDR in some cases.


title: Personalized news recommendation via implicit social experts

abstract: Personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information around the world. A variety of news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing the implicit ''social'' factors (i.e., the potential influential experts in news reading community) among news readers to facilitate news personalization. In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques. We propose PRemiSE, a novel Personalized news Recommendation framework via implicit Social Experts, in which the opinions of potential influencers on virtual social networks extracted from implicit feedbacks are treated as auxiliary resources for recommendation. We evaluate and compare our proposed recommendation method with various baselines on a collection of news articles obtained from multiple popular news websites. Experimental results demonstrate the efficacy and effectiveness of our method, particularly, on handling the so-called cold-start problem.


title: A SDN-oriented DDoS blocking scheme for botnet-based attacks

abstract: DDoS attacks mounted by botnets today target a specific service, mobilizing only a small amount of legitimate-looking traffic to compromise the server. Detecting or blocking such clever attacks by only using anomalous traffic statistics has become difficult, and devising countermeasures has been mostly left to the victim server. In this paper, we investigate how a software-defined network (SDN) can be utilized to overcome the difficulty and effectively block legitimate looking DDoS attacks mounted by a larger number of bots. Specifically, we discuss a DDoS blocking application that runs over the SDN controller while using the standard OpenFlow interface.


Data Mining Classification Techniques: A Recent Survey


title: A Distortion Rectification Method for Distorted QR Code Images Based on Self-Adapting Structural Element

abstract: With the rapid development of Internet of Things and communication technologies, QR code images are widely used in the embedded automatic identification field. However, previous works for the location of QR code have not considered how to extract the vertexes accurately in geometric distorted QR code images. In this paper, utilizing the characteristics of QR code, we propose an effective mathematical morphology method based on self-adapting structural element to obtain the distorted vertexes precisely and revise the distortion. Our method evaluates two data sets. Compared with previous works, the experiments indicate that our method can achieve higher recognition rate and be more adaptive for geometric distorted QR code images. Thus, the proposed method provides valuable application for embedded system and mobile terminal device.


title: Trajectory clustering: a partition-and-group framework

abstract: Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.


title: A Review of Commercial Energy Harvesters for Autonomous Sensors

abstract: Current commercial autonomous sensors are mainly powered by primary batteries. Batteries need to be replaced and hence can become the largest and most expensive part of the system. On the other hand, our environment is full of waste and unused energy such as that coming from the sun or mechanical vibrations. As a result, commercial energy harvesters are increasingly available to power autonomous sensors. This work presents and analyses commercial energy harvesters currently available. First, environmental energy sources are classified and described. Then, energy harvesting principles are described and some guidelines are given to calculate the maximum power consumption allowed and the energy storage capacity required for the autonomous sensor. Finally, commercial energy harvesters are evaluated to determine their capability to power a commercial autonomous sensor in some given circumstances.


title: G.: Ontologies and Information Systems: the Marriage of the Century

abstract: Although is recognized that ontologies may help building better and more interoperable information systems, there is skepticism on the real impact they may have in the future. We believe that ontologies will succeed in the information system arena and no systems will ever be designed without an ontological approach. In this paper we demonstrate the effectiveness of the ontological approach by illustrating three case studies. We show how an ontological framework is able to support semantic interoperability in the domain of fishery, then we present the role of ontologies for managing clinical guidelines and finally we sketch up an ontological analysis aimed at the interoperability of genetics


title: An Interoperable Internet Scale Solution for RFID Network Security

abstract: Security and privacy issues are critically important in RFID networks because EPC data and their associated business events are valuable assets. In this paper, an Interoperable Internet-Scale Security (IISS) framework for RFID networks is proposed. The IISS framework performs authentication and au- thorization based on an aggregation of business rules, enterprise information, and RFID tag information. IISS provides a protocol for several authentication schemes and identity techniques. It also provides an engine for reasoning over business rules from different domains. Moreover, the IISS framework is able to resolve provenance information of RFID tags, which can identify the history of a particular piece of EPC data through the supply chain. The IISS framework and the IISS ontologies to model the information in RFID networks are also described, and how the IISS framework can be developed for access control in RFID enabled supply chains is discussed. Finally, the IISS framework's efficiency is tested using a supply chain EPC simulator as the testing platform, which allows optimization of the IISS protocol's performance. I. INTRODUCTION Since Radio Frequency Identification (RFID) was invented in 1946, RFID technology has had widespread application, including electronic passports, transportation payments, and inventory systems. As RFID deployment continues to in- crease, its associated applications have become more and more network-centric. Most tags are used in supply chains where the Electronic Product Code (EPC) and associated business event data are transmitted through RFID networks. The design of RFID network thus plays a critical role in successfully deploying large-scale applications. Security and privacy issues are critically important for RFID


title: Performance comparison of rasterization-based graphics pipeline and ray tracing on GPU shaders

abstract: The interactive rendering of 3D computer graphics has approached the photorealistic quality, as evident by the vivid shading effects and lush outdoor scenes in recent game engines. Clearly, the traditional 3D graphics APIs are reaching their limits, and the need to switch to more extendable ray-tracing based techniques has arisen. In this work, we explore the fundamental differences between ray tracing based and rasterization based techniques, including how they are supported by the processors and by the programming platforms. We duplicate the typical shading effects in both ray tracing and rasterization, starting from the simple Phong lighting, to slightly more complex Whitted-style shadow and reflection. Although the rasterization-based techniques clearly outperform ray tracing in current generations of graphics processors, we show by more precise quantitative analysis that the performance gaps are not as wide as thought. And the gap may narrow further when the requirement of image quality increases in the future.


title: FPGA-based real-time optical-flow system

abstract: We describe a pipelined optical-flow processing system that works as a virtual motion sensor. It is based on a field-programmable gate array (FPGA) device enabling the easy change of configuring parameters to adapt the sensor to different speeds, light conditions and other environmental factors. We refer to it as a "virtual sensor" because it consists of a conventional camera as front-end supported by an FPGA processing device, which embeds the frame grabber, optical-flow algorithm implementation, output module, and some configuration and storage circuitry. To the best of our knowledge, this is the first study that presents a fully stand-alone optical-flow processing system to include measurements of the platform performance in terms of accuracy and speed.


title: Cognitive load in hypertext reading: A review

abstract: A process model of hypertext reading was used to generate predictions about the effects of hypertext features on cognitive processing during text navigation and comprehension. We evaluated the predictions of the model with respect to the extant literature, focusing on studies in which versions of hypertexts were compared. Consistent with our predictions, the increased demands of decision-making and visual processing in hypertext impaired reading performance. Individual differences in readers, such as working memory capacity and prior knowledge, mediated the impact of hypertext features. For example, readers with low working memory and low prior knowledge were usually disadvantaged in hypertext. Some benefits were observed for learners with low prior knowledge, however, if the hypertext structure was hierarchical and consistent with that of the knowledge domain. We also surveyed the effectiveness of structural features designed to reduce cognitive load, including graphical overviews, restricted access to links, and visible link types. Complex graphical overviews did not reliably enable learning and navigation, whereas navigational support from restricted access and visible link types were helpful. We identified gaps in the empirical literature and suggested future studies to investigate cognitive processes in hypertext reading.


title: Anatomical study of the pisotriquetral joint ligaments using ultrasonography

abstract: OBJECTIVE ::: The aim of this study was to demonstrate that ultrasonography may allow a precise assessment of the primary stabilizers of pisotriquetral joint (pisohamate, pisometacarpal, and ulnar pisotriquetral ligaments). ::: ::: ::: METHODS AND MATERIALS ::: This study was initially undertaken in eight cadavers. Metal markers were placed in the ligaments using ultrasonographic guidance, followed by the dissection of the wrists. High-resolution ultrasonography was then performed in 15 volunteers (30 wrists) for the analysis of the presence, appearance, and thickness of the ligaments. ::: ::: ::: RESULTS ::: At dissection, the metal markers were located in the ligaments or immediately adjacent to them, confirming that they were correctly depicted using ultrasonography. The three ligaments could also be identified in each volunteer. The optimal positioning of the probe and the dynamic maneuvers of the wrist allowing the strain of these ligaments could be defined. No significant changes in the appearance and thickness of the ligaments could be observed. ::: ::: ::: CONCLUSIONS ::: The three ligaments stabilizing the pisotriquetral joint can be identified using ultrasonography. Further studies are now required to know whether this knowledge may be useful in the assessment of pain involving the ulnar part of the wrist.


title: Virtual CPU validation

abstract: Testing the hypervisor is important for ensuring the correct operation and security of systems, but it is a hard and challenging task. We observe, however, that the challenge is similar in many respects to that of testing real CPUs. We thus propose to apply the testing environment of CPU vendors to hypervisors. We demonstrate the advantages of our proposal by adapting Intel's testing facility to the Linux KVM hypervisor. We uncover and fix 117 bugs, six of which are security vulnerabilities. We further find four flaws in Intel virtualization technology, causing a disparity between the observable behavior of code running on virtual and bare-metal servers.


A simple mechanical model for the DELTA-Robot


title: Controllability metrics, limitations and algorithms for complex networks

abstract: This paper studies the problem of controlling complex networks, that is, the joint problem of selecting a set of control nodes and of designing a control input to steer a network to a target state. For this problem (i) we propose a metric to quantify the difficulty of the control problem as a function of the required control energy, (ii) we derive bounds based on the system dynamics (network topology and weights) to characterize the tradeoff between the control energy and the number of control nodes, and (iii) we propose an open-loop control strategy with performance guarantees. In our strategy we select control nodes by relying on network partitioning, and we design the control input by leveraging optimal and distributed control techniques. Our findings show several control limitations and properties. For instance, for Schur stable and symmetric networks: (i) if the number of control nodes is constant, then the control energy increases exponentially with the number of network nodes, (ii) if the number of control nodes is a fixed fraction of the network nodes, then certain networks can be controlled with constant energy independently of the network dimension, and (iii) clustered networks may be easier to control because, for sufficiently many control nodes, the control energy depends only on the controllability properties of the clusters and on their coupling strength. We validate our results with examples from power networks, social networks, and epidemics spreading.


title: Bi-Level Semantic Representation Analysis for Multimedia Event Detection

abstract: Multimedia event detection has been one of the major endeavors in video event analysis. A variety of approaches have been proposed recently to tackle this problem. Among others, using semantic representation has been accredited for its promising performance and desirable ability for human-understandable reasoning. To generate semantic representation, we usually utilize several external image/video archives and apply the concept detectors trained on them to the event videos. Due to the intrinsic difference of these archives, the resulted representation is presumable to have different predicting capabilities for a certain event. Notwithstanding, not much work is available for assessing the efficacy of semantic representation from the source-level. On the other hand, it is plausible to perceive that some concepts are noisy for detecting a specific event. Motivated by these two shortcomings, we propose a bi-level semantic representation analyzing method. Regarding source-level, our method learns weights of semantic representation attained from different multimedia archives. Meanwhile, it restrains the negative influence of noisy or irrelevant concepts in the overall concept-level. In addition, we particularly focus on efficient multimedia event detection with few positive examples, which is highly appreciated in the real-world scenario. We perform extensive experiments on the challenging TRECVID MED 2013 and 2014 datasets with encouraging results that validate the efficacy of our proposed approach.


title: Passivity-Based Controller Design of Grid-Connected VSCs for Prevention of Electrical Resonance Instability

abstract: The time delay in the current control loop of a grid-connected voltage-source converter (VSC) may cause destabilization of electrical resonances in the grid or in the VSC's input filter. Instability is prevented if the input admittance of the VSC can be made passive. This paper presents an analytical controller design method for obtaining passivity. The method is equally applicable to single- and three-phase systems, i.e., in the latter case, for both stationary- and synchronous-frame control. Simulations and experiments verify the theoretical results.


title: Commissioning of the Varian TrueBeam linear accelerator: A multi-institutional study

abstract: PURPOSE ::: Latest generation linear accelerators (linacs), i.e., TrueBeam (Varian Medical Systems, Palo Alto, CA) and its stereotactic counterpart, TrueBeam STx, have several unique features, including high-dose-rate flattening-filter-free (FFF) photon modes, reengineered electron modes with new scattering foil geometries, updated imaging hardware/software, and a novel control system. An evaluation of five TrueBeam linacs at three different institutions has been performed and this work reports on the commissioning experience. ::: ::: ::: METHODS ::: Acceptance and commissioning data were analyzed for five TrueBeam linacs equipped with 120 leaf (5 mm width) MLCs at three different institutions. Dosimetric data and mechanical parameters were compared. These included measurements of photon beam profiles (6X, 6XFFF, 10X, 10XFFF, 15X), photon and electron percent depth dose (PDD) curves (6, 9, 12 MeV), relative photon output factors (Scp), electron cone factors, mechanical isocenter accuracy, MLC transmission, and dosimetric leaf gap (DLG). End-to-end testing and IMRT commissioning were also conducted. ::: ::: ::: RESULTS ::: Gantry/collimator isocentricity measurements were similar (0.27-0.28 mm), with overall couch/gantry/collimator values of 0.46-0.68 mm across the three institutions. Dosimetric data showed good agreement between machines. The average MLC DLGs for 6, 10, and 15 MV photons were 1.33 ± 0.23, 1.57 ± 0.24, and 1.61 ± 0.26 mm, respectively. 6XFFF and 10XFFF modes had average DLGs of 1.16 ± 0.22 and 1.44 ± 0.30 mm, respectively. MLC transmission showed minimal variation across the three institutions, with the standard deviation <0.2% for all linacs. Photon and electron PDDs were comparable for all energies. 6, 10, and 15 MV photon beam quality, %dd(10)x varied less than 0.3% for all linacs. Output factors (Scp) and electron cone factors agreed within 0.27%, on average; largest variations were observed for small field sizes (1.2% coefficient of variation, 10 MV, 2 × 2 cm(2)) and small cone sizes (<1% coefficient of variation, 6 × 6 cm(2) cone), respectively. ::: ::: ::: CONCLUSIONS ::: Overall, excellent agreement was observed in TrueBeam commissioning data. This set of multi-institutional data can provide comparison data to others embarking on TrueBeam commissioning, ultimately improving the safety and quality of beam commissioning.


title: High-Gain Dual-Loop Antennas for MIMO Access Points in the 2.4/5.2/5.8 GHz Bands

abstract: A high-gain, three-antenna system suitable to be concealed inside wireless access points for multiple-input multiple-output (MIMO) applications in the WLAN 2.4/5.2/5.8 GHz bands is presented. The MIMO antenna system is composed of three dual-loop antennas occupying a moderate size of 10 × 20 × 40 mm3. Each dual-loop antenna further comprises a large outer loop and a small inner loop, both operating at 1.0-wavelength resonant mode and sharing common antenna feeding and grounding portions. In addition, the antennas are placed in a sequential, rotating arrangement on a ground plane with an equal inclination angle of 120° to form a symmetrical multiple-antenna structure. Good impedance matching for outer and inner loops over the 2.4 and 5.2/5.8 GHz bands can easily be achieved by tuning the widths of feeding and grounding portions and a small gap therein between. The results show that well port isolation can be obtained together with high-gain, directional radiation characteristics. Calculated envelope correlation is less than 0.007 within the bands of interest. Details of a design prototype are described and discussed in the paper.


title: Two theories of consciousness: Semantic pointer competition vs. information integration

abstract: Consciousness results from three mechanisms: representation by firing patterns in neural populations, binding of representations into more complex representations called semantic pointers, and competition among semantic pointers to capture the most important aspects of an organism's current state. We contrast the semantic pointer competition (SPC) theory of consciousness with the hypothesis that consciousness is the capacity of a system to integrate information (IIT). We describe computer simulations to show that SPC surpasses IIT in providing better explanations of key aspects of consciousness: qualitative features, onset and cessation, shifts in experiences, differences in kinds across different organisms, unity and diversity, and storage and retrieval.


title: Project portfolio selection: Multi-criteria analysis and interactions between projects

abstract: In the project portfolio management, the project selection phase presents the greatest interest. In this article, we focus on this important phase by proposing a new method of projects selection consisting of several steps. We propose as a first step, a classification of projects based on the three most important criteria namely the value maximization, risk minimization and strategic alignment. The second step is building alternatives portfolio by the portfolio managers taking into account the classification of projects already completed in the first step. The third and final step enables the identification of the alternative portfolio to consider the contribution of projects to achieve the organization objectives as well as interactions between projects.


title: Localization of Salient Objects in Scenes through Visual Attention

abstract: There are several real situations in which it is useful to have a system able to detect a salient object and its localization in a given scene in autonomous way. Among these systems are robot vision systems that must detect determined objects, security systems, that must detect stranger people or objects in environment. These systems need to present a very fast performance and accuracy. In this article, we have combined two neural networks, a Kohonen model and a Pulsed Neural Network for the creation of an attributed-saliency map. Thanks to this combination, it is possible not only to detect the salient object in given scene as well as its localization in the image. Several tests have been performed to verify the viability of the model as a mechanism of selection of objects as a part of a visual attention system. The results demonstrate that the technique proposed is very fast for detecting the region of the image corresponding to salient object.


title: A taxonomy and catalog of runtime software-fault monitoring tools

abstract: A goal of runtime software-fault monitoring is to observe software behavior to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing additional defense against catastrophic failure. Although runtime monitoring has been in use for over 30 years, there is renewed interest in its application to fault detection and recovery, largely because of the increasing complexity and ubiquitous nature of software systems. We present taxonomy that developers and researchers can use to analyze and differentiate recent developments in runtime software fault-monitoring approaches. The taxonomy categorizes the various runtime monitoring research by classifying the elements that are considered essential for building a monitoring system, i.e., the specification language used to define properties; the monitoring mechanism that oversees the program's execution; and the event handler that captures and communicates monitoring results. After describing the taxonomy, the paper presents the classification of the software-fault monitoring systems described in the literature.


title: Through-Hole Contactless Slipring System Based on Rotating Magnetic Field for Rotary Applications

abstract: Contactless slipring system based on inductive power transfer is a preferable alternative to mechanical slipring assemblies. They are designed to deliver required output power to a rotary shaft without direct electrical contacts. The current single or multiple-unit through-hole single-phase systems often have limited power transfer capability within a confined physical space; thus, they may not be able to meet the load requirement for certain applications such as wind power pitch control. This paper presents an integrated polyphase through-hole contactless slipring system by generating a rotating primary magnetic field coupled with balanced power pickups at the secondary side with zero mechanical driving torque on the shaft. A new index termed “mutual inductance per pole” is introduced to simplify the analysis of the mutually coupled polyphase system to a single-phase basis. Simulation and practical experiments have demonstrated that, with the same physical size, the proposed contactless slipring setup can transfer 55% more power compared with the existing single-phase counterpart.


title: A new zero-ripple switching dc-to-dc converter and integrated magnetics

abstract: A new switching dc-to-dc converter is synthesized which consists of the least number of storage elements (inductive and capacitive) and switches, and yet truly emulates the ideally desired dc-to-dc transformer having both input and output currents as pure dc quantities with no ripple. This result was facilitated by implementation of a new concept termed integrated magnetics, which leads in some special switching structures to the integration of otherwise independent and separate magnetic components (inductors and transformers) into a single magnetic circuit.


title: Development of climbing robot for steel bridge inspection

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to present a design of climbing robot with magnetic wheels which can move on the surface of steel bridge. The locomotion concept is based on adapted lightweight magnetic wheel units with relatively high attractive force and friction force. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The robot has the main advantages of being compact (352 × – 215 × – 155 mm), lightweight (2.3 kg without battery) and simple mechanical structure. It is not only able to climb vertical walls and follow circumferential paths, but also able to pass complex obstacles such as bolts, steps, convex and concave corners with almost any inclination regarding gravity. By using a servo as a compliant joint, the wheel base can be changed to enable the robot to overcome convex corners. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The experiment results show that the climbing robot has a good performance on locomotion, and it is successful in negotiating the complex obstacles. On the other hand, the limitations in locomotion of the robot are also presented. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Compared with the past researches, the robot shows good performance on overcoming complex obstacles such as concave corners, convex corners, bolts and steps on the steel bridge. Magnetic wheel with the characterization of compact size and lightweight is able to provide bigger adhesion force and friction coefficient.


title: L-Store: A Real-time OLTP and OLAP System

abstract: Arguably data is a new natural resource in the enterprise world with an unprecedented degree of proliferation. But to derive real-time actionable insights from the data, it is important to bridge the gap between managing the data that is being updated at a high velocity (i.e., OLTP) and analyzing a large volume of data (i.e., OLAP). However, there has been a divide where specialized solutions were often deployed to support either OLTP or OLAP workloads but not both; thus, limiting the analysis to stale and possibly irrelevant data. In this paper, we present Lineage-based Data Store (L-Store) that combines the real-time processing of transactional and analytical workloads within a single unified engine by introducing a novel lineage-based storage architecture. We develop a contention-free and lazy staging of columnar data from a write-optimized form (suitable for OLTP) into a read-optimized form (suitable for OLAP) in a transactionally consistent approach that also supports querying and retaining the current and historic data. Our working prototype of L-Store demonstrates its superiority compared to state-of-the-art approaches under a comprehensive experimental evaluation.


title: Key issues in conducting sentiment analysis on Arabic social media text

abstract: The problem of extracting sentiments from text is a very complex task, in particular due to the significant amount of Natural Language Processing (NLP) required. This task becomes even more difficult when dealing with morphologically rich languages such as Modern Standard Arabic (MSA) and when processing brief, noisy texts such as “tweets” or “Facebook statuses”. This paper highlights key issues researchers are facing and innovative approaches that have been developed when performing subjectivity and sentiment analysis (SSA) on Arabic text in general and Arabic social media text in particular. A preprocessing phase to sentiment analysis is proposed and shown to noticeably improve the results of sentiment extraction from Arabic social media data.


title: Can Machine Learn Steganography? - Implementing LSB Substitution and Matrix Coding Steganography with Feed-Forward Neural Networks

abstract: In recent years, due to the powerful abilities to deal with highly complex tasks, the artificial neural networks (ANNs) have been studied in the hope of achieving human-like performance in many applications. Since the ANNs have the ability to approximate complex functions from observations, it is straightforward to consider the ANNs for steganography. In this paper, we aim to implement the well-known LSB substitution and matrix coding steganography with the feed-forward neural networks (FNNs). Our experimental results have shown that, the used FNNs can achieve the data embedding operation of the LSB substitution and matrix coding steganography. For steganography with the ANNs, though there may be some challenges to us, it would be very promising and valuable to pay attention to the ANNs for steganography, which may be a new direction for steganography.


title: Textual Affect Communication and Evocation Using Abstract Generative Visuals

abstract: In order to facilitate interaction in computer-mediated communication and enrich user experience in general, we introduce a novel textual emotion visualization approach, grounded in generative art and evocative visuals. The approach is centered on the idea that affective computer systems should be able to relate to, communicate, and evoke human emotions. It maps emotions identified in the text to evocative abstract animation. We examined two visualizations based on our approach and two common textual emotion visualization techniques, chat emoticons and avatars, along three dimensions: emotion communication, emotion evocation, and overall user enjoyment. Our study, organized as repeated measures within-subject experiment, demonstrated that in terms of emotion communication, our visualizations are comparable with emoticons and avatars. However, our main visualization based on abstract color, motion, and shape proved to be the best in evoking emotions. In addition, in terms of the overall user enjoyment, it gave results comparable with emoticons, but better than avatars.


title: The first decade of the community of inquiry framework: A retrospective

abstract: Abstract This article provides a personal perspective about the development of the seminal papers associated with the Community of Inquiry Framework. The framework and its construction are described. The main part of the paper explores the evolution of the framework and its associated methodology. Finally, research associated with the validation of the framework and new research directions are reviewed.


SPH Based Shallow Water Simulation.


title: A review of algorithms for filtering the 3D point cloud

abstract: Abstract In recent years, 3D point cloud has gained increasing attention as a new representation for objects. However, the raw point cloud is often noisy and contains outliers. Therefore, it is crucial to remove the noise and outliers from the point cloud while preserving the features, in particular, its fine details. This paper makes an attempt to present a comprehensive analysis of the state-of-the-art methods for filtering point cloud. The existing methods are categorized into seven classes, which concentrate on their common and obvious traits. An experimental evaluation is also performed to demonstrate robustness, effectiveness and computational efficiency of several methods used widely in practice.


title: Biomechanical features of gait waveform data associated with knee osteoarthritis: An application of principal component analysis

abstract: This study compared the gait of 50 patients with end-stage knee osteoarthritis to a group of 63 age-matched asymptomatic control subjects. The analysis focused on three gait waveform measures that were selected based on previous literature demonstrating their relevance to knee osteoarthritis (OA): the knee flexion angle, flexion moment, and adduction moment. The objective was to determine the biomechanical features of these gait measures related to knee osteoarthritis. Principal component analysis was used as a data reduction tool, as well as a preliminary step for further analysis to determine gait pattern differences between the OA and the control groups. These further analyses included statistical hypothesis testing to detect group differences, and discriminant analysis to quantify overall group separation and to establish a hierarchy of discriminatory ability among the gait waveform features. The two groups were separated with a misclassification rate (estimated by cross-validation) of 8%. The discriminatory features of the gait waveforms were, in order of their discriminatory ability: the amplitude of the flexion moment, the range of motion of the flexion angle, the magnitude of the flexion moment during early stance, and the magnitude of the adduction moment during stance.


title: MySQL to NoSQL: data modeling challenges in supporting scalability

abstract: Software systems today seldom reside as isolated systems confined to generating and consuming their own data. Collecting, integrating and storing large amounts of data from disparate sources has become a need for many software engineers, as well as for scientists in research settings. This paper presents the lessons learned when transitioning a large-scale data collection infrastructure from a relational database to a hybrid persistence architecture that makes use of both relational and NoSQL technologies. Our examples are drawn from the software infrastructure we built to collect, store, and analyze vast numbers of status updates from the Twitter micro-blogging service in support of a large interdisciplinary group performing research in the area of crisis informatics. We present both the software architecture and data modeling challenges that we encountered during the transition as well as the benefits we gained having migrated to the hy-brid persistence architecture.


title: SysML-Sec: A model driven approach for designing safe and secure systems

abstract: Security flaws are open doors to attack embedded systems and must be carefully assessed in order to determine threats to safety and security. Subsequently securing a system, that is, integrating security mechanisms into the system's architecture can itself impact the system's safety, for instance deadlines could be missed due to an increase in computations and communications latencies. SysML-Sec addresses these issues with a model-driven approach that promotes the collaboration between system designers and security experts at all design and development stages, e.g., requirements, attacks, partitioning, design, and validation. A central point of SysML-Sec is its partitioning stage during which safety-related and security-related functions are explored jointly and iteratively with regards to requirements and attacks. Once partitioned, the system is designed in terms of system's functions and security mechanisms, and formally verified from both the safety and the security perspectives. Our paper illustrates the whole methodology with the evaluation of a security mechanism added to an existing automotive system.


title: Digital circuit designs that are not sensitive to delay promise to allow operation without clocks for future systems-on-a-chip

abstract: SoC design will require asynchronous techniques as the large parameter variations across the chip will make it impossible to control delays in clock networks and other global signals efficiently. Initially, SoCs will be globally asynchronous and locally synchronous (GALS). But the complexity of the numerous asynchronous/synchronous interfaces required in a GALS will eventually lead to entirely asynchronous solutions. This paper introduces the main design principles, methods, and building blocks for asynchronous VLSI systems, with an emphasis on communication and synchronization. Asynchronous circuits with the only delay assumption of isochronic forks are called quasi-delay-insensitive (QDI). QDI is used in the paper as the basis for asynchronous logic. The paper discusses asynchronous handshake protocols for communication and the notion of validity/neutrality tests, and completion tree. Basic building blocks for sequencing, storage, function evaluation, and buses are described, and two alternative methods for the implementation of an arbitrary computation are explained. Issues of arbitration, and synchronization play an important role in complex distributed systems and especially in GALS. The two main asynchronous/synchronous interfaces needed in GALS-one based on synchronizer, the other on stoppable clock-are described and analyzed.


title: Comparison of Different Data Mining Techniques to Predict Hospital Length of Stay

abstract: In this paper we present the performance analysis of different data mining techniques to predict the inpatient hospital length of stay in a super specialty hospital. Data set used for the analysis is real time data taken from super specialty hospital. Pre-processed data set is generated from the electronic discharge summaries obtained from the hospital. This data set consists of 401 records with 16 parameters. In this paper we have investigated four data mining techniques: Multilayer back propagation NN, Naive Bayes Classifier, K-NN method, J48 class of C4.5 decision tree. We found from the analysis that Neural Network has achieved better performance compared to the other three techniques.


title: Latent topic model for audio retrieval

abstract: Latent topic model such as Latent Dirichlet Allocation (LDA) has been designed for text processing and has also demonstrated success in the task of audio related processing. The main idea behind LDA assumes that the words of each document arise from a mixture of topics, each of which is a multinomial distribution over the vocabulary. When applying the original LDA to process continuous data, the word-like unit need be first generated by vector quantization (VQ). This data discretization usually results in information loss. To overcome this shortage, this paper introduces a new topic model named Gaussian-LDA for audio retrieval. In the proposed model, we consider continuous emission probability, Gaussian instead of multinomial distribution. This new topic model skips the vector quantization and directly models each topic as a Gaussian distribution over audio features. It avoids discretization by this way and integrates the procedure of clustering. The experiments of audio retrieval demonstrate that Gaussian-LDA achieves better performance than other compared methods. HighlightsA topic model named Gaussian-LDA is introduced for audio retrieval, which integrates topic modeling and clustering in a framework.The variational inference method is adopted to train the parameters of Gaussian-LDA.It is beneficial to model audio data using latent topic model in the task of audio retrieval.The Gaussian-LDA demonstrates better performance than LDA and other compared methods.


title: Sentiment analysis on E-commerce application by using opinion mining

abstract: Social media is becoming a major and popular technological platform that allows users to express personal opinions toward the subjects with shared interests, opinion are good for decision making to People would want to know others' opinion before taking a decision, while corporate would like to monitor pulse of people in a social media about their products and services and take appropriate actions. This paper reviewed about world are realizing that e-commerce is not just buying and selling over Internet, rather it is improve the efficiency to compete with other giants in the market. Their opinions on specific topic are inevitably dependent on many social effects such as user preference on topics, peer influence, user profile information.


title: Government 2.0: Making Connections between Citizens, Data and Government

abstract: The revolution in information and communication technologies (ICT) has been changing not only the daily lives of people but also the interactions between governments and citizens. The digital government or electronic government (e-government) has started as a new form of public organization that supports and redefines the existing and new information, communication and transaction-related interactions with stakeholders (e.g., citizens and businesses) through ICT, especially through the Internet and Web technologies, with the purpose of improving government performance and processes [1].


title: Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey

abstract: Due to global climate change as well as economic concern of network operators, energy consumption of the infrastructure of cellular networks, or “Green Cellular Networking,” has become a popular research topic. While energy saving can be achieved by adopting renewable energy resources or improving design of certain hardware (e.g., power amplifier) to make it more energy-efficient, the cost of purchasing, replacing, and installing new equipment (including manpower, transportation, disruption to normal operation, as well as associated energy and direct cost) is often prohibitive. By comparison, approaches that work on the operating protocols of the system do not require changes to current network architecture, making them far less costly and easier for testing and implementation. In this survey, we first present facts and figures that highlight the importance of green mobile networking and then review existing green cellular networking research with particular focus on techniques that incorporate the concept of the “sleep mode” in base stations. It takes advantage of changing traffic patterns on daily or weekly basis and selectively switches some lightly loaded base stations to low energy consumption modes. As base stations are responsible for the large amount of energy consumed in cellular networks, these approaches have the potential to save a significant amount of energy, as shown in various studies. However, it is noticed that certain simplifying assumptions made in the published papers introduce inaccuracies. This review will discuss these assumptions, particularly, an assumption that ignores the effect of traffic-load-dependent factors on energy consumption. We show here that considering this effect may lead to noticeably lower benefit than in models that ignore this effect. Finally, potential future research directions are discussed.


title: A Distributed Test System Architecture for Open-source IoT Software

abstract: In this paper, we discuss challenges that are specific to testing of open IoT software systems. The analysis reveals gaps compared to wireless sensor networks as well as embedded software. We propose a testing framework which (a) supports continuous integration techniques, (b) allows for the integration of project contributors to volunteer hardware and software resources to the test system, and (c) can function as a permanent distributed plugtest for network interoperability testing. The focus of this paper lies in open-source IoT development but many aspects are also applicable to closed-source projects.


title: Medium-Term Effects of a Train Derailment on the Physical and Psychological Health of Men

abstract: In July 2013, the derailment of a train caused the death of 47 people and the destruction of Lac-Mégantic’s downtown area (Canada). Three years after this event, a population survey was conducted among a representative sample of 800 adults, including 282 men. Several significant differences were observed among respondents of a survey based on their level of exposure to this tragedy, including their physical (changes in physical health) and psychological health (post-traumatic stress disorder, mood and anxiety disorders, psychological distress, signs of depression, consultation of social workers and psychologists) as well as their use of prescribed (anxiolytics and antidepressants) and nonprescribed drugs. Such results can be explained by the nature, magnitude, and cause of the event.


title: Navigating pluralism: Understanding perceptions of the ecosystem services concept

abstract: Abstract Being open to multiple interpretations allows the ecosystem services concept to operate as a boundary object, facilitating communication and cooperation between different user groups. Yet there is a risk the resultant pluralism limits the capacity of ecosystem services assessments to directly inform decision and policy making, and that the concept could be used to support environmentally or socially harmful activities. Here, we report results from a large mixed methods survey conducted among academics, policymakers and practitioners working in the field of ecosystem services across Europe. We use these results to explore the trade-off that exists between the role of ecosystem services as a boundary object and the needs of policy and decision makers of more standardisation. We conclude this can be done by working towards the standardisation of ecosystem service assessments within specific jurisdictions, whilst maintaining forums for debate, collaboration, and critical reflection within the broader ecosystem services community. We also aim to deduce guiding principles to ensure the ecosystem services concept is not used to support detrimental activities. The consideration of shared and cultural values, the expansion of inter- and transdisciplinary work and the integration of the concept of sustainability are identified as valuable guiding principles to this end.


title: Correlation of apparent diffusion coefficients measured by 3T diffusion-weighted MRI and SUV from FDG PET/CT in primary cervical cancer

abstract: Purpose ::: Diffusion-weighted magnetic resonance imaging (DWI) and fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) are oncological feasible techniques. Currently, apparent diffusion coefficient (ADC) measured by DWI and standard uptake value (SUV) from FDG PET/CT have similar applications in clinical oncology. The aim of this study was to assess the correlation between ADC and SUV in primary cervical cancer.


title: Scalable and near real-time burst detection from eCommerce queries

abstract: In large scale online systems like Search, eCommerce, or social network applications, user queries represent an important dimension of activities that can be used to study the impact on the system, and even the business. In this paper, we describe how to detect, characterize and classify bursts in user queries in a large scale eCommerce system. We build upon the approaches discussed in KDD 2002 "Bursty and Hierarchical Structure in Streams" [3] and apply them to a high volume industrial context. We describe how to identify bursts on a near real-time basis, classify them, and apply them to build interesting merchandizing applications.


title: M-STAR: A Modular, Evidence-based Software Trustworthiness Framework

abstract: Despite years of intensive research in the field of software vulnerabilities discovery, exploits are becoming ever more common. Consequently, it is more necessary than ever to choose software configurations that minimize systems' exposure surface to these threats. In order to support users in assessing the security risks induced by their software configurations and in making informed decisions, we introduce M-STAR, a Modular Software Trustworthiness ARchitecture and framework for probabilistically assessing the trustworthiness of software systems, based on evidence, such as their vulnerability history and source code properties. ::: Integral to M-STAR is a software trustworthiness model, consistent with the concept of computational trust. Computational trust models are rooted in Bayesian probability and Dempster-Shafer Belief theory, offering mathematical soundness and expressiveness to our framework. To evaluate our framework, we instantiate M-STAR for Debian Linux packages, and investigate real-world deployment scenarios. In our experiments with real-world data, M-STAR could assess the relative trustworthiness of complete software configurations with an error of less than 10%. Due to its modular design, our proposed framework is agile, as it can incorporate future advances in the field of code analysis and vulnerability prediction. Our results point out that M-STAR can be a valuable tool for system administrators, regular users and developers, helping them assess and manage risks associated with their software configurations.


Towards a Taxonomy of Real-Time Business Intelligence Systems


title: A motorized and easy-docking wheelchair drive for persons with lower-limb disabilities

abstract: The aim of this study is to develop a driving module that can be attached to a manual wheelchair with an easy-docking mechanism to assist persons with lower-limb disabilities. The driving module consists of a motorized driving mechanism, embedded control boards, and an easy-docking mechanism, which can together be used to convert a manual wheelchair to an electrical wheelchair. The core of the easy-docking mechanism comprises the snatch locks and yokes, with which the driving module can be attached in front of a manual wheelchair and firmly fixed on the wheelchair frame. After the attachment, the wheelchair can be operated like a motorcycle. A brusless DC (BLDC) motor and a wheel dragged the wheelchair, and it was controlled by micro-controllers (MCUs). The MCU determined the motor input based on the throttles, a hall sensor, a brake, a battery, and limit switches. The current driving status was displayed via an LCD panel that was activated by a cluster board. The driving module was tested to evaluate the performances and the results showed that the developed system was helpful in increasing the mobility of persons with lower-limb disabilities.


title: eCushion: An eTextile Device for Sitting Posture Monitoring

abstract: Sitting posture analysis is critical for daily applications in biomedical, education and healthcare fields. However, it remains unclear how to monitor sitting posture economically and comfortably. To this end, we presented an eTextile device, called eCushion, in this paper, which can analyze the sitting posture of human being accurately and non-invasively. First, we discussed the implementation of eCushion and design challenges of sensing data, such as scale, offset, rotation and crosstalk. Then, several effective techniques have been proposed to improve the recognition rate of sitting posture. Our experimental results show that the recognition rate of our eCushion system could achieve 92% for object-oriented cases and 79% for general cases.


Malton: Towards On-Device Non-Invasive Mobile Malware Analysis for ART.


title: Externalities in online advertising

abstract: Most models for online advertising assume that an advertiser's value from winning an ad auction, which depends on the clickthrough rate or conversion rate of the advertisement, is independent of other advertisements served alongside it in the same session. This ignores an important 'externality effect': as the advertising audience has a limited attention span, a high-quality ad on a page can detract attention from other ads on the same page. That is, the utility to a winner in such an auction also depends on the set of other winners. In this paper, we introduce the problem of modeling externalities in online advertising, and study the winner determination problem in these models. Our models are based on choice models on the audience side. We show that in the most general case, the winner determination problem is hard even to approximate. However, we give an approximation algorithm for this problem with an approximation factor that is logarithmic in the ratio of the maximum to the minimum bid. Furthermore, we show that there are some interesting special cases, such as the case where the audience preferences are single peaked, where the problem can be solved exactly in polynomial time. For all these algorithms, we prove that the winner determination algorithm can be combined with VCG-style payments to yield truthful mechanisms.


title: Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation

abstract: It has been recently shown that neural networks can recover the geometric structure of a face from a single given image. A common denominator of most existing face geometry reconstruction methods is the restriction of the solution space to some low-dimensional subspace. While such a model significantly simplifies the reconstruction problem, it is inherently limited in its expressiveness. As an alternative, we propose an Image-to-Image translation network that jointly maps the input image to a depth image and a facial correspondence map. This explicit pixel-based mapping can then be utilized to provide high quality reconstructions of diverse faces under extreme expressions, using a purely geometric refinement process. In the spirit of recent approaches, the network is trained only with synthetic data, and is then evaluated on in-the-wild facial images. Both qualitative and quantitative analyses demonstrate the accuracy and the robustness of our approach.


title: A zero-strain layered metal oxide as the negative electrode for long-life sodium-ion batteries

abstract: Anode materials in sodium-ion batteries can undergo significant volume change upon sodium insertion and extraction, leading to deteriorated cycling performance. Wang et al. report a layered metal oxide anode with zero-strain characteristics, which may lead to extended battery cycle life.


title: Deep Imbalanced Learning for Face Recognition and Attribute Prediction

abstract: Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary deep learning methods typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain inter-cluster margins both within and between classes. This tight constraint effectively reduces the class imbalance inherent in the local data neighborhood, thus carving much more balanced class boundaries locally. We show that it is easy to deploy angular margins between the cluster distributions on a hypersphere manifold. Such learned Cluster-based Large Margin Local Embedding (CLMLE), when combined with a simple k-nearest cluster algorithm, shows significant improvements in accuracy over existing methods on both face recognition and face attribute prediction tasks that exhibit imbalanced class distribution.


title: A Lagrangian dynamics model of integrated transformer incorporated in a multi-phase LLC resonant converter

abstract: In conventional arrangements of three-phase LLC converters, there are at least three magnetic components that occupy a considerable volume and mass of the power converter. Although, the three-phase LLC topology has many advantages over the single-phase one, circuit designers tend to select the single-phase topology because it has a minimal number of magnetic components. In this paper, with the purpose of promoting the industrial applications of the three-phase topology, Lagrangian dynamics is applied to theoretically prove that it is possible to replace the three-discrete transformers by a single integrated transformer. The Lagrangian dynamics theory allowed us to derive a physically motivated model for the integrated transformer, in which each component of the integrated transformer has its own Lagrangian parameter. The remarkable result to emerge from the Lagrangian model is that in a symmetrical design, there is no interphase coupling; this is regardless of the value of the coupling coefficient between the phases. This means that there is no return path for the three ac fluxes, and as a result the magnetic components can be downsized. Therefore, the major advantages of using integrated magnetics in the LLC converter can be concluded as: cost-reduction, reduced weight, and realizing higher power density. Along with the theoretical discussion, experimental validation is provided utilizing a 500W–390V/12V–200kHz prototype.


Jean Piaget's Theory of Development