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Evaluating Postal Systems’ Current State, Roadmap to Automation
No full textPart 6: Intelligent Computational SystemsInternational audienceThis article explores the key aspects and challenges in transforming the postal and package delivery networks to a fully automated and self-learning stage. It analyzes its current state, possible gaps in research and business solutions, identifying the existing technologies, and the possible management challenges. The authors also consider socio-economic factors during the current context analysis stage. The authors reviewed the literature and identified best practices and technological solutions used in the postal delivery field and existing research gaps. The most commonly pointed technological solutions include for example IoT for package tracking and machine learning with big data for workload optimization. A case study with company stakeholders in the form of interview was followed, to identify the best practices used and technological issues in the field. The researchers analyzed the current state and introduced the potential advancements of the target state - a more efficient, technology-driven postal delivery system
Three-Level Zero-Voltage Transition Interleaved Buck Converter with DC Transformer-based Isolation for EV Fast Charging Stations
No full textPart 5: Energy Management and SustainabilityInternational audienceThis paper proposes a new design architecture in which a three-level zero-voltage transition interleaved buck converter (3L-ZVT-IBC) with DC transformer-based isolation is introduced for applications in EV fast charging stations. This 3L-ZVT-IBC accomplishes lossless switching thanks to ZVT ability of proposed idea, enabling a high efficiency. In addition, it also has a lower voltage stress in comparison to the conventional interleaved buck converter, still required duty-cycle lies in the vicinity of 50% for wide operating range of output voltage required for EV fast charging stations (200 V to 850 V). In addition, the proposed architecture guarantees that the multilevel input DC voltages are balanced without any specific balancing technique or extra components, while ensuring the operation with low output inductor ripple for all conditions. In order to validate the 3L-ZVT-IBC, PSIM simulations were carried out, demonstrating the feasibility of the proposed 3L-ZVT-IBC
On the Efficient Architecture for 6G System
No full textPart 1: The 9th Workshop on “5G – Putting Intelligence to the Network Edge” (5G-PINE)International audienceThis paper discusses the proposed approaches to the 6G System architecture against the background of the fundamental principles of the 5G network and challenges towards 6G network, focusing also on drawbacks of already adopted solutions and their further impact on the future 6G System performance. The user-centric architectural framework, based on a dynamic stateless procedural approach, extending the organic core concept, has been proposed that simplifies and accelerates Control Plane interactions, introducing also the mechanisms of network self-cognition, self-awareness, and self-control into the new core
Smart City for Civic Participation: A Conceptual Framework
No full textPart 8: Smart Collaborations and CrowdsourcingInternational audienceThe emergence of Information and Communication Technology has allowed the transformation of urban governance into smart governance, leading cities to fulfill their mission and develop their processes more efficiently and effectively. In this context, civic participation has become fundamental to the success of smart city initiatives. In this vein, this article aims to investigate how smart city initiatives promote civic participation through smart governance. After a systematic review of the literature, the antecedents, moderators and mechanisms associated with this relationship were identified. The antecedents or structural factors are: technological, political & socio-economic and local government contexts. Furthermore, digital exclusion and citizens’ educational background are proposed as instrumental factors. This research concludes that to increase civic participation it is essential to address issues like the digital divide and provide citizen education. Lastly, it is concluded that smart governance is a key mechanism through which smart city initiatives can enable civic participation
The Application of Artificial Intelligence in Diabetes Prediction: A Bibliometric Analysis
No full textPart 1: Artificial Intelligence, Inequalities, and Human RightsInternational audienceThis study aimed to map the evolution and impact of artificial intelligence (AI) in diabetes prediction research from 2013 to 2023. Utilizing Scopus database records, a bibliometric analysis was conducted on documents featuring AI and diabetes prediction keywords. The analysis used the Bibliometrix and VOSviewer tools to evaluate research publication trends, author collaboration, and keyword co-occurrence in the application of AI in diabetes prediction. Data screening, focusing on specific terms in titles and abstracts, ensured the relevance of the documents. The study included diverse document types and subject areas, reflecting the field’s multidisciplinary nature. The findings revealed a significant annual growth rate of 84.86% in AI applications for diabetes prediction, with 1 498 documents from 802 sources highlighting strong scholarly interest. A peak in citation impact in 2018 marks key contributions and diverse research themes in that year. International co-authorship, notably from the USA, India, China, and Saudi Arabia, underscores extensive collaboration. Thematic analysis points to focal areas like ophthalmology in diabetes-related complications and identifies central topics and emerging trends, including the Internet of Things. The bibliometric review highlights a significant interdisciplinary expansion in AI research applied to diabetes prediction, with a marked increase in global collaborations and contributions. The study underscores the importance of AI in enhancing diabetes diagnostics and management, indicating a promising trajectory for future research, healthcare policy, and clinical practice. The evolution of AI, particularly machine learning, in diabetes prediction, demonstrates the potential for innovative solutions in managing this chronic condition
Understanding the Role of Library Anxiety and Attitude as Determinants of Intention to Use a Digital Library System
No full textPart 9: Technology and Social JusticeInternational audienceThere are growing calls to understand the determinants that can enhance the use of digital library systems within universities. Addressing factors related to library anxiety can potentially enhance the end-user experience of using digital library systems. The study aimed to understand the role of library anxiety and attitudes as determinants of the intention to use a digital library system. A quantitative research approach used a survey method to collect data from 316 students at a public university in South Africa. Inferential statistical analysis was used to test associations between variables. The findings showed that impediments such as a) barriers with staff, b) affective barriers, c) comfort library barriers, d) knowledge of the library, e) mechanical barriers, and f) resource barriers affect attitudes towards usage of digital library systems. Further, these identified impediments affected attitudes and intentions to use digital library systems. Based on the findings, implications and suggestions are made on enhancing end-user experiences for digital library systems usage. The study's findings show that a precursor to this is primarily the need to address library anxiety barriers
Pattern Matching in Polyphonic Musical Sequences
No full textPart 2: Recommendation/ClassificationInternational audienceThis paper focuses on the development and implementation of pattern matching algorithms designed for the analysis of musical sequences. The primary objective is to create algorithms capable of efficiently and accurately identifying instances of musical patterns within a dataset encompassing both simple compositions and well-known musical pieces. This goal is achieved through the adaptation and extension of the Tuned-Boyer-Moore algorithm, coupled with the introduction of δ- and δ,γ-approximation techniques. The performance of these algorithms is evaluated on a dataset containing both self-generated pieces as well as well-known pieces. The algorithm consistently was able to detect both exact and approximate pattern occurrences accurately, even when the pieces were subject to changes in rhythm and key. A series of testing rounds involving manipulation of δ and γ values, showcases the algorithms’ adaptability and efficiency
LFENav: LLM-Based Frontiers Exploration for Visual Semantic Navigation
No full textPart 5: Multi Agent/Ontologies/RoboticsInternational audienceRobot navigation in an unknown environment is a challenge task, due to the lack of spatial awareness and semantic understanding of the environment. Previous works mostly rely on learning-based approaches, which need large amount of training data and lack of generalization ability. The emergence of Large Language Models (LLMs) provides a new way for semantic understanding. This paper proposes a method of LLM-based Frontiers Exploration for visual semantic Navigation (LFENav), which leverages the rich semantic prior knowledge of LLMs to find next subgoals with the input natural language instruction. Firstly, the semantic map is incrementally constructed and the frontiers are redefine from the observed RGB-D images. A prompt mechanism is designed to embody the Chain-of-Thought (CoT) merit of LLMs. We use geometric costs to compensate the information gap of LLMs in understanding the spatial layout of scenes. Based above, a novel exploration policy is designed by integrating LLM scores and geometric costs to select better frontiers worthy of exploring. Experiments on Habitat-Matterport 3D dataset shows that the success rate of this method is up to 0.638, which is the best performance compared with the existing methods
A MARL-Based Approach for Easing MAS Organization Engineering
No full textPart V: Multi Agent/Ontologies/RoboticsInternational audienceCommunication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques
Finding Logical Vulnerability in Policies Using Three-Level Semantic Framework
No full textPart 4: LearningInternational audienceWe present the continuation of our work on a three-level framework, which can be used to model and analyze the identification- authentication- authorization policies. Finding the gaps in such policies is challenging. We explore the cases when operations become accessible to the user because of flawed or missing authentication methods. Our objective is to model the domain and find such vulnerabilities. Our proposed framework has three levels. Each level is built on top of a previous one. The first is ontological, where we model the static domain in OWL; the second is logical, where we model the dynamic using SWRL; and the third is analytical level, where we utilize the reasoner to get the results. In this paper, we present the algorithm, which finds vulnerable situations in the policies or confirms that there are no vulnerable situations. We have modelled a couple of policies from different user-based applications to validate our approach as well as demonstrate the feasibility of using it on policies from the actual systems