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Top Cyber Threats: The Rise of Ransomware
No full textInternational audienceRansomware stands out as a particularly malicious type of cyberattack, wielding the potential to inflict severe financial, operational, and reputational harm. The insidious nature of ransomware, with their ability to encrypt or exfiltrate sensitive data, demands a paradigm shift in cybersecurity strategies. Further, the ability to predict and forecast such malware is paramount for bolstering overall cybersecurity resilience. In this paper, we provide a comprehensive overview of the evolution of ransomware, detailing key characteristics and specifications of zero-day ransomware, along with limitations in static and dynamic analyses, with the goal of identifying effective defense strategies. Despite substantial advancements, enduring challenges and notable research gaps persist, necessitating ongoing innovation in cybersecurity. Furthermore, this study serves as a valuable resource for cybersecurity professionals and researchers, shedding light on key trends, challenges, limitations, and potential avenues for future research in the realm of ransomware forecasting
Towards Interconnected Quantum Networks: A Requirements Analysis
No full textInternational audienceMost of nowadays quantum key distribution (QKD) networks rely on trusted nodes. While trusted nodes allow for overcoming range limitations of QKD, their use hinders building interconnected networks as trust in every node has to be granted in such networks. In this paper, we focus on the requirements to enable interconnected QKD networks and sketch a way towards an interconnected multi-vendor/multi-provider QKD research network
Evaluating the Effectiveness of Generative AI in TRIZ: A Comparative Case Study
No full textPart 1: AI-Driven TRIZ and InnovationInternational audienceThe rapid advances in generative AI technologies have sparked a debate among researchers on their role in the innovation process, particularly regarding their problem-solving and idea-generation capabilities. While researchers theorise the potential of generative AI in conjunction with TRIZ (Theory of Inventive Problem Solving), evaluating its current state and understanding its practicality is equally critical. Hence, this paper provides evidence of generative AI’s ability to offer solutions in real innovation projects. Our exploratory study compares the results of an actual innovation project in a professional consulting-like setting using traditionally applied modern TRIZ tools against generative AI-assisted results for the same customer-defined problem. The comparison focuses on the solutions’ degree of similarity, depth, and breadth. Additionally, our research identifies the advantages, disadvantages, and feasibility of using generative AI in problem-solving and innovation projects. Our findings indicate that combining generative AI and TRIZ produces feasible, cross-domain preliminary conceptual directions with satisfactory scientific substantiation. Lastly, we recommend suitable use cases for innovation managers and TRIZ practitioners, highlighting how the TRIZ-GPT combination can save considerable time exploring preliminary concepts and idea generation during problem-solving
The Evolving Landscape of TRIZ: A Generative AI-Powered Perspective
No full textPart 1: AI-Driven TRIZ and InnovationInternational audienceThe surge of Generative AI has revolutionized problem-solving, giving rise to innovative tools that unlock unprecedented solutions and cross-industry breakthroughs within the TRIZ methodology. This paper unveils five groundbreaking Generative AI- integrated tools designed to enhance innovation and problem-solving across diverse domains.1.Mechanism Oriented Search (MOS): Identifies and analyzes specific problem mechanisms, abstracting them for cross-industry comparison, facilitating the discovery of innovative solutions by applying insights from one field to challenges in another.2.Resource Innovator for Non-Engineering: Extends TRIZ to non-engineering fields, focusing on identifying and leveraging unique resources within domains like nursing, education, and communication, empowering users to uncover hidden potential.3.TRIZ FOS-Market Explorer: Facilitates the discovery and analysis of adjacent market opportunities by abstracting the primary function of a product or service and identifying similar functions across various industries, revealing potential new markets.4.Systematic Idea Generation: Employs detailed resource analysis and TRIZ principles to facilitate innovation within existing systems, categorizing resources and suggesting strategic modifications to components or processes.5.Function Redirector: Fosters innovation by redirecting functions and resources towards achieving goals in novel ways, deconstructing primary functions into auxiliary functions to stimulate creative problem-solving.These tools collectively harness the power of Generative AI to revolutionize problem-solving and innovation across various sectors, offering structured analysis, imaginative recombination, and cross-disciplinary insights
Internet of Things. Advances in Information and Communication Technology: 6th IFIP International Cross-Domain Conference, IFIPIoT 2023, Denton, TX, USA, November 2–3, 2023, Proceedings, Part II
No full textInternational audienceBook Front Matter of AICT 68
Advancing Manufacturing with Interpretable Machine Learning: LIME-Driven Insights from the SECOM Dataset
No full textPart 3: Hybrid Intelligence – Decision-Making for AI-Enabled Industry 5.0International audienceThis study introduces an interpretable machine learning (ML) framework tailored for the semiconductor manufacturing industry, with a strong focus on ensuring model transparency and understandability. In a domain where manufacturing efficiency and product quality are of utmost importance, our research introduces bespoke ML models designed to predict product quality with remarkable accuracy while elucidating the factors driving these predictions. Indeed, the pervasive challenge of model opacity impedes the manufacturing industry to fully leverage ML advancements for operational excellence. To address this critical gap, our study introduces an interpretable ML framework. This framework not only enhances model transparency and understandability but also ensures the precision of product quality predictions. Central to our approach is the application of LIME (Local Interpretable Model-agnostic Explanations), which demystifies the predictive mechanisms of ML models. By elucidating the underlying factors influencing product quality predictions, our methodology empowers operation managers with actionable insights for preemptive quality control and process optimization. Utilizing the UCI SECOM dataset, this paper exemplifies how interpretability in ML transcends conventional analytics, facilitating informed decision-making and fostering a culture of operational excellence
Pick-and-Place Robotics Implementation Under the Influence of Lean Manufacturing – A Process Model
No full textPart 1: Lean Thinking Models for Operational Excellence and Sustainability in the Industry 4.0 EraInternational audienceThe article seeks to develop a process model to guide research and practice in the effective integration of robotics for pick-and-place activities in manufacturing firms, where lean management tools and practices are being embraced. Utilizing a multiple case study analysis, the researchers conducted on-site visits to production facilities and analyzed 16 diverse projects, 11 coming from manufacturing companies and 5 projects provided by System Integrators, to gain heterogeneous and wide-spanning insights. The unit of analysis is the single robotic implementation, spanning across various sectors to extract patterns independently from the specific industry. These projects yielded a substantial volume of information, knowledge, and best practices related to the adoption of Robotics. Following a meticulous examination of the case studies, a process model was formulated to guide companies through decision-making, implementation, monitoring, and sustain stages in robotics introduction projects. This research disentangles the influence of lean management in ensuring the optimization of benefits derived from such projects. The process model seeks to offer practical guidance for companies approaching the complexities of robotics integration within manufacturing processes, successfully filling in the pre-existing research gap
What Matters for Managers When Adopting Cobots in Manufacturing Organisations? - The Results of a Survey Study in Portuguese SMEs
No full textPart 2: Human in Command – Operator 4.0/5.0 in the Age of AI and Robotic SystemsInternational audienceCollaborative robots, or cobots, are increasingly used by manufacturing companies to meet the demands for greater flexibility and to adapt to the trend of mass customisation in production. When considering the adoption of cobots, companies enter a critical decision-making phase. This study aims to identify the relevant decision factors for adopting collaborative robots (cobots) in manufacturing medium-sized enterprises (SMEs) in Portugal, using a combined framework of Technology-Organisation-Environment (TOE), Diffusion of Innovations (DOI) theory, and Institutional Theory. Data was collected through an online survey distributed to Portuguese manufacturing companies, yielding 78 valid responses. Analysis conducted using SmartPLS 4 revealed that top management support, resource availability, and industry pressure significantly influence the adoption decision. However, factors such as the relative advantage of cobots, compatibility with existing processes, organisational innovativeness, human resources quality, and external support did not significantly impact SMEs’ adoption of cobots. These findings enhance the understanding of technology management, specifically the process of adopting cobots in manufacturing. The insights from this study help managers focus on the key factors critical for successful cobot adoption, supporting decision-makers in making more informed choices
Utilizing the Shapley Value to Measure Individual Productivity in the Service Industry
No full textPart 4: Mechanism Design for Smart and Sustainable Supply ChainsInternational audienceMeasuring productivity, particularly at the individual employee level, has significant implications for organizational success, such as the standardization and guidance of employee behavior. Although various statistical methods have been applied to measure overall productivity, an alternative approach that considers intricate individual-organizational relationships and data availability to develop a robust measurement model is required. To address this gap, this study adopts the Shapley value concept from cooperative game theory to quantify individual productivity in the service industry. By treating the contribution to hourly revenue as the expected marginal contribution of each employee, two methods are proposed for the different data structures available. Through simulations, these methods are compared with other statistical methods. The results indicate the feasibility and efficacy of the Shapley value approach for accurately measuring individual productivity with satisfactory levels of accuracy, stability, and practicality
Mechanism Design for Agricultural Machinery Sharing
No full textPart 4: Mechanism Design for Smart and Sustainable Supply ChainsInternational audienceThe decline in agriculture, owing to the reduced number of farms, has been countered by adopting advanced technologies aimed at increasing production scale. However, in Japan, despite using such advanced technologies, the unique topographical features render scaling up difficult. Effective collaboration between farmers is required for high productivity in small and medium-sized farms. Although large companies provide top-down services for agricultural machinery sharing (AMS), several small-scale but scattered niche demands remain, some of which are solved via a bottom-up sharing practice based on trust between participating farmers. As the dependence on precarious trust renders the spreading of such a practice challenging, this study proposes an AMS mechanism for replacing trust with monetary incentives. Moreover, the impact of weather uncertainty on the machine use schedule is considered. The mechanism utilizes certain rules to cause the schedule to respond to uncertain weather in the ex-ante stage. Three rules, namely order, release date, and period rules, are proposed, and their characteristics are analyzed through computational experiments. The results reveal that the order rule depends on the participating farmers. The release date rule facilitates farmers in using the machine more frequently, and the period rule tends to equalize farmers’ revenue; however, both rules provide similar results