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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Fluid Antenna-Assisted Simultaneous Wireless Information and Power Transfer Systems

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8285-8290页

作者： Zhou, Liaoshi Yao, Junteng Wu, Tuo Jin, Ming Yuen, Chau Adachi, Fumiyuki Ningbo University faculty of Electrical Engineering and Computer Science Ningbo315211 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Sendai Japan

This paper examines a fluid antenna (FA)-assisted simultaneous wireless information and power transfer (SWIPT) system. Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs. This capability enhances both energy harvesting and communication performance. The system comprises a base station (BS) equipped with multiple FAs that transmit signals to an energy receiver (ER) and an information receiver (IR), both equipped with a single FA. Our objective is to maximize the communication rate between the BS and the IR while satisfying the harvested power requirement of the ER. This involves jointly optimizing the BS's transmit beamforming and the positions of all FAs. To address this complex convex optimization problem, we employ an alternating optimization (AO) approach, decomposing it into three sub-problems and solving them iteratively using first and second-order Taylor expansions. Simulation results validate the effectiveness of our proposed FA-assisted SWIPT system, demonstrating significant performance improvements over traditional FPA-based systems. © 2025 IEEE.

关键词： Convex optimization

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Determining category metadata in open data portals - an approach based on Formal Concept Analysis 32

Determining category metadata in open data portals - an appr...

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32nd Telecommunications Forum, TELFOR 2024

作者： Gligorijevic, Milena Frtunic Bogdanovic, Milos Stoimenov, Leonid University of Niš Faculty of Electronic Engineering Department for Computer Science Niš Serbia

ISBN: (纸本)9798350391053

The efforts for data transparency and open government initiatives have resulted in a large amount of data being published on open data portals. These portals are organized to enhance published data accessibility by providing search mechanisms that utilize metadata such as category, tags, format, and publisher. However, due to the metadata incompleteness, such as missing category information, datasets are harder to find. Therefore, within this paper, we address the issue of missing category information by proposing an approach for categorizing datasets using keywords and their usage within categories. The presented approach is based on the Formal Concept Analysis method, which is used to create a hierarchical order of keywords grounded on their usage within categories on one open data portal. Further, we present the evaluation of the presented method using data from the Canadian open data portal. © 2024 IEEE.

关键词： categorization FCA formal concept analysis metadata enrichment open data open data portals

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Traditional and parameter-efficient fine-Tuning of LLMs for sentiment analysis in the English and Serbian language 11

Traditional and parameter-efficient fine-Tuning of LLMs for ...

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11th International Conference on Electrical, electronic and Computing engineering, IcETRAN 2024

作者： Dordevic, Nikola Stojkovic, Suzana University of Niš Faculty of Electronic Engineering Department of Computer Science Niš Serbia

ISBN: (纸本)9798350386998

Fine-Tuning of large language models is often demanding in terms of computational resources and memory. Consequently, there is a need to explore new methods that can effectively fine-Tune these models without compromising performance. In this paper, we compared traditional and parameter-efficient fine-Tuning of large language model XLM-RoBERTa, across both high-resourced languages like English and low-resourced languages such as Serbian. The model is fine-Tuned for sentiment analysis when it is conducted using text classification. The experiments have shown that parameter-efficient fine-Tuning can produce results that are on par with traditional fine-Tuning, both for the English and the Serbian language. Furthermore, parameter efficient fine-Tuning approach significantly reduces the computational time and memory requirements associated with model training, thereby offering a promising avenue for resource-efficient large language models fine-Tuning. In experiments with the English language, we achieved accuracy using the PEFT approach that was only 3% lower than the accuracy obtained through the traditional approach, while reducing training time by 35% and memory requirements by 42%. In experiments involving the Serbian language, we achieved accuracy using the PEFT approach that was only 1% lower than the accuracy obtained through the traditional approach, while reducing training time by 42% and memory requirements by 69%. © 2024 IEEE.

关键词： Modeling languages

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Efficient Earth Observation Data Visualization Using Dual Orthogonal Equidistant Cylindrical Projection 11

Efficient Earth Observation Data Visualization Using Dual Or...

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11th International Conference on Electrical, electronic and Computing engineering, IcETRAN 2024

作者： Dimitrijević, Aleksandar University of Niš Faculty of Electronic Engineering Department of Computer Science Niš Serbia

ISBN: (纸本)9798350386998

Earth observation (EO) data have seen a constant surge in volume, necessitating efficient storage, retrieval, and sharing mechanisms. Cartographic projections play a vital role in transforming spheroidal surface data into two-dimensional representations, albeit with distortions. The Dual Orthogonal Equidistant Cylindrical (DOEC) projection offers a balance between simplicity and distortion, making it a promising choice for EO data storage and visualization. This paper explores various methods for raster geodata visualization in DOEC projection, addressing challenges of partitioning, distortion, and visualization techniques. Through experimental comparisons, the paper demonstrates the efficiency of these methods, highlighting their applicability in EO data processing and visualization systems. © 2024 IEEE.

关键词： Rasterization

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An Approach for Metadata Enrichment on Open Data Portals 11

An Approach for Metadata Enrichment on Open Data Portals

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11th International Conference on Electrical, electronic and Computing engineering, IcETRAN 2024

作者： Gligorijević, Milena Frtunic Bogdanović, Milos Stoimenov, Leonid University of Niš Faculty of Electronic Engineering Department for Computer Science Niš Serbia

ISBN: (纸本)9798350386998

Open data initiatives have resulted in a large amount of data being published on open data portals. In order to make published data more accessible these portals provide search mechanisms based on metadata like category, tags, format, and publisher. However, due to the lack of metadata information, like category values, search results are incomplete, and data is harder to be found. Therefore, this paper presents an approach for metadata enrichment on open data portals. In particular, the presented approach is focused on suggesting categories one dataset should belong to, based on the combination of tags that is used for its description. This approach relies on the tags usage for describing datasets within categories and the Formal Concept Analysis method for creating a hierarchical order of tags within categories. The presented proposal has been evaluated using data from Ireland's open data portal. © 2024 IEEE.

关键词： FCA formal concept analysis metadata enrichment open data open data portals

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The Serbian Retrieval Augmented Generation System based on Hybrid Search

The Serbian Retrieval Augmented Generation System based on H...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Cenic, Aleksandar Jovanovic, Martin Stojkovic, Suzana University of Niš Faculty of Electronic Engineering Computer Science Department Niš Serbia

ISBN: (纸本)9798350353907

This paper presents a hybrid search based retrieval-augmented generation (RAG) system in the domain of history, in Serbian language. The system was implemented in Python programming language, and is based on Google BERT embedding model, OpenSearch hybrid search, OpenAI GPT 3.5 large language model, and two original Serbian datasets for augmentation and system evaluation. The system performance was evaluated using ROGUE-1, cosine similarity and human evaluation, and its performance was compared to the purely semantic search based version. The system performance was satisfactory in human measurement, but estimated as low in automated metrics. In both metrics the hybrid search showed significantly better performance. © 2024 IEEE.

关键词： Question answering

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Braintransformer: Subject-Wise Patch Embed Transformer for Cross-Subject Brain Visual Information Decoding 21

Braintransformer: Subject-Wise Patch Embed Transformer for C...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Zongxin, Du Lin, Li University of Electronic Science and Technology of China Faculty of Computer Science and Engineering Chengdu610000 China

ISBN: (纸本)9798331519254

Visual information decoding aims to infer the visual content perceived by a subject based on their brain responses, representing a cutting-edge area of neuroscience research. Functional magnetic resonance imaging (fMRI) provides millimeter-level spatial resolution and a relatively high signal-to-noise ratio, making it an ideal tool for neural information decoding. However, practical applications remain limited, primarily due to the reliance on subject-specific models. In this work, we propose BrainTransformer, a novel framework for cross-subject visual information decoding. To overcome the limitations, we develop a self-supervised patch embedding transformer trained using a masked signal modeling approach on large-scale fMRI data, enabling the learning of robust and generalizable neural representations across subjects. By aligning these learned fMRI representations with CLIP's image and text embeddings, BrainTransformer leverages rich semantic information from both visual and textual modalities. This alignment allows us to employ a multimodal latent diffusion model guided by CLIP's embeddings to generate images consistent with brain activity. Experimental results demonstrate BrainTransformer's effectiveness in decoding visual information across subjects, marking a significant step toward scalable and generalizable brain decoding. © 2024 IEEE.

关键词： Signal to noise ratio

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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