Access control in cloud-based computing environments is in essence a security mechanism enabling decisions as to which subjects may access which protected resources and under what operations. The rapid emergence of quantum computing threatens the hardness assumptions underlying classical digital signatures and public-key infrastructures. The authors of this work recently proposed a novel quantum ice-cream model to demonstrate the basic concepts of quantum cryptography in a simple and intuitive manner. In this work we present a novel quantum ice-cream digital signature scheme by using our proposed quantum ice-cream model. Our quantum ice-cream digital signature scheme maps two non-commuting measurement bases to the fl'avor'and 'shape'attributes of the quantum ice-cream model. It achieves information-theoretic security against forgery and repudiation via threshold tests and natively supports signer-verifier-arbiter workflow that is suitable for cloud access control. Although our scheme is presented theoretically, a rigorous mathematical analysis has been carried out using Hoeffding-type inequalities. We also discuss the implementation of our proposed scheme by using weak-coherent BB84-like optics with decoy states. This article demonstrates that our proposed quantum ice-cream digital signature scheme preserves the practicality of memoryless quantum digital signature schemes while aligning with cloud authorization requirements. Furthermore, we draw analogies between the design of our proposed QIDS and robust control theory to provide an interdisciplinary perspective on system stability and security.
Wireless Sensor Networks (WSNs) are widely applied in domains such as environmental monitoring, healthcare, and defense, where accurate localization of sensor nodes is essential for reliable data interpretation. Despite extensive research, localization continues to face challenges related to energy consumption, scalability, and algorithmic efficiency. This paper presents a systematic review of position computation algorithms, emphasizing how different approaches address these challenges. The analysis highlights key trade-offs, such as accuracy versus energy cost and convergence speed versus computational overhead. It also identifies clear research trends, including a shift from traditional range-based and range-free methods toward hybrid and AI-assisted techniques that enhance adaptability in dynamic environments. Persistent open issues remain, notably handling mobility, achieving robustness under noisy conditions, and enabling deployment in large-scale real-world settings. By synthesizing these insights, the study clarifies the current state of WSN localization, outlines unresolved gaps, and points to future directions for energy-aware, scalable, and learning-driven solutions.
This bibliometric study analyzes the evolution of research on digital actors from 1995 to 2023 using data from the Scopus and Web of Science databases. Through a systematic and rigorous selection process, the study identifies publication growth patterns, leading authors and journals, geographical contributions, and the thematic evolution of the field. Unlike previous bibliometric reviews focused broadly on digital twins or virtual avatars, this study uniquely examines the intersection between digital twin technologies and performance-oriented digital actors, integrating perspectives from engineering, artificial intelligence, and digital character creation. The findings delineate an emerging and previously underexplored research subfield and provide a structured framework that highlights conceptual trends, research gaps, and directions for future studies.
The rapid integration of generative artificial intelligence into higher education has sparked contrasting reactions, ranging from technological enthusiasm to ethical concerns. This study aims to qualitatively and interpretatively examine higher education students' attitudes towards using ChatGPT in academic settings. Based on semi-structured interviews with a diverse sample of students and an in-depth semantic analysis of the transcripts, fourteen key variables were identified and grouped into three overarching dimensions: techno-cognitive, academic-pragmatic, and axiological-ethical. The findings reveal ambivalent attitudes shaped by the lack of institutional regulation, performance-driven logic, and individual moral frameworks. This research proposes a conceptual model that expands traditional technology acceptance frameworks by incorporating normative, emotional, and identity-based dimensions. Beyond its empirical contribution, the study underscores the urgency of implementing a critical and ethically grounded pedagogical framework for using AI in higher education.
Large Language Models (LLMs) have accelerated code generation, yet their security implications remain underexplored. This work evaluates the capability of eight general-purpose and code-specialized LLMs to detect weaknesses and vulnerabilities in generated code using CVE and CWE references. Stratified sampling of the CVEfixes dataset across five programming languages is assessed using precision, recall, F1-score, and relevance-oriented metrics (REINP, REINR, and REINF1). Results show limited detection of specific vulnerabilities (CVE), but better performance for general weaknesses (CWE), especially in Python and Ruby. We discuss limitations in model training and prompt conditioning, and outline improvements through dataset diversification, prompt engineering and hybrid human-in-the-loop approaches. The study highlights the current potential and limitations of LLMs for practical vulnerability detection in generated code.
