Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Inductive Grounding Impedance on Lightning Transients in Wind Farm

Electric facilities operating inside wind turbine towers are damaged due to direct-and indirect-lightning, and such accidents sometimes stop operations of plants for a long time. Lightning current distributions and potential rises inside a wind farm are investigated by means of numerical analysis in this paper. The plant is expressed by equivalent circuit models of towers, grounding foundations, and interconnecting cables. Effects of each component, especially the inductive characteristics of the tower grounding, on the lightning current distributions and potential rises are discussed based on the simulated results by Electromagnetic Transients Program (EMTP). The effect of the inductive characteristic of the tower grounding on the potential rise at the lightning struck tower is large, although it is relatively small on the current distribution inside the plant.
A Computational Exploration of Exaggeration

Several NLP studies address the problem of figurative language, but among non-literal phenomena, they have neglected exaggeration. This paper presents a first computational approach to this figure of speech. We explore the possibility to automatically detect exaggerated sentences. First, we introduce HYPO, a corpus containing overstatements (or hyperboles) collected on the web and validated via crowdsourcing. Then, we evaluate a number of models trained on HYPO, and bring evidence that the task of hyperbole identification can be successfully performed based on a small set of semantic features.
Learning Bidirectional Temporal Cues for Video-Based Person Re-Identification

This paper presents an end-to-end learning architecture for video-based person re-identification by integrating convolutional neural networks (CNNs) and bidirectional recurrent neural networks (BRNNs). Given a video with consecutive frames, features of each frame are extracted with CNN and then are fed into the BRNN to get a final spatio-temporal representation about the video. Specifically, CNN acts as a Spatial Feature Extractor, while BRNN is expected to capture the temporal cues of sequential frames in both forward and backward directions, simultaneously. The whole network is trained end-to-end with a joint identification and verification manner. Experimental results on benchmark data sets show that the proposed model can effectively learn spatio-temporal features relevant for re-identification and outperforms existing video-based person re-identification methods.
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.
Thermal Analysis of a SynRM Using a Thermal Network and a Hybrid Model

This paper presents a lumped parameter thermal network and a hybrid model of a synchronous reluctance motor to predict the temperature of its different parts. The lumped parameter thermal network is developed and its parameters are calculated by simplified analytical correlations. The hybrid model consists of a 2D finite element model of the active part of the machine coupled with a lumped parameters thermal circuit of the end-winding region. A synchronous reluctance machine is used to validate the proposed approaches. The results from the proposed methods are compared with the experimental ones obtained from measurements on a prototype machine. The comparison shows a good agreement among the experimental result, analytical thermal network and hybrid model.
Comprehensive Analysis of Virtual Impedance-Based Active Damping for LCL Resonance in Grid-Connected Inverters

LCL filters have been widely applied as the interface between voltage source inverters (VSIs) and power grid. However, inherent LCL resonance complicates the design of current control or even threatens system stability. Extensive approaches have been proposed to deal with the resonance, among which the active damping (AD) methods realized by the feedback of filter state variables are proved to be effective and robust. In this paper, a comprehensive analysis of such AD by utilizing its virtual circuit property is presented. It is found that different filter state variables can be selected to achieve the effective damping functions by emulating proper virtual impedances (VIs). Meanwhile, the connection of the VI with the LCL network is not restricted by the selected state variable but mainly dependent on the applied AD controller, which, in result, enables a thorough investigation of such VI-based AD methods. Consequently, all possible forms of the AD controller to realize different connection types by utilizing different state variables are derived. The optimal selection of the AD controller together with the specific state variable is further analyzed, where the obtained results can be used to guide the design of such AD in practice. Finally, the correctness of the theoretical analyses are verified by the experimental results.
Vouch: A Secure Proof-of-Location Scheme for VANETs

In Vehicular Ad Hoc Networks (VANETs), nodes periodically share beacons in order to convey information about identity, velocity, acceleration, and position. Truthful positioning of nodes is essential for the proper behavior of applications, including the formation of vehicular platoons. Incorrect position information can cause problems such as increased fuel consumption, reduced passenger comfort, and in some cases even accidents. In this paper, we design and evaluate Vouch: a secure proof-of-location scheme tailored for VANETs. The scheme leverages the node positioning capability of fifth generation (5G) wireless network roadside units. The key idea of Vouch is to disseminate periodic proofs of location, combined with plausibility checking of movement between proofs. We show that Vouch can detect position falsification attacks in high-speed scenarios without incurring a large overhead.
A Nil-Aware Answer Extraction Framework for Question Answering

Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA). However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage. A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage. In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer. If the associated passage does not contain any valid answer, the QA system will correctly return Nil. We propose a novel nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step. We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an end-to-end fashion. Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer. Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.
ADvISE: Anomaly Detection tool for blockchaIn SystEms

Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. In this paper we present ADvISE: the first Anomaly Detection tool for blockchaIn SystEms which leverages blockchain meta-data, named forks, in order to collect potentially malicious requests in the network/system while being resilient to eclipse attacks. ADvISE collects and analyzes malicious forks to build a threat database that enables detection and prevention of future attacks.
Towards Emotion-based Adaptive Games: Emotion Recognition Via Input and Performance Features

During game play a variety of undesired emotions can arise, impeding players' positive experiences. Adapting game features based on players' emotions can help to address this problem, but necessitates a way to detect the current emotional state. We investigate using input parameters on a graphics tablet in combination with in-game performance to unobtrusively detect the players' current emotional state. We conducted a user study with 48 participants to collect self-reported emotions, input data from the tablet and in-game performance in a serious game teaching players to write Japanese hiragana characters. We synchronized data, extracted 46 features, trained machine learning models, and evaluated their performance to predict levels of valence, arousal, and dominance modeled as a seven class problem. The analysis shows that random forests achieve good accuracies with F1 scores of . 567 to . 577 and AUC of . 738 to . 740, while using input features or in-game performance alone leads to highly decreased performance. Finally, we propose a game architecture that is able to react to undesired emotion levels by adaptive content generation in combination with emotion recognition.
Event Detection by Change Tracking on Community Structure of Temporal Networks

Event detection is a popular research problem, aiming to detect events from online data sources with least possible delay. Most of the previous work focus on analyzing textual content such as social media postings to detect happenings. In this work, we consider event detection as a change detection problem in network structure, and propose a method that detects change in community structure extracted from communication network. We study three versions of the method based on different change models. Experimental analysis on benchmark data set reveals that change in the community can be used as an indication of an event.
A Stochastic Modelling Framework for Single Cell Migration: Coupling Contractility and Focal Adhesions

The interaction of the actin cytoskeleton with cell–substrate adhesions is necessary for cell migration. While the trajectories of motile cells have a stochastic character, investigations of cell motility mechanisms rarely elaborate on the origins of the observed randomness. Here, guided by a few fundamental attributes of cell motility, I construct a minimal stochastic cell migration model from ground-up. The resulting model couples a deterministic actomyosin contractility mechanism with stochastic cell–substrate adhesion kinetics, and yields a well-defined piecewise deterministic process. Numerical simulations reproduce several experimentally observed results, including anomalous diffusion, tactic migration and contact guidance. This work provides a basis for the development of cell–cell collision and population migration models.
Analysis of Additional Eddy-Current Copper Losses in Large Converter-Fed Hydropower Generators

This paper investigates the effect of converter operation on AC copper losses in the stator and damper windings of a large hydrogenerator. Stator currents are computed using finite element simulations. The computed current waveforms are used as an input to estimate the AC copper losses in the stator due to converter operation using an analytical approach. Damperbar losses are calculated directly in the finite element analysis. The generator is simulated with a two-level voltage source converter and a three-level neutral-point-clamped converter topology. Different carrier frequencies are used to investigate their effect on the AC copper loss. Methods for handling these additional losses are discussed and analysed.
A Computational Approach to Finding Contradictions in User Opinionated Text

The rapid growth of Web 2.0 and wide popularity of social media have brought the challenge of digesting and understanding large amounts of user-generated text. Automatically finding contradictions from user opinionated text is a potential solution to help sense-making and decision-making process from those user opinions. However, the problem of contradiction detection is understudied in social media analysis field. This study presents a computational approach to detecting contradictions in user opinionated text. Specifically, a typology of contradictions was proposed, and then the state-of-art deep learning models were adopted and enhanced by three methods of incorporating sentiment analysis. The enhanced models were evaluated with Amazon's customer reviews. The best model was selected and applied to a collection of tweets from Twitter to demonstrate its usefulness in understanding contradiction semantically and quantitatively in a large amount of user opinionated text.
Design and Control of Single-Phase Grid-Connected Photovoltaic Microinverter with Reactive Power Support Capability

This paper proposes a single-phase photovoltaic (PV) microinverter capable of supplying reactive power to the grid. In the past, researchers mainly focused on obtaining high efficiency, low cost, and high boost ratio. However, as the solar power generation has increased in recent years, the grid regulations have been changed so that PV inverters provide reactive power for the grid stability. Therefore, the proposed PV microinverter is constructed by the bidirectional boost/buck dc-dc converter for reactive power support and achieving high step-up ratio by using coupled inductors. In addition to the design guideline, this paper presents the control strategy of the proposed microinverter. Unlike traditional two-stage microinverters, the dc-link voltage is shaped like the rectified grid voltage. The experimental results verify that the 320-W laboratory prototype can supply reactive power and reaches 96.5% California Energy Commission (CEC) efficiency at unity power factor operation.
Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two main purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning); a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance.
Implementation of a Miniaturized SiC Inverter

This paper presents analysis, design, and implementation of a miniaturized inverters using SiC-MOSFETs. Theoretical analysis and experimental verification in this paper reveals the relationship between the time-domain switching properties and the frequency-domain impedance characteristics. Based on the analysis, damping resistors are inserted into the dc side of the inverter. The damping resistors can effectively reduce the impedance at resonant frequencies in frequency domain, and they also enable to suppress the surge voltage and oscillation across the switching devices in time domain. Moreover, it has theoretically and experimentally been confirmed that the damping resistors also have the capability of reducing the switching power losses in the circuit.
Resampled Priors for Variational Autoencoders

We propose Learned Accept/Reject Sampling (LARS), a method for constructing richer priors using rejection sampling with a learned acceptance function. This work is motivated by recent analyses of the VAE objective, which pointed out that commonly used simple priors can lead to underfitting. As the distribution induced by LARS involves an intractable normalizing constant, we show how to estimate it and its gradients efficiently. We demonstrate that LARS priors improve VAE performance on several standard datasets both when they are learned jointly with the rest of the model and when they are fitted to a pretrained model. Finally, we show that LARS can be combined with existing methods for defining flexible priors for an additional boost in performance.
Robust Position Sensorless Control Against Inductance Variations of Synchronous Reluctance Motors in Low-Speed Region Using High-frequency Current Control

Synchronous reluctance motors (SynRMs) have attracted attention because they have no magnets. The position sensorless control of SynRMs at low speeds is desired, and several methods have been proposed. A new position sensorless control method for SynRMs in a very-low-speed region has been proposed by the authors. It involves superimposing a high-frequency current with a small constant amplitude, using a high-frequency current control system. This paper shows that robust position sensorless control against inductance variations can be realized using this proposed method. Experiments showed that the proposed method is very useful even in a very-low-speed region.
Pilot Studies on the Effects of Tonal Noise Annoyance Impact on Workers Performing Their Primary Tasks

Numerous references indicate that the subjectively assessed annoyance of tonal noise is higher than that of wide band noise. There are no criteria for the impact of tonal noise in assessing the occupational environment for both indoor areas (construction standards) and workplaces (annoyance assessment). As a stressor, noise can contribute to the development of various types of illnesses (e.g, hypertension, peptic ulcers, neurosis), it causes distraction of attention, hinders work, and reduces personnel performance. The pilot studies involved 11 people (6 male and 5 female) fulfilling the audiometric qualification criteria. The group of test participants was diversified in terms of noise sensitivity. The research method used both the questionnaire studies and computer psychological tests checking work performance, attention level, and memory. Four types of generated test signals were developed (filtered noise - A, and three signals with tonal components 125, 1600 and 8000 Hz - B, C, and D) at the same sound level A of 55 dB. Results of the statistical analysis based on the questionnaire studies, as well as for the psychological tests, did not show a statistically significant variation between individual signals. The absence of variation in test results between the individual signals presented in the statistical analysis does not give any reasons for applying correction of tonality measurement results according to ISO 1996–2.
An Instrumented Glove for Control Audiovisual Elements in Performing Arts

The use of cutting-edge technologies such as wearable devices to control reactive audiovisual systems are rarely applied in more conventional stage performances, such as opera performances. This work reports a cross-disciplinary approach for the research and development of the WMTSensorGlove, a data-glove used in an opera performance to control audiovisual elements on stage through gestural movements. A system architecture of the interaction between the wireless wearable device and the different audiovisual systems is presented, taking advantage of the Open Sound Control (OSC) protocol. The developed wearable system was used as audiovisual controller in “As sete mulheres de Jeremias Epicentro”, a portuguese opera by Quarteto Contratempus, which was premiered in September 2017.
VulSeeker-pro: enhanced semantic learning based binary vulnerability seeker with emulation

Learning-based clone detection is widely exploited for binary vulnerability search. Although they solve the problem of high time overhead of traditional dynamic and static search approaches to some extent, their accuracy is limited, and need to manually identify the true positive cases among the top-M search results during the industrial practice. This paper presents VulSeeker-Pro, an enhanced binary vulnerability seeker that integrates function semantic emulation at the back end of semantic learning, to release the engineers from the manual identification work. It first uses the semantic learning based predictor to quickly predict the top-M candidate functions which are the most similar to the vulnerability from the target binary. Then the top-M candidates are fed to the emulation engine to resort, and more accurate top-N candidate functions are obtained. With fast filtering of semantic learning and dynamic trace generation of function semantic emulation, VulSeeker-Pro can achieve higher search accuracy with little time overhead. The experimental results on 15 known CVE vulnerabilities involving 6 industry widely used programs show that VulSeeker-Pro significantly outperforms the state-of-the-art approaches in terms of accuracy. In a total of 45 searches, VulSeeker-Pro finds 40 and 43 real vulnerabilities in the top-1 and top-5 candidate functions, which are 12.33× and 2.58× more than the most recent and related work Gemini. In terms of efficiency, it takes 0.22 seconds on average to determine whether the target binary function contains a known vulnerability or not.
Feature Grouping-Based Outlier Detection upon Streaming Trajectories (Extended Abstract)

The existing detection techniques are not tailored to identify the outlier which is close to its neighbors according to some features, but behaves significantly distinct from its neighbors in terms of the other features. In this paper, we propose a feature grouping-based mechanism, and then present two algorithms to detect outliers (TF-outlier and MO-outlier) upon trajectory streams. The effectiveness and efficiency of our proposal are validated by the experiments on real trajectory data.