Reviewerly/exHarmony huggingface dataset:
Automated Scoring of Translations with BERT Models: Chinese and English Language Case Study

Authors:
raw_author_name: Maocheng Liang
raw_affiliation_string: School of Foreign Languages, Beihang University, Beijing 100191, China

Abstract: With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can reliably assess translation quality and identify high-quality translations for potential recognition. It takes the Han Suyin International Translation Contest as a case study, which is a large-scale and influential translation contest in China, with a history of over 30 years. The experimental results show that the BERT-assist system is a reliable second rater for massive translations in terms of translation quality, as it can effectively sift out high-quality translations with a reliability of r = 0.9 or higher. Thus, the automated translation scoring system based on BERT can satisfactorily predict the ranking of translations according to translation quality and sift out high-quality translations potentially shortlisted for prizes.
Blockchain-based Trust Mechanism for Online Shared Mobility

Authors:
raw_author_name: Haonan Yao
raw_affiliation_string: School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
raw_author_name: Nan Ren
raw_affiliation_string: School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China

Abstract: Economic Management and Big Data Application, pp. 832-842 (2024) No AccessBlockchain-based Trust Mechanism for Online Shared MobilityHaonan Yao and Nan RenHaonan YaoSchool of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China and Nan RenSchool of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, Chinahttps://doi.org/10.1142/9789811270277_0073Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: In order to solve the trust problem in China’s online shared mobility, blockchain technology is used to build a trust mechanism to guide and encourage users’ honest transactions through smart contracts. Then, the agent model is built by AnyLogic software to simulate the behavior of users participating in the transaction. The results show that the willingness of users to perform the contract can be effectively improved under the incentive of the trust mechanism. Finally, some suggestions are put forward to ensure that the trust mechanism can be effectively applied. Keywords: Online Shared MobilityBlockchainSmart Contract FiguresReferencesRelatedDetails Recommended Economic Management and Big Data ApplicationMetrics History KeywordsOnline Shared MobilityBlockchainSmart ContractPDF download
A COMPARATIVE REVIEW OF DATA ENCRYPTION METHODS IN THE USA AND EUROPE

Authors:
raw_author_name: None Temitayo Oluwaseun Abrahams
raw_affiliation_string: Independent Researcher, Adelaide, Australia

Abstract: Data encryption is a critical aspect of modern information security, and understanding the approaches taken by different regions is vital for a comprehensive analysis. In the United States and Europe, data encryption methods vary in implementation, legal frameworks, and overall priorities. In the United States, encryption methods are primarily governed by a combination of federal laws and industry standards. The National Institute of Standards and Technology (NIST) plays a central role in recommending cryptographic standards, while the Department of Commerce oversees export controls on encryption technology. The focus in the U.S. is on balancing national security needs with individual privacy rights. The tension between law enforcement's desire for access to encrypted data for criminal investigations and the right to privacy has sparked debates and legal battles. On the other hand, Europe has taken a more privacy-centric approach to data protection. The General Data Protection Regulation (GDPR) is a cornerstone in the European Union's efforts to safeguard individual privacy rights. GDPR mandates the use of encryption to protect personal data, and failure to implement adequate measures can result in hefty fines. European countries also emphasize the importance of end-to-end encryption in communication services to ensure confidentiality. Both regions prioritize encryption, but their approaches reflect different values and legal philosophies. The U.S. tends to navigate a delicate balance between national security and individual rights, while Europe places a stronger emphasis on the protection of personal data as a fundamental right. In terms of technological implementation, the encryption algorithms adopted in both regions are often aligned with global standards. Advanced Encryption Standard (AES) is widely accepted and implemented in various sectors. However, the choice of key management and the level of regulatory oversight differ, contributing to the nuanced landscape of data protection. In conclusion, a comparative review of data encryption methods in the USA and Europe reveals the complex interplay between security, privacy, and legal frameworks. Understanding these differences is crucial for multinational organizations and individuals navigating the intricate landscape of global data protection.&#x0D; Keywords: Data, Encryption, USA, Europe, Review.
Causal relationship recognition algorithm based on external semantic and contextual structural features

Authors:
raw_author_name: Jiaxin Li
raw_author_name: Kai Shuang

Abstract: Identifying the causal relationship of events plays an important role in determining the development of known events and evaluating the possible outcomes of different decisions. At present, neural network models are widely used to identify the relationships between events. Based on obtaining events, researchers distinguish the relationships between events by mining their semantics. However, due to the complexity of events and the dynamic changes in relationships between events, models often cannot fully meet the needs of accurately identifying causal relationships by only learning simple event descriptions in sentences; Moreover, focusing too much on the events themselves often leads to neglecting the structural features of statements and neglecting the impact of specific structural patterns on the relationships between events. In this article, we propose an event masking algorithm that combines external semantics to address the aforementioned issues. In this algorithm, external semantics are first introduced into the statement to enrich the information behind the event, allowing the model to mine the deep connections between events through a wider range of background knowledge; Then, the event masking module is used to enhance the model's extraction of sentence structured features, mining specific contextual representations that are unrelated to the event. The results show that compared to existing neural network algorithms, the algorithm proposed in this paper improves the F1 value of predictions on publicly available datasets by more than 4%.
Use of Rubber Fruit Seeds in Making Fine Artworks at SMA Negeri 1 Payaraman

Authors:
raw_author_name: Muhammad Kristiawan
raw_affiliation_string: Universitas Bengkulu

Abstract: Rubber plants are very easy to find around SMAN 1 Payaraman because they are widely cultivated by the community around the school. One of the parts contained in the rubber plant is the rubber fruit seeds. Rubber fruit seeds are covered with a hard-textured seed coat, brown, and have a distinctive pattern. The inside of the rubber fruit seeds is usually processed as a food source by the people in the Payaraman area. Meanwhile, the shells of rubber fruit seeds, which have a hard, brownish texture, are only waste and no one has used them yet. The purpose of this research is to find out the process of using rubber fruit seeds in the creation of works of art. In making works of art using rubber fruit seeds, researchers are divided into several stages, namely; determining the theme, determining the type of work, determining the technique of making the work, preparing tools and materials, and making works of art based on the techniques students like. The result of this study is that students can create various works of art from rubber seeds. The types of works of art made from rubber seeds that were made by students were: paintings, key chains, flowers, wall hangings, and ant figures. Utilizing rubber fruit seeds in making works of art, can increase the knowledge of teachers and students to use various kinds of media that exist around the school so that they can become works of art which will certainly add to the selling value of a work of art.
Keynote Talk: How DSN is Contributing to Social Impact in Africa

Authors:
raw_author_name: Opeyemi Osakuade
raw_affiliation_string: DSN-Data Scientists Network (formerly Data Science Nigeria)

Abstract: DSN-Data Scientists Network (formerly Data Science Nigeria) is committed to raising 1 million AI talents and building AI solutions that improve the quality of life and wellbeing of 2 billion people in an emerging market. DSN is the No 1 Artificial Intelligence learning community and solution delivery network in Africa. We won the Matthai Impact Award at Deep Learning Indaba 2019. One of DSN’s papers on the use of AI to address financial inclusion was the best poster at the 21st edition of the ACM Conference on Economics and Computation (EC’ 20), the world’s premier conference on the interface of economics and computer science, organized by the Association for Computing Machinery, New York, United States. 2 Products initiated, conceptualized and developed by DSN were selected in the 2021 Global Top 100 AI Products by UNESCO IRCAI. DSN-Data Scientists Network (formerly Data Science Nigeria) is building cross-country AI learning and research communities, developing AI solutions that enhance the quality of life and wellbeing of 2 billion people in emerging markets such as CareerTech, FinTech, RetailTech, EdTech, PlatformTech and HealthTech. DSN’s work has won academic and social impact awards across the world which has been showcased as a best practice in delivering scalable learning, research and social good solutions development. We released the 1st Artificial Intelligence and Python Book for elementary students and beginners in Africa and we recently opened a dedicated AI Research Lab Hub in Yaba, Lagos for Social Impact. Our renewed focus will see us delivering on our vision through a renewed focus on 3 core areas; A community for learning and research Product development for social impact Partnerships for solution delivery
Project of implementation strategies formation by alternation in education media técnico profesional

Authors:
raw_author_name: José Manuel Salum Tomé

Abstract: Technical-vocational education is one of the fundamental pillars that governs the educational system, and its strengthening is a task assumed by the Ministry of Education through the implementation of the National Policy on Technical-Vocational Training implemented through the National Strategy for Technical-Vocational Training. There are multiple challenges involved in carrying out actions to promote and generate instances of participation and decision-making that enable the involvement of all actors to articulate the educational system and economic development. In this sense, professional technical high schools with a specialty in preschool care assume a strategic and preponderant role in the education sector, since the young people who study the specialty put into practice what they have learned in classrooms and workshops allow them to effectively insert themselves into the world of work, access their professional practice and insert themselves into higher education. And, in most cases, to be an agent of change at the family level to improve the socioeconomic reality of their homes. For this reason, Chile needs to have technicians in the care of middle schools that will allow it to address the challenges of its own development and its growing participation in the preschool care system, with demands of greater added value, dynamic and challenging. In this scenario, alternation training will not only strengthen the specific technical competencies of the specialty, but also the capacity for innovation, entrepreneurship, teamwork, and a set of transversal competencies that will prepare students to work in multiple areas of education.
Fast and Secure Image Encryption System Using New Lightweight Encryption Algorithm

Authors:
raw_author_name: Rasha S. Ali
raw_affiliation_string: Electrical Engineering Department, College of Engineering, Al Iraqia University, Baghdad, Iraq
raw_author_name: Saad N. Alsaad
raw_affiliation_string: Computer Science Department, College of Science, Mustansiriyah University, Baghdad, Iraq

Abstract: The rapid evolution of network technologies, where computer vision and the Internet seamlessly merge, poses significant challenges in safeguarding end-user data. To address the growing need for secure data in smart environments, lightweight and efficient security solutions are in high demand. Notably, the Trivium, a well-known encrypt stream cipher (eSTREAM) project, has been the target of several attacks. This paper proposes an innovative approach for fast and highly secured image encryption using byte scrambling and a modified version of the Trivium algorithm. The Henon map is utilized to generate random numbers for permutation, reducing time and increasing security. Performance is measured on 100 colour images with different several tests. The results present a fast and robust security system for the transmission process. It is more efficient than the traditional Trivium method. The encrypted-decryption time is reduced by 1:24 times, making it a quick security system for surgical telepresence and multimedia visuals.
An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

Authors:
raw_author_name: Herison Surbakti
raw_affiliation_string: Information and Communication Technology, Rangsit University International College, Pathumthani, Thailand
raw_author_name: Prashaya Fusiripong
raw_affiliation_string: Information and Communication Technology, Rangsit University International College, Pathumthani, Thailand

Abstract: The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.
Exploring Multidimensional Embeddings for Decision Support Using Advanced Visualization Techniques

Authors:
raw_author_name: Olga Kurasova
raw_affiliation_string: Institute of Data Science and Digital Technologies, Vilnius University, 08412 Vilnius, Lithuania
raw_author_name: Arnoldas Budžys
raw_affiliation_string: Institute of Data Science and Digital Technologies, Vilnius University, 08412 Vilnius, Lithuania
raw_author_name: Viktor Medvedev
raw_affiliation_string: Institute of Data Science and Digital Technologies, Vilnius University, 08412 Vilnius, Lithuania

Abstract: As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of information, are often not directly understandable. Dimensionality reduction techniques play an important role in transforming multidimensional data into interpretable formats for decision support systems. To address this problem, the paper presents an analysis of dimensionality reduction and visualization techniques that embrace complex data representations and are useful inferences for decision systems. A novel framework is proposed, utilizing a Siamese neural network with a triplet loss function to analyze multidimensional data encoded into images, thus transforming these data into multidimensional embeddings. This approach uses dimensionality reduction techniques to transform these embeddings into a lower-dimensional space. This transformation not only improves interpretability but also maintains the integrity of the complex data structures. The efficacy of this approach is demonstrated using a keystroke dynamics dataset. The results support the integration of these visualization techniques into decision support systems. The visualization process not only simplifies the complexity of the data, but also reveals deep patterns and relationships hidden in the embeddings. Thus, a comprehensive framework for visualizing and interpreting complex keystroke dynamics is described, making a significant contribution to the field of user authentication.
Research on the Resilience Evaluation of Urban Rail Transit Construction Organization Based on the Cloud Matter-Element Model: A Case Study of Nanchang West Station

Authors:
raw_author_name: Wei Liu
raw_affiliation_string: School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, China

Abstract: In the construction of urban rail transit projects, the disturbance of equipment, sudden failure, rainstorms, and other emergencies may bring serious safety risks. Resilience theory emphasizes the ability of the system to resist, adapt, absorb, and learn from risks in the whole process before, during, and after the occurrence of risks. It is introduced into the safety management of construction organization of urban rail transit projects to describe the ability of urban rail transit projects to cope with risks in the whole process of dealing with construction risks. This study defines the connotation of the resilience of the project construction organization and uses the literature frequency statistics method to determine the resilience evaluation indexes. The game theory combination weighting method is used to determine the index weights, and the cloud matter element model is used to establish the evaluation model of construction organization resilience of urban rail transit projects. Taking Nanchang West Station of Phase 1 Project of Nanchang Line 2 as an example, the validity and accuracy of the model are verified. The results show that the resilience grade of the construction organization of the project is “higher resilience,” which is consistent with the actual survey situation, and the evaluation model is reasonable. In addition, in the evaluation results, the key indexes and risk indexes of the project are determined, and the safety management measures of the construction organization of the project are put forward according to the key indexes and risk indexes.
Effects of Assistance Media on Active Learning and Creative Thinking in Online Learning

Authors:
raw_author_name: Hendrikus Midun
raw_affiliation_string: Universitas Katolik Indonesi Santu Paulus
raw_author_name: Mikael Nardi
raw_affiliation_string: Universitas Katolik Indonesi Santu Paulus

Abstract: Online learning is the best learning strategy during the Covid-19 pandemic. However, it has obstacles for students who are still learning to adapt to internet technology. This research aims to determine the effect of using online learning strategies on students' active learning and creative thinking. The subjects of this study were 52 students of the Indonesian Language Education Study Program at the Indonesian Catholic University of Saint Paul. They are taught using the Moodle application. The experimental class was taught using media assistance in the form of WhatsApp, while the control class was not taught. Data collection uses tests and observation sheets. Data analysis used ANOVA with the help of SPSS version 27. The results showed that 1) there were significant differences in student learning activities in online learning with media assistance and students without media assistance; 2) there is no significant difference in creative thinking between students who are taught using media and students who are not given media assistance. Based on the results, findings and conclusions, suggestions will be explained for implementing future learning and further research. Especially the creation of an online learning environment for students who are still adapting to advances in internet technology.
Bearing fault diagnosis based on online symbolic aggregation approximation and streaming deep discriminant analysis

Authors:
raw_author_name: Zixuan Wang
raw_affiliation_string: China Jiliang University, Hangzhou,China, Hangzhou, Zhejiang, 310018, CHINA
raw_author_name: Jiusun Zeng
raw_affiliation_string: Hangzhou Normal University, N0.58 Haishu Road, Cangqian Street, Yuhang District, Hangzhou, Zhejiang, 310036, CHINA

Abstract: Abstract Rolling bearings are one of the important components of many industrial equipment, and the timely detection of faults in these bearings significantly contributes to ensuring equipment safety. To achieve real-time diagnosis of bearing faults, this paper proposes an online fault diagnosis framework that utilizes online symbolic aggregation approximation (SAX) and streaming deep discriminant analysis. This method uses the moving window approach to segment the vibration data collected online, and then uses SAX for symbolic representation.The obtained icons are input into stacked denoising convolutional autoencoder (SDCAE) for&amp;#xD;classification. The model consists of several denoising convolutional autoencoders (DCAEs) and a linear discriminant analysis (LDA) module. To accommodate online data changes, while fixing the main structure of the network, the streaming method is used to update the parameters of LDA. Compared with other traditional approaches for diagnosing bearing faults, the proposed method exhibits distinct advantages. Specifically, it utilizes a stable neural network model, updates classifier parameters in real-time, and demonstrates superior computational efficiency. The application results demonstrate the effectiveness of the proposed method.