Reviewerly/exHarmony huggingface dataset:
An Empirical Analysis and Challenging Era of Blockchain in Green Society

Blockchain is one of the new inventions that can help develop sustainable and sustainable solutions as it is able to provide accountability, transparency, online tracking and robustness, and improve the efficiency of global collaboration. A massive increase in electricity demand has created a new dilemma. Power businesses, startups, commercial banks, policymakers, and researchers are all interested in blockchain technology, which has become the most generally acknowledged IoT Technology. Therefore, blockchain technology efforts from both industry and education have been included in the study. Recently, the blockchain has been employed as a new approach for developing and enhancing green societies. A full examination of all the constraints and potential of the blockchain through technological ideas is required. In this article, tremendous attempts are underway to thoroughly investigate the complete foundation of the green community and how the blockchain contributes to the green community. Finally, this article also highlights the applications and challenges of the green chain-driven community.
A Survey of Cryptographic Algorithms for Encryption and Key Transmission

Protecting digital information is crucial because it may contain sensitive information. Cryptography plays a large role in securing sensitive data. It ensures the privacy and confidentiality of the user's information. In cryptography, plain text is converted into ciphertext using an encryption algorithm, and a key is generated using a key generation algorithm. In the end, the ciphertext is decrypted back to its original plain text by using a decryption algorithm. There are a lot of encryption–decryption and key-generating algorithms, but only a few are feasible. Here, we review and compare different encryption, decryption, and key-generating algorithms to find one that is both reliable and convenient. First, the basics of cryptography are presented in this work. Second, we carry out a literature review to showcase the previous research related to this paper. Next, we present a brief idea of all the algorithms used to find the best method by studying and comparing each algorithm. Our final point of a conclusion is that these algorithms are space-saving as well as secure.
ANALYSIS OF THE FOUNDATIONS OF TRADITIONAL SPIRITUAL AND MORAL EDUCATION IN THE EDUCATIONAL SPACE OF MODERN SOCIETY

This article analyzes the basics of traditional spiritual and moral education, its main goal of assimilation of spiritual values by adolescents and their implementation into practical action; analyzes the concept of "spirituality"; provides a number of recommendations for the spiritual and moral education of the younger generation in the modern educational space.
STAGE FEAR AS A COMMUNICATIVE BARRIER IN THE CLASSROOM IN THE BUSINESS ENGINEERING CAREER OF THE PRIVATE UNIVERSITY OF THE EAST SUBSIDIARY CIUDAD DEL ESTE

Stage fright, also known as stage fright or fear of public speaking, is a recognized phenomenon that affects many people in different contexts. This research focuses on analyzing how stage fright becomes a communication barrier in the educational field, specifically in the classroom. The methodology used is mixed research under the Quantitative and Qualitative approach, data collection was carried out through interviews and survey. The results indicate that stage fright has a significant impact on students’ communication in the classroom. Those who experience stage fright tend to avoid actively participating in discussions, oral presentations, and other activities that require speaking in front of their peers. This negatively affects their learning by missing opportunities to express ideas, ask questions and participate in discussions. Factors that contribute to stage fright in the educational context are identified, such as fear of judgment and negative evaluation, lack of confidence in communication skills, and peer pressure. These factors create anxiety and stress, further hindering students’ ability to communicate effectively. As a result of the research, strategies are proposed to address stage fright and promote effective communication in the classroom. These include relaxation techniques and stress management, creating a supportive and trusting environment, designing activities that promote active participation by all students, and training teachers in managing stage fright.
UTILISING DEEP LEARNING IN SINGAPORE PRIMARY SCHOOL MATHEMATICAL WORD PROBLEMS

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 2314-2320ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0679Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
Hybridized Hierarchical Watermarking and Selective Encryption for Social Image Security

With the advent of cloud computing and social multimedia communication, more and more social images are being collected on social media platforms, such as Facebook, TikTok, Flirk, and YouTube. The amount of social images produced and disseminated is rapidly increasing. Meanwhile, cloud computing-assisted social media platforms have made social image dissemination more and more efficient. There exists an unstoppable trend of fake/unauthorized social image dissemination. The growth of social image sharing underscores potential security risks for illegal use, such as image forgery, malicious copying, piracy exposure, plagiarism, and misappropriation. Therefore, secure social image dissemination has become urgent and critical on social media platforms. The authors propose a secure scheme for social image dissemination on social media platforms. The main objective is to make a map between the tree structure Haar (TSH) transform and the hierarchical community structure of a social network. First, perform the TSH transform on a social image using social network analysis (SNA). Second, all users in a social media platform are coded using SNA. Third, watermarking and encryption are performed in a compressed domain for protecting social image dissemination. Finally, the encrypted and watermarked contents are delivered to users via a hybrid multicast–unicast scheme. The use of encryption along with watermarking can provide double protection for social image dissemination. The theory analysis and experimental results demonstrate the effectiveness of the proposed scheme.
ICT AND SATISFACTION: ANALYSIS OF THE CHARACTERISTICS OF UNIVERSITY STUDENTS

Appears in: EDULEARN23 Proceedings Publication year: 2023Page: 1331 (abstract only)ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0428Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
Magnetic Resonance Image Denoising Based on Laplacian Prior Sparsity Constraint and Nonconvex Second-Order TV Penalty

Magnetic resonance (MR) imaging is considered as a very powerful imaging modality in clinical examination, but the process of image acquisition and transmission will be affected by noise, resulting in the degradation of imaging quality. In this paper, based on the Laplacian prior sparsity constraint and the nonconvex second order total variation (TV) penalty, we propose a MR images denoising model which consists of three terms. Specifically, in the first term, we use the L2-norm as the fidelity term to control the proximity between the observed image and the recovered MR image. Then, we introduce the Laplacian sparse prior constraint as the second term to mitigate the staircase artifacts in the recovered image. In the third term, we adopt the nonconvex second-order TV penalty to preserve important textures and edges. Finally, we use the alternating direction method of multipliers to solve the corresponding minimization problem. Comparative experiments on clinical data demonstrate the effectiveness of our approach in terms of PSNR and SSIM values.
Peningkatan Keaktifan Dan Hasil Belajar Ekonomi Melalui Permainan Menempel Rupiah

The purposes of this study were (1) to examine the increase in the activity of class XII MIPA 6 SMA Negeri 1 Rembang in learning accounting economics by applying the rupiah stick game, and (2) to examine the increase in student learning outcomes in class XII MIPA 6 SMA Negeri 1 Rembang in learning accounting economics with the application of the rupiah sticking game.This Classroom Action Research was conducted in two cycles, each cycle consisting of two meetings. The research subjects were students of class XII MIPA 6 SMA Negeri 1 Rembang with a total of 35 students consisting of 9 male students and 26 female students. The data collection method used is the observation method to determine student activity and the test method to determine student learning outcomes. Data analysis techniques in this study were carried out quantitatively and qualitatively.The results showed an increase in student activity and learning outcomes. After the action was taken, the average level of activity increased from 55.41% in the less active category to 67.19% in the active category. The average pre-cycle learning result was 53, after cycle 1 it increased to 68 and after the implementation of the actions in cycle 2 it became 74. Student completeness in cycle 2 increased by 66% and has met the success indicators that have been set in this study. This proves that the Rupiah Sticking Game Media can be used to increase student activity and learning outcomes.
Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy: erratum

This erratum reports corrections for the original publication, Appl. Opt.61, 2834 (2022)APOPAI0003-693510.1364/AO.450805.
Corrigendum to “PartitionTuner: An operator scheduler for deep‐learning compilers supporting multiple heterogeneous processing units”

https://doi.org/10.4218/etrij.2021-0446 ETRI Journal, Volume 45, Issue 2, April 2023, pp. 318–328. Misun Yu1 | Yongin Kwon1 | Jemin Lee1 | Jeman Park1 | Junmo Park2 | Taeho Kim1 1Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea 2Samsung Electronics, Suwon, Republic of Korea The authors regret that the original authorship list did not include the affiliation of author Junmo Park. The authors would like to apologize for any inconvenience caused.
Learning from Hierarchical Structure of Knowledge Graph for Recommendation

Knowledge graphs (KGs) can help enhance recommendations, especially for the data-sparsity scenarios with limited user-item interaction data. Due to the strong power of representation learning of graph neural networks (GNNs), recent works of KG-based recommendation deploy GNN models to learn from both knowledge graph and user-item bipartite interaction graph. However, these works have not well considered the hierarchical structure of knowledge graph, leading to sub-optimal results. Despite the benefit of hierarchical structure, leveraging it is challenging since the structure is always partly-observed. In this work, we first propose to reveal unknown hierarchical structures with a supervised signal detection method and then exploit the hierarchical structure with disentangling representation learning. We conduct experiments on two large-scale datasets, of which the results well verify the superiority and rationality of the proposed method. Further experiments of ablation study with respect to key model designs have demonstrated the effectiveness and rationality of our proposed model. The code is available at https://github.com/tsinghua-fib-lab/HIKE .
Leadership of university teachers in the age of AI

Under the rapid development of artificial intelligence (AI), enhancing the teacher leadership in university has become an urgent need for the development of higher education.This article uses literature research to explore the definition, characteristics, evaluation indicators, and current status of leadership skills among university teachers.Additionally, the application of AI technology in improving leadership skills is discussed, including data mining, machine learning, and AI-assisted decision-making.Based on this analysis, the article proposes a path to enhance the teacher leadership in university , including strengthening leadership training, enhancing teachers' AI literacy and technological literacy, establishing an AI-assisted work system, promoting innovative teaching models, and optimizing educational resources.These measures will help improve the teacher leadership in university and further promote the modernization of higher education.
Habilidades investigativas y niveles de creatividad en preescolares de 4 años

The present research paper was developed to determine the relationship between investigative skills and levels of creativity, being of a quantitative type and of a nonexperimental descriptive correlational design. The data was gathered through two self-made observation guides validated by expert judgment, titled “Observation Guide for Investigative Skills” and Observation Guide for Creativity Levels“. Forty 4-year-old children were part of the sample from a preschool located in MetropolitanLima. The application of the instruments was carried out through Zoom sessions,in the context of remote learning due to the current Covid-19 pandemic. The results indicate that there is a close relationship among both studied variables. I’m addition, we recognized the importance of working on research and creativity from the preschool stage. &nbsp
A Lead User-Persona approach to eliminate communication barriers among stakeholders in co-design.

An appropriate representative Persona is a critical factor in helping interaction design (IxD) innovation and product marketing succeed. Designers, users, and marketers usually work together as stakeholders to build Persona. However, researchers have noted that differences in understanding among stakeholders lead to Persona being created but not used well. Therefore, utilizing a mobile photographing market investigation project as a case study, this study proposes a new approach called Lead user-Persona. The core aim of the Lead-User Persona method is to help stakeholders reduce differences in persona understanding. Researchers use hybrid qualitative and quantitative approaches to find and analyze users’ information and ultimately build Lead-User Persona. At the end of this project, researchers invited project stakeholders to find IxD insight and fill out the satisfaction report. The results show that the Lead-user Persona can improve stakeholders' satisfaction with communication and creation.
Quality of life assessment in individuals with recurrent remitting multiple sclerosis at a referral center

Introduction: Multiple sclerosis (MS) is a demyelinating and autoimmune disease that affects the central nervous system (CNS). It is the most common CNS disabling disease in young adults, predominantly in the 20-40 age group. Patients with MS have a compromised quality of life. The Short Form Health Survey 36 (SF-36) is the most widely used questionnaire to assess quality of life in patients with MS. Objectives: To analyze the quality of life of patients diagnosed with relapsingremitting multiple sclerosis (RRMS), in a Reference Center for Multiple Sclerosis in Paraíba. Methods: This is a cross-sectional field work of quantitative approach, of descriptive and exploratory nature. The SF-36 questionnaire was applied in April 2021, at the Reference Center for Multiple Sclerosis. At the study site, 390 patients with Multiple Sclerosis were registered. For the research to present a confidence level of 90% and an expected error of 10%, a sample of 58 patients was determined. The statistical data were made by IBM SPSS statistics. The study was approved by the Research Ethics Committee of Joao Pessoa University Center. Results: The data found in the sample are compatible with what is described in literature, reaffirming the predominance of MS for the female gender of 77%. As for the analysis of the patients’ quality of life, the domains with the most impairment were limitation by emotional aspects, followed by vitality and limitation by physical aspects. The use of Betainterferone 1a was associated with a better quality of life in six of the eight domains studied. Conclusion: Patients with RRMS have a decreased perception of Quality of Life. Other factors that affect the quality of life of these patients are gender and the use of betainterferone 1a.
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation

Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally may distort the essential structural information of the target-domain data, leading to inferior performance. To address this issue, we first propose to introduce active sample selection to assist domain adaptation regarding the semantic segmentation task. By innovatively adopting multiple anchors instead of a single centroid, both source and target domains can be better characterized as multimodal distributions, in which way more complementary and informative samples are selected from the target domain. With only a little workload to manually annotate these active samples, the distortion of the target-domain distribution can be effectively alleviated, achieving a large performance gain. In addition, a powerful semi-supervised domain adaptation strategy is proposed to alleviate the long-tail distribution problem and further improve the segmentation performance. Extensive experiments are conducted on public datasets, and the results demonstrate that the proposed approach outperforms state-of-the-art methods by large margins and achieves similar performance to the fully-supervised upperbound, i.e., 71.4% mIoU on GTA5 and 71.8% mIoU on SYNTHIA. The effectiveness of each component is also verified by thorough ablation studies.
Bharatavani Project - Reviving Linguistic Diversity and Cultural Heritage in India: A Case Study

The Bharatavani project, launched in 2016 initiated by the Government of India, addresses the crucial need to preserve and promote indigenous languages and cultures. The paper presents an overview of the project, which focuses on recording socio-cultural and linguistic information about 121 Indian languages and making it accessible to a broader audience. The project leverages technological advancements to document significantly smaller and lesser-known languages and mother tongues in India, to raise awareness and maintain and promote the country's rich linguistic diversity. The Bharatavani project aims to bridge the digital divide and ensure equal access to knowledge and information by emphasizing the importance of incorporating these languages into the digital sphere. Through the creation of e-content, the project offers multimedia resources, including text, audio, video, and images, through the online portal www.bharatavani.in and the Bharatavani Android App. This research highlights the significance of content generation, software development, and web portal creation for selected languages in the first phase, with subsequent plans for translation, online teaching-learning, and language teacher training in the second phase. By embracing the potential of technology, the Bharatavani project aspires to create a Knowledge Society in the digital era, enabling individuals across India to explore and celebrate their linguistic heritage.
Study on the example segmentation method of remote sensing image based on neural network

With the rapid development of modern science and technology, people collect remote sensing data from the altitude perspective at the same time, put forward the use of a variety of remote sensing images to solve the military exploration, meteorological analysis, environmental protection, resource exploration and other basic problems. However, since remote sensing images have the characteristics of too large data, high image resolution and extremely low application efficiency, some scholars have used the image features in residual network problems in their research to solve the problem of remote sensing image target segmentation scale difference based on the attention mechanism and single-step case segmentation framework. In this paper, based on the understanding of the research status of neural networks and remote sensing image application, a remote sensing image segmentation model based on multi-level channel attention is proposed according to the model architecture of convolutional neural networks. The final experimental results show that the neural network based remote sensing image case segmentation technology has positive effects.
Software Self-adaptation and Industry: Blame MAPE-K

If software self-adaptation has to be widely adopted by industry, we need to think big, embrace complexity, provide easily deployed and cost-effective solutions, and justify trust. On fairness, MAPE-K should not solely take the blame. MAPE-K is one of the many interpretations of feedback loops apply to systems for which mathematical models - mostly based on control theory, are difficult to be synthesised. MAPE-K has provided a basic and widely accepted framework for justifying the deployment of feedback loops in software systems. Undoubtedly, it has helped to promote and advance the whole area, but now more concrete and resilient solutions are necessary. This position paper argues that, first, industry has been adopting software self-adaptation, perhaps in a way that may not be recognised by the academic community, second, generic solutions are unfeasible since every software system brings its own challenges, and thirdly, the generic stages associated with a feedback loop, like MAPE-K, are insufficient to solve specific challenges.
A Novel Framework for Multiagent Knowledge-Based Federated Learning Systems

Multiagent systems promote a decentralized and distributed approach that enable the division of complex problems into smaller parts. The use of multiagent systems also enables the representation of physical entities, such as persons, pursuing their own goals in an active and proactive society. Currently developments are promoting the idea of having machine learning models in agents to enable intelligent decisions in agents-side. However, machine learning, required assess to large datasets that cannot be available locally to individual agents, demanding the sharing of data or the use of public available datasets to training models for a given agent. To address this issue, this paper proposes the use of federated learning to enable the existence of a collaborative learning model that respects the data privacy, security, and ownership and can be in compliance with the European General Data Protection Regulation (EU GDPR). This paper proposes a novel framework called Python-based framework for agent-based communities powered by federated learning (PEAK FL) that will provide all the necessary tools to build powerful federated learning solutions based on agent communities. This framework provides the users the ability to implement and test hybrid solutions (multiagent-based federated learning systems) in a simple-to-use way, removing the unnecessary boilerplates.