The paper presents the technique for analysis of text emotional nature which is a key characteristic of Mass media news text. Emotions inherent design its Emotional coloring and become a significant feature of mass media news texts. The technique proposed measures the degree of exposure of emotions and allocates them by rating. Emotional coloring is defined by emotional characteristics and by grammar categories, and a set of rules is applied to regulate wordforms interaction. Techniques for verbal units analysis are examined. The Heavy Natural Language Processing models and Machine learning techniques are considered. They are compared and the optimum one is defined to resolve the problem of Emotional coloring evaluation. A system prototype is developed on the basis of this technique. It allocates news by influence rating according to their key parameters. The examples of texts’ emotional nature recognition results by means of the prototype are presented. The visualization of emotional nature analysis results highlights additional features of the news text’s emotional nature and expresses them in numeric values. It is exposed both by sentences and by the whole news text, with tracking of news Emotional coloring dynamics. The results presented have application in analysis procedure intending to studying Mass media, particularly informational environment with concomitant factors, and their impact on political and social interrelation.

In this study, we show the status of ICT education and find the gaps between rural and urban institutions for providing ICT education in secondary and higher secondary institutions in Bangladesh. For this purpose, we use primary data collected using a survey questionnaire that is answered by ICT teachers engaged in those institutions. The variables used in the questionnaire are the name of the respondents, educational qualification, locations of the institutions, syllabus structure, the total number of students for ICT courses, number of computers, etc. The data were collected from institutions located in urban and rural areas. We apply several statistical functions along with conditional logic to our data for getting the desired result. We find that the students-teacher ratio in secondary (resp., higher secondary) is about 288:1 (resp., 212:1), existing teachers have a heavy academic workload. We also find that there exist low facilities in rural institutions compared to the urban institutions because students-computer ratio (SCR) is 46 in rural areas whereas SCR is 22 in the urban area. Moreover, we find that more than 80% of the teachers conducting ICT classes have graduated from the discipline other than ICT or related discipline. Furthermore, teachers who cannot complete at least 80% of the ICT syllabus in time are mostly non-ICT graduate. Based on these findings, we propose some recommendations to meet the above gaps of the current ICT education in Bangladesh.

Blended learning can be carried out well if it is supported by good content. Good content consists of the subject, performance assignments, discussion forums, and quiz questions that are packaged in an interesting and structured manner. Good content must also be used to measure students’ abilities in the cognitive, affective, and psychomotor domains. One of the efforts that can be made to realize good blended learning content is to develop content by inserting the Tri Kaya Parisudha and Superitem concepts into the Kelase platform. The Tri Kaya Parisudha concept is used as a basis for measuring students’ abilities in the cognitive, affective, and psychomotor domains. Superitem concept is used as the basis for structured content creation (especially on quiz questions and performance tasks) starting from the lowest to the highest level of complexity. Referring to some of those things, this research aimed to provide an overview of the stages of developing blended learning content that integrated the Tri Kaya Parisudha concept and the Superitem concept in the Kelase platform (a case study of Senior High Schools/Vocational High Schools in Bali). This research used the 4D method, which consists of 4 stages, including Define, Design, Develop, and Disseminate. The subjects involved in content testing were four experts. The tools used to collect data were interview guides, photo documentation, and questionnaires. The technique used to analyze the data was descriptive quantitative. The analysis technique in this research was carried out by making a comparison between the five-scale reference effectiveness standard with the percentage level of effectiveness of the blended learning content. The results showed the level of effectiveness of the blended learning content based on Tri Kaya Parisudha-Superitem in the good category with a percentage was 88.667%.

All organizations have a collaborative information system, which is a shared system between employees and teams in the organisation. All such information systems in organizations need to be flawlessly secure. Securing information systems through the latest technologies like Artificial Intelligence, Deep Learning and Blockchain is one of the latest trends in information sciences. This paper tries to explore them in detail through data on user’s login time and time spent on the websites along with user actions. The objective is to develop a model that will be used for authentication of the user. This will allow early detection of frauds so that preventive and remedial actions like blocking access to the user can be initiated well in advance. The dataset used to develop this model is the user log data and technique of logistic regression is used to create the regression model for authentication of the user. Logistic regression-based classification is used on the attributes taken to record and analyze entries recorded on the system leading to identification of a cluster based on normal and suspicious users. The accuracy of logistic regression has been analyzed and implemented to secure the collaborative system. This study will help the researcher to implement the AI in the system. It also discusses its future prospects and the disruptive changes in implementation of Information Systems. Finally, the research considers combining blockchain (BC) and deep learning (DL) with Artificial Intelligence (AI) and discusses the revolutionary changes that would result by rapidly advancing the AI field.

We conducted a training experimentation on computer coding whose aim is to probe ICT skills enhancement of pre-service teachers in Morocco. For that, we have developed and implemented training sessions using a visual programming tool (Scratch) targeting 63 prospective teachers at the Faculty of Educational Sciences (FSE) and the Regional Center for Education and Training Professions (CRMEF) in Nador, Morocco. During these sessions, trainees were introduced to algorithmic thinking where they implemented teaching sequences in their specialty subjects using Scratch. Pre and post surveys were conducted to measure the evolution of the trainees’ perceptions towards the integration of computer coding in the teaching and learning of their specialties. The analysis of the surveys showed the potential of integrating computer coding in the development of learners’ transversal skills. The training revealed different possibilities of exploiting visual block-based programming environments in the teaching and learning process.

Data driven social security fraud detection has been given limited attention in research. Recently, social schemes have seen significant expansion across many developing countries including India. The fundamental aims of social schemes are to alleviate poverty, enhance the quality of life of the most vulnerable and offer greater chances to those relegated to the fringe of society to engage more enthusiastically in the society. Although governments channel billions of dollars every year in support of these social schemes, quite significant number of the eligible people are excluded from the program mainly through fraud and dishonesty. Although fraud is considered an illegal offence and morally reprehensible, it is unfortunate that the prevalence of fraud in social benefit schemes is rampant and a significant challenge to address. In this paper, we studied the viability of machine learning techniques in identifying fraudulent transactions in the context of social schemes. We focus on the detection of the false income level claims made by the fake beneficiaries to get the privileges of government scheme. We used the standard classifiers like Logistic Regression, Decision Trees, Random Forests, Support Vector Machine (SVM), Multi-Layer Perceptron and Naïve Bayes to identify fake beneficiaries of the government scheme from those deserving people. The results show that the Random Forest Classifier perform best providing an accuracy of 99.3% with F1 score of 0.99. The outcome of this research can be used by the government agencies entrusted with the management of the schemes to wade out the abusers and provide the required benefits to the right and deserving recipients.