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Steganography, the art and science of concealing messages within other data, has a rich history spanning centuries. In this review paper, we delve into various aspects of steganography, exploring its techniques, detection methods, evaluation criteria, and practical applications. We analyze steganalysis techniques, discuss time-sensitive steganography, and examine historical cases illustrating the ingenuity and effectiveness of covert communication methods. Through this comprehensive examination, we aim to provide insights into the evolving landscape of steganography and its significance in contemporary digital communication.

Along with growing AI development, it is important to consider both the benefits and risks that come with it. This research paper provides a deep analytical understanding of both aspects and their potential impact on humanity.

Design research topics attract exponentially more attention and consideration among researchers. This study is the first research article that endeavors to analyze selected design research publications using an advanced approach called “text mining”. This approach speculates its results depending on the existence of a research term (i.e., keywords), which can be more robust than other methods/approaches that rely on contextual data or authors’ perspectives. The main aim of this research paper is to expand knowledge and familiarity with design research and explore future research directions by addressing the gaps in the literature; relying on the literature review, it can be stated that the research area in the design domain still not built-up a theory, which can unify the field. In general, text mining with these features allows increased validity and generalization as compared to other approaches in the literature. We used a text mining technique to collect data and analyzed 3553 articles collected in 10 journals using 17,487 keywords. New topics were investigated in the domain of design concepts, which included attracting researchers, practitioners, and journal editorial boards. Such issues as co-innovation, ethical design, social practice design, conceptual thinking, collaborative design, creativity, and generative methods and tools were subject to additional research. On the other hand, researchers pursued topics such as collaborative design, human-centered design, interdisciplinary design, design education, participatory design, design practice, collaborative design, design development, collaboration, design theories, design administration, and service/product design areas. The key categories investigated and reported in this paper helped in determining what fields are flourishing and what fields are eroding

Improving the quality of digital health care through information and communication technology can mainly contribute to the clinical, social, fnancial, and economic systems’ success, especially during the COVID-19 pandemic period. The co-design approach, which unleashes the end-user power, can contribute actively in improving the healthcare systems. It deals with understanding the user behaviors, requirements, and motivations through observation, inspection, task analysis, and feedback techniques. Consequently, both the co-design and digital technologies might empower the management of patients’ health and that of their families. The research strategy is based on a systematic literature review and meta-analysis to summarize how the co-design methodologies handled the existing technology-based health systems for their improvement. Based on the findings, we establish the following hypotheses: (i) A user-centered methodology for service implementation might offer a promising tool to enhance the healthcare services quality before they be launched; (ii) Several limitations can affect the co-design approach in digital health, such as a bias for a patients’ group. Efforts have been made to reduce this risk by identifying bias at an early stage, or different groups should be included in the test phase for example; (iii) Use decision-making devices that handle technologies for patient and clinical healthcare solution

This study aims to investigate the factors that perceive citizens’ intention to adopt smart city technologies in the Arab world. A self-administered questionnaire that included 312 end users as citizens in Amman, Jordan’s capital city, was used in this study. This study uses advanced statistical techniques to test an expanded technology acceptance model (TAM) that incorporates the determinants of perceived usefulness, perceived ease of use, security and privacy, ICT infrastructure and inadequate Internet connectivity, social influence, and demographic profiles. Based on the results, perceived ease of use and ICT infrastructure and Internet connectivity showed positive association with the intention of citizens to adopt smart city services in Jordan. By recognizing the factors that predict citizens’ adoption of smart city services, this study presents some theoretical implications and practical consequences related to smart city service adoption.

Purpose: The nutrition industry is characterized by a high impact on human-made climate change, accounting for as much as 25-30 % of all worldwide greenhouse gas emissions. Deploying eco-labeling to increase people's awareness about the pollution caused by their dietary choices is being introduced in various contexts to promote more sustainable consumption. Design: This experiment aimed to explore the effectiveness of different eco-labeling approaches and quantify the expected effect on consumer behavior. 144 participants was randomly assigned to four groups: a) no label, b) climate-friendly icon for low-emitting choices, c) the CO2 equivalent emissions per meal for all items, and d) a combination icon and numbers. Findings: This survey showed a 9 % reduction in average CO2 equivalent per meal with the certificate alone, 4 % with label and number, but no reduction in the group using only the CO emissions number. Female gender and green attitudes were associated with making a green dietary choice, and the graphical approach was more effective than providing the report as a pure number. Originality: Our results are unique in the sense of comparing impact of the type of labeling realm with an existing commercial label and matching to personal traits with causal modeling.

This research analyses which model would be the most efficient for a movie recommendation system. We have compared a cosine similarity model (CSM) and 4 Neural network models - ANN, CNN, RNN, and RBFNN. We have a cleaned dataset of 45,000 movies, however, we have used a sample of it only. Using the sample as our main dataset each run we got randomized data, we created a user dataset that stores info for user_id, movie_list, and genre-lists, ensuring it is not biased. Using the user dataset, we compute each user’s top three genres they prefer. Then using all of these data sets we train, compile, and apply the various models to recommend 10 movies and their performance. We used a different user dataset for the cosine similarity model because we realized it wouldn’t be efficient. But the same ones for the neural networks. Lastly, we compared the performances of each neural network and cosine similarity model (CSM) to determine the best model. We did 6 runs, 1 run on a sample of 5000 movies and 5000 users each of 50 movies. To confirm the obtained best model, we ran the code 5 more times but this time with a sample of 1000 movies and 1000 users for each of 50 movies.

Automatic Keyword Extraction (AKE) is a fundamental Natural Language Processing (NLP) task that plays a critical role in various applications, including information retrieval, document summarization, and content categorization. The review’s purpose is to gather all the techniques, methodologies, and advancements available in the literature, and present them to researchers, practitioners, and developers interested in the topic, who can use this work as a starting point for their research, or to have a quick insight on what are the main trend in the automatic keyword extraction task. The review provides an in-depth analysis of traditional approaches, supervised and unsupervised, as well as emerging techniques that employ neural networks and deep learning architectures like transformers. This work also provides information regarding existing datasets and benchmarks and how to obtain them, as well as functioning code for building practical applications. At the end of the paper, there is a description of the state-of-the-art for automatic keyterm extraction, paired with a discussion on the evaluation metrics.

GeoSketch is a C# program that combines the joy of sketching with the collision detection for geometric shapes. With GeoSketch, you can unleash your creativity by drawing various geometric shapes and explore their interactions in real-time.