Reviewerly/exHarmony huggingface dataset:
A Pragmatic Analysis of Trump’s Hate Speech

Hate speech is spoken words that reflect the speaker’s offensive, insulting, and/or threatening toward an individual or group based on a particular attribute of that addressee(s) being targeted. Such kind of words is likely to vex person/people into acting rashly against speaker who is doing the vexation (Web source 1). The current study deals with hate speech of Trump pragmatically falling in three categories that are: speech act theory, rhetorical tropes, and impoliteness. It aims at sketching a pragmatic approach to deal with hate speech of political speech or any type of speech. It hypothesizes that, in hate utterances, assertives and commissives classes of Searle’s speech acts theory are used more than the other three. It also hypothesizes that metaphor and allusion as rhetorical tropes are likely to be used within hate speech. Another hypothesis is that both rudeness and bald on record are the most dominant techniques of impoliteness to express hatred.
Comparison of Classification Neural Network for Shape Feature in AI-driven Metaverse Application

Image classification technology is critical to the success of Metaverse applications. The key challenges in big data image classification is finding an efficient and automated method to classify images. Currently, the most popular approach is to use neural network to train an image classifier. Although various neural network models are available for image classification, there is a lack of model selection methods for specific scenarios in AI-driven Metaverse application. To address this issue, we introduced three popular neural networks in the industry and applies them to problem of shape classification in the interdisciplinary field of ceramic culture digitalization technology. Then, we use a two-parameter optimization training scheme with multiple batches to obtain multiple comparable results. Finally, we determine the optimal neural network selection scheme for ceramic type classification by combining the accuracy, number of parameters, reasoning time and other factors with the requirements of Metaverse scenarios.
Adaptive Anti-jitter Optimization for Immersive Streaming in Metaverse Scenarios

In recent years, the swift advancement of Extended Reality (XR) multimedia services has made the realization of metaverse possible. The notion of the metaverse has posited elevated standards for immersive experience, encompassing heightened interactivity, enhanced sociability, streamlined client endpoints, and enriched content immersion. In response to these requirements, cloud rendering architecture has become an important research direction for the next generation of virtual reality experiences. Virtual reality, a key technology in cloud rendering architecture, is integral to achieving high levels of immersion and interactivity in metaverse services. Nonetheless, the content displayed on virtual reality (VR) all-in-one devices is panoramic videos, which differ from conventional planar videos by imposing higher demands on bitrate and bandwidth. Consequently, the transmission of panoramic video poses a considerable challenge. Furthermore, adaptive bitrate algorithms (ABR) face the challenge of balancing panoramic video’s competing quality of experience (QoE) objectives (such as higher video quality, minimising rebuffer events and maintaining video smoothness), especially in intricate and dynamic network environments. In our study, we present a tile-based reinforcement learning ABR algorithm termed TRLBA, which leverages the asynchronous advantage actor-critic (A3C) algorithm to determine the appropriate bitrate for each tile in the forthcoming video segment based on historical and current data. Notably, TRLBA optimizes bandwidth utilization by allocating higher bitrates to tiles within the user’s Field-of-View (FoV, also called viewport) while assigning lower bitrates to those outside the FoV. To make the user experience more intuitive to be represented, we propose a novel QoE model for panoramic video and employ it for comparative evaluation of algorithms. Furthermore, we design a platform to simulate tiled panoramic video downloads and demonstrate the superiority of TRLBA over various open-source ABR algorithms with the highest average QoE score.
Распределенное научное познание – от единства к связи разнообразного: ответ оппонентам

The author consider critical remarks of opponents about the idea of distributed scientific cognition, its connection with the concept of science as a source of epistemic diversity. She notes the importance of the concept of consensus and unification of scientific disciplines, and emphasizes relevance the distribution of scientific cognition binding the diverse in situations of problematic unity.
Properties of Fuzzy Semi-Open Sets with Regards to Fuzzy Ideals

This paper introduces a broadened concept of fuzzy semi-open sets by framing them in the context of fuzzy ideals. Where many of their elementary properties will be presented in terms of theorems and lemmas. Also, many related examples about the validity of semi-open sets and their relationships with fuzzy ideals will be provided and discussed.
A Security Process for the Automotive Service-Oriented Software Architecture

The Automotive Service-Oriented software Architecture (ASOA) is a framework for the effective design, fast deployment, and efficient maintenance of automotive software architectures, facilitating reliable communication among loosely coupled services. It maintains a global view of functional units, allowing them to be updated, replaced, and reconnected so that road vehicles obtain or lose capabilities. While the ASOA lives up to expectations regarding reliability and real-time behavior, it lacks a security concept and, therefore, cannot be safely deployed in automotive systems. This work presents a security process to protect ASOA-based communication from compromise and prevent passenger harm. We propose annotating dataflows with security attributes in a web-based architecture design tool and introducing a central component responsible for converting the communication model into tokens securely distributed to ASOA services. They enable secure communication among components while preserving the desired loose coupling, a critical factor for cost-effective system maintainability. Moreover, we support shielded and technically constrained devices in a heterogeneous domain or zonal vehicle network. After formally verifying our token distribution protocol, we evaluate our security process in a self-driving vehicle and prove that it imposes only negligible overhead during operation.
Smart Contract Vulnerability Detection Based on Automated Feature Extraction and Feature Interaction

Smart contract is the core of blockchain operation, and contract vulnerability will cause huge economic losses. Therefore, effective smart contract vulnerability detection is of vital importance and attracts more and more attention. In this paper, we propose a vulnerability detection model (VDM-AEI) based on automatic feature extraction and feature interaction. For the first time, this model converts smart contracts into gray images and uses VGG16 and GRU models to automatically extract vulnerability features and filter effective features, respectively. Then, a contract graph and an expert knowledge feature vector are constructed by using commonly used methods as part of feature construction. Next, AutoInt and DCN networks are used to build a dual feature interaction network to obtain more abundant vulnerability feature information, which extracts high-dimensional nonlinear features from the low and sparse features of the contract graph feature vector and the expert knowledge-defined feature vector. Finally, all ouput features of GRU, AutoInt and DCN networks are integrated to obtain vulnerability classification results through fully connected neural networks. We conducted extensive experiments on the ESC and VSC datasets for reentrancy vulnerabilities, timestamp dependency vulnerabilities, and infinite loop vulnerabilities. The experimental results prove the effectiveness and accuracy of the VDM-AEI model. Compared with the latest vulnerability detection model CGE, the accuracy rates of the 3 types of vulnerability detection are improved by 10.85%, 6.18%, and 12.34%, respectively. In addition, the predicted F1 scores of VDM-AEI are all greater than 95%, and the recall rate is no less than 94%.
Laboratory in Python

One of the key concepts in informational genomics is the notion of dictionary. Given a genome, many possible dictionaries can be associated with it. Many important properties of the genome depend on the characteristics of these dictionaries and on the ways their words are distributed in the genome. For this reason, all the informational analyses based on dictionaries have a fundamental relevance to the topics presented in this book. In what follows, we will learn how to manage genomic strings, how to efficiently extract dictionaries from them and how to exploit features linked to such dictionaries for retrieving biological information. The examples are made over real genomic strings and the coordinates of known genes that are present in them, which are publicly available in bioinformatic resources. A fundamental data structure for the field of computational genomics is introduced, named “enhanced suffix array”, by means of which dictionary operations over genomes can be performed in linear time. Lastly, since some key aspects regard the informational properties of random strings, the laboratory shows how to create random genomes and how to deal with them.
PASCOINFOG/PASFOG: Privacy-preserving Data Deduplication Algorithms for Fog Storage Systems

The forthcoming Fog storage system should provide end users with secured and faster access to cloud services and minimise storage capacity using data deduplication. This method stores a single copy of data and provides a link to the cloud/fog owners. In client-side data deduplication, the system can reduce network bandwidth levels by duplicate check. This solution fails to cover user privacy and optimise the latency of real-time communications. Motivated by this, this magazine paper develops PrivAcy-preServing data deduplication in Fog stOraGe system (PASFOG) as a data deduplication protocol implemented between cloud storage and users to mitigate brute-force and poison attacks. PASFOG is implemented in fog computing to reduce real-time delay and communication when performing duplicate checks. Also, we propose PrivAcypreServing data dedupliCatiOn in blockchaIN-based Fog stOraGe system (PASCOINFOG) utilised blockchain techniques to realise a reliable system. In PASCOINFOG, when users want to send chunks to the cloud/fog nodes, process the duplicate check and create a new block for the blockchain to reduce real-time latency/communication and protect the cloud from attackers. The proposed protocols can enhance user privacy and reduce real-time communication delay, crucial for consumer electronics applications such as cloud storage and IoT devices.
SemiHAR: Improving Semisupervised Human Activity Recognition via Multitask Learning

Semisupervised human activity recognition (SemiHAR) has attracted attention in recent years from various domains, such as digital health and ambient intelligence. Currently, it still faces two challenges. For one thing, discriminative features may exist among multiple sequences rather than a single sequence since activities are combinations of motions involving several body parts. For another thing, labeled data and unlabeled data suffer from distribution discrepancies due to the different behavior patterns or biological conditions of users. For that, we propose a novel SemiHAR method based on multitask learning. First, a dimension-based Markov transition field (DMTF) technique is designed to generate 2-D activity data for capturing the interactions among different dimensions. Second, we jointly consider the user recognition (UR) task and the activity recognition (AR) task to reduce the underlying discrepancy. In addition, a task relation learner (TRL) is introduced to dynamically learn task relations, which enables the primary AR task to exploit preferred knowledge from other secondary tasks. We theoretically analyze the proposed SemiHAR and provide a novel generalization result. Extensive experiments conducted on four real-world datasets demonstrate that SemiHAR outperforms other state-of-the-art methods.
Does Political Regime Matter in Assessing the Impact of Political Stability on Economic Performance across MENA?

Abstract&#x0D; This paper examines the influence of political stability on economic performance across different political regimes in the Middle East and North Africa (MENA) region. The study uses panel data of MENA sample countries from 1996 to 2018. The pooled OLS, fixed effects, random effects, and Hausman techniques are used to examine the estimated model. The study contributes to the political economy by discriminating the influence of different political regimes across MENA: republics, monarchies, and semi-democracies. The main findings show that when accounting for political regime types across MENA, the political stability of both republic regimes and monarchy regimes positively affects their economic performance. However, the impact tends to be negative for countries under semi-democratic regimes, as these countries suffer from a high level of political instability. Using other institutional indicators, semi-democratic regimes, as expected, outperformed the other political regimes. These results are consistent with the literature on the importance of democracy as a driver for economic enhancement.&#x0D; Keywords: Political Regime; Political Stability; Economic Performance; Democracy;
Snakes and ladders: How do media and RME address the five components of mathematics learning in elementary school?

Mathematics learning depends on five components, namely concepts, skills, processes, attitudes, metacognition, and problem-solving. Apart from that, teachers also apply three teaching principles: the class must be fun, children must understand, and children must be capable. This research aims to develop products used in learning, especially educational materials. Often students need help and are not interested in learning math, especially memorizing multiplication facts. This problem in learning Mathematics can reduce the quality of student learning outcomes. The "M2 Game" product developed multiplies natural numbers with a practical basic mathematics learning method. The type of research used is development research, especially Research and Development. The development phase considers the Hannafin and Peck model, including requirements analysis, design, development, and implementation. The tools used are observation, interviews and questionnaires. Data analysis techniques, the utility of developing them, and their impact on learning will be discussed
The Attitudes of Jordanian Universities Students towards Digital Advertising and its Impact on Their Purchasing Behaviour

This study aimed to identify the attitudes of Jordanian Universities students towards digital advertising and its impact on their purchasing behavior. This study belongs to the analytical descriptive studies. The survey method and questionnaire were used in this study. The questionnaire was distributed to a sample of 308 students from University of Jordan, and Petra University. The appropriate statistical means were used to answer the study questions. The results revealed that the rates of Jordanian Universities student’s internet usage are high, and that students in the public universities are less likely to use the Internet than students in the private universities. The study indicated that scientific and recreational information are the most information that the students seek to obtain, while the economic information was the least. The products sought by the students of through the digital advertising are music and clothing, while the products of household items were the lowest among students.
Application of machine learning in wireless communication

With the rapid development of wireless communication technology, people have put forward higher requirements for the speed and quality of data transmission in wireless communication while enjoying a convenient life. Wireless communication systems are expected to be combined with artificial intelligence to meet these requirements. Machine learning (ML) can rely on different algorithms to process data without explicit programming. It can also optimize wireless systems by solving complex problems that traditional mathematics cannot solve. This paper briefly introduces wireless communication, machine learning, and the necessity of combining machine learning. The potential and applications of machine learning in various aspects of wireless communication, such as channel estimation, spectrum allocation, adaptive interference suppression, etc., are listed. The paper also introduces the various conveniences that machine learning in wireless communication brings to people in practical applications and the potential hazards that improper applications may bring.
Analysis of the principle and state-of-art applications for quantum neural network

The Neural Network is a well-known computational model that widely applied in machine learning (ML) inspired by human brains, which can perform the ML tasks including classification and feature extraction. Contemporarily it has been succeeded in all areas functioning as a powerful tool. Quantum computing is an emerging field based on quantum computers, which is a different calculation logic in the context of quantum dynamic theory providing an exponential computation power edge over traditional computers. Quantum Neural Network (QNN) is an intersection of the two areas, leveraging the advantage of quantum computing in the neural network, providing a strikingly powerful algorithm with promising potential. On this basis, this paper will demonstrate the state-of-art of QNN, which briefly explains the basic principle of QNN and an introduction of several typical QNN models. In addition, the current defects and drawbacks will also be discussed simultaneously. Overall, these results serve as a preliminary introduction to the topic, which shed light on guiding further exploration of quantum computing algorithms.
Chinese chess competition strategy analysis based on the zero-sum game

As a desktop game with a long history, Chinese chess has been widely circulated. In recent years, Chinese chess championships have become increasingly popular. During the competition, in addition to the individual abilities of the participants, the use of different strategies in the competition also become another major factor affecting the outcome of the competition. Based on the fixed ability values of the contestants, this research focuses on discussing the selection of strategies. This paper will adopt points-based match rules in common competitions and quantify the player's ability values for making a data analysis about nine different strategies. Due to the use of a point system in the competition, this research could use zero-sum game for analysis. According to zero-sum game and Nash equilibrium, the payoff matrix could be obtained. Furthermore, a precise linear program model is built. After the calculation, this research presents a result about which strategies should be used in the competition and provide an analytical explanation.
Development of RSA and some attack points

Currently, the relatively popular public key encryption is RSA. It was officially launched in 1978 and given their initials as a name. Different encryption and decryption keys are used in the RSA public-key cryptosystem. Deriving decryption keys from known encryption keys is computationally difficult. Rsa has emerged in various forms since its development. Since the development of RSA, there have been various forms, which means there are various loopholes. Next, we will summarize several interesting attack methods of rsa. It will involve continued fractions and CRT and so on. Through this paper, one can learn a series of knowledge about rsa. As a classic asymmetric password, the value of rsa's existence is relatively high, and exploring its value is something that every password learner should experience. Through this article, one will learn about the most basic attack methods of rsa.
Analysis of faster matrix multiplication

Since the definition of matrices in 1855, matrix multiplication has played a crucial role in a wide range of fields. Over the years, numerous researchers have dedicated their efforts to improving the time complexity of this fundamental operation. This paper aims to delve into the historical development of matrix multiplication algorithms and methodologies employed to achieve these significant advancements in time complexity. By employing various approaches, researchers have been able to improve the time complexity of matrix multiplication, leading to a significant reduction from O () to O (). Across nearly two centuries, this progress is contributed by a lot of extraordinary scientists and researchers. This paper explores the practical implications of these improvements across various domains, such as computer science, physics, economics, and more. The development of more efficient matrix multiplication algorithms has enabled researchers and practitioners to tackle complex problems and explore new frontiers. In the future, with the rapid growth of machine learning techniques, matrix multiplication will continue to evolve and improve.
Application of linear programming in two-player zero-sum games

Linear programming is a useful optimization tool that has been utilized since the 1940s. It has been studied for a long time, and it can be applied to many different areas and fields. One of these applications is the two-player zero-sum game. In this game, each player has a set of possible strategies that is finite. While one player tries to maximize the net payoff, the other player tries to minimize this payoff. Since the problem involves optimizations, linear programming can be involved. This paper analyzes a famous and classic example of the game the Colonel Blotto Game. This game is a military planning problem. Indeed, Linear programming was first developed to solve complex military and planning problems during wartime. This paper utilizes a payoff matrix and some mathematical models to build the mathematical problem of the Colonel Blotto Game. Then the results can be obtained by converting the mathematical problem into a Python Linprog problem. The paper finds the best strategy for Colonel Blotto to maximize the net payoff and how eliminating certain possible strategies will or will not affect the results. Moreover, the paper provides an understanding of the combination of the two-player zero-sum game and linear programming.
Application of modern algebra in cryptography

With the rapid development of the digital age, information security, from personal data to national security, is becoming increasingly crucial. Information security primarily refers to the computation and processing of diverse information in computer systems and information exchange networks in order to safeguard information security. Cryptography is the technical foundation for achieving these objectives. In the early stages of education, advanced mathematics, linear algebra, probability theory, and other fundamental disciplines must be studied, although the practical application of modern algebra, cryptography, number theory, and mathematical knowledge will vary. This study explores the application of current algebra in cryptography, including both traditional and modern cryptographic applications, using a literature review approach. By comparing images from various eras, the researchers discovered that images were classed as "traditional" and "modern" at various times. Moreover, the likelihood of both traditional and modern images being identified throughout the modern era is comparatively higher.