Reviewerly/exHarmony huggingface dataset:
Techniques for Jointly Extracting Entities and Relations: A Survey

Relation Extraction is an important task in Information Extraction which deals with identifying semantic relations between entity mentions. Traditionally, relation extraction is carried out after entity extraction in a “pipeline” fashion, so that relation extraction only focuses on determining whether any semantic relation exists between a pair of extracted entity mentions. This leads to propagation of errors from entity extraction stage to relation extraction stage. Also, entity extraction is carried out without any knowledge about the relations. Hence, it was observed that jointly performing entity and relation extraction is beneficial for both the tasks. In this paper, we survey various techniques for jointly extracting entities and relations. We categorize techniques based on the approach they adopt for joint extraction, i.e. whether they employ joint inference or joint modelling or both. We further describe some representative techniques for joint inference and joint modelling. We also describe two standard datasets, evaluation techniques and performance of the joint extraction approaches on these datasets. We present a brief analysis of application of a general domain joint extraction approach to a Biomedical dataset. This survey is useful for researchers as well as practitioners in the field of Information Extraction, by covering a broad landscape of joint extraction techniques.
Interpretability and Effectiveness of Machine Learning Methods for Sequence Mining in Various Domains

There is a diverse variety of demographic data that can be analyzed with modern methods of data mining to achieve better results. On the one hand, the main chosen task is to compare different methods for the next event prediction and gender prediction, on the other hand, we pay special attention to interpretable patterns describing demographic behavior in the studied problems. There were considered interpretable methods as decision trees and their ensembles and semi- or non-interpretable methods, such as the SVM method with different customized kernels tailored for demographers' needs and neural networks, respectively. The best accuracy results were obtained with two-channel Convolutional Neural Networks.
Anomalies in Transport Data

Abstract Over time, the analysis of traffic data has come down to predicting traffic flows. Building correspondence matrices describing traffic flows, forecasting traffic flows at a specific point and at a specific time interval is what transport analytics did. However, the development of technology and the economy has led to the fact that the penetration of mobile phones and the efforts of telecommunications operators provide complete information about the movements of mobile devices (their users) in modern cities. This means, in particular, that predicting flows is no longer a task. Other issues come to the fore. And one of them is just an analysis of anomalies. This is necessary, first of all, at the stage of operation of Smart City systems.
LBS-based location privacy protection mechanism in augmented reality

With the development of augmented reality(AR) technology and location-based service (LBS) technology, combining AR with LBS will create a new way of life and socializing. In AR, users may consider the privacy and security of data. In LBS, the leakage of user location privacy is an important threat to LBS users. Therefore, it is very important for privacy management of positioning information and user location privacy to avoid loopholes and abuse. In this review, the concepts and principles of AR technology and LBS would be introduced. The existing privacy measurement and privacy protection framework would be analyzed and summarized. Also future research direction of location privacy protection would be discussed.
Applications of Big Data in Large and Small Systems

Nowadays, big data is available in every field due to the advent of computers and electronic devices and the advancement of technology. However, analysis of this data requires new technology as the earlier designed traditional tools and techniques are not sufficient. There is an urgent need for innovative methods and technologies to resolve issues and challenges. Soft computing approaches have proved successful in handling voluminous data and generating solutions for them. This chapter focuses on basic concepts of big data along with the fundamental of various soft computing approaches that give a basic understanding of three major soft computing paradigms to students. It further gives a combination of these approaches namely hybrid soft computing approaches. Moreover, it also poses different applications dealing with big data where soft computing approaches are being successfully used. Further, it comes out with research challenges faced by the community of researchers.
Deep Learning and Approach for Tracking People’s Movements in a Video

Everyday, a large amount of data is produced thanks to technological advances in the field of multimedia, associated with the generalization of their use in many applications. The need to keep control over this content, in terms of data analysis, classification, accurate AI (Artificial Intelligence) algorithms are required to perform this task efficiently and quickly. In this article, we propose an approach using deep learning technologies for the analysis of movement in video sequences. The suggested approach uses images from video splitting to detect objects / entities present and store their descriptions in a standard XML file. As result, we provide a Deep Learning algorithm using TensorFlow for tracking motion and animated entities in video sequences.
Qillah: A Morphological Extension for Identifying Plural-of-Paucity Arabic Words

Plural of paucity is one type of broken plural used in the classical Arabic. It is used when the number of people or objects ranges from three to 10. Based on our evaluation of four current state-of-the-art Arabic morphological analyzers, there is a lack of identification of broken plural words, specifically the plural of paucity. Therefore, this paper presents Qillah (paucity), a morphological extension that is built on top of other morphological analyzers and uses a hybrid rule-based and lexicon-based approach to enhance the identification of plural of paucity. Two versions of the Qillah were developed, one is based on FARASA morphological analyzer and the other is based on CALIMA Star analyzer, as these are some of the best-performing morphological analyzers. We designed two experiments to evaluate the effectiveness of our proposed solution based on a collection of 402 different Arabic words. The version based on CALIMA Star achieved a maximum accuracy of 93% in identifying the plural-of-paucity words compared to the baselines. It also achieved a maximum accuracy of 98% compared to the baselines in identifying the plurality of the words.
Quantum Machine Learning

The chapter starts by reviewing relevant classical machine learning algorithms, categorized as supervised, unsupervised, and reinforcement learning algorithms. The following topics from classical machine learning are described: principal component analysis (PCA), support vector machines (SVMs), clustering, boosting, regression analysis, and neural networks. In the PCA section we describe how to determine the principal components from the correlation matrix, followed by a description of singular value decomposition-based PCA and scoring phase. We describe SVMs from both geometric and Lagrangian method-based points of view and introduce both hard and soft margins as well as the kernel method. In the clustering section we describe K-means, expectation maximization, and K-nearest neighbor algorithms. We also describe how to evaluate the clustering quality. In the boosting section, we describe how to build strong learners from weak learners, with special attention devoted to the AdaBoost algorithm. In the regression analysis section we describe the least-squares estimation, the pseudoinverse approach, and the ridge regression method. In the neural networks' section we describe in detail: perceptron, activation functions, and feedforward networks. The focus then moves to the quantum machine learning (QML) algorithms. We first describe the Ising model and relate it to the quadratic unconstrained binary optimization (QUBO) problem. We then study how to solve the QUBO problem by adiabatic quantum computing and quantum annealing. To perform QML using imperfect and noisy quantum circuits we describe the variational quantum eigensolver and quantum approximate optimization algorithm (QAOA). To illustrate the impact of QAOA we describe how to use it in combinatorial optimization problems and how to solve the MAX-CUT problem. Next, quantum boosting is discussed, and is related to the QUBO problem. Quantum random access memory is described next, allowing us to address the superposition of memory cells with the help of a quantum register. Quantum matrix inversion, also known as the Harrow–Hassidim–Lloyd algorithm, is then described, which is used as a basic ingredient for other QML algorithms such as quantum PCA, which is described as well. In the quantum optimization-based clustering section we describe how the MAX-CUT problem can be related to clustering, and thus be solved by adiabatic computing, quantum annealing, and QAOA. Grover algorithm-based quantum optimization is discussed next. In the quantum K-means section we describe how to calculate the dot product and quantum distance, followed by the Grover search-based K-means algorithm. In the quantum SVM section we formulate the SVM problem using least squares and describe how to solve it using quantum matrix inversion. In the quantum neural networks (QNNs) section we describe feedforward QNNs, quantum perceptron, and quantum convolutional networks.
Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination Through Curriculum-Learning and Competition

This work proposes a scheme that allows learning complex multi-agent behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is formulated as a Markov game, and solved using deep reinforcement learning. We propose a basic multi-agent extension of TD3 for learning the policy of each player, in a decentralized manner. To ease learning, the task of 2v2 soccer is divided in three stages: 1v0, 1v1 and 2v2. The process of learning in multi-agent stages (1v1 and 2v2) uses agents trained in a previous stage as fixed opponents. In addition, we propose using experience sharing, a method that shares experience from a fixed opponent, trained in a previous stage, for training the agent currently learning, and a form of frame-skipping, to raise performance significantly. Our results show that high quality soccer play can be obtained with our approach in just under 40M interactions. A summarized video of the resulting game play can be found in https://youtu.be/pScrKNqfELE.
Assessment of Undergraduate Students’ Readiness for Intercultural Business Communication within the Digital Economy Ecosystem

The article is devoted to the issues of preparing future specialists for intercultural professional interaction in the context of the development of the global digital economy. As part of rapidly evolving standards in this area, issues are urgently needed that require detailed consideration. Namely: What is the role of education in the digital age? What modern teaching methods have already been introduced, and what else can be introduced into the educational process? One of the significant competencies for the implementation of professional and sociocultural activities remains the competence of communicative communication. In order to solve communication problems in an interpersonal and intercultural format, the specialist’s ability to write written communication in a foreign language is distinguished. Proficiency in a foreign language as an important tool for professionally oriented training, knowledge of the format of document texts and practical skills acquired during the training process make it possible to more effectively carry out professional activities in modern economic conditions. The relevance of the study is determined by the many difficulties that specialists face in the course of working with various types of electronic documentation. The study used empirical methods such as observation, experiment, analysis, study and generalization. The article presents the results of a study of the level of preparation of a future specialist for written communication in a foreign language in the professional sphere. An attempt was made to identify the degree of preparedness of students for intercultural foreign-language written business communication in electronic format, to consider the specifics of linguistic and related difficulties. The research prospects lie in the further implementation of modern educational technologies, focused on the widespread use of active teaching methods, which contribute to deepening the professional specialization of the educational process in the era of the digital economy.
An Exploratory Study on Performance Engineering in Model Transformations.

Model-Driven Software Engineering (MDSE) is a widely used approach to deal with the increasing complexity of software. This increasing complexity also leads to the fact that the models used and the model transformations applied become larger and more complex as well. This means that the execution performance of model transformations is gaining in importance. While improving the performance of model transformation execution engines has been a focus of the MDSE-community in the past, there does not exist any empirical study on how developers of model transformation deal with performance issues. Consequently, we conducted an exploratory mixed method study consisting of a quantitative online survey and a qualitative interview study. We used a questionnaire to investigate whether the performance of a transformation is actually important for transformation developers and whether they have already tried to improve the performance of a model transformation. Subsequently, we conducted semi-structured interviews based on the answers to the questionnaire to investigate how transformation developers deal with performance issues, what causes and solutions they found and also what they think could help them to easier find causes. The results of the quantitative online survey show that 43 of 81 participants have already tried to improve the performance of a transformation and 34 of the 81 are sometimes or only rarely satisfied with the execution performance. Based on the answers from our 13 interviews, we identified different strategies to prevent or find performance issues in model transformations as well as different types of causes of performance issues and solutions. Finally, we compiled a collection of additional tool features perceived helpful by the interviewees to address performance issues.
Improved Boundary Uncertainty Estimation For Classifier Selection

Classifier evaluation is a key component of optimal classifier design that traditionally requires validation data separate from the training data. In contrast, a recent classifier evaluation method enabled classifier evaluation directly on the training data, by introducing a new evaluation measure called boundary uncertainty. However, the boundary uncertainty estimation proposed by this method suffered costs that hindered its applicability and accuracy. Therefore, we propose an improved boundary uncertainty estimation method and demonstrate its high utility. Compared to the original method, our improved method is more accurate, transparently applicable, hyperparameter-free, and roughly 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> times faster on the presented benchmark dataset.
Optical Surface Profiling Using Beamlet Deflection Method

Deflectometry and slope measurement techniques are being used for surface profiling and wavefront estimation. In present study a screen having multiple apertures of small size is inserted in the object path which is illuminated by a collimated and test wave. The beamlets are generated and its images at different image planes are used to compute the wavefront slope. These slopes are then used in reconstruction of the wavefront. The preliminary results of the investigations carried out on beamlet deflection technique to estimate the spherical, cylindrical and freeform wavefronts are presented. At longer image plane distances, the calculation of slope value is more accurate and hence the wavefront estimation.
A Dual 32 Nibble Specific Cipher Model for RGB Images Using Lorenz Attractor

In the modern age, where the digital communication systems play a vital part in our day-to-day activities the amount of digital data generated has increased exponentially. Limited security and bandwidth are the major constraints that have to be faced. One of the most excessively used multimedia data are images. A lot of studies indicate that the confidentiality and authenticity of the data are vulnerable from attacks no matter the level or method proposed to secure the data. In this paper, an algorithm is proposed to encrypt color image where Lorenz attractor is used to generate keys that are used in the process of image encryption. The proposed solution is focused of reducing the computational complexity by reducing the encryption process to most significant nibble plane of the image.
Cloud Computing as a Platform for Design-Oriented Applications

Τhe last two decades there is an effort to move traditional CAx application to the cloud so to facilitate the transition of industrial sectors towards the Industry 4.0 model. This paper reviews three computer aided design modules (CAD modules) that exploit the infrastructure of cloud computing systems with respect to the Industry 4.0 technological requirements: 3D model visualization, collaborative design, design review. On the basis of these modules, we present indicative cloud-based design applications from different disciplines and discuss how design-oriented tasks can benefit from cloud computing technology.
Entropy Based Frame Exclusion Framework for Video Transmission over Next Generation Networks

With the growing increase of the video traffic and the increasing expectations of users in terms of the acceptable video quality, achieving the users' Quality of Experience (QoE) while maximising the network resource utilisation to avoid any potential loss of revenue for the ISPs had become a challenge. Traditional Admission Control (AC) algorithms have many limitations in terms of achieving the balance between the perceived QoE and the number of admitted video sessions. This paper proposes a novel framework that exploits video traffic characteristics to present an adaptive admission control technique without compromising the perceived QoE for video traffic. More specifically, we apply an information theoretic tool, namely information entropy, to perform frame selection to the incoming video signals. Experiment results highlight the promise of the studied framework and identify possible future applications.
Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications

In this work, the problem of exploring an unknown environment with a team of agents and search different targets on it is considered. The key problem to be solved in multiple agents is choosing appropriate target points for the individual agents to simultaneously explore different regions of the environment. An intelligent approach is presented to coordinate several agents using a market-based model to identify the appropriate task for each agent. It is proposed to compare the fitting of the market utility function using neural networks and optimize this function using genetic algorithms to avoid heavy computation in the Non-Polynomial (NP: nondeterministic polynomial time) path-planning problem. An indoor environment inspires the proposed approach with homogeneous physical agents, and its performance is tested in simulations. The results show that the proposed approach allocates agents effectively to the environment and enables them to carry out their mission quickly.
Influence of medical epidemic prevention and control on the development of sports events based on FPGA system and machine learning

In year 2020, a large-scale outbreak of pneumonia caused by new coronavirus has affected the development of many industries and enterprises in China. Under the strong leadership of the Chinese government, the development of the epidemic situation in China has been well controlled. The development of various industries also began to show a good situation, many large-scale sports competitions also need to be restored. In order to ensure the normal development of large-scale sports events, we need to consider the development of epidemic situation to determine the time of sports events. Based on the study of FPGA theory, this paper designs a specific scheme of programming and system debugging, which includes a variety of program operations. In order to better predict the situation of the epidemic situation, this paper also uses the basic knowledge of machine learning to establish a relevant model to evaluate the situation of large-scale sports events under the development of the epidemic situation, and provide feasible suggestions for the recovery of large-scale sports events under the epidemic situation.
Key Technology of Games Based on Artificial Intelligence Technology

The continuous development of artificial intelligence technology has triggered technological changes in many industries. Now artificial intelligence in computer bionics has become an indispensable part and is widely used in virtual reality, games and application research of bionic robots. Among them, artificial intelligence all over these fields shows autonomous behavior and user interaction has gradually matured. Therefore, artificial intelligence technology has caused changes in many industries. As the main carrier of programming design, the key technology of game is also affected by artificial intelligence technology. Therefore, it is urgent to develop the key technology reform of core game. Based on the rapid development of artificial intelligence technology, there is a prospect of breakthrough in the key technology of the game. The research purpose of this article is to use artificial intelligence technology to make innovative breakthroughs in key game technologies under the background of current artificial intelligence technology. This article starts from the relevant knowledge and theory of practice, and takes the artificial intelligence era as the background, and makes an in-depth study on the development of key game technologies. In the course of the experiment, follow up the expected purpose and technical goals of the key game technology, and implement rigorous experiments through the theory and relevant realistic background to innovate and break through the key game technology through artificial intelligence technology. Experimental evidence shows that the combination of key game technologies based on artificial intelligence technology has a better development prospect for them, and can build a new model of key game technologies from the perspective of artificial intelligence.