It scorched the grass and was "simply not appropriate or acceptable", said a council.

The gang, who nicknamed themselves The Sticky Bandits, hit 10 firms within six months.

The way smart energy meters work in northern England and Scotland is causing issues for customers, BBC Panorama has been told. The body that represents energy companies, Energy UK, has […]

The post Smart meters in north/south divide, Bitcoin breaks through $82,000 barrier appeared first on Tech Digest.

Bristol City have the squad depth to be a "top team" and challenge in the Championship this season says forward Anis Mehmeti.

Sink your teeth into London's tastiest exhibition.

This is not about sex, and not about The Sex Myth. This is about the old blog, and the growing scandal in News International's paper the rules they played by. And as Prince Humperdinck so eloquently put it, I always think everything could be a trap.

Very early on in blogging as Belle de Jour, I had an email address associated with the blog. It was with one of those free email providers and not very secure. Later, I wised up a touch and moved to doing everything through Hushmail. But for some reason I kept the old email up and running, and checked it occasionally.

So on the day of the book's release in the UK, I logged on to a public library computer in Clearwater, Florida, and had a look at that old account. There was a new message from someone I didn't recognise. I opened it.

The message was from a journo at the Sunday Times. It was short, which struck me as unusual: Come on Belle, not even a little hint? There was an attachment. The attachment started downloading automatically (then if I remember correctly, came up with a "failed to download" message).

My heart sank - my suspicion was that there had been a program attached to the message, some sort of trojan, presumably trying to get information from my computer.

Now, I understood the papers regarded all of this as a game. There were accusations that the anonymity thing was a ruse to pump sales. It wasn't. I was really afraid of losing my job and my career if found out. But I knew the rules they played by. And as Prince Humperdinck so eloquently put it, I always think everything could be a trap.

I did several things:

1. Alerted library staff that I thought there had been a virus downloaded on to the computer, so they could deal with it.

2. Phoned a friend who knew my secret. I explained what happened. He agreed to log in to that email account from where he lived, halfway around the world, open the email and send a reply, so they would have competing IP address information.

3. Alerted the man who owned the .co.uk address pointing to my blog, someone called Ian (who to my knowledge I have never met). He confirmed he had been contacted by the Times and asked if I was indeed in Florida. He told them he didn't know (which was true).

Point 3 is the part that makes me think my suspicions were correct. I hadn't replied to the message from the computer in Florida, so why would they have a Florida IP address? They did get a reply from "my" account, but it would have had an IP address from Australia.

(It's been suggested on Twitter that this could also have been because of a read receipt or embedded images. However, if my memory serves - and it usually does - the service I used did not send read receipts and I had images/HTML off as a matter of habit. There could of course be other explanations for what happened, but it is certainly true that the Times were trying hard to find me. Thanks for the comments, I hope this answers any concerns.)

It is with some interest that I have been following media reports of the alleged conduct of Ashton Kutcher, a well-known campaigner against sex trafficking. As has been pointed out elsewhere, the "problem" his advocacy claims to address is certainly hugely overstated and possibly being manipulated by people who are at least as interested in money and credibility as they are in philanthropy.

Interestingly, on Quora, which Kutcher has called "the smartest place on the internet" (you know, because academic journals and research forums are where the dumb kids hang out), there was a question not long ago which asked, "Why is it so common to include voluntary prostitution in the category of sex trafficking?"

Kutcher stepped in, as did others, in defence of the idea that foreign-born women voluntarily choosing to enter sex work - such as, say, myself (and yes, one of them did mention me specifically) are trafficked. Also people being transported over state and international borders, or something.

When you hear the word "trafficking", maybe you imagine a foreign child being kidnapped and sold into sexual slavery. Not only is that not accurate, it's also not what the lobbyists against sex work even seem to believe themselves. But it is an assumption they appear happy to exploit. As the Quora discussion shows, Kutcher and people like him claim that "trafficking" includes people going into sex work willingly and migrating willingly. In other words, equating consensual sex work with involuntary slavery. Actually a lot of other "rescue industry" types buy that as well. It's a stand with a lot of errors of logic, but it's their platform, they defend it, they own it.

Right. Now, let's check out an article from the Daily Mail dated 03 October 2011 (no link since Istyosty has gone now, HuffPo covers it and so does The Frisky, also it's screensnapped below). It includes quotes from someone who not only claims to know the person Kutcher allegedly cheated with, but who also appears to indicate that the presence of girls like her at celeb parties is, shall we say, not entirely without reimbursement.

Here's the bit in the Mail that caught my eye:

Naumoff, who arranges for good looking girls to be shipped to certain hotspots,  also told the newspaper: 'Sara’s a great girl. My job is to round up hot girls and bus them into clubs in San Diego or Vegas.

The girls get free booze, food, whatever, and they attract rich and famous guys to the clubs. It’s a two-way street. The girls get to meet rich men and the guys get what they want.’

Which is? ‘Sex, obviously.’

Is Naumoff paid to do this? If so, by whom? The Mail doesn't say.

You could be charitable and interpret this as kind of an introduction service. But then again, some of the men in question are already married. You could alternatively think this setup sounds an awful lot like people being reimbursed for travel and sex. Which might not only count as prostitution to some people, but trafficking as well. If you were the sort of person who was inclined to see things that way.

Me? I don't believe anyone who enters any kind of quid-pro-quo relationship, be it sex for money or naked hot tubbing for a drinks tab, and does so willingly, is trafficked. So far so sugar daddy. But read the Quora opinions, and consider what's being quoted in the Mail, and ask yourself whether you think this alleged situation would tick the rescue industry's boxes for "prostitution: trafficking" or not. Whatever would the missus think?

In the round-up: New simulator 'like Star Wars' - Krack • Pirelli likes 'flexibility' of new C6 • Play NZ anthem when McLaren wins - Lawson

An OCR free word spotting method is developed and evaluated under a strong experimental protocol. Different feature sets are evaluated under the same experimental conditions. In addition, a tuning process in the document segmentation step is proposed which provides a significant reduction in terms of processing time. For this purpose, a complete OCR-free method for word spotting in printed documents was implemented, and a document database containing document images and their corresponding ground truth text files was created. A strong experimental protocol based on 800 document images allows us to compare the results of the three feature sets used to represent the word image.

We investigate ambiguity for symmetric difference nondeterministic finite automata. We show the existence of unambiguous, finitely ambiguous, polynomially ambiguous and exponentially ambiguous symmetric difference nondeterministic finite automata. We show that, for each of these classes, there is a family of n-state nondeterministic finite automata such that the smallest equivalent deterministic finite automata have O(2ⁿ) states.

E-mail spam is no more garbage but risk since it recently includes virus attachments and spyware agents which make the recipients' system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning techniques have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should counteract with it. To cope with this, parameters optimization and feature selection have been used to reduce processing overheads while guaranteeing high detection rates. However, previous approaches have not taken into account feature variable importance and optimal number of features. Moreover, to the best of our knowledge, there is no approach which uses both parameters optimization and feature selection together for spam detection. In this paper, we propose a spam detection model enabling both parameters optimization and optimal feature selection; we optimize two parameters of detection models using Random Forests (RF) so as to maximize the detection rates. We provide the variable importance of each feature so that it is easy to eliminate the irrelevant features. Furthermore, we decide an optimal number of selected features using two methods; (i) only one parameters optimization during overall feature selection and (ii) parameters optimization in every feature elimination phase. Finally, we evaluate our spam detection model with cost-sensitive measures to avoid misclassification of legitimate messages, since the cost of classifying a legitimate message as a spam far outweighs the cost of classifying a spam as a legitimate message. We perform experiments on Spambase dataset and show the feasibility of our approaches.

1. Apparition en plein concert ! Cameron Hallyday se montre pour son père David Hallyday, sa tante Laura Smet conquise  Purepeople
2. Duo virtuel avec Johnny, Sylvie Vartan, Laura et Santa invitées… David Hallyday a frappé fort à Paris  Le Parisien
3. David Hallyday et Laura Smet, le frère et la sœur réunis sur scène après 7 ans  Paris Match
4. Rendez-vous culture : show, sang pour sang Hallyday !  TF1+
5. « C’est si émouvant» : David Hallyday rejoint sur scène par sa mère Sylvie Vartan et sa sœur Laura Smet  La Voix du Nord

1. Aide à l'Ukraine : Antony Blinken met la pression sur les Européens avant le retour de Donald Trump  FRANCE 24
2. Les craintes des dirigeants ukrainiens face aux futures nominations annoncées par Donald Trump  Le Monde
3. «La confrontation entre Trump et Poutine est inévitable, mais que va-t-elle coûter à l’Ukraine et à l’Europe ?»  Libération
4. 24H Pujadas du mardi 12 novembre 2024 - 24 h Pujadas, l'info en questions | LCI  TF1+
5. Guerre en Ukraine : "Donald Trump souhaite parvenir à la paix le plus rapidement possible", en acceptant de céder à Poutine les territoires conquis par son armée  L'Indépendant

Aiming at the problems of high error rate, low evaluation accuracy and low investment return in traditional methods, a random forest-based e-bank investment risk evaluation method is proposed. First, establish a scientific e-bank investment risk evaluation index system. Then, G1-COWA combined weighting method is used to calculate the weights of each index. Finally, the e-bank investment risk evaluation index data is taken as the input vector, and the e-bank investment risk evaluation result is taken as the output vector. The random forest model is established and the result of e-banking investment risk evaluation is obtained. The experimental results show that the maximum relative error rate of this method is 4.32%, the evaluation accuracy range is 94.5~98.1%, and the maximum return rate of e-banking investment is 8.32%. It shows that this method can accurately evaluate the investment risk of electronic banking.

The current advertising push methods have low accuracy and poor advertising conversion effects. Therefore, a Weibo marketing advertising push method based on social network data mining is studied. Firstly, establish a social network graph and use graph clustering algorithm to mine the association relationships of users in the network. Secondly, through sparsisation processing, the association between nodes in the social network graph is excavated. Then, evaluate the tightness between user preferences and other nodes in the social network, and use the TF-IDF algorithm to extract user interest features. Finally, an attention mechanism is introduced to improve the deep learning model, which matches user interests with advertising domain features and outputs push results. The experimental results show that the push accuracy of this method is higher than 95%, with a maximum advertising click through rate of 82.7% and a maximum advertising conversion rate of 60.7%.

To reduce the location cost of cross-border e-commerce logistics sites, this article proposes a multi-logistics site location method based on return uncertainty. Firstly, a site selection model is established with the objective function of minimising site construction costs, transportation costs, return costs, and operating costs, and the constraint conditions of return recovery costs and delayed pick-up time; Then, using the Monte Carlo method to simulate the number of returned items, and using an improved chicken swarm algorithm based on simulated annealing, the cross-border e-commerce multi-logistics site location model is solved to complete the location selection. Experimental results show that this method can effectively reduce the related costs of cross-border e-commerce multi-logistics site selection. After applying this method, the total cost of multi-logistics site selection is 19.4 million yuan, while the total cost of the five comparative methods exceeds 20 million yuan.

To achieve the benefits as much as possible, it is required to identify the available PEV capacity and prepare scheduling plans based on that. The analysis revealed that the risk-based scheduling of the microgrid could reduce the financial risk completely from $9.89 to $0.00 and increases the expected operation cost by 24% from $91.38 to $112.94, in turn. This implies that the risk-averse decision-maker tends to spend more money to reduce the expected risk-in-cost by using the proposed downside risk management technique. At the end, by the help of fuzzy satisfying method, the suitable risk-averse strategy is determined for the studied case.

The current study has outlined slow fashion (SF) research trends and created a future research agenda for this field. It is a thorough analysis of the literature on slow fashion. Numerous bibliometric features of slow fashion have been discussed in the paper. This study comprises 182 research articles from the Scopus database. The database was utilised for bibliometric analysis. To identify certain trends in the area of slow fashion, a bibliometric study is done. For bibliometric analysis, the study employed R-software (the Biblioshiny package). Here, VOSviewer software is used to determine the co-occurrence of authors, countries, sources, etc. The study has outlined the gap that still exists in the field of slow fashion. Here, the research outcome strengthens the domain of slow fashion for sustainable consumption. The study findings will be useful for policymakers, industry professionals, and researchers.

This systematic literature study employed bibliometric analysis to identify workplace mindfulness-related methods and practices in literature published from 2000 to 2020 by leading nations, institutions, journals, authors, and keywords. We also assessed the impact of workplace mindfulness research papers. Scopus analysis tools provided a literature report for 638 Scopus articles used in the study. Using VOSviewer, leading nations, institutions, articles, authors, journals, and keyword co-occurrence network maps were constructed. PRISMA was used to identify 56 publications to recognise workplace mindfulness literature's significant achievements. The research's main contribution is a deep review of neurological mindfulness and psychological measuring tools as workplace mindfulness tool categories. The study is the first to use the PRISMA technique to capture the essential contributions of workplace mindfulness papers from 2000 to 2020.

Although artificial intelligence provides a new method to gather, process, analyse data, generate insights, and offer customised solutions, such methods could change how marketers deal with customers, and there is a lack of literature to portray the application of artificial intelligence in marketing. This study aims to recognise and portray the use of artificial intelligence from a marketing standpoint, as well as to provide a conceptual framework for the application of artificial intelligence in marketing. This study uses a systematic literature review analysis as a research method to achieve the aims. Data from 142 articles were extracted from the Scopus database using relevant search terms for artificial intelligence and marketing. The systematic review identified significant usage of artificial intelligence in conversational artificial intelligence, content creation, audience segmentation, predictive analytics, personalisation, paid ads, sales forecasting, dynamic pricing, and recommendation engines and the bibliometric analysis produced the trend in co-authorship, citation, bibliographic coupling, and co-citation analysis. Practitioners and academics may use this study to decide on the marketing area in which artificial intelligence can be invested and used.

A metaverse is a virtual shared space connected to the real world, an alternative reality that enables economic activities, exchanges, and transactions as well as formation of relationships between user avatars and non-player characters (NPCs). Initial experiences of the metaverse were not very satisfactory; new virtual world metaverses may or may not survive as information services or platforms. The purpose of this empirical study is to identify the characteristics of a virtual world metaverse and their effects on intention to continue usage of the platform. Considering the metaverse as a new type of user experience and a powerful mode of communication, we examine the mediating role of these characteristics according to Pine and Gilmore's (1998) experience economy theory, which enriches our understanding of the factors affecting the success of a metaverse. In addition, since social interaction is important in metaverses, we extend Pine and Gilmore's experience economy model by including Schmitt's (2011) relate experience for better understanding.

Differential privacy is usually applied to location privacy protection scenarios, which confuses real data by adding interference noise to location points to achieve the purpose of protecting privacy. However, this method can result in a significant amount of redundant noisy data and impact the accuracy of the location. Considering the security and practicability of location data, an effective differential privacy protection method of location data based on perturbation loss constraint is proposed. After applying the Laplace mechanism under the condition of differential privacy to perturb the location data, the Savitzky-Golay filtering technology is used to correct the data with noise, and the data with large deviation and low availability is optimised. The introduction of Savitzky-Golay filtering mechanism in differential privacy can reduce the error caused by noise data while protecting user privacy. The experiments results indicate that the scheme improves the practicability of location data and is feasible.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Along with amazing advancements in the field of digital payments, this article seeks to provide a summary of research undertaken over the last four decades and to suggest areas in need of additional study. This study employs a two-pronged technique for analysing its data. The first is concerned with performance analysis, and the second with science mapping. The study uses the apps VOS viewer and R-studio to do bibliometric data analysis. From 1982 until May 2022, the most trustworthy database, Scopus, is used to compile a database of 923 publications The findings of this study identify the scope of current research interest, which is explored with critical contributions from a variety of authors, journals, countries, affiliations, keyword analysis, citation analysis, co-citation analysis, and bibliometric coupling, as well as a potential research direction for further investigation in this emerging field.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

Aim/Purpose: The traditional method of teaching e-safety by lecturing is not very effective. Despite learners often being equipped with the right knowledge, they reject the need to act accordingly. There is a need to improve the way digital e-safety is taught. Background: The study compares four different teaching styles, examining how each affected the way students perceive a range of e-safety keywords and consequently the way they approach this issue. Methodology: The semantic differential technique was used to carry out the research. Students completed a semantic differential questionnaire before and after lessons. A total of 405 first year undergraduates took part in the study. Contribution: The paper contributes to the debate on appropriate methods for teaching e-safety, with an aim to influence learners’ attitudes. Findings: Experience-based learning seems to be very effective, confronting students with an e-safety situation and providing them with a negative experience. This teaching method had the biggest influence on students who were deceived by the prepared e-safety risk situation. Recommendations for Practitioners: E-safety instruction can be enhanced by ensuring that lessons provide students with a personal experience. Recommendation for Researchers: The semantic differential technique can be used to measure changes in learners’ attitudes during the teaching process. Impact on Society: Our findings may bring improvements to the way e-safety topics are taught, which could, in turn, evoke in learners a more positive e-safety attitude and a desire to improve their e-safety behavior. Future Research: More research needs to be carried out to examine how the experiential learning method affects the attitudes of younger learners (primary, middle, and high school students).

Aim/Purpose: The purpose of this article is to provide an overview and analysis of the emerging research landscape surrounding the integration of ChatGPT into education. The main problem appears to be that this is a new, rapidly developing research area for which there is no comprehensive synthesis of the current literature. The aim of the article is to fill this gap by conducting a timely bibliometric study to map publication trends, influential works, themes, and opportunities, thus representing the growth and structure of ChatGPT educational research. Background: This article addresses the issue of the lack of a comprehensive synthesis of the new research on ChatGPT in education by conducting a bibliometric analysis. Specifically, the authors use statistical and network analysis techniques to examine the patterns of publication, citation, and keywords and map the growth, contributions, themes, structure, and opportunities in this evolving field. The bibliometric approach provides a comprehensive, evidence-based overview of the current state of the literature to uncover trends and gaps and help researchers improve their understanding of appropriate and effective applications of ChatGPT in educational contexts. Methodology: The authors used bibliometric analysis as the primary method to summarize the new research on ChatGPT in education. We searched the database of the Web of Science Core Collection to find 51 relevant documents from 2023 that included ChatGPT in the title and were classified as ‘educational research.’ The sample consisted of these 51 documents, including articles, early access articles, editorials, reviews, and letters. Statistical techniques examined publication, citation, and keyword patterns. Network analysis visualized citation and co-occurrence networks to reveal intellectual structure. The multifaceted bibliometric approach allowed a comprehensive study of the sample from a productive, conceptual, and intellectual perspective. Contribution: This article conducts comprehensive bibliometric analysis of this emerging research area and synthesizes publication, citation, and keyword data to map the growth and structure of the literature. The results reveal important trends, such as the rapid growth of publications since the release of ChatGPT, initial authorship patterns, the focus on higher education applications, and distinct research clusters around pedagogical, ethical, and assessment issues. Visualizing citation networks identifies seminal studies while mapping co-occurrence clarifies conceptual relationships between topics. The comparative analysis highlights the differences between document types, topics, and time periods. Knowledge mapping highlights gaps in the literature, such as lack of focus on K-12 contexts, and highlights opportunities for further research. Findings: Key findings from this bibliometric analysis of the emerging research land-scape surrounding ChatGPT integration in education include the following: • Since ChatGPT was released in late 2022, the number of releases has increased significantly, indicating rapid growth in this emerging space. • The most cited authors initially came primarily from Anthropic, but over time, the citations spread throughout the research community. • The topics focused primarily on higher education applications, with a clear focus on pedagogical strategies, ethical risks, and implications for assessment. • Citation networks visualized seminal studies, while the co-occurrence of keywords clarified conceptual connections. • Gaps such as applications in the K-12 context were uncovered, and opportunities for further research were highlighted. • The literature is rapidly evolving and requires ongoing monitoring of the development of this field. In general, the analysis presents the productivity, contributors, themes, struc-ture, and opportunities in this emerging area around the integration of ChatGPT in education based on current scientific evidence. The key findings focus on the growing early interest, gaps and developments that can provide insight for researchers and educators. Recommendations for Practitioners: Practitioners should carefully integrate ChatGPT into education based on new evidence, carefully assess contextual applicability, and proactively develop guidelines for ethical and equitable implementation. Ongoing advice, impact monitoring, and research partnerships are crucial to informing best practices. Educators must be vigilant for risks such as privacy, student well-being, and competence impairment while staying abreast of advances in knowledge to dynamically adapt integration strategies. The introduction should empower diverse learners through measured, integrative approaches based on continuous contextual analysis and ethical principles. Recommendation for Researchers: This article recommends that researchers conduct more studies in under-researched contexts, use multiple methods to capture nuanced impacts, increase focus on responsible integration strategies, develop tailored assessments, conduct interdisciplinary collaborations, monitor long-term adoption, mix with interactive explain and publish open access technologies, help guide adoption pathways through actionable studies, and synthesize the exponentially growing literature through updated systematic reviews. Impact on Society: The rapid publication growth and prevailing optimism suggest that the integration of ChatGPT into education will accelerate, increasing the need for rigorous research that guides ethical, responsible innovations that avoid risks and improve outcomes in all educational contexts. The findings have broader implications for guiding adoption trajectories through ongoing evidence synthesis and expanded investigations in under-researched areas to address knowledge gaps. Ultimately, continued monitoring and updated guidance are critical to ensure that ChatGPT’s educational penetration progresses carefully by maximizing benefits and minimizing harms in rapidly evolving AI-powered learning ecosystems. Future Research: Based on the basic mapping provided by this paper, recommended research directions include longitudinal impact studies, research tailored to under-researched contexts such as K-12, qualitative research to capture stakeholder perspectives, development and testing of AI-calibrated assessments as well as explorations that combine conversational and interactive learning technologies, updated systematic reviews, and co-designed implementation research that explain pedagogical strategies that ethically unlock learning potential while mitigating risks in diverse educational environments. Such multilayered tracking can provide critical insights to guide context-specific, responsible ChatGPT integration and monitor impact within rapidly evolving AI-powered education ecosystems.

Aim/Purpose: The teaching of appropriate problem-solving techniques to novice learners in undergraduate software development education is often poorly defined when compared to the delivery of programming techniques. Given the global need for qualified designers of information technology, the purpose of this research is to produce a foundational template for an educational software development methodology grounded in the established literature. This template can be used by third-level educators and researchers to develop robust educational methodologies to cultivate structured problem solving and software development habits in their students while systematically teaching the intricacies of software creation. Background: While software development methodologies are a standard approach to structured and traceable problem solving in commercial software development, educational methodologies for inexperienced learners remain a neglected area of research due to their assumption of prior programming knowledge. This research aims to address this deficit by conducting an integrative review to produce a template for such a methodology. Methodology: An integrative review was conducted on the key components of Teaching Software Development Education, Problem Solving, Threshold Concepts, and Computational Thinking. Systematic reviews were conducted on Computational Thinking and Software Development Education by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process. Narrative reviews were conducted on Problem Solving and Threshold Concepts. Contribution: This research provides a comprehensive analysis of problem solving, software development education, computational thinking, and threshold concepts in computing in the context of undergraduate software development education. It also synthesizes review findings from these four areas and combines them to form a research-based foundational template methodology for use by educators and researchers interested in software development undergraduate education. Findings: This review identifies seven skills and four concepts required by novice learners. The skills include the ability to perform abstraction, data representation, decomposition, evaluation, mental modeling, pattern recognition, and writing algorithms. The concepts include state and sequential flow, non-sequential flow control, modularity, and object interaction. The teaching of these skills and concepts is combined into a spiral learning framework and is joined by four development stages to guide software problem solving: understanding the problem, breaking into tasks, designing, coding, testing, and integrating, and final evaluation and reflection. This produces the principal finding, which is a research-based foundational template for educational software development methodologies. Recommendations for Practitioners: Focusing introductory undergraduate computing courses on a programming syllabus without giving adequate support to problem solving may hinder students in their attainment of development skills. Therefore, providing a structured methodology is necessary as it equips students with essential problem-solving skills and ensures they develop good development practices from the start, which is crucial to ensuring undergraduate success in their studies and beyond. Recommendation for Researchers: The creation of educational software development methodologies with tool support is an under-researched area in undergraduate education. The template produced by this research can serve as a foundational conceptual model for researchers to create concrete tools to better support computing undergraduates. Impact on Society: Improving the educational value and experience of software development undergraduates is crucial for society once they graduate. They drive innovation and economic growth by creating new technologies, improving efficiency in various industries, and solving complex problems. Future Research: Future research should concentrate on using the template produced by this research to create a concrete educational methodology adapted to suit a specific programming paradigm, with an associated learning tool that can be used with first-year computing undergraduates.

Aim/Purpose: The aim of the study is to test whether the perception of self-regulated learning during text reading in online teacher education is improved by using a digital tool for the use of metacognitive strategies for planning, monitoring, and self-assessment. Background: The use of self-regulated learning is important in reading skills, and for students to develop self-regulated learning, their teachers must master it. Therefore, teaching strategies for self-regulated learning in teacher education is essential. Methodology: The sample size was 252 participants with the tool used by 42% or the participants. A quasi-experimental design was used in a pre-post study. ARATEX-R, a text-based scale, was used to evaluate self-regulated learning. The 5-point Likert scale includes the evaluation of five dimensions: planning strategies, cognition management, motivation management, comprehension assessment and context management. A Generalized Linear Model was used to analyse the results. Contribution: Using the tool to self-regulate learning has led to an improvement during text reading, especially in the dimensions of motivation management, planning management and comprehension assessment, key dimensions for text comprehension and learning. Findings: Participants who use the app perceive greater improvement, especially in the dimensions of motivation management (22,3%), planning management (19.9%) and comprehension assessment (24,6%), which are fundamental dimensions for self-regulation in text reading. Recommendations for Practitioners: This tool should be included in teacher training to enable reflection during the reading of texts, because it helps to improve three key types of strategies in self-regulation: (1) planning through planning management, (2) monitoring through motivation management and comprehension assessment, and (3) self-assessment through comprehension assessment. Recommendation for Researchers: The success of the tool suggests further study for its application in other use cases: other student profiles in higher education, other teaching modalities, and other educational stages. These studies will help to identify adaptations that will extend the tool’s use in education. Impact on Society: The use of Metadig facilitates reflection during the reading of texts in order to improve comprehension and thus self-regulate the learning of content. This reflection is crucial for students’ knowledge construction. Future Research: Future research will focus on enhancing the digital tool by adding features to support the development of cognition and context management. It will also focus on how on adapting the tool to help other types of learners.

Aim/Purpose: This study explores the subject structure, social networks, research trends, and issues in the domain that have the potential to derive an overview of the development of virtual reality-based learning media in vocational education. Background: Notwithstanding the increasingly growing interest in the application of virtual reality in vocational learning, the existing research literature may still leave out some issues necessary for a comprehensive understanding. This study will point out such areas that need more exploration and a more comprehensive synthesis of the literature by conducting a bibliometric analysis. It will be interesting to keep track of the changing concepts and methodologies applied in the development of VR-based learning media in vocational education research. Methodology: This review was carried out using bibliometric methodology, which can highlight patterns of publication and research activity in this hitherto little studied area. The results of the study have the potential to lead to evidence-based priority in VR development, which will tailor work for vocational contexts and set the compass against the growing worldwide interest in this area. The study provides a descriptive analysis of publications, citations, and keyword data for 100 documents published between the years 2013 and 2022 from the Scopus database, which is conducted to illustrate the trends in the field. Contribution: This study also counts as a contribution to understanding the research hotspots of VR-based learning media in vocational education. Through bibliometric analysis, this study thoroughly summarized the relevant research and literature laying a knowledge foundation for researchers and policy makers. Additionally, this analysis identified knowledge gaps, recent trends, and directions for future research. Findings: The bibliometric analysis revealed the following key findings: 1. A growing publication trajectory, with output increasing from 7 articles in 2013 to 25 articles in 2022. 2. The United States led the contributions, followed by China, and Germany. 3. The most prominent authors are affiliated with American medical institutions. 4. Lecture proceedings include familiar sources that reflect this nascent domain. 5. Citation analysis identified highly influential work and researchers. 6. Keyword analysis exposed technology-oriented topics rather than learning-oriented terms. These findings present an emerging landscape with opportunities to address geographic and pedagogical research gaps. Recommendations for Practitioners: This study will be beneficial for designers and developers of VR-based learning programs because it aligns with the most discussed and influential VR technologies within the literature. Such an alignment of an approach with relevant research trends and focus can indeed be very useful for the effective application and use of VR-based learning media for quality improvements in vocational students' learning. Recommendation for Researchers: In fact, in this bibliometric review of VR integration within vocational classrooms, a future call for focused research is presented, especially on teaching methods, course design, and learning impact. This is a framework that seeks to establish its full potential with effective and integrated use of VR in the various vocational curricula and settings of learners. Impact on Society: From the findings of the bibliometric analysis, it is evident that virtual reality technologies (VR) have significantly led to transformation within educational media. There is no denying that the growing interest and investment in the integration of virtual reality into vocational education has been well manifested in the substantive increase in publications in the last decade. This shows what the innovation driving factor is in the United States. At the same time the rapid contributions from China signal worldwide recognition of the potential of VR to improve technical skills training. This study points the way for more research to bridge critical gaps, specifically how VR tools can be used in vocational high school classrooms. Furthermore, research should be aligned to meet specific needs of vocational learners and even promote international cross-border partnerships, pointing out the potential of virtual reality to be a universally beneficial tool in vocational education. The examination of highly cited articles provides evidence of the potential of VR to be an impactful pedagogical tool in vocational education. The findings suggest that researchers need to move forward looking at the trajectory of VR in vocational education and how promising it is in defining the future for innovative and effective learning methodologies. Future Research: This study is an exceptionally valuable contribution, a true landmark in the field of dynamic development, and one that denotes very meaningful implications for the future course of research in the dynamically developing field of bibliometric analysis of VR-based learning media for vocational education. The increase in the number of publications emanates from growing interests in the application of virtual reality (VR) technologies in vocational education. The high concentration of authorship from the USA, along with the ever increasing contributions from China, spotlights the increasing worldwide recognition of the impact of immersive technologies in the enhancement of training in technical skills. These are emerging trends that call for research to exemplify the diverse views and global teamwork opportunities presented by VR technologies. The study also highlights critical areas that need focused attention in future research endeavors. The fact that the embedding of VR tools into classrooms in vocational high schools has been poorly researched points to the major gap in pedagogical research within authentic educational settings. Therefore, further investigations should evaluate teaching methods in VR, lesson designs, and the impacts of VR in specific vocational trades. This supports the need for learner-centered frameworks that are tailor-made to the needs of vocational learners. This calls for more direct and focused investigations into identified research gaps noting a growing dominance in the field of health-related research with the most cited articles in this field, to integrate virtual reality into additional vocational education contexts. In this way, the gaps present an opportunity for researchers to make significant contributions to the development of interventions responsive to the unique needs of vocational learners; this will contribute to strengthening the evidence base for the worldwide implementation of VR within vocational education systems. This was recommended as the intention of such a bibliometric analysis: supporting the potential of VR as a pedagogical tool in vocational contexts and providing grounding for a strong and focused future research agenda within this burgeoning area of educational technology.

The use of electronic means of a public official in carrying out their duties may lead to an instance wherein the person discloses confidential information, which can significantly impact their obligations. After verifying this act as part of electronic misconduct, disciplinary action is enforced upon the concerned party to rectify and ensure proper functioning in delivering public services without any disturbance or infringement. The study presents several significant findings regarding the absence of comparative regulations concerning electronic violations and their judicial evidence. It provides recommendations such as modifying legislative frameworks to enhance public utility disciplinary systems and incorporating rules for electric violations. The fundamental focus revolves around assessing, verifying, and punishing digital misconduct by management or regulatory bodies. Additionally, this research employs descriptive-analytical methods comparing the Jordanian Law with its Egyptian counterpart in exploring these issues.

This research introduces a hybrid honeypot architecture to bolster security within software-defined networks (SDNs). By combining low-interaction and high-interaction honeypots, the proposed solution effectively identifies and mitigates cyber threats, including port scanning and man-in-the-middle attacks. The architecture is structured into multiple modules that focus on detecting open ports using Vilhala honeypots and simulating targeted and random attack scenarios. This hybrid approach enables comprehensive monitoring and detailed packet-level analysis, providing enhanced protection against advanced online threats. The study also conducts a comparative analysis of different attack detection methods using tools like KFSensor and networking shell commands. The results highlight the hybrid honeypot system's efficacy in filtering malicious traffic and detecting security breaches, making it a robust solution for safeguarding SDNs.

Corporate social responsibility (CSR) and sustainability are gaining worldwide recognition. The question of whether CSR and sustainability programs benefit an organisation's financial success is still being debated. This study aims to verify this phenomenon by examining the current literature pattern on this relationship using bibliometric and systematic review analysis. It further provides a taxonomy for understanding this association. VOSviewer is used to obtain comprehensive dataset mapping and clustering in the field. The manuscript offers promising insights regarding academia by assessing the pattern of publication trends, the most influential author in the area, and analysing the methodological and theoretical underpinnings of CSR, sustainability and firm performance linkage. The outcome of this study provides exploratory insights into research gaps and avenues for future research.

This research aims to establish a valid and accurate measurement scale and identify consumer-driven characteristics for minimalism. The study has employed a hybrid approach to produce items for minimalism. Expert interviews were conducted to identify the items for minimalism in the first phase followed by consumer survey to obtain their response in second phase. A five-point Likert scale was used to collect the data. Further, data was subjected to reliability and validity check. Structural equation modelling was used to test the model. The findings demonstrated that there are five dimensions by which consumers perceive minimalism: decluttering, mindful consumption, aesthetic choices, financial freedom, and sustainable lifestyle. The outcome also revealed a high correlation between simplicity and well-being. This study is the first to provide a reliable and valid instrument for minimalism. The results will have several theoretical and practical ramifications for society and policymakers. It will support policymakers in gauging and encouraging minimalistic practices, which enhance environmental performance and lower carbon footprint.

Voice Activity Detection (VAD) distinguishes speech segments from noise or silence areas. An efficient and noise-robust VAD system can be widely used for emerging speech technologies such as wireless communication and speech recognition. In this paper, we propose two versions of an unsupervised Arabic VAD method based on the combination of the Short-Time Energy (STE) and the Spectral Centroid (SC) features for formulating a typical threshold to detect speech areas. The first version compares only the STE feature to the threshold (STE-VAD). In contrast, the second compares the SC vector and the threshold (SC-VAD). The two versions of our VAD method were tested on 770 sentences of the Arabphone corpus, which were recorded in clean and noisy environments and evaluated under different values of Signal-to-Noise-Ratio. The experiments demonstrated the robustness of the STE-VAD in terms of accuracy and Mean Square Error.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

Aiming to improve the accuracy, recall, and F1 value of data classification, this paper proposes an applied university innovation and entrepreneurship data classification method based on the nearest neighbour criterion. Firstly, the decision tree algorithm is used to mine innovation and entrepreneurship data from applied universities. Then, dynamic weight is introduced to improve the similarity calculation method based on edit distance, and the improved method is used to realise data de-duplication to avoid data over fitting. Finally, the nearest neighbour criterion method is used to classify applied university innovation and entrepreneurship data, and cosine similarity is used to calculate the similarity between the samples to be classified and each sample in the training data, achieving data classification. The experimental results demonstrate that the proposed method achieves a maximum accuracy of 96.5% and an average F1 score of 0.91. These findings indicate a high level of accuracy, recall, and F1 value for data classification using the proposed method.

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

Due to the problems of low clustering accuracy and efficiency in traditional similar resource clustering methods, this paper studies an English MOOC similar resource clustering method based on grey correlation. Principal component analysis was used to extract similar resource features of English MOOC, and feature selection methods was used to pre-process similar resource features of English MOOC. On this basis, based on the grey correlation method, the pre-processed English MOOC similar resource features are standardised, and the correlation degree between different English MOOC similar resource features is calculated. The English MOOC similar resource correlation matrix is constructed to achieve English MOOC similar resource clustering. The experimental results show that the contour coefficient of the proposed method is closer to one, and the clustering accuracy of similar resources in English MOOC is as high as 94.2%, with a clustering time of only 22.3 ms.

The conventional methods for identifying English online course learning behaviours have the problems of low recognition accuracy and high time cost. Therefore, a multimodal data fusion-based method for identifying English online course learning behaviours is proposed. Firstly, the analytic hierarchy process is used for decision fusion of multimodal data of learning behaviour. Secondly, based on the fusion results of multimodal data, weight coefficients are set to minimise losses and extract learning behaviour features. Finally, based on the extracted learning behaviour characteristics, the optimal classification function is constructed to classify the learning behaviour of English online courses. Based on the transfer information of learning behaviour status, the identification of online course learning behaviour is completed. The experimental results show that the recognition accuracy of the proposed method is above 90%, and its recognition accuracy is and can shorten the recognition time of learning behaviour, with high practical application reliability.

In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.

In order to enhance the accuracy of teaching resource recommendation results and optimise user experience, a personalised recommendation method for English teaching resources on the MOOC platform based on data mining is proposed. First, the learner's evaluation of resources and resource attributes are abstracted into the same space, and resource tags are established using the Knowledge graph. Then, interest preference constraints are introduced to mine sequential patterns of user historical learning behaviour in the MOOC platform. Finally, a graph neural network is used to construct a recommendation model, which adjusts users' short-term and short-term interest parameters to achieve dynamic personalised teaching recommendation resources. The experimental results show that the accuracy and recall of the resource recommendation results of the research method are always higher than 0.9, the normalised sorting gain is always higher than 0.5.

This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.

In order to improve the comprehensiveness of evaluation results and reduce errors, a teaching reform quality evaluation method based on fuzzy comprehensive evaluation is proposed. Firstly, on the premise of meeting the principles of indicator selection, factor analysis is used to construct an evaluation indicator system. Then, calculate the weights of various evaluation indicators through fuzzy entropy, establish a fuzzy evaluation matrix, and calculate the weight vector of evaluation indicators. Finally, the fuzzy cognitive mapping method is introduced to improve the fuzzy comprehensive evaluation method, obtaining the final weight of the evaluation indicators. The weight is multiplied by the fuzzy evaluation matrix to obtain the comprehensive evaluation result. The experimental results show that the maximum relative error of the proposed method's evaluation results is about 2.0, the average comprehensive evaluation result is 92.3, and the determination coefficient is closer to 1, verifying the application effect of this method.

Presently, there are many existing academic studies on the development, protection and operation of intellectual property management (IPM). Therefore, provides a comprehensive econometric analysis in order to provide scholars, with a clearer understanding of the evolution and development of IP management research during 2000 to 2022. The study is aiming to help scholars to better discern the expanding IPM research field from a multidimensional perspective. The database used for this analysis is the Web of Science Core Collection database. After retrieval through keywords and using a variety of tools such as CiteSpace, VOSviewer, Bibliometrix and HistCite, 1033 documents were refined to conduct the econometric analysis, and produce graphs. The findings indicate that the US is a highly active country/region in the field IP management research, and its expanding IP management research is branching out into other disciplines. The study also presents the future directions and possible challenges for IPM in technology management.