Reviewerly/exHarmony huggingface dataset:
Supply Chain Management in Agriculture using Blockchain

Authors:
raw_author_name: None Prof. Mundhe Bhalchandra
raw_affiliation_string: Sahyadri Valley College of Engineering, Rajuri, Maharashtra, India

Abstract: Block chains are now firmly established as a digital technology that combines cryptographic, data management, networking, and incentive mechanisms to support the verification, execution, and recording of transactions between parties. While block chain technologies were originally intended to support new forms of digital currency for easier and secure payments, they now hold promise as a new foundation for all forms of transactions. Agribusiness stands to become a key beneficiary of this technology as a platform to execute ‘smart contracts’ for transactions, particularly for high-value produce. First it is important to distinguish between private digital currencies and the distributed ledger and block chain technologies that underlie them. The distributed and cross- border nature of digital currencies like Bit coin means that regulation of the core protocols of these systems by central banks is unlikely to be effective. Monetary authorities are focused more on understanding ‘on-ramps’ and ‘off-ramps’ that constitute the links to the traditional payments system rather than being able to monitor and regulate the currency itself. In contrast to the digital currency feature of block chain, the distributed ledger feature has the potential for widespread use in agribusiness and trade financing, especially where workflows involve many different parties with no trusted central entity
Multi-objective optimization design of scramjet nozzle based on grey wolf optimization algorithm and kernel extreme learning machine surrogate model

Authors:
raw_author_name: Mingming Guo
raw_affiliation_string: Science and Technology on Scramjet Laboratory, China Aerodynamics Research and Development Center, Mianyang 621000, China; Southwest University of Science and Technology, Mianyang 621000, China
raw_author_name: Ye Tian
raw_affiliation_string: Key Laboratory of Cross-Domain Flight Interdisciplinary Technology, Mianyang 621000, China; Science and Technology on Scramjet Laboratory, China Aerodynamics Research and Development Center, Mianyang 621000, China; Southwest University of Science and Technology, Mianyang 621000, China
raw_author_name: Jialing Le
raw_affiliation_string: Key Laboratory of Cross-Domain Flight Interdisciplinary Technology, Mianyang 621000, China; Science and Technology on Scramjet Laboratory, China Aerodynamics Research and Development Center, Mianyang 621000, China; Southwest University of Science and Technology, Mianyang 621000, China
raw_author_name: Dongqing Zhang
raw_affiliation_string: Science and Technology on Scramjet Laboratory, China Aerodynamics Research and Development Center, Mianyang 621000, China

Abstract: This study delves into the parametric design of the scramjet nozzles, utilizing the Catmull–Rom curve, to meet the high-performance design requirements. It establishes a high-dimensional, multi-objective optimization design method based on a surrogate model for the nozzle. In addition, this research proposes a surrogate model for nozzle performance to enhance the accuracy of the traditional surrogate model and prevent the multi-objective optimization design method from optimizing in an incorrect direction. This model incorporates the grey wolf optimization (GWO) algorithm and kernel extreme learning machine (KELM). Various machine learning algorithms are compared and analyzed, demonstrating that the performance parameters predicted by the GWO-KELM model are the most accurate, and the generalization of GWO-KELM is verified. Utilizing the particle swarm optimization algorithm assisted by GWO-KELM, the multi-objective optimization of the nozzle is further investigated. This study obtains the optimal Pareto front, analyzes the distribution of design variables in the Pareto solution set, and reveals the impact of geometric parameters on nozzle performance. Comparing the representative nozzle from the Pareto front with the truncated maximum thrust nozzle, it is found that the thrust, lift, and outlet Mach number increase by 3.3%, 12.2%, and 0.5%, respectively, while the outlet height decreases by 5.3%. This research contributes to overcoming the limitations of traditional design methods, which are typically time-consuming. The proposed GWO-KELM surrogate model effectively tackles the issue of low prediction accuracy.
Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

Authors:
raw_author_name: Ruben De Smet
raw_affiliation_string: Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
raw_author_name: Tom Godden
raw_affiliation_string: Department of Engineering Technology (INDI), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
raw_author_name: Kris Steenhaut
raw_affiliation_string: Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium; Department of Engineering Technology (INDI), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
raw_author_name: An Braeken
raw_affiliation_string: Department of Engineering Technology (INDI), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium

Abstract: Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library “curve25519-dalek”. We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.
Graph convolution networks for social media trolls detection use deep feature extraction

Authors:
raw_author_name: Muhammad Asif
raw_affiliation_string: School of Media, Hunan University of Science and Engineering, Yongzhou, 425199, Hunan, China
raw_author_name: Muna Al-Razgan
raw_affiliation_string: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 22452, 11495, Riyadh, Saudi Arabia
raw_author_name: Yasser A. Ali
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, 11543, Riyadh, Saudi Arabia
raw_author_name: Long Yunrong
raw_affiliation_string: School of Media, Hunan University of Science and Engineering, Yongzhou, 425199, Hunan, China

Abstract: Abstract This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate relationships inherent in social media data. The practical implications of our work are significant, despite some limitations in the model's performance. While the model accurately identifies toxic content more than half of the time, it struggles with precision, correctly identifying positive instances less than 50% of the time. Additionally, its ability to detect all positive cases (recall) is limited, capturing only 40% of them. The F1-score, which is a measure of the model's balance between precision and recall, stands at around 0.4, indicating a need for further refinement to enhance its effectiveness. This research offers a promising step towards more effective monitoring and moderation of toxic content on social platforms.
A Survey on Secure Eye: Blink-Based Morse Authentication

Authors:
raw_author_name: None Ravi
raw_affiliation_string: Global Academy of Technology, Bangalore, Karnataka, India

Abstract: Real-time password authentication methods are widely used by people, but they can be exploited or defeated with the help of a hot track or high-speed scan. By using blinding hand motions in place of visible fingerprints, pin verification offers a more secure method of password entry. The procedure of blinking the eyes while taking many picture frames and coming up with a PIN is known as blink-based authentication. This paper provides a range of real-time applications that combine instant-based PIN, facial detection, and One-time Passwords to eliminate shoulder-to-shoulder and scratch tracking
Analysis and prospect of power transformer fault diagnosis technology

Authors:
raw_author_name: Yong Gao
raw_affiliation_string: Guizhou Electric Power Test Research Institute (China)
raw_author_name: Jie Wang
raw_affiliation_string: Guangdong Power Grid Corporation (China)

Abstract: The transformer stands as a vital component within the power system, serving a crucial role in voltage regulation and energy transmission throughout the entire process of electricity generation, transmission, distribution, and consumption. Furthermore, it holds a pivotal position in facilitating Alternate Current (AC) and Direct Current (DC) conversion within DC transmission. This paper begins by offering an overview of common anomalies and fault categories that arise during transformer operation, focusing on the role of power transformers within system operations. It subsequently delves into the contemporary methods and diagnostic processes used for identifying and diagnosing transformer faults. The paper then proceeds to expound upon cases involving winding deformation and insulating oil failure, illustrating instances of these particular types of faults. Finally, the paper outlines two prospective avenues for transformer fault diagnosis technology. The first entails the incorporation of artificial intelligence theories in the realm of transformer fault diagnosis. The second involves the development of an expert system for comprehensive transformer fault diagnosis, grounded in artificial intelligence technology.
Education and Training Program to Improve Preparedness and Increased Access to Energy Workforce for Engineering Technology Graduates

Authors:
raw_author_name: Kuldeep Rawat
raw_affiliation_string: Elizabeth City State University

Abstract: Abstract Elizabeth City State University (ECSU) implemented a comprehensive education and training program to strengthen the renewable energy curriculum and increase the pipeline of qualified workers, especially underrepresented minorities for the growing energy industry. The overarching goal is to meet the current and future energy industry workforce needs, especially in the renewable energy sector. The project activities were designed based on three tenets, which include mentoring, research, and education/training. The project components included, enhancing renewable energy courses, establishing a wind and solar energy systems training laboratory, a year-round hands-on project-based training, a residential summer bridge program, undergraduate research experience, and summer internships. The activities afforded through the project will help ensure that the energy sector has a sustained pipeline of a qualified workforce. The project implementation and challenges, project delivery components, and results from pretests and posttests and participant surveys are presented in the paper.
Logical activation functions for training arbitrary probabilistic boolean operations

Authors:
raw_author_name: Jed A. Duersch
raw_affiliation_string: Sandia National Laboratories, Livermore, CA 94550, USA
raw_author_name: Tommie A. Catanach
raw_affiliation_string: Sandia National Laboratories, Livermore, CA 94550, USA
raw_author_name: Niladri Das
raw_affiliation_string: Sandia National Laboratories, Livermore, CA 94550, USA

Abstract: In this work, we introduce a family of novel activation functions for deep neural networks that approximate n-ary, or n-argument, probabilistic logic. Logic has long been used to encode complex relationships between claims that are either true or false. Thus, these activation functions provide a step towards models that can efficiently encode information. Unfortunately, typical feedforward networks with elementwise activation functions cannot capture certain relationships succinctly, such as the exclusive disjunction (p xor q) and conditioned disjunction (if c then p else q). Our n-ary activation functions address this challenge by approximating belief functions (probabilistic Boolean logic) with logit representations of probability and experiments demonstrate the ability to learn arbitrary logical ground truths in a single layer. Further, by representing belief tables using a basis that associates the number of nonzero parameters with the effective arity of each belief function, we forge a concrete relationship between logical complexity and sparsity, thus opening new optimization approaches to suppress logical complexity during training. We provide a computationally efficient PyTorch implementation and test our activation functions against other logic-approximating activation functions on both traditional machine learning tasks as well as reproducing known logical relationships.
Unleashing the power of AI: a systematic review of cutting-edge techniques in AI-enhanced scientometrics, webometrics and bibliometrics

Authors:
raw_author_name: Hamid Reza Saeidnia
raw_affiliation_string: Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran
raw_author_name: Elaheh Hosseini
raw_affiliation_string: Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran
raw_author_name: Shadi Abdoli
raw_affiliation_string: Department of Information Science, Universite de Montreal, Montreal, Canada
raw_author_name: Marcel Ausloos
raw_affiliation_string: School of Business, University of Leicester, Leicester, UK

Abstract: Purpose: The study aims to analyze the synergy of Artificial Intelligence (AI), with scientometrics, webometrics, and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields. Design/methodology/approach: By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends, and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science, and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles. Findings: (i) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis, and knowledge mapping, in a more objective and reliable framework. (ii) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis, and recommender systems. (iii) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining, and recommender systems are considered as the potential of AI integration in the field of bibliometrics. Originality/value: This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics, and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.
Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

Authors:
raw_author_name: Nashwa Shaker
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt
raw_author_name: Diaa AbdElminaam
raw_affiliation_string: Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt
raw_author_name: Omar Adel
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt

Abstract: Online payment fraud detection is crucial for safeguarding e-commerce transactions against sophisticated fraudsters who exploit system vulnerabilities. This paper proposes an efficient framework for predicting online payment fraud, employing six diverse machine learning algorithms, namely constant, CN7Rule induction, KNN, Tree, Random Forest, Gradient boosting, SVM, Logistic regression, Naive Bayes, Ada boost, Neural network, and stochastic gradient descent, on three distinct datasets. The gradient-boosting algorithm consistently outperformed others through rigorous testing, achieving an impressive accuracy rate of 99.7%. This algorithm demonstrated resilience across various testing scenarios, establishing itself as the most effective online payment fraud detection solution. With the highest accuracy score of 99.7% in all testing phases, gradient boosting is optimal for preemptive measures against fraudulent activities in electronic transactions, providing a robust defense mechanism for e-commerce platforms.
Signature Scheme Based on Alternant Codes

Authors:
raw_author_name: Regis Freguin Babindamana
raw_affiliation_string: Faculté des Sciences et Techniques, Université Marien NGOUABI

Abstract: In this paper, we present a version of the signature on subcodes of generalized Reed-Solomon codes defined on a subfield. We show that the use of alternating codes reduces the key size and the use of subcodes has the caracteristic of hiding the code structure. This makes the system more secure.
Generation of Revision Identifier (rsid) Numbers in MS Word

Authors:
raw_author_name: Dirk H.R. Spennemann
raw_affiliation_string: Charles Sturt University
raw_author_name: Claire L Singh
raw_affiliation_string: Albury NSW 2640; PO Box 789; School of Dentistry and Medical Sciences, Charles Sturt University

Abstract: The 2007 implementation of the Office Open XML standard for Microsoft Word introduced the assignation of individual revision save identifiers (Rsid) to document editing sessions that end in a save action. The relevant standards ECMA (2016) and ISO/ IEC 29500-1:2016 (2016) stipulate that these Rsid should be allocated randomised but with increasing numerical value, thereby documenting the progress of the editing. As MS Word is the most ubiquitous word processing software, Rsid appear to be a useful tool to examine and provide evidence for a wide range of common document generation editing and modification processes and file management operations, with implications for document analysis including, but not limited to academic integrity issues in student assignment submissions (e.g. contract cheating). This paper presents the results of a series of experiments conducted to assess whether and how well MS Word implements the ECMA and ISO/ IEC standards. The results show that the number of allocated Rsid indeed increases with each edit and save action, with the previous Rsids carried over and retained. The newly allocated Rsid, however, do not conform to the standard as the numerical value of a Rsid associated with a save action may be larger or smaller than any or all of those allocated during that of the previous save actions. The allocation of a new Rsid is not necessarily caused by an edit event but that a new Rsid can also be generated if a file is saved as rtf or if it is sent as an e-mail from within MS Word, although the file was not edited in any way. Rsid numbers are not generated if a person opens a MS Word document, reads it and closes the file without saving, making this action impossible to detect. MS Word template files on a given machine contain document (root) Rsid numbers that are generated when a newly installed application is launched for the first time. As these will be embedded as legacy Rsid into every new file generated from that template file, they act as signatures for all MS Word documents that are created. The experiments have shown that user behaviour has a direct influence on the number of Rsid represented in a given file. Although the implementation of Office Open XML chosen by Microsoft is not compliant with the relevant standards, and thus Rsid cannot be used determine the exact chronological order of all editing sequences within a given document, the Rsid retain their value for document forensics as they are associated with specific edit events, and illuminate the document writing and editing process.
Peningkatan Hasil Belajar Menulis Recoun Text pada Siswa Kelas Viiig Melalui Model Pembelajaran Problem-Based Learning di SMP Negeri 1 Sambungmacan

Authors:
raw_author_name: Arin Arianti
raw_affiliation_string: Universitas Veteran Bangun Nusantara Sukoharjo, Jawa Tengah

Abstract: This research aims to enhance the learning outcomes of recount text writing for kelas VIIIG SMP Negeri 1 Sambungmacan in the academic year 2023—2024 through the implementation of the problem-based learning instructional model. The objectives of this research are (a) to ascertain the effectiveness of the problem-based learning model in improving the ability to write recount texts, (b) to understand the process of students' learning outcomes improvement, and (c) to determine the extent of the improvement in recount text writing skills for eighth-grade students at SMP Negeri 1 Sambungmacan in the academic year 2023—2024 before and after the implementation of the problem-based learning model. This study follows a Classroom Action Research (CAR) design, conducted over two cycles, each involving four stages: planning, implementation, observation, and reflection. The research subjects are 30 students kelas VIIIG at SMP Negeri 1 Sambungmacan in the academic year 2023—2024. Both quantitative and qualitative analyses were employed for data analysis. The results of the analysis indicate an improvement in recount text writing learning outcomes in the first cycle with the topic of "unforgettable experiences," where the average score was 73.53, falling into the less satisfactory category. However, in the same material in the second cycle, the average score increased to 84.03. Based on these findings, it is concluded that the problem-based learning model is suitable for enhancing students' learning outcomes in recount text writing with the topic of "unforgettable experiences." Moreover, the problem-based learning model can be applied to other English language topics and across various subjects.