Hasil untuk "Computer Science"

Swathi Muthyala Ramesh, Kristen M. Donnell

Frequency selective surfaces (FSSs) are arrays of conductive elements or apertures that exhibit frequency-dependent reflection and transmission properties. Their electromagnetic response is influenced by geometry and environmental conditions, making them attractive for wireless strain-sensing applications. However, temperature variations can produce frequency shifts similar to those caused by strain, reducing measurement accuracy. This work investigates the effects of intrinsic temperature compensation on two common FSS unit cell geometries&#x2014;loop and patch&#x2014;through comprehensive simulation analysis. The results show that loop-based cells offer superior thermal stability, while patch-based cells provide greater strain sensitivity, illustrating the tradeoff between thermal robustness and mechanical responsiveness. A patch-type FSS strain sensor was designed, fabricated, and characterized under varying temperature and strain. The sensor achieves a strain sensitivity of ~150 MHz per 1%<inline-formula> <tex-math notation="LaTeX">${\varepsilon }_{l}$ </tex-math></inline-formula>, while temperature-induced drift is limited to ~12 MHz over a 200&#x00B0;C range, confirming the effectiveness of the intrinsic compensation strategy. The results provide valuable insights for optimizing FSS-based sensor design in structural health monitoring applications and balancing thermal stability with mechanical sensitivity to ensure reliable performance in thermally dynamic environments.

L. Raghavendar Raju, M. Venkata Krishna Reddy, Sridhar Reddy Surukanti et al.

Abstract Edge–cloud computing has emerged as an important paradigm for modern Internet of Things (IoT) workflow applications, enabling low latency and on-demand resource allocation. In scenarios with heterogeneous deadlines and varying workloads, SLA compliance requires efficient coordination between edge and cloud resources. However, cloud-centric scheduling and heuristic approaches tend to lack adaptability to rapidly changing system conditions and, as a result, experience long waiting times (the same applies to QoS). To tackle these issues, we present IntelliScheduler, a hybrid actor–critic deep reinforcement learning framework for adaptive task scheduling in an edge–cloud system. Our framework presents a runtime-aware state representation combined with a learning-based decision mechanism, backed by a multi-buffer experience replay architecture. Second, a learning-based optimal task scheduling (LbOTS) algorithm is developed to minimise total task execution delay by discovering optimal deployment decisions across edge and cloud computational resources using latency-aware reward modelling. We assess the proposed approach by conducting extensive simulation experiments under different workloads. We evaluate LbOTS across various experimental scenarios and report up to 13% higher normalised reward, 67% lower training loss, 52–66% lower operational cost, and 80–90% lower rejection rate compared to PSO, MBO, and MOPSObaselines, achieving approximately 15–75% better QoE. Though the current assessment is simulation-based, the adaptive learning formulation is highly relevant for application in dynamic edge–cloud scheduling scenarios.

Yang Cai, Yunli Hao, Yongfang Qi

The niche situation can reflect the advantages and disadvantages of biological individuals in the ecosystem environment as well as the overall operational status of the ecosystem. However, higher-order niche systems generally exhibit complex nonlinearities and parameter uncertainties, making it difficult for traditional Type-1 fuzzy control to accurately handle their inherent fuzziness and environmental disturbances in complex environments. To address this, this paper introduces the backstepping control method based on Type-2 T-S fuzzy control, incorporating the niche situation function as the consequent of the T-S backstepping fuzzy control. The stability analysis of the system is completed by constructing a Lyapunov function, and the adaptive law for the parameters of the niche situation function is derived. This design reflects the tendency of biological individuals to always develop in a direction beneficial to themselves, highlighting the bio-inspired intelligent characteristics of the proposed method. The results of case simulations show that the Type-2 backstepping T-S fuzzy control has significantly superior comprehensive performance in dealing with the complexity and uncertainty of high-order niche situation systems compared with the traditional Type-1 control and Type-2 T-S adaptive fuzzy control. These results not only verify the adaptive and self-development capabilities of biological individuals, as well as their efficiency in environmental utilization, but also endow this control method with a solid practical foundation.

Leonhard Ziegler, Michael Grabatin, Daniela Pöhn et al.

Abstract While self-sovereign identities (SSI) have been gaining more traction, the topic of SSI security has yet to be addressed. Especially regarding response procedures to security incidents, no prior work is available. However, incident response processes are essential to systematically respond to a security incident in a timely manner. We first evaluate the current state-of-the-art by conducting a literature survey and contacting organizations that offer SSI. The insights underpin the subject’s relevance, highlighting that incident response capabilities are just starting to be developed. Contributing to this development, we identify the challenges of building a security incident response process for SSI. Mainly, the decentralized nature inhibits the utilization of known best practices, which all focus on building a centralized incident response capability. However, even in the case of SSI, some centralized entities may exist. Therefore, we design two variants of SIR processes: one more centralized and one more decentralized. For the latter, the problem size is reduced in the first step by identifying all the stakeholders within an SSI ecosystem and then analyzing possible proactive and reactive measures each participant can access. This procedure leads to the grouping of SSI system participants into three distinct domains of incident response. For each domain, different capabilities for handling incidents are introduced depending on the involved stakeholders, their infrastructure, and their goals. To demonstrate the procedures, incident scenarios for each domain highlight the workflows during incident handling.

Bakul Akter, Silvia Aishee, Abdullah Hridoy et al.

Etoricoxib (ETC), a selective cyclooxygenase enzyme (COX-2) inhibitor, is widely utilized to manage pain and inflammation. Nevertheless, its therapeutic efficacy is limited by poor aqueous solubility, low bioavailability, and significant cardiovascular risks, including increased blood pressure, thrombosis, and the potential for myocardial infarction. This study aimed to address these limitations through structural modifications of etoricoxib. A total of 21 derivatives were designed by introducing various functioning sets at the R3, R2, and R1 sites of ETC. Quantum chemical calculations were performed to assess alterations in physicochemical properties, such as HOMO–LUMO energy gaps, electrostatic potential, enthalpy, and dipole moments. Notably, most of the derivatives showed improved binding affinities, particularly ETC9 and ETC19, demonstrating the highest binding interactions in molecular docking studies (-10.1 and -10.8 kcal/mol, respectively). Furthermore, molecular dynamics (MD) simulations accomplished by exploiting the YASARA dynamics software program with the AMBER14 energy field throughout 100 ns revealed that the ETC9 and ETC19 derivatives exhibited enhanced stability and flexibility profiles compared to the parent drug, ETC. ADMET and PASS predictions confirmed the drug-like properties of most derivatives, particularly ETC19 and ETC9, which also showed improved absorption, better blood-brain barrier penetration, and reduced toxicity. These outcomes underscore the prospect of the de novo-designed etoricoxib analogues as safer and more effective alternatives, effectively addressing the pharmacological limitations and safety concerns associated with the parent drug.

XIAO Zhipeng, HE Shufeng, TIAN Chunqi

This study presents a facial emotion recognition network based on UniRepLKNet to address the difficulty in effectively capturing feature information and preventing key facial information from occupying a more prominent position in the facial emotion recognition process. Moreover, to extract facial emotional features more accurately, the study designs a masked polarized self-attention module that combines U-Net and a polarized self-attention mechanism. This module can deeply mine the dependency between channels and spaces. It can also strengthen the influence of local key information of the face on emotion recognition through a multi-scale feature fusion strategy. The study optimizes UniRepLKNet, a universal large kernel Convolutional Neural Network (CNN), and proposes the EmoRepLKNet neural network structure. In EmoRepLKNet, the mask-polarized self-attention module enables the network to extract key information for facial emotion recognition. Combined with the wide receptive field of large kernel CNN, facial emotions can be recognized effectively. Experimental results show that on the facial emotion recognition dataset FER2013, EmoRepLKNet achieves an accuracy of 76.20%, outperforming existing comparison models and significantly improving facial emotion recognition accuracy compared to that of UniRepLKNet. Additionally, on the single-label portion of the RAF-DB dataset, the proposed method achieves an accuracy of 89.67%.

Tahiya Chowdhury

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.

Ivan Baburin, Matthew Cook, Florian Grötschla et al.

In this work, we investigate the computational aspects of asynchronous cellular automata (ACAs), a modification of cellular automata in which cells update independently, following an asynchronous schedule. We introduce flip automata networks (FAN), a simple modification of automata networks that remain robust under any asynchronous update schedule. We show that asynchronous automata can efficiently simulate their synchronous counterparts with a linear memory overhead, which improves upon the previously established quadratic bound. Additionally, we address the universality gap for (a)synchronous cellular automata -- the boundary separating universal and non-universal automata, which is still not fully understood. We tighten this boundary by proving that all one-way asynchronous automata lack universal computational power. Conversely, we establish the existence of a universal 6-state first-neighbor automaton in one dimension and a 3-state von Neumann automaton in two dimensions, which represent the smallest known universal constructions to date.

Seung Ju Kim, Hyeon-Ji Lee, Chul-Ho Lee et al.

Abstract Neuromorphic hardware enables energy-efficient computing, which is essential for a sustainable system. Recently, significant progress has been reported in neuromorphic hardware based on two-dimensional materials. However, traditional planar-integrated architectures still suffer from high energy consumption. This review systematically explores recent advances in the three-dimensional integration of two-dimensional material-based neuromorphic hardware to address these challenges. The materials, process, device physics, array, and integration levels are discussed, highlighting challenges and perspectives.

Li Yizhan, Dong Lu, Fan Xiaoxiao et al.

Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.

Zhenwen He, Xianzhen Liu, Chunfeng Zhang

Three-dimensional voxel models are widely applied in various fields such as 3D imaging, industrial design, and medical imaging. The advancement of 3D modeling techniques and measurement devices has made the generation of three-dimensional models more convenient. The exponential increase in the number of 3D models presents a significant challenge for model retrieval. Currently, these models are numerous and typically represented as point clouds or meshes, resulting in sparse data and high feature dimensions within the retrieval database. Traditional methods for 3D model retrieval suffer from high computational complexity and slow retrieval speeds. To address this issue, this paper combines spatial-filling curves with octree structures and proposes a novel approach for representing three-dimensional voxel model sequence data features, along with a similarity measurement method based on symbolic operators. This approach enables efficient similarity calculations and rapid dimensionality reduction for the three-dimensional model database, facilitating efficient similarity calculations and expedited retrieval.

Sagnik Dakshit

The emergence of Large Language Models (LLMs) has significantly impacted the field of Natural Language Processing and has transformed conversational tasks across various domains because of their widespread integration in applications and public access. The discussion surrounding the application of LLMs in education has raised ethical concerns, particularly concerning plagiarism and policy compliance. Despite the prowess of LLMs in conversational tasks, the limitations of reliability and hallucinations exacerbate the need to guardrail conversations, motivating our investigation of RAG in computer science higher education. We developed Retrieval Augmented Generation (RAG) applications for the two tasks of virtual teaching assistants and teaching aids. In our study, we collected the ratings and opinions of faculty members in undergraduate and graduate computer science university courses at various levels, using our personalized RAG systems for each course. This study is the first to gather faculty feedback on the application of LLM-based RAG in education. The investigation revealed that while faculty members acknowledge the potential of RAG systems as virtual teaching assistants and teaching aids, certain barriers and features are suggested for their full-scale deployment. These findings contribute to the ongoing discussion on the integration of advanced language models in educational settings, highlighting the need for careful consideration of ethical implications and the development of appropriate safeguards to ensure responsible and effective implementation.

Michael Holly, Jannik Hildebrandt, Johanna Pirker

Skills in the field of computer science (CS) are increasingly in demand. Often traditional teaching approaches are not sufficient to teach complex computational concepts. Interactive and digital learning experiences have been shown as valuable tools to support learners in understanding. However, the missing social interaction affects the quality of the learning experience. Adding collaborative and competitive elements can make the virtual learning environment even more social, engaging, and motivating for learners. In this paper, we explore the potential of collaborative and competitive elements in an interactive virtual laboratory environment with a focus on computer science education. In an AB study with 35 CS students, we investigated the effectiveness of collaborative and competitive elements in a virtual laboratory using interactive visualizations of sorting algorithms.

Daniel López-Fernández, Ricardo Vergaz

Contribution: The combination of ChatGPT with traditional learning resources is very effective in computer science education. High-performing students are the ones who are using ChatGPT the most. So, a new digital trench could be rising between these students and those with lower degree of fundamentals and worse prompting skills, who may not take advantage of all the ChatGPT possibilities. Background: The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences in computer science education are needed to better understand these tools and know how to use them to their fullest potential. Research Questions: This article addresses three questions. The first two explore the degree of use and perceived usefulness of ChatGPT among computer science students to learn database administration, where as the third one explore how the utilization of ChatGPT can impact academic performance. Methodology: This contribution presents an exploratory and correlational study conducted with 37 students who used ChatGPT as a support tool to learn database administration. The student grades and a comprehensive questionnaire were employed as research instruments. Findings: The obtained results indicate that traditional learning resources, such as teacher explanations and student reports, were widely used and correlated positively with student grade. The usage and perceived utility of ChatGPT were moderate, but positive correlations between student grade and ChatGPT usage were found. Indeed, a significantly higher use of this tool was identified among the group of outstanding students.