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

1. Ottawa businesses brace for possible Canada Post strike before the holiday season  CTV News Ottawa
2. Tougher work conditions for postal workers at the heart of labour dispute, union says  The Globe and Mail
3. Ottawa businesses worry about potential postal disruption  CBC.ca
4. Calgary workers 'ready to go' if Canada Post, CUPW unable to reach agreement by Friday: union president  Calgary Herald
5. ‘Feeling the pinch’: Potential postal strike may be first of many, Manitoba prof says  Global News Toronto

Special counsel Jack Smith looking at stepping down before Trump takes office and is discussing how to end prosecutions  CNN

Online journal dedicated to nursing informatics

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 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.

Smart cities research is growing all over the world seeking to understand the effect of smart cities from different angles, domains and countries. The aim of this study is to analyse how the smart city ICTs (e.g., big data analytics, AI, IoT, cloud computing, smart grids, wireless communication, intelligent transportation system, smart building, e-governance, smart health, smart education and cyber security) are related to government. services and society welfare from the perspective of China. This research confirmed a positive correlation of smart city ICTs to e-Govt. Services (e-GS). On the other hand, the research showed a positive influence of smart city ICTs on society's welfare. These findings about smart cities and ICTs inform us how the thought paradigm to smart technologies can cause the improvement of e-GS through economic development, job creation and social welfare. The study offers different applications of the theoretical perspectives and the management perspective which are significant to building a society during recent technologised era.

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.

File transfer is always handled by a separate service, sometimes it is a third-party service in videoconferencing. When sending files during a video session, file data flow and video stream are independent of each other. Encryption is a mature method to ensure file security. However, it still has the chance to leave footprints on the intermediate forwarding machines. These footprints can indicate that a file once passed through, some protocol-related logs give clues to the hackers' later investigation. This work proposes a file-sending scheme through the video stream using blockchain and steganography. Blockchain is used as a file slicing and linkage mechanism. Steganography is applied to embed file pieces into video frames that are continuously generated during the session. The scheme merges files into the video stream with no file transfer protocol use and no extra bandwidth consumed by the file to provide trackless file transmission during the video communication.

At present, there is a need for an authentic and fast approach to certificate verification. Which verifies and authenticates the certificates to reduce the extent of duplicity and time. An academic certificate is significant for students, the government, universities, and employers. Academic credentials play a vital role in the career of students. A few people manipulate academic documents for their benefit. There are cases identified where people produced fake academic certificates for jobs or higher education admission. Various research works are developing a secure model to verify genuine academic credentials. This research article proposed a new security model which contains several security algorithms such as timestamps, hash function, digital signature, steganography, and blockchain. The proposed model issues secure digital academic certificates. It enhanced security measures and automated educational certificate verification using blockchain technology. The advantages of the proposed model are automated, cost-effective, secured, traceable, accurate, and time-saving.

In the present scenario, the security of medical images is an important aspect in the field of image processing. Support vector machines (SVMs) are a supervised machine learning technique used in image classification. The roots of SVM are from statistical learning theory. It has gained excellent significance because of its robust, accurate, and very effective algorithm, even though it was applied to a small set of training samples. SVM can classify data into binary classification or multiple classifications according to the application's needs. Discrete wavelet transform (DWT) and singular value decomposition (SVD) transform techniques are utilised to enhance the image's security. In this paper, the image is first classified using SVM into ROI and RONI, and thereafter, to enhance the images diagnostic capabilities, the DWT-SVD-based hybrid watermarking technique is utilised to embed the watermark in the RONI region. Overall, our work makes a significant contribution to the field of medical image security by presenting a novel and effective solution. The results are evaluated using both perceptual and imperceptibility testing using PSNR and SSIM parameters. Different attacks were introduced to the watermarked image, which shows the efficacy and robustness of the proposed algorithm.

Image encryption is a critical and attractive issue in digital image processing that has gained approval and interest of many researchers in the world. A proposed hybrid encryption method was implemented by using the combination of the Nahrain chaotic map with a well-known optimised algorithm namely the grey wolf optimisation (GWO). It was noted from analysing the results of the experiments conducted on the new hybrid algorithm, that it gave strong resistance against expected statistical invasion as well as brute force. Several statistical analyses were carried out and showed that the average entropy of the encrypted images is near to its ideal information entropy.

Word of mouth (WOM) and electronic word of mouth (eWOM) are very effective and important communication tools to persuade consumers for purchasing the products/services. These become more significant with products that are difficult to assess before consumption, e.g., hospitality. The tourism industry is reviving, and the consumer is conscious when booking a particular destination. Thus, it is important to understand how WOM and eWOM are impacting the various factors in distinct ways while choosing the tourist destination. The seven factors identified, for the present study, are channel engagement, expertise, homophily, resource helpfulness, source credibility, tie-strength, and trustworthiness. The PLS-SEM was used to test the theoretical model of this study. The study shows that both WOM and eWOM impact an individual in different ways. The expertise of the reviewer is the most important factor in the case of WOM and channel engagement is the most significant factor for eWOM. Resource helpfulness is common for both WOM and eWOM.

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.

Constrained programming is the concept used to select possible alternatives from an incredibly diverse range of candidates. This paper proposes an AI-assisted Backtracking Scheme (AI-BS) by integrating the generic backtracking algorithm with Artificial Intelligence (AI). The detailed study observes that the extreme dual ray associated with the infeasible linear program can be automatically extracted from minimum unfeasible sets. Constraints are used in artificial intelligence to list all possible values for a group of variables in a given universe. To put it another way, a solution is a way of assigning a value to each variable that these values satisfy all constraints. Furthermore, this helps the study reach a decreased search area for smart backtracking without paying high costs. The evaluation results exhibit that the IB-BC algorithm-based smart electricity schedule controller performs better electricity bill during the scheduled periods than comparison approaches such as binary backtracking and binary particle swarm optimiser.

To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimised using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image pre-processing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97% and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

The spread of COVID-19 results in disruption, uncertainty, complexity, and ambiguity in all businesses. Employees help companies achieve their aims. To manage human resources sustainably, analyse organisational strategy. This thorough research study attempts to find previously unidentified challenges, cutting-edge techniques, and surprising decisions in human resource management outside of healthcare organisations during the COVID-19 pandemic. The narrative review examined corporate human resource management measures to mitigate COVID-19. Fifteen publications were selected for the study after removing duplicates and applying the inclusion and exclusion criteria. This article examines HR's COVID-19 response. Human resource management's response to economic and financial crises has been extensively studied, but the COVID-19 pandemic has not. This paper reviewed the literature to reach its goal. The results followed the AMO framework for human resource policies and procedures and the HR management system. This document suggests COVID-19 pandemic-related changes to human resource management system architecture, policies, and practises. The study created a COVID-19 pandemic human resource management framework based on the literature. The COVID-19 pandemic had several negative effects, including social and behavioural changes, economic shock, and organisational disruption.

Numerous Indian economic sectors have been impacted by the COVID-19 epidemic, with many being forced to the verge of extinction. As a result, this essay analyses the importance of supply chains for grapes and the manufactured goods made from them, including beverages and currants, in a specific state that happens to be India's top grape-producing region. In order to identify the sites of rupture brought on by the pandemic and to recommend policy changes to create a resilient system, a value chain analysis is performed. Value chain management has emerged as one of the key strategies businesses use today to boost productivity and costs when they are up against greater rivalry in the marketplace, however, with several new challenges, such as concerns over security, environmental protection, compensation, and business accountability. According to the value chain study, the level of value addition for intermediary agents, such as pre-harvest contractors, has increased after COVID-19 at the expense of farmers. Various policy approaches are explained to enhance the grape value chain using the knowledge gained from Porter's value chain results and supply and demand shocks.

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.

With the expansion of business scope, the environment faced by enterprises has also changed, and competition is becoming increasingly fierce. Traditional financial systems are increasingly difficult to handle complex tasks and predict potential financial risks. In the context of the digital economy era, the booming financial sharing services have reduced labour costs and improved operational efficiency. This paper designs and implements an intelligent financial sharing platform, establishes a fund payment risk early warning model based on an improved support vector machine algorithm, and tests it on the Financial Distress Prediction dataset. The experimental results show that the effectiveness of using F2 score and AUC evaluation methods can reach 0.9484 and 0.9023, respectively. After using this system, the average financial processing time per order decreases by 43%, and the overall financial processing time decreases by 27%. Finally, this paper discusses the role of intelligent financial sharing platform in accounting transformation and optimisation of production.

Intelligent digital virtual technology has become an indispensable part of modern construction, but there are also some problems in its practical application. Therefore, it is necessary to strengthen the design of intelligent building image processing systems from many aspects. Starting from image digital processing methods, this paper studies the digital twin virtual design scene construction method and related algorithms, converts the original image into a colour digital image through a greyscale algorithm, and then combines morphological knowledge and feature point extraction methods to complete the construction of a three-dimensional virtual environment. Finally, through the comparison of traditional image processing effects with smart building images based on digital twins and BIM technology, the results show that the optimised image processing results have higher clarity, sharper contrast, and a sensitivity increased by 5.84%, presenting better visual effects and solving the risk of misjudgement caused by inaccurate image recognition.

The research uses a machine learning approach to appraising the validity of customer aptness for a loan. Banks and non-banking financial companies (NBFC) face significant non-performing assets (NPAs) threats because of the non-payment of loans. In this study, the data is collected from Kaggle and tested using various machine learning models to determine if the borrower can repay its loan. In addition, we analysed the performance of the models [K-nearest neighbours (K-NN), logistic regression, support vector machines (SVM), decision tree, naive Bayes and neural networks]. The purpose is to support decisions that are based not on subjective aspects but objective data analysis. This work aims to analyse how objective factors influence borrowers to default loans, identify the leading causes contributing to a borrower's default loan. The results show that the decision tree classifier gives the best result, with a recall rate of 0.0885 and a false- negative rate of 5.4%.

In this paper we discuss the use of automated assessment in a variety of computer science courses that have been taught at Israel Academic College by the authors. The course assignments were assessed entirely automatically using Checkpoint, a web-based automated assessment framework. The assignments all used free-text questions (where the students type in their own answers). Students were allowed to correct errors based on feedback provided by the system and resubmit their answers. A total of 141 students were surveyed to assess their opinions of this approach, and we analysed their responses. Analysis of the questionnaire showed a low correlation between questions, indicating the statistical independence of the individual questions. As a whole, student feedback on using Checkpoint was very positive, emphasizing the benefits of multiple attempts, impartial marking, and a quick turnaround time for submissions. Many students said that Checkpoint gave them confidence in learning and motivation to practise. Students also said that the detailed feedback that Checkpoint generated when their programs failed helped them understand their mistakes and how to correct them.

Teaching advanced programming can be a challenge, especially when the students are pursuing different majors with diverse analytical and problem-solving capabilities. The purpose of this paper is to explore the efficacy of using a particular problem as a vehicle for imparting a broad set of programming concepts and problem-solving techniques. We present a classic brain teaser that is used to communicate and demonstrate advanced software development concepts and techniques. Our results show that students with varied academic experiences and goals, assuming at least one procedural/structured programming pre-requisite, can benefit from and also be challenged by such an exercise. Although this problem has been used by others in the classroom, we believe that our use of this problem in imparting such a broad range of topics to a diverse student population is unique.

Technology skills are assumed to be a necessity for college and career success, but technology is constantly evolving. Thus, development of students’ technology skills is an on-going and persistent issue. Standards from the Partnership for 21st Century Skills and the International Society for Technology in Education encourage educators to teach skills that help students adapt to changing working environments. These skills resemble the National Association for Gifted Children’s program and teacher preparation standards. Descriptive research about what is already occurring in classrooms has been done, but the information is frequently limited to a list of activities. A qualitative multi-case phenomenological study of six Alabama teachers of the gifted examined how they use and shape technology experiences with students, and promote student learning of 21st century skills. The teachers were chosen for the case study due to their reputation as teachers skilled in using technology with students. Lesson plans, interviews, and observations were used to discover themes between the teachers. Findings from the research indicate that educational technology use with students is shaped by factors such as teacher attitudes and expertise, available equipment and support, pedagogical decisions related to working with technology, and the particular student group participating in the technology use.

The ways people connect, interact, share, and communicate have changed due to recent developments in information technology. These developments, categorized as social media, have captured the attention of business executives, technologists, and education professionals alike, and have altered many business models. Additionally, the concept of social media impacts numerous sub-disciplines within business and has become an important issue with operational, tactical, and strategic considerations. Despite this interest, many business schools do not have courses involving social media technologies and applications. In those that do, the placement and focus of the course varies considerably. This article provides motivation and insight into the process of developing an approach for effectively teaching social media use in business. Additionally, it offers implementation examples of courses taught at three major universities. The article concludes with lessons-learned that will give instructors practical guidance and ensure that social media courses taught in a business school provide students with a solid basis for integrating social media into business practice.

An online Moodle Workshop was evaluated for peer assessment effectiveness. A quasi-experiment was designed using a Seminar in Professionalism course taught in face-to-face mode to undergraduate students across two campuses. The first goal was to determine if Moodle Workshop awarded a fair peer grader grade. The second objective was to estimate if students were consistent and reliable in performing their peer assessments. Statistical techniques were used to answer the research hypotheses. Although Workshop Moodle did not have a built-in measure for peer assessment validity, t-tests and reliability estimates were calculated to demonstrate that the grades were consistent with what faculty expected. Implications were asserted to improve teaching and recommendations were provided to enhance Moodle.

Since the advent of the iPhone and rise of mobile technologies, educational apps represent one of the fastest growing markets, and both the mobile technology and educational app markets are predicted to continue experiencing growth into the foreseeable future. The irony, however, is that even with a booming market for educational apps, very little research regarding the quality of them has been conducted. Though some instruments have been developed to evaluate apps geared towards student learning, no such instrument has been created for teacher resource apps, which are designed to assist teachers in completing common tasks (e.g., taking attendance, communicating with parents, monitoring student learning and behavior, etc.). Moreover, when teachers visit the App Store or Google Play to learn about apps, the only ratings provided to them are generic, five-point evaluations, which do not provide qualifiers that explain why an app earned three, two, or five points. To address that gap, previously conducted research related to designing instructional technologies coupled with best practices for supporting teachers were first identified. That information was then used to construct a comprehensive rubric for assessing teacher re-source apps. In this article, a discussion that explains the need for such a rubric is offered before describing the process used to create it. The article then presents the rubric and discusses its different components and potential limitations and concludes with suggestions for future research based on the rubric.

Immigrant students face a multitude of problems, among which are poor social adaptation and school integration. On the other hand, although digital narrations are widely used in education, they are rarely used for aiding students or for the resolution of complex problems. This study exploits the potential of digital narrations towards this end, by examining how the development and presentation of an autobiographical digital narration can assist immigrant students in overcoming their adaptation difficulties. For that matter, a female student presenting substantial problems was selected as the study’s subject. Data was collected from all the participating parties (subject, teacher, classmates) using a variety of tools, before, during, and after the intervention. It was found that through the digital narration she was able to externalize her thoughts and feelings and this, in turn, helped her in achieving a smoother integration in the school environment. In addition, the attitudes and perceptions of the other students for their foreign classmate were positively influenced. The intervention was short in duration and it did not require special settings. Hence, it can be easily applied and educators can consider using similar interventions. On the other hand, further research is recommended to establish the generalizability of the study’s findings.

The pilot research presented here explores the classroom use of Emerging Literacy in Mathematics (ELM) software, a research-based bilingual interactive multimedia instructional tool, and its potential to develop emerging numeracy skills. At the time of the study, a central theme of early mathematics curricula, Number Concept, was fully developed. It was broken down into five mathematical concepts including counting, comparing, adding, subtracting and decomposing. Each of these was further subdivided yielding 22 online activities, each building in a level of complexity and abstraction. In total, 234 grade one students from 12 classes participated in the two-group post-test study that lasted about seven weeks and for which students in the experimental group used ELM for about 30 minutes weekly. The results for the final sample of 186 students showed that ELM students scored higher on the standardized math test (Canadian Achievement Test, 2008) and reported less boredom and lower anxiety as measured on the Academic Emotions Questionnaire than their peers in the control group. This short duration pilot study of one ELM theme holds great promise for ELM’s continued development.

Aim/Purpose: The present study investigated and compared students’ attention in terms of time-on-task and number of distractors between using a touchscreen and a pen tablet in mathematical problem-solving activities with virtual manipulatives. Background: Although there is an increasing use of these input devices in educational practice, little research has focused on assessing student attention while using touchscreens or pen tablets in a mathematics classroom. Methodology: A qualitative exploration was conducted in a public elementary school in New Taipei, Taiwan. Six fifth-grade students participated in the activities. Video recordings of the activities and the students’ actions were analyzed. Findings: The results showed that students in the activity using touchscreens maintained greater attention and, thus, had more time-on-task and fewer distractors than those in the activity using pen tablets. Recommendations for Practitioners: School teachers could employ touchscreens in mathematics classrooms to support activities that focus on students’ manipulations in relation to the attention paid to the learning content. Recommendation for Researchers: The findings enhance our understanding of the input devices used in educational practice and provide a basis for further research. Impact on Society: The findings may also shed light on the human-technology interaction process involved in using pen and touch technology conditions. Future Research: Activities similar to those reported here should be conducted using more participants. In addition, it is important to understand how students with different levels of mathematics achievement use the devices in the activities.

Aim/Purpose: The dynamic nature of the information systems (IS) field presents educators with the perpetual challenge of keeping course offerings current and relevant. This paper describes the process at a College of Business (COB) to redesign the introductory IS course to better prepare students for advanced business classes and equip them with interdisciplinary knowledge and skills demanded in today’s workplace. Background: The course was previously in the Computer Science (CSC) Department, itself within the COB. However, an administrative restructuring resulted in the CSC department’s removal from the COB and left the core course in limbo. Methodology: This paper presents a case study using focus groups with students, faculty, and advisory council members to assess the value of the traditional introductory course. A survey was distributed to students after implementation of the newly developed course to assess the reception of the course. Contribution: This paper provides an outline of the decision-making process leading to the course redesign of the introductory IS course, including the context and the process of a new course development. Practical suggestions for implementing and teaching an introductory IS course in a business school are given. Findings: Focus group assessment revealed that stakeholders rated the existing introductory IS course of minimal value as students progressed through the COB program, and even less upon entering the workforce. The findings indicated a complete overhaul of the course was required. Recommendations for Practitioners: The subject of technology sometimes requires more than a simple update to the curriculum. When signs point to the need for a complete overhaul, this paper gives practical guidance supplemented with relevant literature for other academicians to follow. Recommendation for Researchers: Students are faced with increasing pressure to be proficient with the latest technology, in both the classroom where educators are trying to prepare them for the modern workplace, as well as the organization which faces an even greater pressure to leverage the latest technology. The newly designed introductory IS course provides students, and eventually organizations, a better measure of this proficiency. Future Research: Future research on the efficacy of this new course design should include longitudinal data to determine the impact on graduates, and eventually the assessment of those graduates’ performance in the workplace.