Reviewerly/exHarmony huggingface dataset:
Blocking Linear Cryptanalysis Attacks Found on Cryptographic Algorithms Used on Internet of Thing Based on the Novel Approaches of Using Galois Field (GF (232)) and High Irreducible Polynomials

Attacks on the Internet of Things (IoT) are not highly considered during the design and implementation. The prioritization is making profits and supplying services to clients. Most cryptographic algorithms that are commonly used on the IoT are vulnerable to attacks such as linear, differential, differential–linear cryptanalysis attacks, and many more. In this study, we focus only on linear cryptanalysis attacks. Little has been achieved (by other researchers) to prevent or block linear cryptanalysis attacks on cryptographic algorithms used on the IoT. In this study, we managed to block the linear cryptanalysis attack using a mathematically novel approach called Galois Field of the order (232), denoted by GF (232), and high irreducible polynomials were used to re-construct weak substitution boxes (S-Box) of mostly cryptographic algorithms used on IoT. It is a novel approach because no one has ever used GF (232) and highly irreducible polynomials to block linear cryptanalysis attacks on the most commonly used cryptographic algorithms. The most commonly used cryptographic algorithms on the IoT are Advanced Encryption Standard (AES), BLOWFISH, CAMELLIA, CAST, CLEFIA, Data Encryption Standard (DES), Modular Multiplication-based Block (MMB), RC5, SERPENT, and SKIPJACK. We assume that the reader of this paper has basic knowledge of the above algorithms.
The Handling of the Covid-19 Pandemic and Regional or Local ‘Disobedience’

The aim of this chapter is to analyse the contrasts that emerged in the management of the Covid-19 pandemic in Italy between the State and the Regions. The regional structure enshrined in the Italian Constitution had to deal with a health emergency that had never occurred before in the history of the Republic, which highlighted significant difficulties in the coordination between the different governmental bodies and required the intervention of the administrative judge and, most recently, of the Constitutional Court. After a description of the local and regional measures adopted, this chapter underlines the fact that the Constitutional Court preferred to apply a unitary discipline that would efficiently preserve people’s equal right to health and manage constitutional freedoms.
Explainable reinforcement learning (XRL): a systematic literature review and taxonomy

Abstract In recent years, reinforcement learning (RL) systems have shown impressive performance and remarkable achievements. Many achievements can be attributed to combining RL with deep learning. However, those systems lack explainability, which refers to our understanding of the system’s decision-making process. In response to this challenge, the new explainable RL (XRL) field has emerged and grown rapidly to help us understand RL systems. This systematic literature review aims to give a unified view of the field by reviewing ten existing XRL literature reviews and 189 XRL studies from the past five years. Furthermore, we seek to organize these studies into a new taxonomy, discuss each area in detail, and draw connections between methods and stakeholder questions (e.g., “how can I get the agent to do _?”). Finally, we look at the research trends in XRL, recommend XRL methods, and present some exciting research directions for future research. We hope stakeholders, such as RL researchers and practitioners, will utilize this literature review as a comprehensive resource to overview existing state-of-the-art XRL methods. Additionally, we strive to help find research gaps and quickly identify methods that answer stakeholder questions.
A new adversarial attack method based on Shapelet applied to traffic flow prediction model based on GCN

In this article, we propose a Shapelet based adversarial attack method for GCN based traffic flow prediction models. Firstly, we extract shape patterns from the original traffic flow data to obtain a set of shape feature Shapelets with significant discrimination. Then, we use Shapelet to generate adversarial samples using a method called reverse attack. Finally, we input adversarial samples into the GCN model to estimate their impact on traffic flow prediction. We evaluated the proposed attack algorithm on two real traffic datasets. Our algorithm can mislead model predictions, reduce the speed of model predictions, and lead to incorrect congestion prediction results. This study provides a new approach to adversarial attacks against time data. It also provides a new method for improving the robustness of traffic flow prediction models. Future research can further explore more complex and covert attack methods to address evolving security challenges.
Research on detection of insulator damage based on improved YOLOv4-tiny network

With the rapid development of China's economy, the whole society's demand for electricity is becoming more and more extensive. Insulator is the guarantee of smooth operation of transmission line, and is also one of the equipment which is prone to failure. How to detect insulator damage with high precision and high efficiency and repair it is one of the key factors of unimpeded power. This paper proposes a method for detection of insulator damage based on improved YOLOv4-tiny network. This method is mainly aimed at improving the main feature extraction module and feature fusion module of traditional YOLOv4-tiny network. In order to solve the problem of missing detection of small targets, adaptive attention mechanism is introduced to improve the module of feature extraction to improve the accuracy of detection. In addition, in order to further improve the detection accuracy of insulator damage and balance the detection time, a multi-attention CSAR model is proposed to improve the performance of the feature fusion module. Finally, images collected from different weather conditions are used as test sets to verify the effect of the improved model proposed in this paper through experiments. According to the experimental results, the detection accuracy of the proposed method can reach 98%, and the detection time is controlled within 10ms, which meets the basic requirements of detection of insulator damage.
Pedestrian target detection algorithm based on improved YOLO v5

In order to solve the problem of low accuracy of pedestrian target detection and recognition caused by occlusion between people when people are crowded with each other, an improved method of introducing SENet channel attention mechanism based on YOLOV5 neural network was proposed. By introducing SENet channel attention mechanism to further explore the relationship between the feature channels in the image, the feature extraction of the human target area is enhanced, and the accuracy rate of human target detection and recognition is well improved. Compared with the general YOLO V5 algorithm, the improved algorithm introduced by SENet attention mechanism proposed in this paper has a lower missed rate of target detection and a higher correct rate of target detection and recognition when people are crowded with each other, and also has the real-time performance of the general YOLO V5 algorithm.
Optimized data storage and retrieval in blockchain systems leveraging zero-knowledge proofs and sharding techniques

With the widespread adoption of blockchain technology across diverse sectors[1], the imperative for efficient data storage and retrieval mechanisms has intensified[2]. This paper introduces an innovative approach that synergizes Zero-Knowledge Proofs (ZKP) and sharding techniques to bolster data privacy and optimize storage within blockchain systems. We present an exhaustive analysis of prevailing blockchain data storage mechanisms, pinpoint their inherent privacy vulnerabilities, and elucidate the potential of ZKP in mitigating these concerns. Furthermore, we propose a sharding-centric solution that not only fortifies data privacy but also augments the efficiency of data retrieval[3]. Experimental results, derived from real-world scenarios, validate the robustness and efficiency of our proposed methodology.
Research on optimization of UAV patrol inspection path planning for the intelligent warehouse

The Unmanned Aerial Vehicle (UAV) inspection path planning for intelligent warehouse is a typical indoor path planning technology. Through the UAV intelligent inspection of each work area in the intelligent warehouse and workshop, we can solve some issues occurred in the work area timely. In order to solve the problem of path planning efficiently, an improved hybrid optimization algorithm is proposed to minimize the inspection path, which combines intelligent optimization algorithms of particle swarm optimization and genetic algorithm. By optimizing the structure of the genetic algorithm and finding the optimal parameter combination, it is easier to converge to the shortest path for the intelligent warehouse. Simulation results show that, compared with the traditional algorithms, the convergence ability of the optimized hybrid algorithm has been greatly improved, which has also been verified.
Teaching Alternative Learning System (ALS) is not a Joke: From the Lens of ALS Mobile Teachers

This study determined the lived experiences of ALS mobile teachers in the Division of San Jose City, Nueva Ecija, during the School Year 2021- 2022. This study formulated guide questions and consulted the thesis advisory committee. Evidence was gathered from 12 Alternative Learning System, Division of San Jose City mobile teachers. In terms of sex, they were equally distributed as male and female. Most teachers were married, eight were Teacher I, and the rest were Teacher II and III. Regarding educational attainment, eight participants had earned MA units, three were college graduates, and one had a master’s degree. All the respondents were rated “outstanding” in the latest IPCRF rating (SY 2021-2022). The study found that all ALS teacher participants were well-trained and knowledgeable about the processes of implementing the ALS curriculum. This was based on their consolidated responses. All of them answered similarly and followed DO 13, s. 2019 or The Policy Guidelines on Implementing Enhanced Alternative Learning System 2.0. except for the recent pandemic years, students could not be assessed for their A and E tests. The processes involved are ADSOCMOV, mapping, enrollment, administration of FLT, actual learning sessions, portfolio assessment, and A and E tests. Before implementing the DepEd mentioned above Order, all the participants attended a nine-day regional training entitled “ALS K-12 BEC for ALS Teacher” held on September 26 to October 04, 2019, at Otel Pampanga, San Fernando Pampanga. The results showed that the difficulties encountered by the participants were the unavailability of a community learning center for ALS and absenteeism. The strategies that the participants observed are seeking community support and intervention made by the mobile teachers. The participants of this study recommended that incoming ALS teachers should be ready, flexible, approachable, patient, courageous, passionate, dedicated, capable, and compassionate. They should also be hard workers and good communicators, and most importantly, they must have a heart for teaching.
Design and implementation of intelligent analysis software for teaching behavior based on near-field speech

The use of a finer-grained classroom language analysis coding form facilitates a more in-depth analysis of teachers' classroom language behavior. This study uses a finer-grained T-coding system to encode texts converted from teachers' near-field recordings, makes a preliminary exploration of multiple methods of visual teacher teaching strategies for coding sequences, and writes Python scripting software based on machine learning algorithms. This software can effectively assist teachers in self-observation and improve teaching quality.
A SOA-based unified service framework design and implementation

In order to promote the application of the cross-platform and plug-and-play, a development framework based on Service Oriented Architecture (SOA) is proposed. In order to achieve interface unification and service encapsulation, the concept of service template is firstly designed to standardize the generation of services instances as well as the service publishing, query and subscription so as to improve service efficiency. Besides, to fulfil the plug-and-play feature for services, a complete life cycle management is also designed to realize the dynamic service management, in which the concept of mirror service is introduced to realize the service versatility in different scenarios. The results show that SOA based framework can realize the service decoupling and merging easily, so that the service efficiency can be achieved.
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.
Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography

The safeguarding of intellectual property and maintaining privacy for video content are closely linked to the effectiveness of security protocols employed in internet streaming platforms. The inadequate implementation of security measures by content providers has resulted in security breaches within entertainment applications, hence causing a reduction in the client base. This research aimed to enhance the security measures employed for video content by implementing a multi-key approach for encryption and decryption processes. The aforementioned objective was successfully accomplished through the use of hybrid methodologies, the production of dynamic keys, and the implementation of user-attribute-based techniques. The main aim of the study was to improve the security measures associated with the process of generating video material. The proposed methodology integrates a system of mathematical equations and a pseudorandom key within its execution. This novel approach significantly augments the degree of security the encryption mechanism provides. The proposed methodology utilises a set of mathematical equations that are randomly employed to achieve encryption. Using a random selection procedure contributes to the overall enhancement of the system’s security. The suggested methodology entails the division of the video into smaller entities known as chunks. Following this, every segment is subjected to encryption using unique keys that are produced dynamically in real-time. The proposed methodology is executed via Android platforms. The transmitter application is tasked with the responsibility of facilitating the streaming of the video content, whereas the receiver application serves the purpose of presenting the video to the user. A careful study was conducted to compare and contrast the suggested method with other similar methods that were already in use. The results of the study strongly support the safety and dependability of the procedure that was made available.
Improving basic terminology regarding valuation in accounting and reporting

For any organization, the basis of its accounting and reporting is valuation. At the same time, as our research has shown, today the basic terminology in this area still needs significant improvement. This article is devoted to improving this terminology. The following methods were used in the research: analysis and synthesis, comparison, grouping method, logical approach, systematic approach, method of analogies. As a result of the research, this article critically analyzes the basic terminology used in relation to the category of valuation in various accounting and non-accounting sources, and presents the author’s view of this terminology, meeting the requirements of modern times. The results obtained in this work will be of interest to a wide range of readers who analyze various problems of accounting and reporting, including the valuation of accounting objects and reporting elements. Also, the results reflected in this article can be used in the educational process of higher educational institutions and in the practical work of accounting departments of all modern organizations (including healthcare). In addition, the results of this study can be used in the development and improvement of relevant regulatory documents on accounting and reporting.
Is it better to have more layers and nodes of neural networks in deep learning?

Deep learning is an essential part of machine learning, and neural network is an important technology in machine learning all the time. Neural networks can simulate the neural net and synaptic structure of the biological brain to complete data analysis and problem processing and can summarize the law from a time experience like human beings. For the human brain, the more layers and synapses the human brain neural network has, the deeper the level of abstraction of the input features established for some unknown things, so the deeper the accuracy of people's understanding is relative. Then, can the same logic be applied to neural networks in deep learning? Or, to put it differently, the deeper the neural network is and the more nodes it has, the better its performance must be.
Meta-learning-based recommendation method for self-supervised hybrid comparison learning

A self-supervised mixed comparison recommendation method based on meta-learning (MSHCL) is proposed to address the problem of poor recommendation accuracy in social recommendation algorithms. Collaborative filtering based on graph neural networks can model the inter-node interactions between users and items, and make effective use of higherorder neighbor information. However, its representation is very susceptible to interaction noise, and thus the great potential of node-level information is not well utilized. To address the above issues, We first learn the embedding of nodes through the network view and meta-path view to fully capture the heterogeneous network structure. In addition, self-supervised learning as a novel learning method using unlabeled data effectively mitigates the data sparsity problem, which in turn fuses self-supervised learning into hybrid contrast learning model training. We have done empirical and ablation studies on a real dataset to demonstrate that the MSHCL model outperforms the current mainstream methods.
Design and implementation of an intelligent recommendation system for product information on an e-commerce platform based on machine learning

The rapid growth of e-commerce platforms has resulted in an overwhelming abundance of product information, leading to choice paralysis and information overload for users. To address these challenges and enhance the user experience, an intelligent recommendation system based on machine learning is proposed. This paper aims to design and implement such a system for e-commerce platform product information. Firstly, we review the relevant theories and technologies of recommendation systems, including the application of Kafka message queues and the Lambda architecture, as well as two common recommendation system types: content-based recommendation and collaborative filtering. Then, we propose a Bayesian pairwise recommendation algorithm based on one-class collaborative filtering and Bayesian pairwise ranking and optimize the model. The performance and effectiveness of the algorithm are validated through experimental evaluation and comparison with other benchmark algorithms. Regarding recommendation system design, we propose the corresponding architecture and modules. Importantly, we conduct functional and non-functional testing to verify the usability and performance of the system. Through this research, we provide a design and implementation solution for an intelligent recommendation system for e-commerce platforms, offering references for improving the accuracy of product recommendations and user experience. Future research can continue optimizing the algorithms and expanding the recommendation system's functionality and scope.
Potentials of Blockchain Technology for Developing Countries

The spread of blockchain technology within industrialized countries has progressed increasingly in recent years, revealing numerous potentials. In consideration of the numerous challenges that developing countries are facing on a social, economic and environmental level, it is necessary to explore the opportunities of the new technology for these countries. The goal of this paper is to identify ways in which developing countries can benefit from the use of blockchain technology. For this purpose, the following research question is posed: What are the opportunities for societies in developing countries through blockchain technology? To answer the central question, an integrative review was conducted. Specifically, the work examined the opportunities and risks of three application areas: supply chains, the public sector, and cryptocurrencies. The focus is primarily on the area of supply chains. The results show that the use of blockchains in supply chains can increase transparency, reduce companies' running costs and make logistics processes more efficient. This is offset by high implementation costs. For the use of blockchains in politics and administration, opportunities for fighting corruption as well as increasing the efficiency of the systems could be shown. The research on the scope of cryptocurrencies found that the use of decentralized cryptocurrencies and central bank digital currencies can promote the financial inclusion of developing countries. Future research could concretize the evidence of cost savings from blockchains in supply chains using business metrics. An investigation of the cost-benefit ratio by means of ex-post analyses of existing projects could empirically consolidate the findings.
Current and Future Opportunities for 3D Printing in Modern Board Games

Additive manufacturing (3D printing) is a set of disruptive technologies where parts are no longer produced by removing material until the desired form is achieved, but rather build the intended product layer by layer. With significant benefits in the efficient use of resources, optimizing performance, and allowing individual customization of each print, 3D printing techniques have been more widely adopted worldwide, and modern board games are no exception. However, until now its use is mostly hobbyists creating and sharing 3D models for improving player experience in two aspects: customization (upgrade) of game components, and solutions for organization/setup/logistics. Nevertheless, 3D printing opens opportunities also for game designers/developers and publishers. This paper presents a glimpse of the current use of 3D printing in modern board games and discusses opportunities for the future, highlighting how both players and professionals can take advantage of this technology.