Reviewerly/exHarmony huggingface dataset:
A Technique to Avoid Blockchain Denial of Service (BDoS) and Selfish Mining Attack

Blockchain denial of service (BDoS) and selfish mining are the two most crucial attacks on blockchain technology. A classical DoS attack targets the computer network to limit, restrict, or stop accessing the system of authorized users which is ineffective against renowned cryptocurrencies like Bitcoin, Ethereum, etc. Unlike the conventional DoS, the BDoS affects the system's mechanism design to manipulate the incentive structure to discourage honest miners to participate in the mining process. In contrast, in a selfish mining attack, the adversary miner keeps its discovered block private to fork the chain intentionally that aiming to increase the incentive of the adversary miner. This paper proposed a technique to successfully avoid BDoS and selfish mining attacks. The existing infrastructure of blockchain technology does not need to be changed a lot to incorporate the proposed solution.
Environment-aware Dynamic Resource Allocation for VR Video Services in Vehicle Metaverse

With the development of communication technology and virtual reality (VR) technology, virtual Metaverse services are gradually entering people’s lives to provide immersive experience. As one of the important travel tools for people, vehicles have the opportunity to become the carrier of Metaverse, thereby enhancing the driving experience and entertainment experience of vehicle users (VUs). However, due to the high-speed movement of vehicles, how to dynamically adapt to environmental changes to allocate transmission and computing resources so that VUs can better experience VR services in the Metaverse has become a challenge. To this end, in this paper, we propose an environment-aware dynamic resource allocation scheme for VR video services in vehicle Metaverse, aiming to efficiently allocate computing and communication resources to maximize the quality of experience (QoE) of VUs when requesting VR video services. Specifically, we first establish the system model which includes network model, communication model, and VR video model. Then, considering the dynamic changes in the driving environment, we design a QoE model for each VU based on its VR video buffer. After that, we design a deep deterministic policy gradient (DDPG) algorithm to optimally allocate communication and computing resources to maximize the QoE of each VU. The simulation results show that our scheme can bring the highest reward to the VUs compared with the benchmark schemes.
Incentive Based Federated Learning Data Dissemination for Vehicular Edge Computing Networks

The advancements in vehicular networks and applications brings about a huge demand for vehicles to process computation-intensive tasks such as machine learning (ML) algorithms. There is a need for an efficient solution to meet up these challenges. Federated learning (FL), which is distributed and does not require the transmission of dataset to the server, is more suitable to the resource constraint vehicular edge network. However, not all vehicles will participate in FL training without any reward. This paper proposes an incentive-based federated learning scheme for vehicular networks. Vehicles participating in federated learning are selected using an auction algorithm based on maximized social welfare. Simulations are conducted to evaluate the performance of the proposed scheme.
Phishing Attack Awareness Among College Students

Phishing is a form of cyber-attack that uses social engineering techniques to trick individuals into providing sensitive information or clicking on malicious links, often through email or text messages. Social engineering is the psychological manipulation of people into performing actions or disclosing sensitive information. In phishing, attackers use social engineering tactics such as creating a sense of urgency, posing as a trustworthy entity, and creating a sense of familiarity to manipulate the victim into taking the desired action. These attacks can have serious consequences, including financial loss and identity theft. In this research, we used an email phishing technique that asked respondents to change their password on an email account to find out awareness of phishing attacks on college students. The research results show that a small percentage of a certain amount of people are still unaware of phishing attacks and fell into the attacker's trap. Several respondents who are still deceived by social engineering using phishing emails indicate that it is necessary to conduct indoctrination and campaigns to raise the awareness of each student.
Digital Forensics Investigation and Incident Response in Internet of Things (IoT-DFIR): Challenges and Models

Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuously exchanging data via sensors. Due to increased IoT security concerns, digital forensic investigators now possess greater knowledge and abilities to investigate IoT devices. IoT-DigFor are required in terms of cybercrime due to the rapid growth in the number of electronic devices and the collection and consumption of enormous amounts of data. IoT systems with billions of devices have produced a significant amount of evidence, which presents a significant challenge to digital investigators and practitioners who must connect with IoT devices to conduct fast and thorough investigations into cybercrimes. This article presents detailed information about forensics in the IoT environment. The IoT forensic paradigm and the challenges that design-based security requirements and IoT system security offer for IoT-DigFor are covered in great detail at the beginning of the article.
How to create separate bibliographies in a Quarto document

&lt;i&gt; &lt;/i&gt; tl;dr If you want to skip the explanation and justification for why you might want separate bibliographies, you can skip down to the example section, or just go see some example files at GitHub. Why use separate bibliographies? In academic articles, it’s common to have a supplemental appendix with extra tables, figures, robustness checks, additional math, proofs, and other details.
Comparison among Classical, Probabilistic and Quantum Algorithms for Hamiltonian Cycle Problem

The Hamiltonian cycle problem (HCP), which is an NP-complete problem, consists of having a graph <i>G</i> with nodes and <i>m</i> edges and finding the path that connects each node exactly once. In this paper we compare some algorithms to solve a Hamiltonian cycle problem, using different models of computations and especially the probabilistic and quantum ones. Starting from the classical probabilistic approach of random walks, we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine (QTM), which can be a useful conceptual project tool for quantum algorithms. Introducing several constraints to the graphs, our analysis leads to not-exponential speedup improvements to the best-known algorithms. In particular, the results are based on bounded degree graphs (graphs with nodes having a maximum number of edges) and graphs with the right limited number of nodes and edges to allow them to outperform the other algorithms.
A Study on Positive Integer Points of Elliptic Curve y <sup>2</sup> = 7mx(x <sup>2</sup> + 512)

Abstract By using elementary methods such as the properties of congruence and Legendre symbols, it is proved that the elliptic curve y 2 = 7 mx ( x 2 + 512) has at most 1 positive integer point when q ≡ 5(mod8) is an odd prime number.
A Comparative Study of Bitcoin’s Price Prediction Using Regression Models

The rising popularity and increasing financial acceptance of cryptocurrency are having a profound impact on global scale. Unlike the current fiat currencies, bitcoins offer a unique possibility to predict their price. Despite the fact that many individuals are investing in cryptocurrencies, little is known about their dynamic properties and predictability, which puts money at risk. The aim of this paper is to evaluate and compare different regression algorithms in order to forecast the price of most popular cryptocurrency – Bitcons. Secondary bitcoin historical data from Kaggle which features an updated daily record of 24 variables over a seven-year period ARE considered. Since the bitcoin data is so volatile, we implemented an effective pre-processing of data in order to have a better prediction result. The different models applied include – Linear Regression, Ridge Regression, LASSO Regression and Elastic Net Regression model. However, elastic net performed better with an RMSE of 0.0228 without showing signs of overfitting.
Workshop Keterampilan Berbicara (Speaking): Membimbing Siswa SMA dalam Memahami dan Mengungkapkan Ide dengan Jelas

English speaking skills are an important aspect in developing high school students' communication skills. This workshop aims to improve high school students' speaking skills, especially in understanding and expressing ideas clearly. The workshop will cover a variety of communication strategies and techniques involving active interaction, group discussions, and hands-on exercises. Through a practical approach, workshop participants will be empowered to overcome common obstacles in speaking, including unclear expression of ideas, appropriate use of body language, and effective word choice. The expected result of this workshop is an increase in students' ability to compose and convey ideas in a structured and convincing manner. Apart from that, it is also hoped that there will be an increase in students' self-confidence in speaking in public. Thus, this workshop focuses not only on academic aspects, but also on developing interpersonal skills that are important for future success.&#x0D;
Exploration and Evaluation of Prompting Methods for Text Style Transfer

Text Style Transfer is a task concerned with modifying the attributes of a text while leaving its meaning unchanged. In recent years, this task has gained much attention due to the promising performance of deep learning models. However, progress is still slow due to an overreliance on large datasets and a lack of reliable evaluation methodology. To address the data challenge, the present research proposes the usage of prompting techniques which have shown very good performances on other natural language generation tasks in few-shot settings. Furthermore, considering the lack of conventional evaluation methods, this study explores various methods employed in previous research. This includes automatic metrics, but also human evaluation. Prompting outperforms fine-tuning according to computed metrics and to human assessment, but there seems to be little correlation between the human judgement and automatic metrics assessing then meaning conservation and the fluency of generated text.
AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query model

As an emerging knowledge representation model in the domain of knowledge graphs, geographic knowledge graph can take full advantage of semantic, spatial and temporal information to facilitate answering spatio-temporal questions and completing relations. However, the representation of geographic knowledge graphs still has issues such as the difficulty of unified heterogeneous spatio-temporal data modelling, weak ability to answer spatio-temporal queries for dynamic multiobjective problems, and low efficiency of graph querying. This paper presents a grid-augmented geographic knowledge graph (AugGKG) based on the GeoSOT global subdivision grid model and time slice subgraph architecture. AugGKG discretely normalizes the spatio-temporal data of the graph, which involves five types of nodes and two types of relations. By using the geo-hidden layer of the graph and geocoding algebraic operations, the AugGKG can quickly answer complex multiobjective spatio-temporal queries and complete implicit spatio-temporal relations. Compared with existing geographic knowledge graphs (YAGO, GeoKG and GEKG), the comparative experiments verified the obvious advantages of AugGKG in terms of uniformity of accuracy, completeness, and efficiency. Hence, AugGKG is expected to be regarded as an innovative and robust geographic knowledge graph that can perform fast computation and relation completion for complex spatio-temporal queries in future geospatial question answering applications.
Signal and Information Processing in Networks

Networks are omnipresent in the realm of science, serving as a central focus in our modern world [...].
ANALYSIS OF EXISTING FEDERAL PROJECTS ON THE FORMATION OF VALUE ORIENTATIONS OF YOUNG PEOPLE

The analysis of existing federal projects on the formation of value orientations of young people is important and relevant at the present time. The rapid development of technology, public relations and social problems lead to the fact that young people today face many challenges and choices. In such conditions, the formation of value orientations that will help young people navigate in life and make important decisions becomes especially important. An analysis of existing federal projects on the formation of value orientations of young people allows us to determine which methods and approaches are currently used to help young people understand complex issues and make the right choice. This makes it possible to identify the strengths and weaknesses of existing projects, as well as to determine which requirements and expectations of young people are not taken into account. Also, the analysis of existing federal projects on the formation of value orientations of young people can become the basis for the development of new projects and programs that will be focused on the current needs of young people and aimed at solving existing problems. In addition, such an analysis can help in assessing the effectiveness of existing projects and identifying areas that require additional support. This article analyzes federal projects aimed at the formation of value orientations among young people. The purpose of the study is to identify the main characteristics of existing projects. The authors review and systematize existing federal projects, analyze their goals, methods, and audience. In the course of the study, the authors identify the strengths and weaknesses of the projects, identify key problems and recommendations for their improvement. The study has practical significance for the development and implementation of more effective strategies for the formation of value orientations among young people at the federal level. Based on the results obtained, the authors draw conclusions about the need to develop projects aimed at instilling family, traditional, ethical, moral, cultural, and environmental values in the younger generation.
A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.
Adaptive Defense Mechanisms Against Phishing Threats in 6G Wireless Environments

Phishing attacks remain a persistent and evolving cybersecurity threat, particularly in the context of 6G wireless networks. This paper introduces an innovative approach to combat phishing threats, leveraging advanced techniques tailored to the unique challenges of 6G environments. Our research focuses on enhancing the security posture of 6G networks by deploying adaptive defense mechanisms for real-time phishing attack detection and prevention. In this study, we employ cutting-edge deep learning models specifically customized to the 6G landscape. A multi-layer neural network architecture is utilized, fortified with advanced activation functions optimized for the dynamic nature of 6G wireless communication. The proposed model is trained on an extensive and diverse dataset, carefully curated to include phishing and legitimate activities specific to 6G networks, enabling robust learning and broad generalization.
Variability-Aware Architecture for Human-Chatbot Interactions: Taming Levels of Automation

Human-chatbot interactions have become increasingly complex and dynamic. These interactions depend on a multitude of context-aware factors, including those related to the people involved, the environment, the tasks to be accomplished, and the system quality attributes. A key challenge is how to capture the context-aware factors in human-chatbot interactions that influence the levels of automation in autonomous systems. Another challenge is how to incorporate these factors in the architectural design of these systems to create personalized and contextually appropriate responses. This paper proposes a variability-aware feature-oriented architectural design for enhancing human-chatbot interactions. The architecture is designed to accommodate the dynamic and varying requirements of autonomous computing systems, with a particular focus on chatbots. The paper presents the steps involved in the architectural design and demonstrates its instantiation through a specific scenario involving customer service for financial services chatbots.
Multi-Class Classification of Hindi Text using Machine Learning

This study focuses on the multi-class classification of Hindi texts using ML techniques. The goal is to develop an efficient model that can accurately classify Hindi text documents into various predefined categories. We explore the preprocessing steps necessary for cleaning and preparing the text data, tokenization, stop-word removal, and stemming. Feature extraction methods such as TF-IDF and word embeddings are employed to represent the text data numerically. It includes ML algorithm like Supervised Learning algorithm (Naïve Bayes, SVM, Decision tree and Random Forest) are experimented with to determine the best-performing model. The dataset used for training and testing consists of a diverse range of Hindi texts from different domains. The experimental results provide insights into the effectiveness of different approaches and algorithms, shedding light on the challenges and opportunities of Hindi texts. Keywords: Multi-Class Classification, Hindi Texts, Machine Learning Algorithms, Natural Language Processing, Text Classification, Feature Extraction, Evaluation Metrics.
IMPLEMENTATION ON ARTMART A KEYWORD-BASED AI IMAGE GENERATOR

The swift advancement of Artificial Intelligence (AI) has had a profound effect on a number of sectors, including computer vision, image processing, and the creative arts, where jobs are now completed in a whole new way. Artificial intelligence (AI) has gained popularity recently for producing realistic photos. This system uses artificial intelligence APIs to analyze the input text and keywords to generate corresponding images that accurately represent the given description. The project is expected to have a significant impact on the automation of image generation and reduce the workload of graphic designers and content creators. An overview of the main goals, working methods, and importance of the AI Image Generator Project are given in this abstract. The main objective of the project is to create and implement cutting-edge neural networks and machine learning algorithms to produce high-quality photographs while also improving current ones. The project aims to tackle issues of content manipulation, super-resolution style transfer, and picture synthesis by utilizing state-of-the-art deep learning techniques.We concentrate on the technical components of the research, talking about the training datasets, neural network topologies, and iterative training procedures that improve the model's performance.. The project's development is fueled by extensive research into image generation and manipulation techniques incorporating both supervised and unsupervised learning paradigms We also highlight, the project's adoptability as it can input sources including textual descriptions sketches and existing images. In conclusion, the Al Image Generator Project is poised to reshape the landscape of image creation and manipulation through Al-driven Innovation. This abstract provides a glimpse into the transformative capabilities and ethical considerations of the project emphasizing its potential impact on diverse Industries and the broader technological landscape.
Detailed Implementation about Street Fighter Game using Python

This paper examines the varied cultural meanings of computer game play in competitive and professional computer gaming and live-streaming. A fighting game is a genre of video game that involves combat between two or more characters. Fighting game combat often features mechanics such as blocking, grappling, counter-attacking, and chaining attacks together into "combos". Characters generally engage in battle using hand-to-hand combat—often some form of martial arts. The fighting game genre is related to, but distinct from, the beat 'em up genre, which pits large numbers of computer- controlled enemies against one or more player characters. The controls for Final Fight consist of an eight-way joystick and two buttons for attacking and jumping respectively. Pressing the attack button repeatedly when attacking an enemy Pressing the attack and jump buttons simultaneously allows the player to perform a special attack that strikes all surrounding enemies, but will drain a small portion of the player's health. Enemies can be grabbed simply by walking into one of them. When an enemy is grabbed, the player can perform a grab attack by pressing the attack button or perform a throw by tilting the joystick left or right. A thrown enemy can be tossed at another for additional damage. Items such as weapons, health recovery items, and items awarding extra points can be picked up by standing over one and pressing the attack button. Weapons have limited uses and will disappear if the player is disarmed by an enemy too much or when the player moves to a new area.
INTROENUMERABILITY, AUTOREDUCIBILITY, AND RANDOMNESS

We define $\Psi $-autoreducible sets given an autoreduction procedure $\Psi $. Then, we show that for any $\Psi $, a measurable class of $\Psi $-autoreducible sets has measure zero. Using this, we show that classes of cototal, uniformly introenumerable, introenumerable, and hyper-cototal enumeration degrees all have measure zero.By analyzing the arithmetical complexity of the classes of cototal sets and cototal enumeration degrees, we show that weakly 2-random sets cannot be cototal and weakly 3-random sets cannot be of cototal enumeration degree. Then, we see that this result is optimal by showing that there exists a 1-random cototal set and a 2-random set of cototal enumeration degree. For uniformly introenumerable degrees and introenumerable degrees, we utilize $\Psi $-autoreducibility again to show the optimal result that no weakly 3-random sets can have introenumerable enumeration degree. We also show that no 1-random set can be introenumerable.