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A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI. [ABSTRACT FROM AUTHOR]

The notion of indistinguishability operator plays a central role in a large number of problems that arise naturally in decision-making, artificial intelligence, and computer science. Among the different issues studied for these operators, the aggregation problem has been thoroughly explored. In some cases, the notion of indistinguishability operator can be too narrow and, for this reason, we can find two different extensions of such notion in the literature. On the one hand, modular indistinguishability operators make it possible to measure the degree of similarity or indistinguishability with respect to a parameter. On the other hand, relaxed indistinguishability operators delete the reflexivity condition of classical indistinguishability operators. In this paper, we introduced the notion of modular relaxed indistinguishability operator unifying under the same framework all previous notions. We focused our efforts on the study of the associated aggregation problem. Thus, we introduced the notion of modular relaxed indistinguishability operator aggregation function for a family of t-norms extending the counterpart formulated for classical non-modular relaxed indistinguishability operators. We provided characterizations of such functions in terms of triangle triplets with respect to a family of t-norms. Moreover, we addressed special cases where the operators fulfill a kind of monotony and a condition called small-self indistinguishability. The differences between the modular and the non-modular aggregation problem were specified and illustrated by means of suitable examples. [ABSTRACT FROM AUTHOR]

Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions. [ABSTRACT FROM AUTHOR]

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease. [ABSTRACT FROM AUTHOR]

Affective computing is a multidisciplinary field encompassing artificial intelligence, natural language processing, linguistics, computer science, and social sciences. This field aims to deepen our comprehension and capabilities by deploying inventive algorithms. This article presents a groundbreaking approach, the Cumulative Attribute-Weighted Graph Neural Network, which is innovatively designed to integrate trimodal textual, audio, and visual data from the two multimodal datasets. This method exemplifies its effectiveness in performing comprehensive multimodal sentiment analysis. Our methodology employs vocal inputs to generate speaker embeddings trimodal analysis. Using a weighted graph structure, our model facilitates the efficient integration of these diverse modalities. This approach underscores the interrelated aspects of various emotional indicators. The paper's significant contribution is underscored by its experimental results. Our novel algorithm achieved impressive performance metrics on the CMU-MOSI dataset, with an accuracy of 94% and precision, recall, and F1-scores above 92% for Negative, Neutral, and Positive emotion categories. Similarly, on the IEMOCAP dataset, the algorithm demonstrated its robustness with an overall accuracy of 93%, where exceptionally high precision and recall were noted in the Neutral and Positive categories. These results mark a notable advancement over existing state-of-the-art models, illustrating the potential of our approach in enhancing Sentiment Recognition through the synergistic use of trimodal data. This study's comprehensive analysis and significant results demonstrate the proposed algorithm's effectiveness in nuanced emotional state recognition and pave the way for future advancements in affective computing, emphasizing the value of integrating multimodal data for improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%. [ABSTRACT FROM AUTHOR]

Lecture Notes in Computer Science (LNCS) is a globally recognised publication outlet for the field of Computer Science, including in South Africa. In this study, spanning from 1973 to 2022, we investigated the research participation of South African based authors in LNCS. The publication output and citation impact of these authors were compared to the global Computer Science and LNCS output. The authorship patterns and collaborative behaviour of South African LNCS papers were explored, and a keyword or topic analysis also conducted. Of the total of 518 662 LNCS papers published globally between 1973 and 2022, South African based researchers contributed 1150 papers (0.22%). The LNCS papers from South Africa exhibit a strong collaborative publication culture, with 1043 (91%) co-authored and 107 (9%) singleauthored works. Local LNCS researchers prefer institutional collaboration (43%), followed by international (37%) and national collaboration (11%). Europe emerged as the most significant collaboration partner for LNCS researchers in South Africa. Of the 1150 papers, 836 (73%) had received citations, while 314 (27%) had not. On average, papers published by South African based authors received 6.05 citations, compared to the global LNCS average of 9.49 citations per paper. A keyword analysis revealed that the majority of papers by South African authors focus on artificial intelligence. The results indicate that, although LNCS serves as a reputable dissemination platform for Computer Science research output both globally and locally, South African authors should consider publishing more journal articles to build and improve their researcher profiles. Significance: * The study shows that LNCS is the most frequent publication outlet for Computer Science researchers, globally and in South Africa. * The study offers insight into the publication output, authorship patterns, collaborative behaviour and citation impact of South African based Computer Science researchers. [ABSTRACT FROM AUTHOR]

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The integration of artificial intelligence (AI) is revolutionizing the field of education. The possibilities of AI in education range from providing intelligent tutoring to facilitating computational thinking, a key skill in an AI-based society. As education systems around the world adapt to technological advances, the need for teachers to develop AI skills becomes crucial. This paper explores the use of AI on mobile devices to improve the preparation of future mathematics teachers. We acknowledge the need for teachers to be skilled in the use of AI tools to foster collaborative, effective, and ethical learning environments. We also provide a case study of our implementation of the generative AI ChatGPT in the preparation of future mathematics teachers at the Faculty of Mathematics, Physics and Computer Science at Comenius University in Bratislava. After this implementation, we analyzed students' opinions on the application of AI in selected areas. We present the results of this analysis and the implications for the practical use of AI in the university education of future mathematics teachers. Finally, based on our study results, we highlight the possibilities of the successful use of AI in different aspects of teaching and its temporary limitations, which are likely to be reduced by further technological developments. [ABSTRACT FROM AUTHOR]

As part of ongoing research bridging ethnomethodology and computer science, in this article we offer an alternate reading of Alan Turing's 1936 paper, "On Computable Numbers". Following through Turing's machinic respecification of computation, we hope to contribute to a deflationary position on AI by showing that the activities attributed to AIs are achieved in the course of methodic hands-on work with computational systems and not in isolation by them. Turing's major innovation was a demonstration that mathematical and logical operations could be broken down into elementary, mechanically executable operations, devoid of intellectual content. Drawing out lessons from a re-enactment of Turing's methods as a means of reflecting on contemporary artificial intelligence (AI), including the way those methods disappear into the technology, we will suggest the interesting question raised in "On Computable Numbers" is less about the possibilities of designing machines that "can think" (cf. Turing, 1950), but the practical work we do, and which is made possible, when we ourselves set out to think like machines. [ABSTRACT FROM AUTHOR]

Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as "golden" benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each 'ground-truth' description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases. [ABSTRACT FROM AUTHOR]

The document is an editorial from the Bulletin of the Polish Academy of Sciences: Technical Sciences. It introduces a special section on IIoT (Industrial Internet of Things) and AI-based optimization. The aim of the section is to present the latest developments and applications in these areas, including methods and systems in IIoT, IoT infrastructure, and applications of AI in IoT. The topics covered align with current research trends in computer science, telecommunications, and mechanical engineering. The document also highlights the importance of integrating intelligent machines and systems in industry and the role of AI in solving complex problems. [Extracted from the article]

The recent surge of generative artificial intelligence (AI) in higher education presents a fascinating landscape of opportunities and challenges. AI has the potential to personalize education and create more engaging learning experiences. However, the effectiveness of AI interventions relies on well-considered implementation strategies. The impact of AI platforms in education is largely determined by the particular learning environment and the distinct needs of each student. Consequently, investigating the attitudes of future educators towards this technology is becoming a critical area of research. This study explores the impact of generative AI platforms on students' learning performance, experience, and satisfaction within higher education. It specifically focuses on students' experiences with varying levels of technological proficiency. A comparative study was conducted with two groups from different academic contexts undergoing the same experimental condition to design, develop, and implement instructional design projects using various AI platforms to produce multimedia content tailored to their respective subjects. Undergraduates from two disciplines—Early Childhood Education (n = 32) and Computer Science (n = 34)—participated in this study, which examined the integration of generative AI platforms into educational content implementation. Results indicate that both groups demonstrated similar learning performance in designing, developing, and implementing instructional design projects. Regarding user experience, the general outcomes were similar across both groups; however, Early Childhood Education students rated the usefulness of AI multimedia platforms significantly higher. Conversely, Computer Science students reported a slightly higher comfort level with these tools. In terms of overall satisfaction, Early Childhood Education students expressed greater satisfaction with AI software than their counterparts, acknowledging its importance for their future careers. This study contributes to the understanding of how AI platforms affect students from diverse backgrounds, bridging a gap in the knowledge of user experience and learning outcomes. Furthermore, by exploring best practices for integrating AI into educational contexts, it provides valuable insights for educators and scholars seeking to optimize the potential of AI to enhance educational outcomes. [ABSTRACT FROM AUTHOR]

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications. [ABSTRACT FROM AUTHOR]

Abstract A drug molecule is a substance that changes an organism’s mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Artificial intelligence in the workplace is becoming increasingly common. These tools are sometimes used to aid users in performing their task, for example, when an artificial intelligence tool assists a radiologist in their search for abnormalities in radiographic images. The use of artificial intelligence brings a wealth of benefits, such as increasing the efficiency and efficacy of performance. However, little research has been conducted to determine how the use of artificial intelligence assistants might affect the user's cognitive skills. In this theoretical perspective, we discuss how artificial intelligence assistants might accelerate skill decay among experts and hinder skill acquisition among learners. Further, we discuss how AI assistants might also prevent experts and learners from recognizing these deleterious effects. We then discuss the types of questions: use-inspired basic cognitive researchers, applied researchers, and computer science researchers should seek to answer. We conclude that multidisciplinary research from use-inspired basic cognitive research, domain-specific applied research, and technical research (e.g., human factors research, computer science research) is needed to (a) understand these potential consequences, (b) design artificial intelligence systems to mitigate these impacts, and (c) develop training and use protocols to prevent negative impacts on users' cognitive skills. Only by answering these questions from multidisciplinary perspectives can we harness the benefits of artificial intelligence in the workplace while preventing negative impacts on users' cognitive skills. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibility and poor generalisation of findings, undermining scientific value and contributing to global research waste. To address these issues and focusing on the learning aspect of the AI field, we present RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR offers automated generation of transparent and usable reports, aiming to enhance the quality and reproducibility of AI studies. To demonstrate the versatility of our tool, we applied it to benchmark datasets from health, computer science, and STEM (Science, Technology, Engineering, and Mathematics) domains. Furthermore, we showcase RENOIR's successful application in recently published studies, where it identified classifiers for SET2D and TP53 mutation status in cancer. Finally, we present a use case where RENOIR was employed to address a significant pharmacological challenge—predicting drug efficacy. RENOIR is freely available at https://github.com/alebarberis/renoir. [ABSTRACT FROM AUTHOR]

Water droplets help life in nature survive, thrive, and evolve. With water droplet serving as one of the indispensable elements in the Internet of Things (IoT), many droplet‐oriented technologies, such as microfluidics, droplet manipulation, electrowetting, and energy harvesting, make rapid progress driven by material science, computer science, and medicine. Droplet‐based wearable devices are endowed with advantages such as flexibility, sensing ability, and automation for various parameter detection. Besides, the continuous exploration of droplet manipulation has led to the emergence of a wide variety of manipulation methods. Meanwhile, electrowetting that utilizes external fields modifying liquid–solid surfaces has found its applications in various areas, including droplet transportation, microfabrication, and healthcare. The energy generation from water droplets also presents exciting opportunities for the development of novel electricity generators. These approaches for droplet utilization underscore the immense potentials and versatilities of droplet‐based technologies in the IoT landscape. Hence, this mini review presents the fundamental droplet‐based technologies by summarizing their working mechanisms and methods, device structures, and applications. Given the challenges in materials, fabrication, and system integration, this review shows the overall development roadmap in terms of improved functionality and performance and highlights the opportunities toward multifunctional, self‐sustainable, and intelligent systems, which is called for IoT construction. [ABSTRACT FROM AUTHOR]

We report the results of a 4800-respondent survey among students at a technical university regarding their usage of artificial intelligence tools, as well as their expectations and attitudes about these tools. We find that many students have come to differentiated and thoughtful views and decisions regarding the use of artificial intelligence. The majority of students wishes AI to be integrated into their studies, and several wish that the university would provide tools that are based on reliable, university-level materials. We find that acceptance of and attitudes about artificial intelligence vary across academic disciplines. We also find gender differences in the responses, which however are smaller the closer the student's major is to informatics (computer science). [ABSTRACT FROM AUTHOR]

The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism's problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core. [ABSTRACT FROM AUTHOR]

Hollywood screenwriters worry about Artificial Intelligence (AI) replacements taking over their jobs. Famous museums litigate to protect their art from AI infringement. A major retailer scraps a machinelearning based recruitment program that was biased against women. These are just a few examples of how AI is affecting the world of work, learning, and living. MIS and computer science students are among the professional groups who are embarking into careers with nebulous frontiers obscured by the outcroppings brought on by AI. Computer Science and Information System curriculum task forces have recognized the increasing ethical and professional implications developers’ work can have beyond the scope of the programmers’ code. In this article, the authors examine the professional, legal, and ethical implications of copyrights and algorithmic bias resulting from development of AI-enhanced applications and offer suggestions for addressing these topics in courses considering changes to the CC2020 and IS2020 Model Curriculum frameworks. [ABSTRACT FROM AUTHOR]

Autosomal recessive cerebellar ataxias (ARCAs) are a heterogeneous group of neurodegenerative disorders affecting primarily the cerebellum and/or its afferent tracts, often accompanied by damage of other neurological or extra-neurological systems. Due to the overlap of clinical presentation among ARCAs and the variety of hereditary, acquired, and reversible etiologies that can determine cerebellar dysfunction, the differential diagnosis is challenging, but also urgent considering the ongoing development of promising target therapies. The examination of afferent and efferent visual system may provide neurophysiological and structural information related to cerebellar dysfunction and neurodegeneration thus allowing a possible diagnostic classification approach according to ocular features. While optic coherence tomography (OCT) is applied for the parametrization of the optic nerve and macular area, the eye movements analysis relies on a wide range of eye-tracker devices and the application of machine-learning techniques. We discuss the results of clinical and eye-tracking oculomotor examination, the OCT findings and some advancing of computer science in ARCAs thus providing evidence sustaining the identification of robust eye parameters as possible markers of ARCAs. [ABSTRACT FROM AUTHOR]

Starting from the contribution of such thinkers as the famous Giordano Bruno (1583) and the great mathematician and physicist Henri Poincaré (1889) and the surprising discovery of the meteorologist Edward Lorenz (1963), we consider the expansion of the mathematics of chaos in this article, paying attention to topology, qualitative geometry, and Catastrophe Theory, on the one hand, and addressing the possibilities derived from the new Computer Science as Quantum Algorithms and the advances in Artificial Intelligence, on the other. We especially highlight the section on computing chaos, which we consider to be new calculation and analysis instruments, such as machine learning and its algorithm called reservoir computing, through which we can know the dynamics of a chaotic system. With past data, with equations like Karamoto–Sivashinsky, one can improve predictions of the system eight times further ahead than in previous methods. Integrating the machine learning approach and traditional model-based prediction, one could obtain accurate predictions twelve Lyapunov times. As we know, in the framework of chaos theory, it is habitually accepted that the idea of long-term prediction seems impossible because we live under a veil of uncertainty. But with technological advances, the landscape begins to change, both in chaos theory and in its applications, especially in the field of economics, to which we devote particular attention, carrying out as an example the analysis of the evolution of the Madrid Stock Exchange in the 2006–2013 crisis. Above all this, a reflection of a general nature is necessary to enlighten us on the possibility of opening a new horizon. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. In this survey, we discuss the state of the art, opportunities and open research questions in applying RL to generative AI. In particular, we will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives; as a way for generating outputs while concurrently maximizing an objective function; and, finally, as a way of embedding desired characteristics, which cannot be easily captured by means of an objective function, into the generative process. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area. [ABSTRACT FROM AUTHOR]

This document discusses the integration of advanced technologies and algorithms to enhance the performance, efficiency, and reliability of network systems in various application areas such as smart cities, smart homes, industrial automation, and transportation systems. The special issue focuses on future complex cyber-physical networks (CCPNs) and smart sensors, with a particular emphasis on control and optimization strategies. The document provides a summary of 10 original research articles and 1 review article that were accepted for publication in the special issue, covering topics such as remote sensing image recognition, fusion filtering, transmission line planning, optimal filtering for sensor networks, traversal exploration and path coverage, map-merging for UAVs, area coverage control for robot networks, adaptive deskewing algorithm for document images, intelligent positioning for climbing robots, and pneumatic control of valve position. The review article discusses recent developments and methodologies for the secure control of complex cyber-physical networks. The authors express their gratitude to the authors and reviewers for their contributions and acknowledge the support of the Editor-in-Chief and the Editorial Office of Sensors. [Extracted from the article]

The editorial discusses the complexity and rapid evolution of the field of computer science. It acknowledges that the breadth and depth of knowledge required to fully understand computing make it challenging for one person to grasp. The editorial highlights the theoretical and practical aspects of computing, as well as the continuous learning required to keep up with technological advancements. It also emphasizes the collaborative nature of computing projects and the need for specialization and teamwork. The author expresses gratitude to the experts who have contributed to the journal and wishes the readers a successful 2024. [Extracted from the article]