Reviewerly/exHarmony huggingface dataset:
A Batch-Service Queueing Assisted Blockchain System for Supply Chain Management

In recent years, blockchains have attracted a great deal of interest from researchers, engineers, and institutions. Additionally, the implementation of blockchains has begun to revitalisea large number of applications, such as e-finance, smart homes, smart health, social security, logistics, and so on. As supply chains become increasingly global, management and control become more complicated. Blockchain technology is establishing promise in tackling several supply chain management (SCM) concerns as a distributed digital ledger platform that ensures security, traceability, and transparency. Government, societal, and consumer pressures have encouraged us to consider how blockchain can help us meet our sustainability goals and improve supply chain sustainability. True blockchain-led supply chain and corporate transformation is still in its early phases. This chapter examines the possible application of blockchain technology (BT) and smart contracts to SCM. In order to study the operations of blockchain based supply chain management a queueing based model is developed. It assesses the measures of the model, such as the mean transactions per unit time, the mean transactions in the unconfirmed transaction pool, the mean time required for transaction confirmation, and the average waiting time of transactions in the unconfirmed transaction pool. Additionally, it evaluates the characteristics of the supplychain (SC) system.
A Cross-Institution Information-Sharing Scheme Based on a Consortium Blockchain

In today’s data-driven world, efficient and secure cross-institution information-sharing is an urgent challenge. Traditional information-sharing methods based on access controlling often suffer from issues such as privacy breaches and high communication complexity. To address this issue, this paper proposes a cross-institution information-sharing solution based on a consortium blockchain, in which it combines on-chain transaction consensus with off-chain institution storage, thereby facilitating collaboration among nodes from different institutions on the blockchain. To enhance the efficiency and security of transactions on the blockchain, we also introduce a dynamic and adaptive Practical Byzantine Fault Tolerance (DA-PBFT) consensus protocol, which permits nodes to dynamically join and exit the blockchain network, consequently improving network scalability. Through a reputation mechanism, we swiftly identify and remove faulty and malicious nodes, enhancing the trustworthiness of nodes in the information-sharing network based on consortium blockchain, thereby improving consensus efficiency. We have also employed encryption techniques to enhance the privacy and integrity of data during the process of cross-institution information sharing. A comprehensive analysis of the communication complexity in the information-sharing network confirms the effectiveness and security of our proposed solution. We offer a unique solution to improve the efficiency and security of cross-institution information-sharing while ensuring data integrity and privacy. By addressing the challenges of privacy breaches and high communication complexity in information sharing, we establish a foundation for secure cross-institution data exchange.
Opini Siswa terhadap Siswa yang Mengalami Kecanduan Game Online di Kelas XI IPS 1 SMAN 2 Tanggul

The emergence of deviant behavior among teenagers is caused by their inability to control their emotions. The tendency of teenagers to engage in deviant behavior can be controlled if they have well-developed emotional intelligence. Good emotional intelligence can help teenagers manage their emotions appropriately, enabling them to adapt to their environment. Family is one of the factors that influence emotional intelligence because the relationship between parents and teenagers has an impact on the emotional intelligence of teenagers. The aim of this study is to determine the difference in emotional intelligence between students whose parents are divorced and those whose parents are not (intact) at SMA PGRI Purwoharjo. This study is a comparative quantitative research involving 38 students with divorced parents and 38 students with intact parents at SMA PGRI Purwoharjo, selected using quota sampling technique. The study utilized an emotional intelligence scale (30 items, α = 0.866) adapted from Wardani's research (2019). The results of this study indicate that there is no difference in emotional intelligence between students with divorced parents and those with intact parents at SMA PGRI Purwoharjo (r = 0.759, p &gt; 0.05).
Penerapan Metode Naïve Bayes pada Sistem Seleksi Penerima Bantuan Bedah Rumah

Home renovation is a succor given by the government for poor people to relieve their economics burden. Of CV Garuda Metta Consultant have team work there are with Dinas Cipta Karya and Dinas Bina Marga In Tambakrejo, Bojonegoro City, there is succor that coming from the region and the institution itself. The succor can be obtained if the candidates are suitable with the requirements and the criteria, such as land floor, bamboo wall, and unsafe roof condition. In addition, the criteria also specified on family income, land area of the house, and number of families. To select the candidates who get a chance of house renovation. This study will make a decision support system to help the get of home renovation receivers by the government in Sambirejo that using Naïve Bayes method. The purpose of this research is to provide suggestion for the priority of receivers of home renovation and to help the selectors in order that the receivers of home renovation in the Tambakrejo, Bojonegoro City in line with the target. This result of this research is a decision support system for the determination of the home renovation recipients based on defined criteria.
Sistem Pakar Untuk Mendiagnosa Kerusakan Hardware Laptop Menggunakan Metode Certainty Factor

Currently the development of information technology is increasingly sophisticated and increasing. All fields ineveryday life can not be separated from technology, especially laptops. Almost all fields use a laptop to get work done. This can cause problems when using a laptop, namely if one of the devices / components suffered damage. When these problems arise, sometimes ordinary users did not understand and had to take it to a technician, where the workmanship and cost was more expensive. Not that’s all, access to the laptop service is quite far which will take time and a fairly long time, only for problems that should be solved on their own. For simplify and speed up the solution, we need and application that can provide solutions that are precise, accurate and efficient. An expert system is a system that harnessing human-to-computer knowledge. One of the methods used in expert system is the certainty factor method, where this method helps overcome the problem of uncertainty, and know the level of user confidence in the problem at hand. Based on the research results, the results of program testing carried out with experts, and this has an accuracy rate of 90% on 10 types of damage tested.
Pengelompokan Pupuk Bersubsidi Di Kabupaten Bojonegoro Menggunakan Metode K -Means Clustering

Subsidized fertilizer is a form of the government's commitment to increasing agricultural productivity, increasing food production and sustainable food security, now the price of fertilizer in Bojonegoro district is getting more expensive, this encourages researchers to raise it as the title of the thesis. The purpose of the K-Means Clustering method is to increase knowledge and understanding of computer-based systems. The K-Means algorithm is an iterative clustering algorithm that partitions datasets into a number of K Clusters that have been set at the beginning. The K-Means algorithm is simple to implement and execute, relatively fast, adaptable, commonly used in practice. From the problem of clustering subsidized fertilizers in Bojonegoro Regency, it can be solved using the ¬K-Means method. The K-Means algorithm can precisely group large amounts of data. The determination of the central point (centroid) in the initial process of the K-Means algorithm has a significant influence on the cluster yield. Based on the research conducted, the authors tested the best cluster grouping using the Davies Bouldin Index (DBI). It was found that the results of 3 clusters were the best with a DBI value of 0.527 while 4 clusters with a DBI value of 0.649 and 5 clusters with a DBI value of 0.677. The smaller the resulting value in the test, the better the result will be. Meanwhile, the Mean Square Error (MSE) produced in fertilizer clustering is 0.588 and RMSE is 0.962.&#x0D;
A Dynamic Thresholds based Anomaly Detection Algorithm in Energy Consumption Process of Industrial Equipment

In the context of dual-carbon strategy and dual-energy consumption control targets, energy conservation of industrial equipment is becoming more and more important. However, because of field barrier and experience dependency, energy conservation efficiency is low even with the spreading of IIoT, which leads to low utilization rate of IoT data in turn. Meanwhile, as an important part in energy conservation process, anomaly detection of energy consumption provides the fundamental for realization of energy saving. Data-driven anomaly detection algorithm are mature in academic area while rarely accepted in industrial area, because of interpretability issue of algorithm and complexity properties of industry activities. As a contribution to energy conservation activity in industry, from the view of data-driven anomaly detection of energy consumption of industrial equipment, this paper points out the capabilities that algorithm needs to own (unsupervised, real-time, adaptive, robust, universality), defines volatility, surge as main anomalies for detection, and propose a dynamic threshold based detection algorithm and estimate its feasibility on a real dataset. Experiment result shows average P, R, F1 score 72.1%, 80.1% and 73.1% separately, with remarking 12.1%, 40.1% and 33.1% improvements comparing with baseline model, and 2.37%, 18.9% and 11.2% improvements to DSPOT. Our work in this paper provides a positive effect for improving the efficiency of energy-saving analysis.
A Note on Invertible Neutrosophic Square Matrices

The purpose of this article is to study the adjoint and inverse of neutrosophic matrices, where the inverse of a neutrosophic square matrix is defined and studied in terms of neutrosophic determinant and neutrosophic adjoint. It is shown by examples that, the converse part of the result “M is invertible if and only if detM ̸= 0” is not true, proved by Mohammad Abobala et al. in.2 Also some of the properties of neutrosophic adjoint are discussed.
Natural and Artificial Intelligence: Points of Contact

The study concerns the problem of the natural intelligence specificity which is cor­related with the prenatal stage of mind forming. At the same time, only those as­pects of the system’s functioning are the subject of consideration in research that become visible from the methodological perspective. These are the modes of non-duality and integrity of the very space of existence, in which not only the main po­tentialities of natural intelligence are laid, but the peculiarities of the contents that have their source in a given period are established also. When person becomes conscious such contents (and mechanisms for their provision) move into the un­conscious sphere. However, they continue to influence the entire universe of hu­man cognition in a significant way: they determine not only the nature and styles of thinking, but also set the cognition’s dynamics that are permissible in such a space of opportunity. In a generalized form, these contents can be characterized as intensional ones, while contents with which the dissociated mind of technocratic culture adult person deals are mainly extensional. Artificial intelligence, that has no knowledge of a non-dual mode of becoming, deals with the extensional facet of the natural mind modeling. To make an approximation to the potentials of natu­ral intelligence functioning more achievable, it is proposed to modify the structure of artificial intelligence systems’ “experience” by adding a mode of free navigation in the space provided for data analysis. Also it seems useful to modify the concept of rationality, by expanding it with two types of contents, whose genesis was corre­lated with non-dual-holistic mode of the mind functioning.
Volatility in Cryptocurrency Prices During COVID-19

This paper examines how COVID-19 affects the volatility and forecasting of Bitcoin price. It compares the cryptocurrency market with traditional financial markets using descriptive statistics and risk indicators. The cryptocurrency market experienced significant fluctuations during the pandemic, aff
TCAM-GNN: A TCAM-based Data Processing Strategy for GNN over Sparse Graphs

The graph neural network (GNN) has recently become an emerging research topic for processing non-euclidean data structures since the data used in various popular application domains are usually modeled as a graph, such as social networks, recommendation systems, and computer vision. Previous GNN accelerators commonly utilize the hybrid architecture to resolve the issue of “hybrid computing pattern” in GNN training. Nevertheless, the hybrid architecture suffers from poor utilization of hardware resources mainly due to the dynamic workloads between different phases in GNN. To address these issues, existing GNN accelerators adopt a unified structure with numerous processing elements and high bandwidth memory. However, the large amount of data movement between the processor and memory could heavily downgrade the performance of such accelerators in real-world graphs. As a result, the processing-in-memory architecture, such as the ReRAM-based crossbar, becomes a promising solution to reduce the memory overhead of GNN training. Furthermore, the ternary content addressable memory (TCAM) can parallel search the stored data with a given input, then output the matching result, which is a great choice to efficiently search the connected vertex through the edge list of the graph. In this work, we present the TCAM-GNN, a novel TCAM-based data processing strategy, to enable high-throughput and energy-efficient GNN training over ReRAM-based crossbar architecture. The proposed TCAM-based data processing approach smartly utilizes the TCAM crossbar to access and search graph data. Several hardware co-designed data structures and placement methods are proposed to fully exploit the parallelism in GNN during training. In addition, to resolve the precision issue, we propose a dynamic fixed-point formatting approach to enable GNN training over crossbar architecture. An adaptive data reusing policy is also proposed to enhance the data locality of graph features by the bootstrapping batch sampling approach. Overall, TCAM-GNN could enhance computing performance by 4.25× and energy efficiency by 9.11× on average compared to the neural network accelerators.
Bayes-Enhanced Multi-View Attention Networks for Robust POI Recommendation

POI recommendation is practically important to facilitate various Location-Based Social Network (LBSN) services, and has attracted rising research attention recently. Existing works generally assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. However, in real application scenarios, the check-in data can be rather unreliable (e.g. sparse, incomplete and inaccurate) due to both subjective and objective causes including positioning error and user privacy concerns. The data uncertainty issue may lead to significant negative impacts on the performance of the POI recommendation, but is not fully explored by existing works. To this end, this paper investigates a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network (BayMAN for short) to effectively address it. Specifically, we construct three POI graphs to comprehensively model the dependencies among the POIs from different views, including the personal POI transition graph, the semantic-based POI graph and distance-based POI graph. As the personal POI transition graph is usually sparse and sensitive to noise, we design a Bayes-enhanced spatial dependency learning module for data augmentation from the local view. A Bayesian posterior guided graph augmentation approach is adopted to generate a new graph with collaborative signals to increase the data diversity. Then both the original and the augmented graphs are used for POI representation learning to counteract the data uncertainty issue. Next, the POI representations of the three view graphs are input into the proposed multi-view attention-based user preference learning module. By incorporating the semantic and distance correlations of POIs, the user preference can be effectively refined and finally robust recommendation results are achieved. We conduct extensive experiments over three real-world LSBN datasets. The results show that BayMAN significantly outperforms the state-of-the-art methods in POI recommendation when the available check-ins are incomplete and noisy.
Malware Infections in the U.S. during the COVID-19 Pandemic: An Empirical Study

The COVID-19 pandemic has changed the world in many ways, especially in the landscape of cyber threats.The pandemic has provided cybercriminals with more opportunities to commit crimes due to more people engaging in online activities, along with the increased use of computers for school, work, and social events.The current study seeks to explore cybercrime trends, in particular malware infections, during the COVID-19 pandemic.Thus, this study examines the relationship between the number of malware infections, COVID-19 positive cases, closed non-essential businesses, and closed K-12 public schools in the United States.Data utilized in this study derives from (1) Kaspersky Cyberthreat Real-Time Map, (2) Centers for Disease Control and Prevention (CDC), and (3) COVID-19 US State Policy Database over the course of six months from January of 2020 to June of 2020.The findings of this study reveal that there are associations between the number of malware infections, COVID-19 positive cases, and closed non-essential businesses.However, interestingly, there is no link between the number of malware infections and closed K-12 public schools.Policy implications and the limitations of this study are also discussed.
The Future of Cybercrime Prevention Strategies: Human Factors and A Holistic Approach to Cyber Intelligence

New technology is rapidly emerging to fight increasing cybercrime threats, however, there is one important component of a cybercrime that technology cannot always impact and that is human behavior.Unfortunately, humans can be vulnerable and easily deceived making technological advances alone inadequate in the cybercrime fight.Instead, we must take a more holistic approach by using technology and better understanding the human factors that make cybercrime possible.In this issue of the International Journal of Cybersecurity Intelligence and Cybercrime, three studies contribute to our knowledge of human factors and emerging cybercrime technology so that more effective comprehensive cybercrime prevention strategies can be developed.
Modern Human Resource Management System Based on Cloud Computing Platform

Abstract This paper creates a human resource management system based on a cloud computing platform to achieve effective data analysis and calculation. The effectiveness of resource output is controlled through similarity characterization, fuzzy division of resource data, defining the division area, and then obtaining resource association rules. The system classifies the information of working employees according to the probability data, determines the characteristic attributes of the talent level vector of new employees, and can determine the initial level of new employees through the matching value. It is found that the gender R in the human resource management system increases significantly by 0.132, the R*R value is 0.624, and the behavioral performance is relatively high at 0.09, which indicates that the cloud computing platform can provide a scientific basis for the important decisions of the enterprise.
Application of University English Teaching Model Based on Artificial Intelligence Technology

Abstract This paper is based on cloud computing technology in artificial intelligence and a clustering algorithm controlled to divide teaching resource attributes and reduce packet loss rates in the system. Data mining is used to reduce the number of iterations and improve processing efficiency, a learner model is designed, and the teaching process and assessment tasks are constructed to realize the teaching model. Two natural classes of students at the University of S. underwent an instructional experiment. The results showed that the experimental class reached about 70% of the overall students in the achievement range of 85-95 and 95-100. The college English teaching model based on artificial intelligence technology’s feasibility and effectiveness are proven by the significant improvement in performance compared to the control class.
Reform and Innovation of Civic and Political Education Based on Deep Enhanced Learning Methods

Abstract The use of an initial state value function and an optimal strategy are used in this paper to solve educational problems based on deep reinforcement learning. Deep reinforcement learning’s approximate function is defined, and the matrix model is created by training tuning using learning methods like gradient descent. To analyze the modeling process of reinforcement learning, reward values are added to the Markov decision transfer matrix and the expected value of cumulative returns is calculated. The weights are trained using the Bellman equation to enhance the algorithm’s stability. In evaluating the effect of reform and innovation in Civic Education, the teacher education concept is rated as 10 points. The reform and innovation of civic education, combined with deep reinforcement learning, can promote the reform of education and teaching modes, improving the efficiency and quality of education.
Reinforcement Learning Technology Applied to the Innovative Model of Informatisation of Classroom Teaching in Educational Economics

Abstract This paper constructs the architecture of the online teaching platform for education economics based on the MVC pattern coupled with three-tier architecture. The optimal value function is solved through the experience playback mechanism and the strategy function of reinforcement learning, and the value function is used to realize the update of the deep Q learning parameters, which is used to realize the guided teaching of students in the platform. A test analysis was conducted to verify the effectiveness of using the online teaching platform for education economics courses. The results show that the waiting time for teaching resource requests in the 0-3s range of this paper’s platform is 29.71% and 51.56% lower than that of the B/C and Web platforms, respectively. The mean value of students’ learning attitude toward the platform was 108.558, and the highest satisfaction score was 0.7088. The online teaching platform can effectively improve students' learning attitude and promote the innovation of information technology in education economics classroom teaching.