Reviewerly/exHarmony huggingface dataset:
A Reputation-enhanced Shard-based Byzantine Fault-tolerant Scheme for Secure Data Sharing in Zero Trust Human Digital Twin Systems

&lt;p&gt;This paper presents a blockchain-enabled data sharing framework for latency-sensitive computing services.&lt;/p&gt;
Shard-based and Reputation-enhanced Byzantine Fault-tolerant Scheme for Secure Data Sharing in Latency-sensitive Computing Services

&lt;p&gt;This paper presents a blockchain-enabled data sharing framework for latency-sensitive computing services.&lt;/p&gt;
Evaluating Websites Using Opinion Mining

In the age of social media and online review platforms, there is no lack of individuals who want to-and doexpress their sentiments and opinions on the internet using one, or more, of the numerous social media or review platforms.The mere existence of these platforms welcomes the public to voice their sentiments.However, given the increasing rate at which internet users are expanding worldwide, making sense of these opinions is becoming increasingly challenging.On the other hand, major corporations are working tirelessly to understand these opinions and extract the gist of them for their benefit.These benefits are usually related to the organisations' efforts towards maintaining their reputation.In this paper, we aim to propose a system that uses user reviews of a website, extracts sentiment expressing words and determines if the overall user reception towards that website is positive, negative or neutral.
MUNPE:Multi-view Uncorrelated Neighborhood Preserving Embedding for unsupervised feature extraction

&lt;p&gt;In order to identify the shared subspace between two views, in Canonical Correlation Analysis (CCA), a popular multi-view dimension reduction technique tries to maximize correlation between the views. Although, there are frequently more than two views in many actual applications, it can only process data with two views. In prior studies, data with more than two viewpoints were managed using either linear correlation or higher degree polynomial correlation. The link between multiview data viewed from diverse perspectives is established by these two types of correlation, that is linear correlation and high order correlation, each of which has a different effect on view consistency. In this paper, we propose a Multi-view Uncorrelated Neighborhood Preserving Embedding (MUNPE), which simultaneously considers two distinct types of correlation to give flexible view consistency. While keeping the local structures of each perspective, the MUNPE also takes into account the complementaries of numerous viewpoints. The MUNPE makes the characteristics gathered by numerous projections for each view uncorrelated in order to get many projections and reduce the duplication of low-dimensional data. Iterative methods are used to resolve the MUNPE, and the algorithm’s convergence has been demonstrated. The testing on the Multiple Feature and other synthetic data sets were successful for MUNPE. It is observed that performance is better than MLPP[1], MSE[2], MLLE[3], GCCA[4], MCCA[5] algorithms.&lt;/p&gt;
SENet: Sequential Encoders for One-Shot Learning

&lt;p&gt;One-shot object recognition is a challenging task for deep neural networks. It becomes more difficult when there is a domain shift between support and query samples. The generalization of a deep model on an unknown domain with different distributions is another problematic task in on-shot recognition. This work introduces a novel deep network architecture named SENet and a multi-step meta-training strategy to overcome the aforementioned problems. To the best of our knowledge, this work outperforms all state-of-the-art models in one-shot traffic sign recognition and one-shot logo identification by superior results. Although the SENet model achieves comparable results for the traditional GTSRB benchmark, it has about 6X fewer parameters than its competitors. The smaller size of the SENet architecture enables it to be used in resource-constrained devices in many applications such as smart vehicles. The experimental results depict that our proposed SENet performance is ameliorated by large margins, with up to 20% accuracy for one-shot classification and 30% area under the curve (AUC) for image retrieval tasks.&lt;br&gt; &lt;/p&gt;
Simplified Algorithms for Order-Based CoreMaintenance

Abstract Graph analytics attract much attention from both research and industry communities. Due to its linear time complexity, the $k$-core decomposition is widely used in many real-world applications such as biology, social networks, community detection, ecology, and information spreading. In many such applications, the data graphs continuously change over time. The changes correspond to edge insertion and removal. Instead of recomputing the $k$-core, which is time-consuming, we study how to maintain the $k$-core efficiently. That is, when inserting or deleting an edge, we need to identify the affected vertices by searching for more vertices. The state-of-the-art order-based method maintains an order, the so-called $k$-order, among all vertices, which can significantly reduce the searching space. However, this order-based method is complicated to understand and implement, and its correctness is not formally discussed.In this work, we propose a simplified order-based approach by introducing the classical Order Data Structure to maintain the $k$-order, which significantly improves the worst-case time complexity for both edge insertion and removal algorithms. Also, our simplified method is intuitive to understand and implement; it is easy to argue the correctness formally. Additionally, we discuss a simplified batch insertion approach.The experiments evaluate our simplified method over 12 real and synthetic graphs with billions of vertices. Compared with the existing method, our simplified approach achieves high speedups up to 7.7x and 9.7x for edge insertion and removal, respectively.
Comparative Study ofSearch Engine Optimization Algorithms On Retrieval Time andPrecise Data Entry for Websites

A search engine is a complex software in that a finder visits numerous websites and their pages to find essential data. It is the primary source to find content on the World Wide Web. Search Engine Optimization (SEO) methods have been invented to make user searching smoother. Although search engines are intelligent, sometimes they also provide irrelevant data. As a result, researchers have found a solution to this problem by implementing SEO techniques. SEO techniques make websites more visible and produce organic search results. Thus, this study proposed the implementation of Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) for SEO problems on online shopping websites and educational websites. These algorithms are evaluated based on retrieval time and precise data entry by using precision and recall. Moreover, PageSpeed Insights is used to check the speed index of the websites. The research outcome found that PSO outperformed ANN for both shopping and educational websites. PSO has a minimal retrieval time, which is 0.04 seconds for an online shopping website and 0.10 seconds for an educational website. As for precision and recall, online shopping websites have been proven to have the highest precision score of 0.67 and 0.63 recall score. The simulation analysis results show thatfuture researchers should concentrate more on determining the significance of each SEO approach and the best blend for various sectors.
AComparative Study of Artificial Neural Network and Genetic Algorithm in Search Engine Optimization

Search engine optimization applies search principles in search engines to assign a higher ranking to the most suitable webpage. Nowadays, information searching is done ubiquitously on the World Wide Web with the help of search engines. However, the process needs to be efficient and produce accurate results simultaneously. In this research, the objectives are to implement and evaluate the Artificial Neural Network and Genetic Algorithms. The accuracy result for both algorithms is compared by implementing keyword ranking, Search Engine Result Page visibility, and time retrieval for document-based and e-commerce websites. To achieve them, firstly, the problem and data are defined. Next, two datasets are importedfrom Kaggle and transformed into a more helpful format. Then, the Artificial Neural Network and Genetic Algorithms are implemented on these datasets in Python using Jupyter Notebook tools. Subsequently, the accuracy of these datasetskeyword ranking, Search Engine Result Page visibility, and time retrieval areobserved based on the output and graph. Lastly, an analysis of the results is performed. Conclusively, the Genetic Algorithm demonstrates a higher percentage of accuracy results than the Artificial Neural Network algorithm in keyword ranking and SERP visibility. However, the accuracy results of time retrieval are vice versa. The results in Genetic Algorithm show 9.0%, 9.0%, and 3.0% in the e-commerce dataset for keyword ranking and 4.0%, 51.0%, and 1.0% in the document-based dataset for SERP visibility. Next, the Artificial Neural Network algorithm shows results of 8.0%, 7.0%, and 7.0% in the e-commerce dataset and 3.0%, 50.0%, and 4.0% in the document-based dataset for time retrieval. Therefore, the results validated the ability of the Genetic Algorithm as one of the most applied algorithms in the search engine optimization field.
Machine Learning Research On Breast And Lung Cancer Detection

As the diagnosis of these cancer cells at late stages causes greater pain and raises the likelihood of death, the initial-state cancer finding is crucial to giving the patient the proper care and reducing the risk of dying from cancer. The publication offers a chance to research breast and lung cancer detection techniques as well as various algorithms for cancer early detection. With the aid of various image kinds and test results data sets, hybrid approaches are utilized to identify lung and breast cancer based on the size and form of the cells. The basic concept of breast and lung cancer block diagram is also explained in this study, with an emphasis on the difficulties and potential future applications of cancer detection and diagnosis techniques.
Evaluating Blockchain-Based Supply Chain Challenges (A Survey)

Blockchain technology is an emerging technology that will have a significant impact on the business of organizations. One of the influential areas of blockchain technology is the supply chain sector. Supply chain is a network of people and companies that focuses on the production and distribution of a specific product or service from the stage of primary producers to customers and consumers. Most supply and distribution networks face problems with managing all these components together. Many problems of current supply chains can be solved through blockchain technology; Because blockchain offers us very good and pristine ways to record, transfer and share information. The literature in this field is more focused on the technological sector and pays less attention to other sectors. In this research, it is emphasized the need to pay attention to the relationships between supply chain partners and change management when adopting this technology. Also, the current research identifies some of the challenges of adopting blockchain technology with a comprehensive view for the first time and places these challenges in a new classification. These challenges are classified in the groups of organizational challenges, inter-organizational challenges, external/environmental challenges, and technological challenges.
The Role of Academic Advising of Students Research Teams in Shaping Student Engagement and Learning Ownership in Russian Universities

This review research article explores the existing literature on academic advising of student research teams in Russian Universities, with a focus on the role of academic advisors in promoting student engagement and learning ownership. This article focuses on the role of academic advising in promoting student engagement and learning ownership in research teams. The article also highlights the potential benefits of research teams for students, including opportunities to engage in authentic problem-solving, develop important skills, and make valuable professional connections. However, challenges can arise when working in groups, and academic advisors can play a critical role in helping students overcome these challenges by providing guidance and support throughout the research process. The article provides recommendations for academic advisors seeking to enhance student engagement and learning outcomes through effective academic advising practices. The study follows a systematic approach and utilizes a narrative synthesis approach to analyze and synthesize the data, making the findings grounded in empirical evidence. The study identifies a range of challenges that students may face while participating in research teams, and how academic advisors can help them overcome these challenges. The authors suggest that effective academic advising should be student-centered, emphasizing communication skills and providing ongoing support to students throughout their academic journey. The article also presents a set of roles for academic advisors in promoting student involvement and learning ownership. Finally, the authors provide recommendations for future research in this area, including investigating different styles of advising, exploring the impact of technology on academic advising, and examining the effectiveness of academic advising for diverse student populations. Ultimately, this review research article aims to contribute to ongoing discussions about the role of experiential learning within higher education institutions and provide guidance for academic advisors seeking to support student success within research teams.
Reconstruction of the rules on the indirect infringement of online copyright

As the situation of online copyright infringement became increasingly severe, the copyright indirect infringement rules for Internet Service Providers came into being for breaking the predicament of accountability produced by the principle of copyright legality. In order to maintain the scientificity and integrity of our Internet Service Providers' copyright indirect infringement rules system, we should: on one hand, adhere to the unified system design path of the imputation principle and abandon the application of the exemption principles such as "safe harbor" rules; on the other hand, clarify the difference between the rules of copyright inducement infringement and the contributory infringement under the guidance of the theory of joint infringement, and clarify the independent value of copyright vicarious liability on the basis of the rule of "obligation of safety guarantee" in the Tort Law.
Winning Robotics Competition Strategy: Theory of Creating Projects in Robotics Education

The subject of robotics in education has a significant impact on the development of skills of students. Studying the main factors motivating robotics to participate in competitions, project competitions, and win prizes is always relevant. There are many competitions in robotics. In particular, one of the competitions for developing robotics in education is WRO. However, it is essential to know and research several factors that can lead to a place in a competition and support young coaches and participants. Higher education institutions that already have courses in educational robotics can benefit from the proposed approach, which allows them to develop new projects with robotics elements.
Distance Education through Information and Communication Technologies

The article deals with distance education through information and communication technologies, which has many advantages compared to traditional forms of education but also disadvantages. On the one hand, it allows to rationalise the work of teachers, helps to break down stereotyped activities, and makes it possible to interest and activate students. Still, on the other hand, not enough attention is paid to addressing psychological, sociological, and pedagogical problems. Flexibility and convenience can be included among the positives, i.e., learning for students at times that suit them best and in environments that stimulate them or where they feel comfortable.
Exploring the Status of Teaching Listening and Speaking Skills after the Introduction of their Tests in Secondary Level Education

Among the four skills of a language, listening and speaking are often considered to be more frequently used skills in real life communication. Many studies show that English listening and speaking skills were less or rarely taught in the classroom mainly because these were not tested in public or internal exams in Bangladesh. But the National Curriculum (2012) also suggests testing these skills. The Ministry of Education (2015) consequently released a circular to administer these tests in the internal exams in secondary level education. This research intended to study the teaching of these two skills, the practice of tests of these two skills, and the challenges in administering the tests. It finds that the teaching practices of these two skills in the classroom have increased compared to those in the past but not at an optimal level. These tests are not given in the terminal examinations and administered only as continuous assessments. This study finds that 67% help learners develop their listening skills by using English in the class, 60% teach listening activities, and 53% teachers teach speaking skills. However, 30% schools still do not give their students these two tests. Practicing TEFL teachers identified some challenges in administering these tests. Their suggestions for solving these problems are also given.
Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection

This paper presents our submission to the SIGMORPHON 2023 task 2 of Cognitively Plausible Morphophonological Generalization in Korean. We implemented both Linear Discriminative Learning and Transformer models and found that the Linear Discriminative Learning model trained on a combination of corpus and experimental data showed the best performance with the overall accuracy of around 83%. We found that the best model must be trained onboth corpus data and the experimental data of one particular participant. Our examination of speaker-variability and speaker-specific information did not explain why a particular participant combined well with the corpus data.We recommend Linear Discriminative Learning models as a future non-neural baseline system, owning to its training speed, accuracy, model interpretability and cognitive plausibility. In order to improve the model performance, we suggest using bigger data and/or performing data augmentation and incorporating speaker and item-specifics considerably.
Designing for Presence: Embodied Interaction in Computer-Mediated Realities

There is only embodied interaction in computer-mediated realities. We use our bodies to sense, interact with, process, and remember environments. Effective computer-mediated realities, i.e. mixed and virtual realities, depend on the user's feeling of being part of those environments. We are explaining our approach towards a theoretical model for this sense of presence. Based on techno-psychological, philosophical, and socio-psychological models we are presenting a theory for the understanding of and design for such mediated realities. We emphasize the role of the human body as a sensing and interacting instrument and argue that presence is achieved by self-controlled navigation, object interaction, and subject interaction. Presence therefore derives from body-related possibilities of interaction with the environment. We demonstrate this through the definition of a theoretical presence model for embodied interaction in computer-mediated realities.This article is dedicated to Martin K Purvis—one of the few modern renaissance scholars who is not only well educated and well read, but who continues to inspire young and curious minds, We hope that our thoughts presented here will inspire and entertain Martin as well as other readers with interests in the crossing boundaries of technology, psychology, and philosophy.
BioHackEU22 Report for Project 31: The What &amp;amp; How in data management: Improving connectivity between RDMkit and FAIR Cookbook

This report describes the work completed during the ELIXIR Biohackathon 2022 for project 31: The What &amp;amp; How in data management: Improving connectivity between RDMkit and FAIR Cookbook. The project covered 3 subjects: the technical connectivity between the two primary resources, an editorial alignment and gap analysis of their content, and the creation of user journeys incorporating the wider ELIXIR Research Data Management (RDM) ecosystem.
An Adaptive Ensemble Framework for Addressing Concept Drift in IoT Data Streams

&lt;p&gt;In the modern era of digital transformation, the evolution of the fifth-generation (5G) wireless network has played a pivotal role in revolutionizing communication technology and accelerating the growth of smart technology applications. As an integral element of smart technology, the Internet of Things (IoT) is grappling with the problem of limited hardware performance. An IoT system based on cloud and fog computing is a highly efficacious solution, but it often faces concept drift challenges in real-time data processing due to the dynamic nature of IoT environments, leading to a degradation in performance. Con- sequently, this work proposes a drift-adaptive ensemble framework called Adaptive Exponentially Weighted Average Ensemble (AEWAE) consisting of three stages: IoT data preprocessing, base model learning, and online ensembling. It is a data stream analytics framework that integrates dynamic adjustments of ensemble methods to tackle various scenarios. Experimental results on two public IoT datasets exhibit that our proposed framework surpasses current methodologies, achieving exceptional precision and effectiveness in IoT data stream analytics.&lt;/p&gt;
MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

&lt;p&gt;Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more. The deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore. Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node. Finding 2-edge connected components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithms. Processing a big graph in a parallel and distributed fashion saves considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. This research proposes a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ”BiECCA”) and presents its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework and uses an existing algorithm to find connected components (CCs) in a graph as a sub-step. Finally, we suggest a few novel ideas and approaches as extensions to our work.&lt;/p&gt;