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Transition Invariants in the Analysis of Concurrent Systems Modelled by Petri Nets
No full textPart 6: Intelligent Computational SystemsInternational audiencePetri nets are a well-known mathematical apparatus that is commonly applied in the modeling of concurrent systems. Their main advantage relates to the possibility of graphical modeling, which results in the readable and intuitive specification of the system. Moreover, Petri nets are widely supported by analysis techniques, including formal verification methods. This paper focuses on the possible application of transition invariants to the analysis of Petri net-based concurrent systems. Such an approach seems to be much less popular in the literature than the analysis of place invariants. Nevertheless, our preliminary results indicate that analysis of transition invariants may result in very interesting information about the modeled system, especially in terms of crucial properties (e.g., liveness, boundedness)
Optimizing Vehicle-to-Vehicle Energy Sharing with Predictive Modeling
No full textPart 2: The 13th Workshop on “Mining Humanistic Data” (MHDW)International audienceThis paper presents a novel AI model designed to address two critical challenges in electric vehicle (EV), range prediction and charging optimization. The primary objective of our work is to autonomously predict if EV charging is needed and to provide precise estimates of EV range through machine learning algorithms. Our key contribution is the integration of a binary decision-making element into the charging optimization process, which predicts the need for charging and enhances the efficiency of energy management to mitigate range anxiety. This contribution stands in stark contrast to existing systems that focus solely on range prediction. Furthermore, when implemented within a vehicle-to-vehicle (V2V) framework, our model lays the groundwork for autonomous energy governance and facilitates efficient energy exchange among EVs
Efficient Energy Disaggregation Using DBSCAN: A Novel Approach for Enhanced Energy Management
No full textPart 2: The 13th Workshop on “Mining Humanistic Data” (MHDW)International audienceIn the rapidly evolving technology landscape, smart homes are becoming increasingly common, driven by the demand for integrated management of information and services. However, despite advancements, managing electricity consumption efficiently remains a significant challenge, primarily due to the lack of detailed usage data and the complexity of predicting device behavior. This study addresses these challenges by utilizing the DinRail Cerberus meter for granular data collection on household electricity use and applying the DBSCAN clustering algorithm for unsupervised learning. Our research aims to develop a forecasting system that accurately discerns the operational status of household devices-active or inactive-based on energy consumption patterns. This innovative approach promises to revolutionize energy management in smart homes, offering detailed insights into device usage that facilitate more informed decisions for efficient electricity consumption
Computational Thinking and Computing linked to Problem-solving across the Primary Curriculum: Groggan Primary School, Northern Ireland Case Study: IFIP Task Force on ‘Sustaining relevant digital inclusive education for young people (5-18 years of age)’
No full textLessons Learned from EdTech Integration During the COVID-19 Pandemic
No full textPart 5: ICT Curriculum and EducationInternational audienceThe integration of Information and Communication Technology (ICT) into education, such as EdTech, in low- and middle-income countries has provoked various global debates. Meanwhile, the worldwide COVID-19 pandemic since 2020 has transformed the situation, elevating ICT as essential for education access. Thus, to systematically address the issues that need consideration when introducing EdTech at the national level, a case review of EdTech implementations in Bhutan and Nepal was conducted. We selected these countries as cases for comparison from the perspective of project timeline, including responses to the COVID-19 pandemic. The review took place from a socio-technical perspective encompassing the “conceptualizing and planning phases” and “execution phase” of the project life cycle. As a result, the cases were organized by their respective phases, and similarities and differences between the two cases were analyzed. After the analysis and comparison, generalizable lessons were identified; the usefulness of low-tech media, the importance of locality, and the necessity of capacity/infrastructure development were the major factors in realizing effective EdTech projects. These insights will contribute to the effective design and implementation of EdTech in the future
A Pilot Mobile App to Collect South African Child Language Data
No full textPart 7: Research in Indigenous African LanguagesInternational audienceTo create effective early childhood development resources and language development intervention programs, we must be informed of linguistic developmental benchmarks. To be informed, we need data - in quality and quantity. In South Africa, there is a project undergoing language data collection in 10 of our 12 official languages, in the form of standardised questionnaires answered by caregivers. For the final phase of data collection, approximately 20000 questionnaire responses are required. To do so, we present the pilot version of a multi-modal mobile app. This app is the product of an extensive user-centered co-design process with stakeholders ranging from lead researchers to young mothers, where their feedback has been the main design influence. It has supplementary audio support, as a mode of presenting the questionnaire and data collection. The pilot app contains a subset of the questionnaire in three languages - South African English, isiXhosa and Afrikaans
A Social Justice Analysis of an African Open Science Initiative
No full textPart 9: Technology and Social JusticeInternational audienceThe case study presented in this paper can be argued to embody the representation of two dominant discourses that frame the persistent problem of lack of African research visibility in global knowledge systems. The first proceeds from a deficit perspective, suggesting endemic dysfunctions within in-country research systems, while the second recognizes potential systemic injustices, which are deemed to create unequal opportunities for knowledge production and dissemination for African scholars. Four selected interventions from the case study are analyzed using a social justice framework, developed in the paper, that synthesizes these two critical perspectives. It was found that all but one of the interventions emanate from a country deficit discourse and that the actions taken tend mainly to ameliorate, not challenge this perspective. The paper concludes by demonstrating through the social justice lens why the African research visibility issue continues to persist and hints that counter narratives initiated by the case study could offer an alternative outcome
Incentivized Federated Learning with Local Differential Privacy Using Permissioned Blockchains
No full textPart 7: Differential PrivacyInternational audienceFederated Learning (FL) is a collaborative machine learning approach that enables data owning nodes to retain their data locally, preventing its transfer to a central server. It involves sharing only the local model parameters with the server to update a global model, which is then disseminated back to the local nodes. Despite its iterative convergence, FL has several limitations, such as the risk of single-point failure, inadequate incentives for participating nodes, and potential privacy breaches. While Local Differential Privacy (LDP) is often used to mitigate privacy concerns, the other challenges of FL have not yet been addressed comprehensively, even for Locally Differentially Private Federated Learning (LDP-FL). We propose an integrated approach that uses permissioned blockchains to guard against a single point of failure and a token-based incentivization (TBI) mechanism for encouraging participation in LDP-FL. In our scheme, participating nodes receive tokens upon sharing their model parameters, which can subsequently be used to access updated global models. The number of tokens awarded for parameter sharing is determined by ϵ - the privacy factor of LDP, ensuring that the nodes do not overly obfuscate the data they share. We demonstrate the feasibility of our approach by developing the Blockchain-based TBI-LDP-FL framework (hereinafter, referred to as BTLF) on HyperLedger Fabric. Extensive results of experimentation establish the efficacy of BTLF
Machine-Learning Phishing Detection Model Used in the E-Banking Environment
No full textInternational audienceThe exponential expansion of Internet usage has given rise to a significant upsurge in cyberattacks, which have caused damage to brand reputation, privacy and financial information, and identities. Phishing, an enduring cyber threat, has emerged as a substantial concern because it can cause considerable financial detriment to economies and erode users’ confidence in e-commerce and online banking. The purpose of this research is to identify phishing websites through the development of a Phishing URL Detection Model (PUDM) utilising machine learning techniques. The model utilises machine-learning algorithms and a standard UCI machine-learning library dataset. The performance of the proposed methods surpassed that of several machine learning algorithms when compared to related work for this study. A feature importance analysis was performed to ascertain the features that would be employed to distinguish phishing URLs from authentic ones. The study determined that the Google Index feature had the greatest impact on determining the validity of website URLs. In contrast, the XGBoost classifier demonstrated the highest performance, attaining an F1 score of 92.72%. The results of this study have the potential to significantly enhance the security measures in place for organisations, clients, and website proprietors
Navigating Unpredictability: Collaborative Networks in Non-linear Worlds: 25th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2024, Albi, France, October 28–30, 2024, Proceedings, Part I
No full textInternational audienceThis two-volume set, IFIP AICT 726 and 727, constitutes the refereed proceedings of the 25th IFIP WG 5.5 Working Conference on Virtual Enterprise, PRO-VE 2024, held in Albi, France, during October 28–30, 2024.The 56 full papers presented in these two volumes were carefully reviewed and selected from 113 submissions. The papers presented in these two volumes are organized in the following topical sections:Part I: AI and collaboration; Human-machine collaboration; Emotions and collaborative networks; Collaborative ecosystems: Skills for resilient futures; Collaborative ecosystems: Technologies for resilient futures; Uncertainty and collaboration in supply chain; Collaborative networks as driver of innovation in organizations 5.0: Models; Collaborative networks as driver of innovation in organizations 5.0: Participation; Trust and trustworthy technologies in collaborative networks.Part II: Empowering vulnerable populations well-being through collaborative networks; Collaborative manufacturing systems in the digital era; Fostering collaborative and interoperable digital models for digital twins: Methods; Fostering collaborative and interoperable digital models for digital twins: Cases; Zero defects and zero waste strategies in industrial collaborative networks; Simulation frameworks; Collaborative decision making; Design of collaborative environments