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To analyze the past, present and future of a particular research field, classic papers are usually studied because they identify the highly cited papers being a relevant reference point in that specific research area. As a result of the possible mapping between high quality research and high citation counts, highly cited papers are very interesting. The objective of this study is to use the H-classics method, which is based on the popular h-index, to identify and analyze the highly cited documents published about aggregation operators in the research area of group decision making. According to the H-classics method, this research area is represented by 87 citation classics, which have been published from 1988 to 2014. Authors, affiliations (universities/institutions and countries), journals, books and conferences, and the topics covered by these 87 highly cited papers are studied. [ABSTRACT FROM AUTHOR]

Common personalization approaches involve re-ranking search results. In such way, documents likely to be preferred by the user are presented higher. In this paper, we focus on research-paper retrieval. We propose a scientometric re-ranking approach based on the scientometric preferences of a particular researcher. The researcher creates its own definition of document quality by the mean of scientometric indicators. These indicators are the base of the scientometric score calculation, which serves to results re-ranking. The originality of our approach was the incorporation of different scientometric indicators into researcher’s preferences which have significantly improved ranking performance. [ABSTRACT FROM AUTHOR]

The high costs of environmentally responsible and socially just approaches are some of the factors that hinder the implementation of sustainable projects in the industrial sector. However, upstream collaborative integration in the corrugated cardboard industry in southeastern Brazil is a reality. In this context, this study aimed to highlight the structure of a collaborative sustainable project in the context of the corrugated cardboard industry, to analyze ESG risk management in the corrugated cardboard industry in the Southeast Region of Brazil, the relationship between ESG risk management and social responsibility in corrugated cardboard industries, the strengths and weaknesses of risk management models in use by the consulted participants, and to propose innovations regarding risk analysis, applicable in sustainable projects with a collaborative bias. The methodology adopted was the case study, with consultation with managers in professional activity in the industrial sector, corroborated by document analysis and systematic review of the literature. We identified preliminary risk analysis (PRA) and failure mode analysis (FMEA) as key practices used by managers in the corrugated paper industry in the region. These practices are insufficient in the convergence of objectives, engagement with the community, and extension of the lifecycle of products and processes. Our findings show that the sustainability of a project may be an extension of the risk analyses carried out in the initial phase, mainly about strategic decision-making and the operationalization of investments. Without bias to the need for risk analysis, the life cycle analysis is also an effective way of highlighting the critical factors, weaknesses, and strengths of a sustainable project. The study indicates the so-called GUT matrix approach (gravity – urgency – tendency) as the most suitable for innovation in risk analyses oriented towards sustainable projects in the analyzed sector. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) have emerged as pivotal tools for enhancing network security in the 21st century, offering innovative approaches that surpass traditional methods. This paper conducts a comprehensive literature review to explore how AI and ML contribute to advancing network security. The findings underscore the critical role of these technologies in detecting and mitigating cyber threats, automating threat responses, and predicting future vulnerabilities. The research highlights how AI and ML can significantly enhance the accuracy and efficiency of security processes, thereby bolstering an organization's overall cybersecurity posture. Additionally, the study emphasizes the importance of seamlessly integrating AI and ML with existing security architectures to maximize their benefits. Despite the significant potential of these technologies, the study identifies several gaps in the literature, such as the need for more empirical research on the effectiveness of AI and ML-based security solutions and the practical challenges of implementing these technologies in real-world scenarios. Finally, this paper discusses the limitations of the current research and suggests future research directions to further explore the convergence of network security, AI, and ML. [ABSTRACT FROM AUTHOR]

Nowadays, it's evident that a system's, product's, or service's User Experience (UX) is a crucial component of its success. User Experience (UX) is a pivotal factor in the success of systems, products, and services, yet UX research often faces challenges in processing the vast amounts of structured and unstructured data involved. Prolonged analysis times result from the complexity and variability of unstructured data and the lack of optimized tools for its processing. This paper addresses these issues by conducting a systematic literature review to assess current methodologies and tools for data collection and analysis in UX research. A total of 120 peer-reviewed publications, sourced from IEEE Xplore, ScienceDirect, and other databases between 2018 and 2023, were reviewed. Through detailed analysis, 17 key papers were selected based on their empirical results, relevance, and focus on AI-driven data processing techniques. The findings indicate that models such as LSTM, GRU, and NLP are increasingly effective in handling both structured and unstructured data, enabling more efficient analysis and insight extraction. These AI-driven techniques not only streamline the research process but also offer scalable, flexible solutions that cater to various UX research contexts, allowing for more accurate decision-making and improved user experiences. [ABSTRACT FROM AUTHOR]

In the era of digital transformation, Robotic Process Automation (RPA) has emerged as a pivotal technology for enhancing operational efficiency and sustainability in logistics. The primary goal of this paper is to explore the potential of RPA in optimizing logistics processes, specifically focusing on its impact on energy efficiency, cost reduction, and overall operational performance. The research aims to identify and address the challenges and opportunities presented by RPA, offering a structured framework for its implementation in large-scale logistics operations. By automating routine and repetitive tasks, RPA reduces human error, accelerates process execution, and significantly lowers energy consumption in both warehouse and transportation logistics. The integration of RPA with existing logistics systems, such as warehouse management systems and transportation management systems, facilitates seamless operations and real-time data analysis. The scientific novelty of the study lies in the development of a comprehensive framework for integrating RPA into logistics systems, highlighting not only the immediate operational benefits but also long-term sustainability and efficiency improvements. This paper also provides insights into the best practices associated with RPA deployment in logistics, supported by case studies and quantitative analysis. [ABSTRACT FROM AUTHOR]

This paper explores the concept of a Metauniversity as an innovative tool for inclusive learning in higher education. Leveraging cutting-edge virtual reality (VR) and augmented reality (AR) technologies, particularly Unity and Meta XR platforms, the Metauniversity creates a comprehensive and accessible educational environment. The study focuses on two key aspects: the inclusive nature of the platform and its multiplayer capabilities. By allowing students to create and customize avatars, the Metauniversity accommodates diverse physical and cultural characteristics, promoting equality and comfort in the learning process. The multiplayer features facilitate real-time interaction, collaborative projects, and knowledge sharing, enhancing the overall educational experience. The paper also delves into the technical architecture of the multiplayer system, demonstrating how various components interact to create a seamless, immersive learning environment. This research highlights the potential of Metauniversity to revolutionize educational approaches, offering new opportunities for personal and professional development while fostering a tolerant and harmonious learning community. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is now used in many sectors but its transformative impact on cybersecurity is unmatched. Cybersecurity is seen to rely heavily on artificial intelligence (AI), which has brought about automation of responses, detection of network threats and security consciousness. This paper examines various modern techniques involving deep learning, machine learning and behavior analysis among others that are used in dealing with the increasing number and complexity of cyber threats. The research also looks at how automated cybersecurity response and decision-making capacities have been transformed through AI. It also investigates into security intelligence modeling as a means of offering actionable insights towards strengthening organizational resilience. Additionally, the goal of this paper is to provide a nuanced understanding of how AI is reshaping cybersecurity and to outline future pathways for research and development in this dynamic field. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of edge computing and Blockchain technologies within the context of digital transportation, focusing on the implementation of autonomous vehicle networks. By leveraging edge computing's real-time data processing capabilities alongside Blockchain's secure, decentralized data management, the paper outlines how these technologies can enhance vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, improve traffic management, and optimize predictive maintenance. The proposed framework includes deploying edge computing nodes along key transportation routes and utilizing Blockchain for secure data exchange and automated transactions via smart contracts. The study also addresses key challenges, such as scalability, data synchronization, and cybersecurity, while exploring the regulatory implications of these innovations. The paper emphasizes the potential of these technologies to create safer, more efficient, and resilient transportation systems and calls for interdisciplinary collaboration to realize these advancements fully. [ABSTRACT FROM AUTHOR]

This paper suggests a secure architecture for Remote Patient Monitoring (RPM) systems that integrate Azure IoT Hub, Azure Digital Twin, and OPC UA in order to enhance the security of patient data, data privacy, and personalized healthcare services. RPM systems track real-time health data of patients through wearable devices. In this process, they face significant security challenges specifically related to data encryption, access control, and compliance with the security requirements of regulations like HIPAA and GDPR. To overcome these concerns, the proposed architecture in the paper utilizes OPC UA for secure and compliant communication between healthcare devices and the cloud. An additional level of security is provided by the implementation of pseudonymization. Moreover, the personally identifiable information (PII) of the patient is removed before transferring to the cloud and hence assures compliance. Azure IoT Hub makes encrypted data flow in the cloud which is then transferred to Human Digital Twin (HDT) for real-time analysis and enhancing personalized healthcare. The integration of multiple layers of security, including role-based access control (RBAC) and encryption, in the architecture protects against data breaches and unauthorized access. The suggested architecture provides adequate security features compared to existing RPM systems, as indicated by statistical analysis using the Chi-Square test. This test was performed for security metrics like Data Encryption, Access Controls, Secure Data Transfer, Data Privacy, Regulatory Compliance, Cloud Security, and Audit Logging. [ABSTRACT FROM AUTHOR]

This paper explores the innovative applications of Generative Artificial Intelligence (GenAI) for strengthening the cybersecurity of vehicular networks. With the advent of intelligent transport systems and autonomous vehicles, the cybersecurity landscape has evolved significantly, which necessitating new strategies to tackle sophisticated threats. GenAI provides advanced capabilities for automating defenses, enhancing threat intelligence, and fostering dynamic security frameworks in vehicular networks. However, the incorporation of GenAI also introduces new risks, requiring robust ethical, legal, and technical oversight. This research paper outlines the current state of GenAI in vehicular network cybersecurity, showcases the Vehicular Threat Intelligence Flowchart (VTIF), focuses on the threat detection rule algorithm in VTIF, highlights the potential benefits and challenges, and proposes future research directions for developing resilient and ethical cybersecurity mechanisms. [ABSTRACT FROM AUTHOR]

A real-time object-tracking device is a crucial technology that continuously locates and monitors moving objects. This paper explores the fundamental principles of such a device, emphasizing its role in creating a compact tracker weighing 19 grams and utilizing GPS data with GSM connectivity to broadcast the location of moving objects to a cloud server and then to client devices through web and mobile applications. Current tracker devices on the market are limited in terms of real-time monitoring, size, weight, and accuracy for small moving objects. Therefore, this paper demonstrates the contribution of a lightweight real-time tracker, Prototype-1, designed to overcome these limitations. Experimental results show that Prototype-1 achieves lower Mean Absolute Error, Root Mean Square Error, and Standard Deviation compared to commercial devices, indicating higher accuracy and consistency. While tested using bird tracking as a case study, the device's applicability extends to various moving objects. This advancement enhances the state of the art in real-time tracking technology. [ABSTRACT FROM AUTHOR]

The reliability of software interfaces has become increasingly recognized as a crucial factor impacting the quality, manageability, and efficiency of various software systems. Traditional approaches to software reliability assessment often overlook interface-level reliability, focusing primarily on system-level metrics. However, the growing emphasis on user-centered design has amplified the demand for reliable and user-friendly software interfaces. This paper introduces a novel information model for evaluating the reliability of software interface design, which is crucial for enhancing assessment approaches in software engineering. The proposed model integrates usability testing and reliability attributes early in the software development life cycle (SDLC), particularly in the interface design phase, to enhance the system's overall performance and user experience. The paper describes the current state of models and methods used to evaluate software reliability at the interface design level, outlining the problems that the proposed new model aims to solve. It presents the theoretical basis for the model and provides information on its integration into the SDLC. Analytical examples are used to corroborate the proposed model, demonstrating its usefulness in the early project stages and its ability to improve software interface reliability and practicality. The paper also discusses challenges in adoption and suggests areas for future theoretical and empirical research. By developing a more comprehensive perspective on software quality, this work paves the way for practical, reliable, and user-friendly interfaces across various software domains. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of the safe region approach for an approximate continuous nearest neighbour strategy is studied and evaluated. In location based services (LBS), a safe region is utilized for processing continuous queries locally on a user's device by many proposed strategies. However, for some strategies, the safe region may not contain all of the points that are in the area covered by it, which leads to approximate query processing. This paper explores a cluster-based safe region approach that is proposed in the literature. The goal is to determine if 100% accuracy can be achieved. Results show that factors such as the number and size of the clusters, the underlying cluster model, and the distribution of the data play a factor in the accuracy of the continuous query using the cluster-based safe region. [ABSTRACT FROM AUTHOR]

As the significance of simulation in medical care and intervention continues to grow, it is anticipated that a simplified and low-cost platform can be set up to execute personalized diagnoses and treatments. 3D Slicer can not only perform medical image analysis and visualization but can also provide surgical navigation and surgical planning functions. In this paper, we have chosen 3D Slicer as our base platform and monocular cameras are used as sensors. Then, We used the neural radiance fields (NeRF) algorithm to complete the 3D model reconstruction of the human head. We compared the accuracy of the NeRF algorithm in generating 3D human head scenes and utilized the MarchingCube algorithm to generate corresponding 3D mesh models. The individual's head pose, obtained through single-camera vision, is transmitted in real-time to the scene created within 3D Slicer. The demonstrations presented in this paper include real-time synchronization of transformations between the human head model in the 3D Slicer scene and the detected head posture. Additionally, we tested a scene where a tool, marked with an ArUco Maker tracked by a single camera, synchronously points to the real-time transformation of the head posture. These demos indicate that our methodology can provide a feasible real-time simulation platform for nasopharyngeal swab collection or intubation. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks (SNNs) have shown significant advantages in developing efficient artificial intelligence systems with cognitive characteristics, owing to their biological authenticity and temporal encoding characteristics. However, there is currently a lack of effective training algorithms for SNNs. In contrast, the brain can maintain accurate, efficient, and flexible decision-making in complex dynamic environments, mainly due to the joint function of multiple brain regions in the biological nervous system. Among them, Basal Ganglia (BG)-based reward learning mechanisms are crucial in action decision-making, but research on it is relatively scarce and most of it cannot cope with complex environments. Therefore, this paper explores the mechanism of biological reward learning and proposes an SNN-based action decision-making model with an Actor-Critic (AC) architecture. Toefficiently train the model and cope with complex situations, this paper proposes a novel cortico-striatal synaptic plasticity learning rule. Finally, the model is tested on the OpenAI Gym environment, and the experiment supported the effectiveness of the model. Compared with Deep Q-learning (DQN) and Proximal Policy Optimization (PPO) algorithms, this model demonstrates good learning performance and remains biologically plausible. [ABSTRACT FROM AUTHOR]

The walking stability of legged robot in unknown environment is closely related to its perceptual ability of environment. This paper proposes a method to identify the terrain where the robot is located online based on robot ontology information such as joint encoders' data and current data, combined with support vector machine. The robot interacts with the terrain in a jumping manner. Then, the feature extraction of three terrain (fat ground, slope, sand) is carried out by means of current acquisition, slope estimation and collision force estimation, and the data model is established correspondingly. The method proposed in this paper is validated in experimental results. Based on the model, the robot can achieve accurate perception for these three types of terrains. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) are gaining prominence for their efficacy in data integration and knowledge representation within Current Research Information Systems (CRIS) Systems. By employing a semantic data model to represent entities, attributes, and their relationships, they prove versatile across scientific applications. In the era of Science platformization, particularly within CRIS systems, KGs serve to amalgamate diverse data sources and formats, facilitating the creation of interconnected data models. This enables stakeholders to access comprehensive, consistent information pertinent to their endeavors. This paper examines the pivotal roles of KGs in current and future data integration within CRIS systems, emphasizing their contributions to scientific reasoning. While their benefits include flexible knowledge modeling, support for semantic queries, and interoperability with various data sources, they face systemic limitations, particularly in methodological and technological aspects, hindering classical scientific investigations. The paper underscores the necessity for novel approaches to address these limitations, offering insights, use cases, and best practices for implementing KGs in CRIS systems. This paves the way for research institutions and scientific organizations to enhance their data analytics capabilities and support scientific reasoning effectively. [ABSTRACT FROM AUTHOR]

Research data is increasingly recognised as an essential output type in the context of research documentation. This paper aims to examine the possible use cases for showcasing research datasets in research information systems. The analysis indicates that three primary use cases play a key role in the implementation of a dataset module in an institutional or in a regional/national CRIS. The paper describes the use cases and places them in the specific context of the University of Vienna, while also referring to existing case studies of other institutions. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into current research information systems (CRIS) is becoming increasingly prevalent, driven by the need for enhanced operational efficiency and data accessibility. While AI offers promising opportunities for improving CRIS functionalities, it also raises significant ethical challenges that must be addressed. This paper examines the ethical implications of AI implementation in CRIS, focusing on issues such as autonomy, discrimination, data protection, and user rights. Drawing on insights from machine, technology, digital, and robot ethics, the paper explores strategies for mitigating these challenges and fostering responsible AI use in CRIS. Additionally, it highlights the impact of AI technology on CRIS staff and emphasizes the importance of considering societal and personal effects alongside technological advancements. By raising awareness of ethical considerations and promoting ethical AI practices, this paper aims to contribute to the development and acceptance of AI technologies in CRIS, particularly in the light of forthcoming regulations such as the EU AI Act. [ABSTRACT FROM AUTHOR]

The paper analyses the implications of the emergence of artificial intelligence (AI), especially generative AI, on current research information systems (CRIS). It reviews the recent European regulations for high-risk AI systems, the Spanish AI strategy, and the IntelComp project as use cases. The study found that the maturity of CRIS systems, coupled with the increasing complexity due to data aggregation, sets the stage for innovative AI applications. The paper proposes key domains where AI can impact and be applied in CRIS, including data management, research assessment, and advanced analytics. It also provides examples of how generative AI can be leveraged to enhance scientific information management within CRIS. The findings highlight the need to ensure the responsible and ethical development of AI technologies in the research domain. [ABSTRACT FROM AUTHOR]

Integrating Artificial Intelligence (AI) into Current Research Information Systems (CRIS) offers significant opportunities to enhance research management. This paper explores AI's potential to automate data handling, improve analytical capabilities, and enhance user experiences within CRIS. Key areas of impact include data enrichment, advanced information retrieval, trend analysis, and predictive analytics. The paper also addresses the challenges and ethical considerations of AI integration, such as data privacy, security, and algorithmic bias. Insights from a Live Poll at the CRIS2024 conference reveal high familiarity with AI among participants, optimism about its potential, and recognition of implementation challenges. By overcoming these obstacles, AI can transform CRIS, making research management more efficient and effective. The paper concludes by advocating for collaboration and dialogue to guide the responsible integration of AI in CRIS, ensuring alignment with stakeholder interests. [ABSTRACT FROM AUTHOR]