Reviewerly/exHarmony huggingface dataset:
VR Sickness in Continuous Exposure to Live-action 180°Video

The goal of this study was to determine the factors that determine the degree of VR sickness in order to improve the audiovisual experience of VR videos or games. We used a simulator sickness questionnaire to evaluate the degree of VR sickness for nine types of live-action 180-degree videos, with a combination of different movement speeds and fields of view (FOV). Among the 40 participants we tested, those suffering from motion sickness had more serious symptoms than those without motion sickness. Although statistical tests failed to show significant differences related to the movement speeds or fields of view, our results suggested that VR exposure time was the most important factor influencing VR sickness.
Linking Trust to Cyber-Physical Systems

Cyber-physical systems deeply connect artefacts, systems and people. As we will use these systems on a daily basis, the question arises to what extent we can trust them, and even more, whether and how we can develop computational models for trust in such systems. Trust has been explored in a wide range of domains, starting with philosophy via psychology, sociology, and economy. Also in automation, computing and networking definitions, models and computations have emerged. In this paper we discuss our initial considerations on linking concepts and models of trust and cyber-physical systems. After structuring and selecting basic approaches in both areas, we propose appropriate links, which can be used as action areas for future development of trust models and computations in cyber-physical systems.
Improvement of the Algorithm Based on Dark Channel Prior Defogging

Abstract The dark channel prior defogging algorithm is a method for rough estimation of transmittance. The dark channel prior method is not obvious enough for the sky. The paper uses a rough estimation model based on the relationship between depth of field and transmittance. A dehazing method that makes the transmittance more refined is proposed on a priori basis. The atmospheric light value is calculated from the atmospheric scattering model, and the image is converted from the RGB color space to the HSV color space and the S (saturation) component is extracted as a supplement to the transmittance. The final transmittance is obtained by the method of transmittance fusion, and a clear image is restored according to the atmospheric scattering model. The experimental results show that the method can effectively eliminate the fog, and the restored image better preserves the image details.
Estimation of complexity by using an object oriented metrics approach and its proposed algorithm and models

The high pace emergence of software applications and associated technologies have enabled us an efficient and luxurious. This research paper focus the procedure of investigating the different object oriented metrics by taking an example and finally evaluate complexities, statistical measurements. A proposed model, flowchart and algorithm are used for measuring the metrics in the software project. Comparison done between the programming languages like C++ and Python and conclude that Python is better than C++.
Effect of system energy on quantum signatures of chaos in the two-photon Dicke model

We have studied entanglement entropy and Husimi $Q$ distribution as a tool to explore chaos in the quantum two-photon Dicke model. With the increase of the energy of a system, the linear entanglement entropy of a coherent state prepared in the classical chaotic and regular regions becomes more distinguishable, and the corresponding relationship between the distribution of time-averaged entanglement entropy and the classical Poincar\'e section has clearly been improved. Moreover, Husimi $Q$ distribution for the initial states corresponding to the points in the chaotic region in the higher-energy system disperses more quickly than that in the lower-energy system. Our results imply that higher system energy has contributed to distinguishing between the chaotic and regular behavior in the quantum two-photon Dicke model.
Improving Reinforcement FALCON Learning in Complex Environment with Much Delayed Evaluation via Memetic Automaton

The Fusion Architecture for Learning, COgnition, and Navigation (FALCON) is an extension of the self-organizing neural network i.e., Adaptive Resonance Theory (ART), which has been successfully applied in many reinforcement learning tasks, and demonstrated fast and stable real-time learning capabilities. However, the learning of reinforcement FALCON relies on the positive feedbacks obtained from the environment, which may not be always available in many real-world applications. Although TD-FALCON has been proposed in the literature, to integrate the temporal difference method to estimate the payoff value when immediate reward is not available, the accuracy of the estimation also relies on the received feedback from environment. In complex environments with much delayed evaluation, the reinforcement FALCON may be hard to learn the proper knowledge to adapt in the given task quickly. To the best of our knowledge, there is no existing work has been conducted to improve the reinforcement FALCON learning in such environment. Taking this cue, inspired from the science of memetics, in this paper, we propose to improve the reinforcement FALCON learning in complex environment where positive reward is hard to achieve, via memetic automaton. In particular, by defining the particular representation of memes in the context of FALCON, the corresponding designs of meme selection and meme transmission for meme evolution are presented, to transfer the knowledge meme from well-learned agents in simple environment to improve the learning performance of FALCON agents in complex environment. Lastly, simulations of FALCON based multi-agent system using the mine navigation task platform, confirmed the efficacy of the proposed memetic model.
Region of interest-based adaptive segmentation for image compression using hybrid Jaya–Lion mathematical approach

This paper intends to propose a new image compression technique, which is processed under various sequences of progressions. The initial process is the image segmentation and is handled by Adaptive Active Contour Model (ACM), which divides or segments the image into two regions: ROI (Region of Interest) and non-ROI. In this, the adaptiveness of this ACM is influenced with the concept of optimization. JPEG-LS algorithm is used to handle the ROI regions, and the wavelet-based lossy compression algorithm is used to handle the non-ROI region. The result of both JPEG-LS algorithm and wavelet-based compression model is combined in terms of bit-stream combination and outputs the compressed image. Subsequently, the compressed image is subjected for image decompression, which will be the reverse process of compression. This will include the bit-stream separation, which is then separately processed under both the JPEG-LS decoding and wavelet-based decomposition for attaining the ROI and non-ROI regions. Finally, the original image is attained precisely. Further, the major contribution of this work falls in the adaptiveness under optimization. The weighting factor and maximum iteration in ACM are optimally selected and for this a new hybrid optimization model that hybridizes the concept of Jaya Algorithm and Lion Algorithm is introduced.
Social, Ethical and Ecological Issues in Wearable Technologies

The rapid growth of Internet of Things (IoT) has given rise to a plethora of wearable devices integrated into daily life, however achieving end-user’s long-term adoption is still an issue. The purp ...
The Power of Machine Learning in Petroleum Geoscience: Biostratigraphy as an Example

Summary The application of data science and machine learning is transforming petroleum geoscience workflows. Routine, yet time-consuming and important tasks can be made more efficient by the application of machine learning-based assisted interpretation, freeing the geoscientist to carry out tasks with greater value. Accuracy, reproducibility, and understanding of uncertainty are also improved and greater insight can be gained. Biostratigraphic data is very common in the industry but requires deep specialised knowledge and significant time to interpret, hence it can be underutilized. However, the form of the data makes it suitable for the application of machine learning techniques. The applications of machine leaning have been tested on biostratigraphic data from a set of typical industry wells to facilitate the interpretation of biozone/age and paleoenvironment. Application of Random Forest and Naïve Bayesian algorithms achieved results comparable to standard human interpretation, although pre-processing of the data (e.g. removal of spurious reworked or caved data) proved beneficial. Critical to the success of the project was the close working relationship between data scientists and subject matter experts in order to capture the nuances of biostratigraphic data and its interpretation. The work forms a case study for application to other geoscience data types.
Detection Based Tracking of Unmanned Aerial Vehicles

Object tracking is one of the fundamental problems of computer vision, which has many difficulties such as fast camera motion, occlusion and similar objects. Today, small and lightweight single board computers with very high processing power have been developed. Real-time processing of the computer vision applications on unmanned aerial vehicles has become possible with the integration of such single board computers within UAVs. In this study, a hybrid method is developed to detect and track UAVs by another UAV. A deep learning based approach which is one of the fastest and most accurate method in the literature, YOLOv3 and YOLOv3-Tiny (You Only Look Once), are utilized to detect the UAV at the beginning of the video and when tracking of the UAV is failed. Kernelized Correlation Filter (KCF) is used for real time tracking purpose of the detected UAVs. A dataset is created that consists different UAVs to train and test YOLOv3. Performance of the proposed methods are evaluated on this dataset.
Coherence constraints in facial expression recognition

This paper investigates the role of coherence constraints in recognizing facial expressions from images and video sequences. A set of constraints are introduced to bridge a pool of Convolutional Neural Networks (CNNs) during their training stage. Constraints are inspired by practical considerations on the regularity of the temporal evolution of the predictions, and by the idea of connecting the information extracted from multiple representations. We study CNNs with the aim of building a versatile recognizer of expressions in static images that can be further applied to video sequences. First, the importance of different face parts in the recognition task is studied, considering appearance and shape-related features. Then we focus on the Semi-Supervised learning setting, exploiting video data, where only a few frames are supervised. The unsupervised portion of the training data is used to enforce three types of coherence, namely temporal coherence, coherence among the predictions on the face parts and coherence between appearance and shape-based representation. Our experimental analysis shows that coherence constraints improve the quality of the expression recognizer, thus offering a suitable basis to profitably exploit unsupervised video sequences, also in cases in which some portions of the input face are not visible.
Assessing Semantic Similarity Between Concepts Using Wikipedia Based on Nonlinear Fitting

Feature-based methods of semantic similarity with Wikipedia achieve fruitful performances on measuring the “likeness” between objects in many research fields. However, since Wikipedia is created and edited by volunteers around the world, the preciseness of these methods more or less are influenced by the incompleteness, invalidity and inconsistency of the knowledge in Wikipedia. Unfortunately, this problem has not got enough attention in the existing work. To address this issue, this paper proposes a novel feature-based method for semantic similarity, which has three parts: low frequency features removal, the similarities of generalized synonyms computing, and weighted feature-based methods based on nonlinear fitting. Moreover, we show that our new method can always get a better Pearson correlation coefficient on one or more benchmarks through a set of experimental evaluations.
A Fast Method for Defogging of Outdoor Visual Images

Background: Capturing image in severe atmospheric catastrophe especially in fog critically degrades the quality of an image and thereby reduces its visibility of which in turn affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring clear vision is the prime requirement of any image. In the last few years, many approaches have been directed towards solving this problem. Methods: In this article, a comparative analysis has been made on different existing image defogging algorithms and then a technique has been proposed for image defogging based on dark channel prior strategy. Results: Experimental results show that the proposed method shows efficient results by significantly improving the visual effects of images in foggy weather. Also the much higher computational time of the existing techniques has been reduced in this paper by using the proposed method. Discussion: Qualitative assessment evaluation was performed on both benchmark and real time data sets for determining the efficacy of the technique used. Finally, the whole work is concluded with the relative advantages and shortcomings of the proposed technique.
Special Issue on Machine Learning for Robotics and Swarm Systems

In recent years, machine-learning applications have been rapidly expanding in the fields of robotics and swarm systems, including multi-agent systems. Swarm systems were developed in the field of robotics as a kind of distributed autonomous robotic systems, imbibing the concepts of the emergent methodology for extremely redundant systems. They typically consist of homogeneous autonomous robots, which resemble living animals that build swarms. Machine-learning techniques such as deep learning have played a remarkable role in controlling robotic behaviors in the real world or multi-agents in the simulation environment. In this special issue, we highlight five interesting papers that cover topics ranging from the analysis of the relationship between the congestion among autonomous robots and the task performances, to the decision making process among multiple autonomous agents. We thank the authors and reviewers of the papers and hope that this special issue encourages readers to explore recent topics and future studies in machine-learning applications for robotics and swarm systems.
Feature GANs: A Model for Data Enhancement and Sample Balance of Foreign Object Detection in High Voltage Transmission Lines

The suspension of foreign objects on high-voltage transmission lines is extremely harmful to the safety of the line. If it is not handled in time, it will easily cause phase-to-phase short circuit of the transmission line and even cause forest fires. Foreign object suspension is a small probability event with fewer existing samples. To use CNN to perform target classification detection, there is a problem of insufficient sample or sample imbalance. Aiming at the above problems that often occur in engineering applications of CNN, we propose a data enhancement algorithm based on GANs. The main idea of this algorithm is: Firstly, the pre-training model is used to extract the feature map of sample, and GANs is used to learn the feature map directly. Then, the feature map generated by GANs and the original data are used to train the classification layer of the pre-training model, so as to achieve the purpose of data enhancement and balancing samples, and then enhance the classification ability of the model. The experimental results show that the classification performance of several classical CNN models can be improved significantly by using this method in the case of insufficient sample and sample imbalance.
Contribution of the Resampling Stage to the Execution Time of Particle Filter

Particle filter is a serial Monte Carlo estimation algorithm. It represents the posterior probability density function with particles and their weights. As time progresses, the normalized weight of one of particles becomes nearly one, while the normalized weights of the remaining ones get close to zero. A common way to solve this problem, known as the degeneracy problem, is resampling. In resampling, the particles with larger weights are replicated, and the particles with smaller weights are eliminated. To tackle the numerical instability problem that is encountered by some of the resampling methods, the Metropolis resampling method is proposed by Murray and his co-workers. Unfortunately, Metropolis is liable to non-coalesced global memory access patterns on the GPU. In this work, we point to the Metropolis-C1 and Metropolis-C2 resampling methods which are proposed earlier. Then we examine the contribution of the stages of the particle filter to the total execution time by increasing the number of particles on a tracking application on the GPU. We use the Sampling Importance Resampling (SIR) method, which is a common particle filter. In the experiments, Metropolis resampling consumes the biggest portion of the execution time of the SIR particle filter. The share of Metropolis increases as the number of particles grows. It can be argued that this is because of non-coalesced global memory access patterns. To reach this conclusion it is sufficient to i) Compare the results of Metropolis, which has non-coalesced access patterns, with Metropolis-C1 and Metropolis-C2, which have confined non-coalesced access patterns, ii) See that the previous stages of the SIR particle filter are not subject to non-coalesced access patterns.
Weakly Supervised Joint Entity-Sentiment-Issue Model for Political Opinion Mining

Microblogging has become an important source of opinion-rich data that can be used for understanding public opinion. In this paper, we propose a novel weakly supervised probabilistic topic model, Joint Entity-Sentiment-Issue (JESI), for political opinion mining from Twitter. The model automatically identifies the target entity of the expressed sentiment, the issues discussed and the sentiment towards the issues and entity simultaneously. Unlike other machine learning approaches to opinion mining which require labelled data for training classifiers, JESI requires only a small number of seed words for each entity and issue, and a sentiment lexicon. The model is evaluated on a dataset of tweets collected during the 2016 Australian Federal Election. Experimental results demonstrate that JESI outperforms baselines for sentiment, entity and issue classification, especially achieving higher recall and F1.
Context Influence on Sequence to Sequence Turkish Spelling Correction

In this paper, we implement a Turkish spelling correction system using sequence to sequence (seq2seq) deep neural networks (DNN). In the baseline system, the letters of the misspelled and reference words are fed into the seq2seq model as input and outputs, respectively. In order to improve the performance, we utilize right and left context of the misspelled words. The context words are coded into three letter sequences. The first three consonants of context words are taken when the length of the word is longer than three. The seq2seq models are trained using a large text corpus collected from internet. We randomly add substitution, deletion and insertion spelling errors to the words in the training set. In the experiments, we observe that, the context-dependent model improves the correction performance by 9.2% absolute when compared to the baseline seq2seq model in which no context is utilized.
Optimizing the Execution Costs of High-Performance Geophysics Software on the Cloud

Summary The cloud computing paradigm is enabling the execution of high performance computing software and is a potential alternative to traditional clusters. Not only that, but public providers, such as Microsoft and Amazon, offer a wide variety of virtual machine specifications and prices. Correctly choosing the proper hardware specification for the software can reduce execution times and money. In light of this, we developed an algorithm that automatically selects the virtual machine configurations with the best cost per performance ratio in the Amazon Web Service cloud platform for a running software, using its performance in differently priced configurations as input to the selection. Our results showed that the algorithm is capable of selecting the instances that offers the best cost per performance within minutes, approaching the optimal cost as the dataset size increases.
Efficient Deep Approximation of GMMs

The universal approximation theorem states that any regular function can be approximated closely using a single hidden layer neural network. Some recent work has shown that, for some special functions, the number of nodes in such an approximation could be exponentially reduced with multi-layer neural networks. In this work, we extend this idea to a rich class of functions, namely the discriminant functions that arise in optimal Bayesian classification of Gaussian mixture models (GMMs) in $\mathds{R}^n$. We show that such functions can be approximated with arbitrary precision using $O(n)$ nodes in a neural network with two hidden layers (deep neural network), while in contrast, a neural network with a single hidden layer (shallow neural network) would require at least $O(\exp(n))$ nodes or exponentially large coefficients. Given the universality of the Gaussian distribution in the feature spaces of data, e.g., in speech, image and text, our results shed light on the observed efficiency of deep neural networks in practical classification problems.
GIS and Mapping

Regardless of their role, many librarians are finding themselves in need of mapping or data visualization tools. Whether they are helping patrons with geo-location questions, looking for visual rep...
GTH-UPM at TASS 2019: Sentiment Analysis of Tweets for Spanish Variants.

This article describes the system developed by the Grupode Tecnologia del Habla at Universidad Politecnica de Madrid, Spain (GTH-UPM) for the competition on sentiment analysis in tweets: TASS2019. The developed system consisted of three classifiers: a) a systembased on feature vectors extracted from the tweets, b) a neural-based classifier using FastText, and c) a deep neural network classifier using contextual vector embeddings created using BERT. Finally, the averaged probabilities of the three classifiers were calculated to get the final score. The final system obtained an averaged F1 of 48.0% and 48.4% for the dev set on the mono and cross tasks respectively, 46.0% and 45.0% for the mono and cross tasks for the test set.
Barcelona Court clarifies level of originality required for an industrial design to obtain cumulative protection under design and copyright law

Abstract Casa OTS Holding, S.L. and others vs. BD Barcelona 1972, S.L. and others, Judgment 764/2019 of the Provincial Court of Barcelona (section 15th), 26 April 2019 This judgment analyses what level of originality is required for the cumulation of industrial design and copyright protection, and the value that expert reports may have in such assessment.
The Human(s) in the Loop—Bringing AI and HCI Together

This document presents the motivation, objectives and target audience, theme, submissions, structure as well as the expected outcome.