L’info du jour | 12 novembre - Mi-journée

Donald Trump confie à Elon Musk et Vivek Ramaswamy "l'efficacité gouvernementale"

L’info du jour | 13 novembre - Mi-journée

Improving website structures is the main task of a website designer. In recent years, numerous web engineering researchers have investigated navigation recommendation systems. Page recommendation systems are critical for mobile website navigation. Accordingly, we propose a smart and adaptive navigation recommendation system based on reinforcement learning. In this system, user navigation history is used as the input for reinforcement learning model. The model calculates a surf value for each page of the website; this value is used to rank the pages. On the basis of this ranking, the website structure is modified to shorten the user navigation path length. Experiments were conducted to evaluate the performance of the proposed system. The results revealed that user navigation paths could be decreased by up to 50% with training on 12 months of data, indicating that users could more easily find a target web page with the help of the proposed adaptive navigation recommendation system.

« La bisexualité d’Alain Delon était impossible à révéler » : une nouvelle biographie par Bernard Violet  Le Parisien

1. Aux Etats-Unis, Elon Musk chargé par Donald Trump de « démanteler la bureaucratie gouvernementale »  Le Monde
2. Le ministre de la fonction publique, Guillaume Kasbarian, félicite Elon Musk, nommé par Donald Trump pour « démanteler la bureaucratie »  Le Monde
3. 6750 milliards de dollars : à quoi sert le colossal budget fédéral des États-Unis que Elon Musk veut raboter?  Le Figaro
4. Donald Trump nomme Elon Musk à la tête d’un ministère de l’«efficacité gouvernementale»  Libération
5. "J'en ai rêvé, Musk va le faire": l'hommage de Valérie Pécresse au "comité de la hache anti-bureaucratique"...  BFM Paris Ile-de-France

1. Stop Using Chrome On Your iPhone, Warns Apple—Millions Of Users Must Now Decide  Forbes
2. 4 new Chrome improvements for iOS  The Keyword
3. Chrome on your iPhone can search using pictures and words at the same time  The Verge
4. Google Rolls Out Four New Chrome Features for iOS  iPhone in Canada
5. Chrome 131 for iOS adding new Google Drive, Maps integrations  9to5Google

Overwhelmed families surrendering custody of their children to CAS  CTV News Northern Ontario

1. Bear encounter recorded in Whistler Village prompts conservation officer warning  CTV News Vancouver
2. Tourists 'shocked' to see man approach black bear in Whistler  Pique Newsmagazine

1. UN chief warns COP29 summit to pay up or face climate-led disaster for humanity  The Globe and Mail
2. Climate Summit, in Early Days, Is Already on a ‘Knife Edge’  The New York Times
3. At COP29 summit, nations big and small get chance to bear witness to climate change  The Globe and Mail
4. Terence Corcoran: COP29 hit by political ‘dunkelflaute’  Financial Post
5. COP29: Albania PM goes off script to ask 'What on Earth are we doing?'  Euronews

1. Trump says Elon Musk, Vivek Ramaswamy will lead the Department of Government Efficiency  The Globe and Mail
2. Why is Elon Musk becoming Donald Trump's efficiency adviser?  BBC.com
3. Elon Musk and Vivek Ramaswamy will lead new 'Department of Government Efficiency' in Trump administration  CTV News
4. George Conway: Musk, Ramaswamy to lead ‘nonexistent department’  The Hill

Online journal dedicated to nursing informatics

(Belga) Sept personnes ont été tuées lundi lors d'une double fusillade près de San Francisco, en Californie, ont indiqué les médias américains sur la base des déclarations de la police locale.

Le suspect a été arrêté, a annoncé sur Twitter le bureau du shérif du comté de San Mateo, qui comprend la ville de Half Moon Bay où ont eu lieu les drames. "Il n'y a plus de danger pour la population à cette heure", a-t-il assuré. Les deux fusillades sont intervenues dans des exploitations agricoles proches l'une de l'autre, ont précisé les médias. Cette nouvelle tuerie intervient moins de 48 heures après qu'un tireur a tué 11 personnes dans un club de danse près de Los Angeles. (Belga)

by Mitchell Longan[1] Introduction The European Parliament is currently considering a proposal to address problems of piracy and other forms of copyright infringement associated with the digital world.[2] Article 13 of the proposed Directive on Copyright in the Digital Single

“Fifteen Years of Evolution of Law, Technology and Society” To celebrate SCRIPTed’s 15th birthday, we are hosting a conference on Monday 28 January (3pm-7pm) at Evolution House here in Edinburgh. For more details, the programme, and (free) registration, see our

by Bashar H. Malkawi Copyright is an engine for knowledge. Although copyright creates monopoly, it should not be considered as a good in itself, but as a tool which can be used to achieve desirable objectives in society. Against the

By Lucas Miotto Lopes and Jiahong Chen The future of real-time appeal Knowing when to say or do something is often just as important as knowing what to say or do. The right advice at the wrong time is not

C'est la rentrￜe politique. Et qui dit rentrￜe, dit universitￜ d'ￜtￜ. Cette annￜe, la Rￜpublique en marche a vu les choses en grand en organisant ￜ la fois des journￜes...

L'information est passï¿œe inaperï¿œue mais elle ne manque pas de sel. Alors que la gestion de la crise du covid-19 par le gouvernement est trï¿œs contestï¿œe (au retard dans la livraison de masques s'ajoute...

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.

Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.

The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid's susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL's indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.

Access to quality healthcare is a fundamental right but unfortunately, India suffers from gender disparities in healthcare access. Telemedicine has the potential to improve access to healthcare services for women by eliminating traditional barriers. Therefore, our research aims to investigate the factors influencing the adoption of telemedicine among Indian women. This study has collected 442 responses and analysed them through structural equation modelling. The result indicates a strong and positive connection between the willingness to adopt telemedicine services and factors like performance expectancy, perceived benefits, e-health literacy, and perceived reliability. Notably, perceived reliability emerges as the most impactful predictor, closely followed by perceived benefits, while factors like effort expectancy and user resistance show no significant influence. This underscores the pivotal role of reliability and perceived benefits in shaping women's inclination toward adopting telemedicine. The study provides practical insights for telemedicine providers and policymakers to customise strategies and policies for effective promotion.

Drawing on the MOA framework, this study establishes a research model that explains the usage intention of robo-advisors. In the model, three predictors that consist of technology relative advantage, technology herding, and technology familiarity influence usage intention of robo-advisors directly and indirectly via the partial mediation of trust. At the same time, the effects of the three predictors on trust are hypothetically moderated by learning goal orientation and perceived performance risk respectively. Statistical analyses are provided using the data of working professionals from the insurance industry in Taiwan. Based on its empirical findings, this study discusses important theoretical and practical implications.

The performance of a building's internal environment, which includes the air temperature, lighting and acoustics, is what determines the quality of the environment inside the building. We present a thermal model for achieving thermal comfort in buildings that makes use of a multimodal analytic framework as a solution to this challenge. In this study, a multimodal combination is used to evaluate several temperature and humidity sensors as well as an area image. Additionally, a CNN and LSTM combination is used to process the image and sensor data. The results show that heating setback and interior set point temperatures, as well as mechanical ventilation based on real people's presence and CO<SUB align=right>2 levels, are all consistently reduced when ICT-driven intelligent solutions are used. The CNN-LSTM model has a goodness of fit that is 0.7258 on average, which is much higher than both the CNN (0.5291) and LSTM (0.5949) models.

Aiming at the problem of aggravation of traffic congestion caused by unstable signal control of traffic signal control system, the Multi-Agent Deep Deterministic Policy Gradient-based Traffic Cyclic Signal (MADDPG-TCS) control algorithm is used to control the time and data dimensions of the signal control scheme. The results show that the maximum vehicle delay time and vehicle queue length of the proposed algorithm are 11.33 s and 27.18 m, which are lower than those of the traditional control methods. Therefore, this method can effectively reduce the delay of traffic signal control and improve the stability of signal control.

Physical Layer Security (PLS) and relay technology have emerged as viable methods for enhancing the security of wireless networks. Relay technology adoption enhances the extent of coverage and enhances dependability. Moreover, it can improve the PLS. Choosing relay and jammer nodes from the group of intermediate nodes effectively mitigates the presence of powerful eavesdroppers. Current methods for Joint Relay and Jammer Selection (JRJS) address the optimisation problem of achieving near-optimal secrecy. However, most of these techniques are not scalable for large networks due to their computational cost. Secrecy will decrease if eavesdroppers are aware of the relay and jammer intermediary nodes because beamforming can be used to counter the jammer. Consequently, this study introduces a multi-agent Q-learning-based PLS-enhanced secured joint relay and jammer in dual-hop wireless cooperative networks, considering the existence of several eavesdroppers. The performance of the suggested algorithm is evaluated in comparison to the current algorithms for secure node selection. The simulation results verified the superiority of the proposed algorithm.

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

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.

In order to reduce the challenges encountered by learners and educators in engaging in educational activities, this paper classifies learners' roles in virtual learning communities, and explores the role of behaviour characteristics and their positions in collaborative knowledge construction networks in promoting the process of knowledge construction. This study begins with an analysis of the relationship structure among learners in the virtual learning community and then applies the FCM algorithm to arrange learners into various dimensional combinations and create distinct learning communities. The test results demonstrate that the FCM method performs consistently during the clustering process, with less performance oscillations, and good node aggregation, the ARI value of the model is up to 0.90. It is found that they play an important role in the social interaction of learners' virtual learning community, which plays a certain role in promoting the development of artificial intelligence.

Elections hold immense significance in shaping the leadership of a nation or organisation, serving as a pivotal moment that influences the trajectory of the entity involved. Despite their centrality to modern democratic systems, elections face a significant hurdle: widespread mistrust in the electoral process. This pervasive lack of confidence poses a substantial threat to the democratic framework, even in the case of prominent democracies such as India and US, where inherent flaws persist in the electoral system. Issues such as vote rigging, electronic voting machine (EVM) hacking, election manipulation, and polling booth capturing remain prominent concerns within the current voting paradigm. Leveraging blockchain for electronic voting systems offers an effective solution to alleviate the prevailing apprehensions associated with e-voting. By incorporating blockchain into the electoral process, the integrity and security of the system could be significantly strengthened, addressing the current vulnerabilities and fostering trust in democratic elections.

E-government serves as a vital channel for citizen interactions with the public sector, where user enjoyment is of paramount importance. To date, few studies have comprehensively examined the determinants of citizen enjoyment in e-government. To address this research gap, we administered a survey and gathered data from 363 Australian residents using myGov for tax filing. Our analysis revealed a pronounced discrepancy between reported enjoyment and the intention to continue using the services. Although users demonstrated a strong intent to use e-government services, this intent did not uniformly align with enjoyment. Additionally, informed by self-determination theory, we developed and tested an e-government service enjoyment model to study the impacts of effort expectancy, technophilia, technology humanness, and engagement in fostering user enjoyment. Unexpectedly, the results showed that information privacy concerns, commonly seen as a deterrent in e-government adoption, did not significantly affect enjoyment. Our findings advance the discourse on e-government service improvement.

The aim of the study is to examine the determinants of digitalisation in public sector. This research is particularly relevant as digital transformation has become a crucial factor in modernising public sector and enhancing service delivery to citizens. The method of the systematic literature review (SLR) was implemented by searching documents on the Scopus database. The initial research reached the 7902 documents and after specifying the keywords the authors found 207 relevant documents. Finally; after the careful read of their abstracts and the use of inclusion and exclusion criteria; the most cited and relevant 32 papers constituted the final sample. Findings highlighted the focus of the literature on technological factors such as the sense of trust and safety as well as the ease of use in the adoption of digital governance; emphasising the need for effective; trustworthy and user-friendly digital services. The most discussed internal factors were leadership and organisational culture. The study offers a deeper understanding of the factors that shape the successful implementation of digital governance initiatives.

Drawn from technology acceptance model, this study establishes a theoretical framework for the analytical interpretation of factors affecting job-seekers intention to use e-recruitment websites. Using the data obtained from 379 respondents in India, ten hypotheses derived from the experimental model are evaluated using a structural equation modelling technique. Vividness, perceived usefulness (PU), and attitude have been shown to have a significant positive impact on the behavioural intentions (BIs) of job-seekers, although perceived ease of use (PEOU) did not. Furthermore, perceived internet stress (PIS) is observed to be a significant antecedent PEOU; and PEOU is of PU. Such findings broaden our knowledge of e-recruiting in various ways and offer qualitative insights into the potential impact of website functionality on the attractiveness of job-seekers.