Reviewerly/exHarmony huggingface dataset:
Multi-Interactive College English Teaching Under the Network-Based Multimedia Environment

As a new teaching method, multi-interactive teaching has been more and more recognized by educators in this fast-growing information age due to its advantage of facilitating communication. However, more empirical studies still need to be done to investigate its effect. This study aims to investigate the impact of multi-interactive teaching method on college students’ English proficiency and their learning motivation through a quasi-experimental design. The Experimental Group (44 students) was taught using multi-interactive teaching approach while the Control Group (44 students) learned English in a conventional way over a span of 14 weeks. T-test was adopted to examine whether there was significant difference in English proficiency (listening, reading, writing, and translation) and their learning motivation between the two groups. The findings revealed that except writing, the Experimental Group obtained significantly higher scores than the Control Group. Hence, it is recommended that multi-interactive teaching be used as an effective method to promote students’ English proficiency and learning motivation in College English classes.
A Discussion of the Pedagogical Benefits of Nodal Knowledge in University Physics

Focusing on nodal knowledge in university physics, this paper describes the definition and categorisation of nodal knowledge, revealing its characteristics and its connection to core knowledge. Through in-depth analyses, it is found that nodal knowledge is an important link in the physics subject that promotes and extends core knowledge, embodying the characteristics of fundamentality, selectivity, transition and diversity. In addition, this paper also highlights the superiority of using nodal knowledge for teaching, which not only helps students reduce the burden of memorisation, but also promotes the construction of a clear structure in the knowledge network and the understanding of the ins and outs of knowledge. Crucially, nodal knowledge makes it easier for students to understand and derive subject knowledge, thus helping them to master it more effectively.
Impact of Artificial Intelligence and Internet of Things on Performance Management: A Systematic Review

The study aims at assessing the outcome of AI and IoT to operational efficiency in business processes for various sectors. Exploring the Synthesis of the existing literature, combined with empirical evidence, this research examines the transformation possibility of AI and IoT Technologies in the improvement of the company. The centric findings have proved that there are significant upsurges in metrics of performance such as efficiency, productivity, quality, and customer satisfaction across different sectors of industries which are like healthcare, manufacturing, and retailing. Healthcare is another example. In this sector AI and IoT combination reduced the patient queue times by 50% while in manufacturing produced more products while spending 33% less capital costs. Similar to the case of brick and mortar stores, they recorded a 25% increment in their sales through AI-aided demand forecasting and the use of IoT in the inventory management. The findings of this research therefore point the way for the tremendous role played by AI and IoT in driving operational excellence, decision making processes and innovation in performance management. The research leads to the identification of major issues and concerns such as data privacy, security and technology integration, that require additional attention. Eventually, by utilizing AI and IoT technologies, companies find new models of sustainable development and market advantages which are of great help in the context of the current business conditions.
A Systematic Performance Review of Security Methods for the Cyberworld

Governments and organisations globally increasingly recognise the importance of cybersecurity as a critical measure against cyber threats in our highly interconnected society. Establishing robust security measures has become paramount in safeguarding sensitive information and infrastructure. Biometric systems have emerged as critical components in various sectors, including industry, civilian applications, and rhetoric, to enhance security measures. This paper provides an overview of diverse security approaches in the cyber world and examines several research works, highlighting their strengths and weaknesses in implementation. We critically analyse existing methodologies and address potential shortcomings, aiming to improve the effectiveness of security measures. Additionally, we explore emerging trends and novel research directions in the field of biometric and rhetorical security. The study delves into contemporary biometric toolkits, examining their functionalities and applications across domains. Furthermore, we discuss digital ornamental models, evaluating their efficacy in enhancing cybersecurity measures. Through comparative analysis, we identify key differences and areas for improvement in existing security frameworks. In conclusion, this paper proposes a generic computer security model tailored to address the evolving challenges of cybersecurity. We highlight potential applications of this model in society, emphasising the importance of proactive measures to mitigate cyber threats effectively. Through comprehensive analysis and innovative approaches, we aim to contribute to advancing cybersecurity practices in contemporary society.
Development of Digital Literacy Skills among Learners in Public Primary Schools in Homabay County, Kenya

Digital literacy direct learners towards innovative, creative and transformational learning in the 21st century. It is one of the core competency in the Competency Based Curriculum. The objective of this study was to assess the availability of ICT infrastructure in public primary schools to develop learners’ digital skills. Constructivist learning theory guided the study. Concurrent embedded mixed method was used. A population of 5713 was targeted for study where a sample of 571 respondents composed of 8 head teachers, 78 teachers and 485 grade six learners were selected. Stratified random, simple random and purposive sampling procedures were used to select the study sample. Data collection instruments included interviews, questionnaires, focused group discussion and observation schedule. Descriptive statistics were used to analyse quantitative and qualitative data. Some of the key findings of the study were majority of public primary schools in Kasipul lack internet connectivity and only 32.1% have computer labs. The results further indicated that teachers have basic knowledge and skills in digital literacy. However, only 23.2% of teachers have been trained in digital literacy. The study recommends that Ministry of education in collaboration with Ministry of Information and Communication Technology enhance internet connectivity in all primary schools to support implementation of digital literacy to develop learners’ digital skills
Analysis of EFL Learners' Writing Challenges in the Digital Era

The Writing Intensive Course emphasizes fundamental English writing elements, providing a prime chance for students to improve written communication. Educators face challenges in enhancing expressiveness, but the large millennial class size, familiar with digital technology, presents a potential advantage. Millennials, as digital natives, show enthusiasm for digital-context learning, offering an opportunity for effective instruction in writing. The primary objective of this research is to find out the problems or challenges students undergo while learning to write English in the digital era. This quantitative research examined 174 EFL students to give particular responses that work with brief reviews of their challenges in English writing. Unable to express (10.9%), improve skills (9.8%), and develop ideas (5.2%) in writing stand strong as the biggest three difficulties in English writing. The diverse challenges, collected from participant samples, reveal consistent issues for both active and nonactive students. The study emphasizes the value of digital-assisted courses, offering hope for enhanced English writing and communication skills among SL/FL learners in a more accessible and effective manner. In brief, this study highlights internal factors as the primary challenges in writing class, with students struggling with self-expression, doubts about improvement, and generating ideas. Various challenges, including optimizing roles and feedback, underscore the importance of addressing obstacles for enhanced writing proficiency in digital-assisted courses
Emotional Regulation Ability on Test Results: Factors Affecting Motivation and Learning Achievement

The ability to regulate emotions in the acceptance of test results can impact both the increase and decrease of motivation. Unfortunately, there has not been much research explaining this phenomenon. Therefore, this study aimed to examine the influence of emotions on the acceptance of test results on students' learning motivation. This research used an ex post facto approach with a cause-and-effect correlation design. The population consisted of 300 students at SMPN 2 Kuta Selatan. Using purposive sampling, 30 students were involved as research samples. The research instrument consisted of 21 statements from the DAS questionnaire about test results, 20 statements about student motivation, and a formative test. Data were analyzed descriptively, using correlation statistics and paired sample t-tests. The research results showed that there was a significant relationship between the ability to manage depression, anxiety, and stress in the acceptance of test results. In addition, students with better emotional management skills tended to have positive motivation if their test results were disappointing. Meanwhile, students with low emotional abilities also had low motivation. For these affecting factors on students’ achievements, this study suggests a more holistic pedagogical approach and intervention as essential components in efforts to improve their learning achievement
The development of blockchain technology in manufacturing: A bibliometric analysis

Many manufacturing organizations have adapted their governance systems, strategies, business models, and strategic processes in the "New Normal" and technologies like Blockchains have irreversibly transformed how people live and work. Despite extensive scholarly research, analysing emerging trends in Blockchain research remains critical. Unfortunately, the use of bibliometric analysis to study Blockchain has been limited, with little understanding of how its various research areas are progressing. Bibliometric analysis, a useful method for examining the development of research, has not been widely used in the context of blockchain. The progression of its numerous research areas is also yet unknown. This study seeks to fill these gaps by employing bibliometric analysis to assess scientific progress and research focus on Blockchains in manufacturing. We used Vos Viewer to conduct this bibliometric analysis to evaluate 265 papers published from 2017 to 2022 using co-authorship and co-occurrence of keywords analysis. We have identified the most productive authors, institutions, countries, and top research fields, highlighting the essential advancements in the Blockchain field. The findings demonstrate how keywords related to Blockchain have changed over time and describe the research hotspots. The originality of this research lies in its application of bibliometric analysis to examine the development and emerging trends in Blockchain research within the manufacturing domain while offering novel insights into the productivity of authors, institutions, and countries, as well as identifying research hotspots and future research directions. This study contributes to the inadequate body of literature on bibliometric analysis in Blockchain research and provides useful guidance for researchers and practitioners in the field.
Protecting ownership rights of ML models using watermarking in the light of adversarial attacks

In this paper, we present and analyze two novel—and seemingly distant—research trends in Machine Learning: ML watermarking and adversarial patches. First, we show how ML watermarking uses specially crafted inputs to provide a proof of model ownership. Second, we demonstrate how an attacker can craft adversarial samples in order to trigger an abnormal behavior in a model and thus perform an ambiguity attack on ML watermarking. Finally, we describe three countermeasures that could be applied in order to prevent ambiguity attacks. We illustrate our works using the example of a binary classification model for welding inspection.
Application of glasses‐free augmented reality localization in precision neurosurgery and literature review

Abstract To summarize the characteristics and therapeutic effects of glasses‐free augmented reality (AR) in neurosurgery, a case series of seven patients undergoing resection of intracranial lesions at the Neurosurgery Department of Hangzhou Xixi Hospital from November 2019 to October 2021 was collected. During the surgeries, glasses‐free AR was used for multi‐angle and multi‐modal image localization. The intraoperative positioning errors of all seven patients who underwent body surface localization with glasses‐free AR, in addition to routine surgical procedures, were less than 10 mm. These findings suggest that glasses‐free AR holds great promise for precision neurosurgery.
Pedagogical Response to the Needs of Preschool Children in a Disadvantaged Situation

The aim of this article is to analyse the pedagogical response of Croatian ECEC teachers to the needs of preschool children in a disadvantaged situation. The quality of pedagogical practise to prevent unfavourable developmental trajectories in children in a disadvantaged situation, particularly in the Croatian system, is poorly researched. A vignette describing an imaginary child in a disadvantaged situation was used and 35 preschool teachers from 15 ECEC institutions described in writing in an online application how they would support the child in his or her development. The qualitative thematic analysis was conducted according to the five components of the strengths-based approach (relationships, empowerment, collaboration, strengths and focus on change). This approach assumes that through reciprocal relationships between children, parents and teachers, previously agreed goals are achieved and personal resources, motivation and belief in the possibility of achieving sustainable change are unleashed. The results show that ECEC teachers are predominantly oriented towards individual components of the strengths-based approach (quality of relationships, focus on children's strengths), while the implementation of the entire strengths-based approach in its entirety is lacking. It is emphasised that the strengths-based approach needs to be more strongly affirmed in Croatian ECEC system so that its principles are recognisable in the pedagogical practise of all ECEC teachers.
Basic statistical methods in determining criteria weights

International Journal of Information Technology & Decision MakingAccepted Papers No AccessBasic statistical methods in determining criteria weightsUzeyir FidanUzeyir Fidanhttps://doi.org/10.1142/S0219622024500093Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 3 August 2022 Accepted 18 February 2024 PDF download
Cryptography and DRM: A study of digital copyright protection in the gaming industry

This paper explores the role of cryptography and encryption technology in developing digital rights management (DRM) in the gaming industry. DRM aims to protect the rights of creators by preventing unauthorized copying and distribution of digital media. Cryptography plays a crucial role in DRM, ensuring the privacy, authenticity, and integrity of data. The early stages of game DRM involved physical disc methods, such as unique disc characteristics and online activation. However, these methods faced challenges such as compatibility issues and unauthorized copying. Modern DRM technologies combine advanced encryption techniques with account-binding mechanisms, requiring users to activate and access game content on specific accounts. Cryptography plays a key role in DRM by encrypting game data into unreadable formats, ensuring that only users with valid decryption keys can access the content, and ensuring secure distribution and consumption of digital content. The paper also discusses third-party DRM technologies, such as Denuvo, which employ strong encryption and obfuscation methods. However, third-party DRM may impact performance, inconvenience users, and lead to compatibility issues. The paper emphasizes the necessity of continually refining DRM technologies to balance copyright protection and consumer rights in the evolving gaming industry.
Multilingual sentiment analysis during the pandemic using deep learning models

High mood swings and increased mortality with the COVID-19 outbreak, mood changes are a concern. COVID-19 is extremely contagious, and by December 20, 2021, the global death toll from COVID-19 exceeded 5.35 million. Under such circumstances, people changed their original way of life and isolated themselves from home to work online. Most people in this situation expressed their emotions through social media. In order to understand the changes of people's emotions during the pandemic, this research analyzed the emotions of people's messages posted on social media in different countries through the SenWave dataset by different deep learning models. The dataset contains 6 languages (English, Arabic, Spanish, French, Italian and Chinese) with more than 70,000 tweets, in this paper, only tweets collected from Twitter are concerned, which contains 11 labels. The dataset will be preprocessed and transferred into numerical vector, then TextCNN, BERT, and BERT-TextCNN models are implemented for the above problems. BERT model reached 0.69 and 0.53 on precision and recall respectively on Twitter dataset.
Breast cancer classification based on hybrid machine learning model

This study proposes a hybrid model that combines K-Means clustering and Random Forest classification as an approach for breast cancer classification. The objective is to exploit the advantages of unsupervised clustering and supervised classification techniques to enhance the accuracy and robustness of classification models. The dataset underwent preprocessing procedures encompassing the handling of missing values, feature normalization, and feature selection. Missing values were addressed through appropriate methods, and features were scaled and selected based on variance threshold or correlation analysis. Subsequently, K-Means clustering was applied to the preprocessed data to assign cluster labels to each sample. The study then proceeded to train a Random Forest classifier by incorporating both the cluster labels and the raw gene eigenvalues as mixed features. This integration of gene expression values and cluster labels provides supplementary information to the classifier, enabling the capture of more intricate patterns within the data. The Random Forest classifier was trained using optimized parameters determined through parameter tuning, including the number of trees, maximum depth, and minimum number of split samples. Extensive experiments and evaluations conducted in this study revealed that the hybrid model outperformed the standalone Random Forest classification. The incorporation of K-Means clustering facilitated the discovery of underlying data structures and patterns, ultimately enhancing the classifier's discriminatory ability. The hybrid model exhibited superior accuracy, precision, recall, and F1 scores, demonstrating its efficacy in accurately classifying breast cancer samples.
An exploration and practice of an optimized online annotation solution based on PDF slicing and SVG

With the continuous development of network technology, online annotation has become an essential tool in fields such as education, training, and document collaboration. However, traditional annotation techniques are plagued by several limitations, including low security, non-erasable annotations after uploading, and difficulties in communication across different systems. To address these issues, this paper proposes an SVG-based online annotation technique that is built upon PDF file slicing. This approach aims to enhance the deficiencies of traditional annotation techniques and improve user interaction. The research begins by introducing the background and significance of online annotation technology, elucidating the application prospects of PDF slicing technology and SVG in annotation. The paper then provides a detailed description of the annotation algorithm and the implementation process. Additionally, it discusses the experimental setup, results, and analysis. The results indicate that the SVG-based online annotation technology can effectively enhance annotation efficiency and real-time interaction while enabling erasure of historical data and facilitating cross-platform communication. Compared to traditional annotation techniques, the SVG-based approach exhibits higher practicality and flexibility, making it suitable for various types of document annotations and collaborative scenarios.
Large language models capsule: A research analysis of In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) methods

In the context of natural language processing (NLP), this paper addresses the growing need for efficient adaptation techniques for pre-trained language models. It begins by summarizing the current landscape of NLP, highlighting the challenges associated with fine-tuning large language models like BERT and Transformer. The paper then introduces and analyzes three categories of parameter-efficient fine-tuning (PEFT) approaches, namely, In-Context Learning (ICL)-inspired Fine-Tuning, Low-Rank Adaptation PEFTs (LoRA), and Activation-based PEFTs. Within these categories, it explores techniques such as prefix-tuning, prompt tuning, (IA)3, and LoRA, shedding light on their advantages and applications. Through a comprehensive examination, this paper concludes by emphasizing the interplay between performance, parameter efficiency, and adaptability in the context of NLP models. It also provides insights into the future prospects of these techniques in advancing the field of NLP. To summarize, this paper offers a detailed analysis of PEFT methods and their potential to democratize access to cutting-edge NLP capabilities, paving the way for more efficient model adaptation in various applications.