Hasil untuk "Electronic computers. Computer science"

Qingfu Zhang, Aimin Zhou, Shi-Zheng Zhao et al.

J. Hafner

J. Sabater-Mir, C. Sierra

W. Bainbridge

J. Behler

Joseph Faisal Nusairat

Mujaheed Abdullahi, Yahia Baashar, Hitham Alhussian et al.

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

I. Siddiqi

Benyou Wang, Qianqian Xie, Jiahuan Pei et al.

Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing tasks. This also benefits the biomedical domain: researchers from informatics, medicine, and computer science communities propose various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks. However, the cross-discipline characteristics of biomedical PLMs hinder their spreading among communities; some existing works are isolated from each other without comprehensive comparison and discussions. It is nontrivial to make a survey that not only systematically reviews recent advances in biomedical PLMs and their applications but also standardizes terminology and benchmarks. This article summarizes the recent progress of pre-trained language models in the biomedical domain and their applications in downstream biomedical tasks. Particularly, we discuss the motivations of PLMs in the biomedical domain and introduce the key concepts of pre-trained language models. We then propose a taxonomy of existing biomedical PLMs that categorizes them from various perspectives systematically. Plus, their applications in biomedical downstream tasks are exhaustively discussed, respectively. Last, we illustrate various limitations and future trends, which aims to provide inspiration for the future research.

Jian-Dong Yao, Wen-Bin Hao, Zhi-Gao Meng et al.

This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant (VPP) networks using multi-agent reinforcement learning (MARL). As the energy landscape evolves towards greater decentralization and renewable integration, traditional optimization methods struggle to address the inherent complexities and uncertainties. Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator and individual VPPs, optimizing economic efficiency while maintaining grid stability. We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay. Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. The MARL framework shows particular strength in scenarios with high renewable energy penetration, where it improves system performance by 11.95% compared with traditional methods. Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.

Benjamin Monmege, Julie Parreaux, Pierre-Alain Reynier

Weighted Timed Games (WTG for short) are the most widely used model to describe controller synthesis problems involving real-time issues. Unfortunately, they are notoriously difficult, and undecidable in general. As a consequence, one-clock WTGs have attracted a lot of attention, especially because they are known to be decidable when only non-negative weights are allowed. However, when arbitrary weights are considered, despite several recent works, their decidability status was still unknown. In this paper, we solve this problem positively and show that the value function can be computed in exponential time (if weights are encoded in unary).

Konrad Miziński, Sławomir Przyłucki

The aim of this study was to investigate the impact of eBPF technology on the performance of network solutions in Kubernetes clusters. Two configurations were compared: a traditional iptables-based setup and eBPF based solution via the Cilium networking plugin. Performance tests were conducted, measuring throughput, latency, CPU usage, and memory consumption under unloaded and loaded conditions. The results indicate that the traditional configuration achieved higher throughput and lower latency in unloaded scenarios. However, under load, the eBPF-enabled cluster demonstrated advantages, including reduced CPU and memory usage and slightly improved latency. This study highlights the potential of eBPF as an efficient technology for Kubernetes environments, particularly in scenarios demanding high performance and resource efficiency.

Johandri Vosloo, Kenneth R. Uren, George van Schoor

The Tennessee Eastman process serves as a benchmark system for the evaluation of fault diagnosis techniques. Current simulator implementations are available in FORTRAN and in a C-mex S-function in MATLAB. The C-mex file is a conversion of the FORTRAN code to C for implementation in MATLAB. Both implementations have the limitation that not all the variables and parameters are directly accessible. Hence, a complete and open Tennessee Eastman process simulator was developed in Simulink to allow for total access to all parameters and variables and better Simulink integration. This implementation will give researchers more freedom towards the design of control and fault diagnosis techniques.

Jia Wang, Yingxue Wang, Yanan Chen

It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples’ current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.

C. Meng

Aleksander Muc

The static and fatigue analysis of plates made of 2D woven roving composites with holes is conducted. The parametrization of convex holes is proposed. The experimental results of the specimens without holes and with different shapes of notches are discussed. The experiments and the appropriate procedures are carried out with the aid of ASTM codes. The fatigue behavior is considered with the use of the low cycle fatigue method. The analysis is supplemented by numerical finite element modeling. The present work is an extension of the results discussed in the literature. The damage of plates with holes subjected to tension always occurs at the tip of the holes, i.e., (x = a, b = 0), both for static and fatigue failure. The originality and the novelty of this approach are described by the failure’s dependence on two parameters: n and the ratio of the a/b ratio characterizing the hole geometry. The fuzzy approach is employed to reduce the amount of experimental data.

Youhua Wang, Yitao Qiu, S. K. Ameri et al.

Electronic tattoos (e-tattoos), also known as epidermal electronics, are ultra-thin and ultra-soft noninvasive but skin-conformable devices with capabilities including physiological sensing and transdermal stimulation and therapeutics. The fabrication of e-tattoos out of conventional inorganic electronic materials used to be tedious and expensive. Recently developed cut-and-paste method has significantly simplified the process and lowered the cost. However, existing cut-and-paste method entails a medical tape on which the electronic tattoo sensors should be pasted, which increases tattoo thickness and degrades its breathability. To address this problem, here we report a slightly modified cut-and-paste method to fabricate low-cost, open-mesh e-tattoos with a total thickness of just 1.5 μm. E-tattoos of such thinness can be directly pasted on human skin and conforms to natural skin texture. We demonstrate that this ultra-thin, tape-free e-tattoo can confidently measure electrocardiogram (ECG), skin temperature, and skin hydration. Heart rate and even respiratory rate can be extracted from the ECG signals. A special advantage of such ultra-thin e-tattoo is that it is capable of high-fidelity sensing with minimized motion artifacts under various body movements. Effects of perspiration are found to be insignificant due to the breathability of such e-tattoos.Flexible electronics: cutting plotters cut costs of electronic tattoosTattoos able to record heart rate and skin conditions have been fabricated with a low-cost cutting plotter. An international collaboration led by YongAn Huang and Nanshu Lu from the Huazhong University of Science and Technology in Wuhan, China, and the University of Texas at Austin, USA, have used this tool—a computer-controlled knife commonly employed to cut paper, vinyl and other materials in custom shapes—to define metallic serpentines on a plastic layer deposited on tattoo paper. The layer is so thin—about one thousandth of a millimeter—that the whole device is imperceptible when transferred on the skin, yet it adheres perfectly without blocking normal perspiration. Applied to human chest, these inexpensive metallic sensors monitor key health parameters, such as skin temperature and heart electric signal, without being affected by sweat and motion artefacts.

Guillermo De Ita, Pedro Bello, Mireya Tovar

A relevant problem in combinatorial mathematics is the problem of counting independent sets of a graph <i>G</i>, denoted by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></semantics></math></inline-formula>. This problem has many applications in combinatorics, physics, chemistry and computer science. For example, in statistical physics, the computation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></semantics></math></inline-formula> has been useful in studying the behavior of the particles of a gas on a space modeled by a grid structure. Regarding hard counting problems, the computation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></semantics></math></inline-formula> for a graph <i>G</i> has been key to determining the frontier between efficient counting and intractable counting procedures. In this article, a novel algorithm for counting independent sets on grid-like structures is presented. We propose a novel algorithm for the computation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><msub><mi>G</mi><mrow><mi>m</mi><mo>,</mo><mi>n</mi></mrow></msub><mo>)</mo></mrow></semantics></math></inline-formula> for a grid graph with <i>m</i> rows and <i>n</i> columns based on the ‘Branch and Bound’ design technique. The splitting rule in our proposal is based on the well-known vertex reduction rule. The vertex in any subgraph from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>G</mi><mrow><mi>m</mi><mo>,</mo><mi>n</mi></mrow></msub></semantics></math></inline-formula>, which is to be selected for the reduction rule, must have four internal incident faces. The ramification process is used to build a computation tree. Our proposal consists of decomposing the initial grid graph until outerplanar graphs are obtained as the ‘basic subgrids’ associated with the leave nodes of the computation tree. The resulting time-complexity of our proposal is inferior to the time-complexity of other classic methods, such as the transfer matrix method.

Arcel Christian Austria, John Simon Fabros, Kurt Russel Sumilang et al.