Reviewerly/exHarmony huggingface dataset:
Fundamental Frequency Accommodation in Multi-Party Human-Robot Game Interactions: The Effect of Winning or Losing

In human-human interactions, the situational context plays a large role in the degree of speakers' accommodation.In this paper, we investigate whether the degree of accommodation in a human-robot computer game is affected by (a) the duration of the interaction and (b) the success of the players in the game.30 teams of two players played two card games with a conversational robot in which they had to find a correct order of five cards.After game 1, the players received the result of the game on a success scale from 1 (lowest success) to 5 (highest).Speakers' f o accommodation was measured as the Euclidean distance between the human speakers and each human and the robot.Results revealed that (a) the duration of the game had no influence on the degree of f o accommodation and (b) the result of Game 1 correlated with the degree of f o accommodation in Game 2 (higher success equals lower Euclidean distance).We argue that game success is most likely considered as a sign of the success of players' cooperation during the discussion, which leads to a higher accommodation behavior in speech.
Computational aspects of probabilistic extensions

In this article we propose a new approach to computing of functions with random arguments.Approach based on the idea of dimension reduction by to calculating some integrals and the application of numerical probability analysis.We apply one of the basic concepts of numerical probabilistic analysis as the probabilistic extension to computing a function with random arguments.To implement this technique, a new method based on parallel recursive calculations is proposed.Numerical examples are presented demonstrating the effectiveness of the proposed approach.
The Integration of Computer Systems into the expansive field of Video Games

In today's day and age, the recent generations are the targets of video game companies.Most companies asociated have goals that correspond with their target market.The companies create what the consumers want, which are video games.Everyone knows what are video games, but what are its comprised components?With that said, this research paper aims to provide information to the field of computer systems, more specifically, its integration with video games.Another objective of this paper is to give awareness of how computer systems work to give video games an extraordinary feel.This is important because individuals need to understand the various topics tackled by video games.These topics would include various computer systems like Spatial imaging, which is the ability to interact with 3D objects in a specific environment.Artificial neural networking and electronic sensors are some of the few computer systems to be tackled.The applications will be further studied so that the information on these systems can help give knowledge to people.
Kite: Automatic Speech Recognition for Unmanned Aerial Vehicles

This paper addresses the problem of building a speech recognition system attuned to the control of unmanned aerial vehicles (UAVs). Even though UAVs are becoming widespread, the task of creating voice interfaces for them is largely unaddressed. To this end, we introduce a multi-modal evaluation dataset for UAV control, consisting of spoken commands and associated images, which represent the visual context of what the UAV sees when the pilot utters the command. We provide baseline results and address two research directions: (i) how robust the language models are, given an incomplete list of commands at train time; (ii) how to incorporate visual information in the language model. We find that recurrent neural networks (RNNs) are a solution to both tasks: they can be successfully adapted using a small number of commands and they can be extended to use visual cues. Our results show that the image-based RNN outperforms its text-only counterpart even if the command-image training associations are automatically generated and inherently imperfect. The dataset and our code are available at this http URL.
Dimensionality Reduction and Its F-Transform Representation

The aim of this contribution is to connect two techniques: the F-transform on a space with a fuzzy partition and dimensionality reduction.We show how a space with a fuzzy partition can be characterized in terms of the graph Laplacian.After making the spectral analysis of the corresponding Laplacian matrix we compute its eigenvectors and show their relevance to the F-transform basic functions.
NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets

This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019.We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task.The network is trained along with Twitter GloVe pre-trained word embeddings.
Fusion of BiLSTM and GMM-UBM Systems for Audio Spoofing Detection

In this paper, we present our contribution to the ASVspoof 2019 challenge.The main task for this challenge is to find countermeasures that generalize well for different spoofing attacks against automatic speaker verification systems.Some of the approaches used by the authors during participation in the challenge are presented.Described anti-spoofing systems mostly rely on using constant Q cepstral coefficients (CQCC) features and bidirectional long-short term memory (BiLSTM) networks for genuine/spoof audio classification.Fusion of BiLSTM and GMM-UBM system is presented.This approach could give significant improvement to baseline systems results without any data augmentation, especially on physical access (PA) condition.Presented systems give 15.2% min-tDCF relative improvement for logical access (LA) condition and 61.5% min-tDCF relative improvement for PA condition, compared to the best baseline systems results.
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Neural networks are state-of-the-art models for machine learning problems. They are often trained via back-propagation to find a value of the weights that correctly predicts the observed data. Back-propagation has shown good performance in many applications, however, it cannot easily output an estimate of the uncertainty in the predictions made. Estimating this uncertainty is a critical aspect with important applications. One method to obtain this information consists in following a Bayesian approach to obtain a posterior distribution of the model parameters. This posterior distribution summarizes which parameter values are compatible with the observed data. However, the posterior is often intractable and has to be approximated. Several methods have been devised for this task. Here, we propose a general method for approximate Bayesian inference that is based on minimizing α-divergences, and that allows for flexible approximate distributions. We call this method adversarial α-divergence minimization (AADM). We have evaluated AADM in the context of Bayesian neural networks. Extensive experiments show that it may lead to better results in terms of the test log-likelihood, and sometimes in terms of the squared error, in regression problems. In classification problems, however, AADM gives competitive results.
Blockchain Technology Helps the Development of Meteorological Informatization

With the impact of the Internet, the application of information technology promotes the continuous change and development of meteorological field.Based on the intrinsic motivation of the development and change of meteorological field, the impetus of blockchain technology to meteorological informatization is discussed in this paper.Firstly, the application status and development opportunities of information technology in meteorological field are analyzed.Then, the new requirements of meteorological information business development driven by information technology are presented.Finally, the paper deals with the role of blockchain technology in promoting the modernization of meteorological information and points out that the combination of blockchain technology with artificial intelligence and big data will further develop and improve meteorological information technology in many aspects.
Peranan Teknologi Dalam Peningkatan Mutu Pendidikan Pembelajaran Bahasa Di SMAN 1 Katingan Hulu Kabupaten Katingan

&lt;p&gt;Technology becomes a thing that has an influence on the life needs of society as a whole, both to fulfill economic, social, cultural and educational needs. One of the things that is concerned by the government at this time is the progress of the quality of education which is the basis for educating the Indonesian people as a whole. The breakthrough made by the government to improve the quality of education is to implement a curriculum based learning system (K-13) in which students both at the elementary school level, junior high school and high school must be creative and innovative in implementing learning in the classroom. The application of this curriculum cannot be separated from the use of technology where teachers and students can develop dynamic and fun learning. One of the subjects offered in K-13 learning is language learning where these subjects become the foundation of students to carry out communication and interaction everyday. Therefore, the aim of the study is to know how to improve quality in language learning in SMAN-1 Katingan Hulu, Katingan Regency.&lt;/p&gt;
Winning the Sussex-Huawei Locomotion-Transportation Recognition Challenge

The Sussex-Huawei Locomotion-Transportation Recognition Challenge presented a unique opportunity to the activity-recognition community to test their approaches on a large, real-life benchmark dataset with activities different from those typically being recognized. The goal of the challenge was to recognize eight locomotion activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). This chapter describes the submissions winning the first and second place. They both start with data preprocessing, including a normalization of the phone orientation. Then, a wide set of hand-crafted domain features in both frequency and time domain are computed and their quality evaluated. The second-place submission feeds the best features into an XGBoost machine-learning model with optimized hyper-parameters, achieving the accuracy of 90.2%. The first-place submission builds an ensemble of models, including deep learning models, and finally refines the ensemble’s predictions by smoothing with a Hidden Markov model. Its accuracy on an internal test set was 96.0%.
Road Quality Assessment Using International Roughness Index Method and Accelerometer on Android

The quality of road conditions can determine comfort in driving. To find out the condition of a road whether it has good surface quality, it can use an accelerometer sensor contained in an android smartphone. This research uses the International Roughness Index (IRI) method combined with the accelerometer sensor and the Global Positioning System (GPS). Application of the results of this study can be used to facilitate the contractor maker and road repair, so they can find out which points need to be repaired. Testing is done using two different vehicles, car and motorcycle. Smartphones with road quality detection applications are attached to the car and motorcycle vehicles using a phone holder. This is to record vibration that occurs while the vehicle is moving based on road conditions. The vibration recording results are then validated in a visual observation to determine the accuracy of the assessment results. Based on the test results the level of accuracy on the car is 90% and the motorcycle is 30%.
Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

Recently, due to easy accessibility of smartphones, digital cameras and other video recording devices, a radical enhancement has been experienced in the field of digital video technology.Digital videos have become very vital in court of law and media (print, electronic and social).On the other hand, a widely-spread availability of Video Editing Tools (VETs) have made video tampering very easy.Detection of this tampering is very important, because it may affect the understanding and interpretation of video contents.Existing techniques used for detection of forgery in video contents can be broadly categorized into active and passive.In this research a passive technique for video tampering detection in spatial domain is proposed.The technique comprises of two phases: 1) Extraction of features with proposed Video Binary Pattern (VBP) descriptor, and 2) Extreme Learning Machine (ELM) based classification.Experimental results on different datasets reveal that the proposed technique achieved accuracy 98.47%.
Querying massive RDF data using Spark

Semantic web technologies are increasingly used in different domains.The core technology of the Semantic Web is the RDF standard.Today with the growth of RDF data it requires systems capable of handling these large volumes of data and responding to very complex queries at the join level.With the increase in RDF data volumes available, many research efforts have been made to allow for distributed and efficient evaluation of SPARQL queries.In this paper, we propose a new solution based on Apache Spark for massive querying and RDF data.This new system allows the processing of complex SPARQL queries on large volumes of RDF data stored in the Hadoop file system or a NoSQL database management system.Our approach translates the SPARQL query of the user to a Spark script in order to take advantage of the speed of processing and the performance of Spark compared to other existing systems, thanks to an experiment carried out on RDF datasets.
Design of Interval Type 2 Fuzzy Fault-Tolerant Controller for a Non-Minimum Phase System: Application to quadruple conical tank system

Quadruple Conical Tank System (QCTS) is benchmark a laboratory setup for testing of various linear and nonlinear control algorithms for the multivariate control system.The multiple process configuration for multivariate input-output can be obtained from quadruple conical tank system.This article is concerned with designing a novel Fault-Tolerant Control (FTC) using Interval Type 2 Fuzzy Control (IT2FLC) subject to actuator and system component (Leak) faults.Additionally, paper explores the pen loop response with given operating points of quadruple conical tank system linearized model for non-minimum phase configuration obtained from MATLAB.The proposed control design method is simulated on prototype model of quadruple conical tank process for onminimum phase configuration.The performance of the proposed controller is studied through extensive simulations with regulatory and servo responses.Through comparative studies of proposed controller with other model based H ∞ , LQR, 2 DoF Decentralized PI Controller and model free Type 1 Fuzzy Logic controller (T1FLC) responses in MATLAB simulation environment, the effect of interaction, coupling, time variations and change in dynamic behaviour of QCTS confirmed.
Abductive reasoning in normal residuated logic programming via bipolar max-product fuzzy relation equations

Fuzzy relation equations have recently been extended to a more general framework in which the unknown variables appear together with their logical negation connectives, giving rise to the called bipolar fuzzy relation equations.This paper shows an abductive procedure for a special kind of normal residuated logic program by means of bipolar maxproduct fuzzy relation equations.From this procedure, interesting properties relating the models of a given normal residuated logic program to the solutions of its corresponding bipolar max-product fuzzy relation equation is deduced.
Active Annotation: Bootstrapping Annotation Lexicon and Guidelines for Supervised NLU Learning

Natural Language Understanding (NLU) models are typically trained in a supervised learning framework.In the case of intent classification, the predicted labels are predefined and based on the designed annotation schema while the labeling process is based on a laborious task where annotators manually inspect each utterance and assign the corresponding label.We propose an Active Annotation (AA) approach where we combine an unsupervised learning method in the embedding space, a humanin-the-loop verification process, and linguistic insights to create lexicons that can be open categories and adapted over time.In particular, annotators define the y-label space on-the-fly during the annotation using an iterative process and without the need for prior knowledge about the input data.We evaluate the proposed annotation paradigm in a real use-case NLU scenario.Results show that our Active Annotation paradigm achieves accurate and higher quality training data, with an annotation speed of an order of magnitude higher with respect to the traditional human-only driven baseline annotation methodology.
Fault Tolerant Control Design Based on Takagi-Sugeno Fuzzy Logic: Application to a Three-Tank System

This paper deals with a novel Fault-Tolerant Control (FTC) strategy for non-linear systems without hardware redundancy in case of actuator and system component failures.These failures correspond to blocking or loss of effectiveness of an actuator, leakage of tank bottom and pipes which could lead to a large degradation in performances.The abrupt and increasing nature faults are introduced into the system, followed by the simulation results.The proposed method is based on a conventional PID controller and T-S Fuzzy controller in order to maintain the acceptable performance subject to faults.The effectiveness of this approach is evaluated through the application to a non-linear pilot plant, a three-tank system.A simulation result is given with IAE and ISE error to illustrate the effectiveness of the proposed approach.
Mining data quality rules based on T-dependence

Since their introduction in 1976, edit rules have been a standard tool in statistical analysis.Basically, edit rules are a compact representation of non-permitted combinations of values in a dataset.In previous work, edit rules are mined automatically as valuecombinations showing strong negative correlation tested under stochastic independence using the traditional notion of lift.In this paper, we generalize the traditional notion of lift to that of T -lift, where stochastic independence is generalized to T -dependence.We show several interesting properties of edit rules under different T -lift measures and proof that edit rules under the minimum tnorm can be computed efficiently by use of frequent pattern trees.Experiments support this result and show that there is a weak to medium correlation in the rank order of edit rules obtained under the minimum and product t-norms.
Uncertainty-aware similarity measures – properties and construction method

A concept of uncertainty-aware similarity measure is being defined and discussed.The aim of the paper is to support the opinion that both definition and construction of such measure should take into account the epistemic nature of compared incomplete (uncertain) information.This approach, however, generates new challenges resulted from the computational complexity of the problem.We define a set of properties to be satisfied by uncertainty-aware similarity measure and we propose a new technique of constructing such measures for Interval-Valued Fuzzy Sets.
Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events

Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public.Twitter captures large volumes of discussions and messages related to these events, in real-time.Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent.In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights.We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.
ON THE NUMBER OF EVENT APPEARANCES IN A MARKOV CHAIN

The paper presents the estimate for the total variation distance between the distribution of the number of appearances of homogeneous disjoint events in a segment of strongly ergodic Markov chain on the finite state space and accompanying Poisson distribution (i.e., Poisson distribution with a parameter equal to the expectation of the random variable under consideration).For this purpose the Chen-Stein method was used.As a result Poisson and normal limit theorems for the number of events appearances are derived.The considered scheme describes the well-known number of runs on consecutive letters, the number of f -recurrent runs, etc., and can be used for describing the properties of distribution of the special form scan statistic.
Generalized Association Rules for Sentiment Analysis in Twitter

Association rules have been widely applied in a variety of fields over the last few years, given their potential for descriptive problems. One of the areas where the association rules have been most prominent in recent years is social media mining. In this paper, we propose the use of association rules and a novel generalization of these based on emotions to analyze data from the social network Twitter. With this, it is possible to summarize a great set of tweets in rules based on 8 basic emotions. These rules can be used to categorize the feelings of the social network according to, for example, a specific character.
Ensemble learning using frequent itemset mining for anomaly detection

Anomaly detection is vital for automated data analysis, with specific applications spanning almost every domain.In this paper, we propose a hybrid supervised learning of anomaly detection using frequent itemset mining and random forest with an ensemble probabilistic voting method, which outperforms the alternative supervised learning methods through the commonly used measures for anomaly detection: accuracy, true positive rate (i.e.recall) and false positive rate.To justify our claim, a benchmark dataset is used to evaluate the efficiency of the proposed approach, where the results illustrate its benefits.
An improved imperialist competitive algorithm for global optimization

The imperialist competitive algorithm (ICA), inspired by sociopolitical behavior in the real world, is a new optimization algorithm.The ICA shows great potential to solve complex optimization problems.In order to improve the ICA's exploration ability and speed up its convergence, two improved schemes are proposed in this paper.The first scheme presents a new possession probability in the imperialistic competition phase.Inspired by geopolitics, not only the power of the empire but also the distance between the imperialists are taken into account in calculating the new possession probability.The second scheme introduces the wavelet mutation operator into the original ICA so as to improve its exploration ability.The improved ICAs (IICAs) are tested on several benchmark functions and then used to design the optimum parameters of tuned mass damper and tune the parameters of a fractional order PID controller of an automatic voltage regulator (AVR) system.Results show that the IICAs outperform the original ICA in terms of solution quality and convergence speed.