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28th International Conference on Principles of Distributed systems, OPODIS 2024

作者： Cichoń, Jacek Dworzański, Dawid Gotfryd, Karol Department of Fundamentals of Computer Science Faculty of Information and Communication Technology Wroclaw University of Science and Technology Poland

ISBN: (纸本)9783959773607

We study the reliability of the following simple mechanism for spreading information in a communication network in the presence of random message loss. Initially, some nodes have information that they want to distribute throughout the network. Each node that has received the information tries to broadcast it to all its neighbors. However, due to interference or communication failures, each transmission between two nodes is broken independently with some fixed probability. This transmission mechanism is the basis for the extrema propagation technique, proposed and analyzed in [2, 3, 10]. Extrema propagation is a simple but powerful method of spreading the extreme values stored by the nodes. In a fully reliable environment, only the number of rounds equal to the network diameter is required for all nodes to be informed. It was shown empirically in [2] that it also performs well in the presence of link failures and message loss. Extrema propagation is an algorithmic framework that was adopted for designing efficient method for distributed data aggregation, such as estimation of cardinalities, sums, and averages, estimation of data distribution via histograms and random sampling (cf. [3, 19]). In this paper, we propose a formal network model in which random transmission failures occur and analyze the operation time of the extrema propagation technique for any connected network graph. We provide new general probabilistic upper bounds on the number of rounds needed to spread the extreme values that depend only on the number of nodes, the diameter of the network, and the probability of successful transmission. For some special families of graphs, we also derive specific, more accurate estimates. Our theoretical and experimental results confirm the reliability and efficiency of the extrema propagation technique in faulty or noisy environments. © Jacek Cichoń, Dawid Dworzański, and Karol Gotfryd.

关键词： Mobile ad hoc networks

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Multifrequency measurement system with no frequency ambiguity based on two frequency-space mappings

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Optical Engineering 2025年第3期64卷

作者： Zhang, Jiahong Ma, Di Li, Peiqi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Applications Kunming China

Multifrequency measurement system with no frequency ambiguity based on two frequency-space mappings has been designed and investigated. By simultaneously mapping the unknown frequencies with a certain frequency shift onto non-flat optical frequency combs (OFCs) and obtaining two beat frequency signals using two balanced photodetectors (BPD), the channel where the measured signal is located can be determined, achieving instantaneous measurement of multifrequencies while effectively addressing five possible types of frequency ambiguity. Simulation results indicate that when the comb line spacing of the OFC is designed as 2 GHz, the measurement range of the system is 0.1 to 30 GHz, with a measurement error within ±1 MHz and a measurement resolution of 2.7 MHz. Furthermore, by adjusting the comb line spacing of the OFC to 4 GHz, the frequency measurement range can be extended to 60 GHz. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Electric frequency measurement

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A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP

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IEEE Transactions on Neural systems and Rehabilitation Engineering 2025年 33卷 1562-1573页

作者： Cao, Beining Li-Ting Tsai, Charlie Zhou, Nan Do, Thomas Lin, Chin-Teng University of Technology Sydney Computational Intelligence and Brain-Computer Interfaces Laboratory Australian AI Institute Faculty of Engineering and Information Technology School of Computer Science UltimoNSW2007 Australia

Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm’s superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency. © 2001-2011 IEEE.

关键词： Augmented reality

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An Efficient Two-State Feature Attention-Based GRU for Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第15期49卷 95-112页

作者： Mohammed, Dhurgham Ali Patel, Kalyani A. Faculty of Computer Science Gujarat University Ahmedabad India Department of Computer Science Faculty of Education for Girl University of Kufa Iraq K.S. School of Business Management and Information Technology Gujarat University India

Text classification has become crucial for mechanically sorting documents into specific categories. The goal of classification is to assign a predefined group or class to an instance based on its characteristics. To attain precise text categorization, a feature selection scheme is employed to categorize significant features and eliminate irrelevant, undesirable, and noisy ones, thereby reducing the dimensionality of the feature space. Many advanced deep learning algorithms have been developed to handle text classification drawbacks. Recurrent neural networks (RNNs) are broadly employed in text classification tasks. In this paper, we referred to a novel Two-state GRU based on a Feature Attention strategy, known as Two-State Feature Attention GRU (TS-FA-GRU). The proposed framework identifies and categorizes word polarity through consecutive mechanisms and word-feature capture. Furthermore, the developed study incorporates a pre-feature attention TS-FA-GRU to capture essential features at an early stage, followed by a post-feature attention GRU that mimics the decoder’s function to refine the extracted features. To enhance computational performance, the reset gate in the ordinary GRU is replaced with an update gate, which helps to reduce redundancy and complexity. The effectiveness of the developed model was tested on five benchmark text datasets and compared with five well-established traditional text classification methods. The proposed TS-FA-GRU model demonstrated superior performance over several traditional approaches regarding convergence rate and accuracy. Experimental outcomes revealed that the TS-FA-GRU model achieved excellent text classification accuracies of 93.86%, 92.69%, 94.73%, 92.46%, and 88.23 on the 20NG, R21578, AG News, IMDB, and Amazon review dataset respectively. Moreover, the results indicated that the proposed model effectively minimized the loss function and captured long-term dependencies, leading to exceptional outcomes when compared to the

关键词： Recurrent neural networks

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Rüppell’s fox optimizer: A novel meta-heuristic approach for solving global optimization problems

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Cluster Computing 2025年第5期28卷 1-77页

作者： Braik, Malik Al-Hiary, Heba Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

A new swarm-based, nature-inspired meta-heuristic method, called Rüppell’s Fox Optimizer (RFO), is designed and examined in this study to address global optimization problems. RFO takes inspiration from the natural and intelligent communal foraging practices of Rüppell’s foxes, both during the day and at night. The optimizer mathematically simulates a variety of chief foraging activities of Rüppell’s foxes using their acute eyesight, hearing, and scent to hunt, to accommodate both exploration and exploitation aspects throughout the optimization action. The RFO algorithm simulates the rotating eyes’ feature of Rüppell’s foxes to a field of view of about 260°, as well as the feature of rotating their ears to a field of hearing of about 150°. Further, a cognitive component, ρ, is designed to oversee the evolution from global search to local search as well as the equilibrium between exploration and exploitation within the search domain. Based on Rüppell’s foxes’ foraging qualities, RFO reveals a variety of foraging habits. The dynamic patterns and activities of Rüppell’s foxes were mathematically mimicked in this study to create an efficient global optimizer, which when applied to optimization problems, ultimately yields feasible solutions. The performance level of RFO was thoroughly examined through a comparison with 12 other algorithms using three challenging test sets: Congress on Evolutionary Computation 2017 (CEC 2017) group with 29 test functions of various dimensions, the Congress on Evolutionary Computation 2019 (CEC 2019) with 10 test functions, and CEC 2020 set with 10 test functions. Using mean rank of Friedman’s test, RFO outperformed other comparative algorithms by 31.25%, 34.04%, 32.36%, and 55.13% on the 10-, 30-, 50-, and 100-dimensional CEC 2017 test set, respectively, 57.11% on the CEC 2019 test suite with 10 dimensions, 48.59%,and 51.68% on the 10- and 20-dimensional CEC 2020 set, respectively. The applicability of RFO is thoroughly examined on 6 clas

关键词： Heuristic methods

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Adaptive Cooperative Quality Weight Spectrum Sensing for Mitigating Byzantine Attacks in Cognitive Radio

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IEEE Access 2025年 13卷 80930-80940页

作者： Srisomboon, Kanabadee Pipanmekaporn, Luepol Prayote, Akara Lee, Wilaiporn Division of Electrical Engineering Department of Electrical and Computer Engineering Faculty of Engineering Bangkok10800 Thailand King Mongkut’s University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand

Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results to mislead the fusion center on channel availability decisions. By using traditional voting rule, the cooperative spectrum sensing performance deteriorates significantly due to incorrect local sensing results. Then, reliability weight strategy becomes the popular solution to avoid incorrect sensing results from BAs and unreliable cognitive radio users (CRUs). However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Based on existing techniques, the BAs and CRUs may be assigned with low reliability weights or distinguished from the data fusion account. However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Then, existing techniques still suffer from BAs and negative impact of unreliable CRUs. In this paper, we propose the adaptive cooperative quality weight algorithm for mitigating the Byzantine attack issue by distinguishing the BAs and CRUs from the data fusion account while selecting only useful CRUs since the number of members in the account is also the important factor for cooperative spectrum sensing. In our proposed algorithm, we adopt a stable preference ordering towards ideal solution (SPOTIS) for determining the reliability of SUs which shows low computational complexity as compared to other reliability weight-based techniques. To achieve high sensing performance, our global decision threshold is adapted according to the reliability of reliable users. From the simulation results, our proposed algorithm significantly improves global detection probability and total error probability compared to the traditional votin

关键词： Sensors Reliability Barium Radio spectrum management Cooperative communication Cognitive radio Data integration Signal to noise ratio Heuristic algorithms Dynamic spectrum access

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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary information

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Advances in Atmospheric sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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LLMs Encounter Critical Elements Prompts: Semantically Guided Partial Supervision Skeleton-Based Action Recognition

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IEEE Sensors Journal 2025年第10期25卷 17350-17363页

作者： Xin, Wentian Liu, Yi Fu, Xianping Wang, Huibing Miao, Qiguang Liu, Ruyi Li, Long Pun, Chi-Man Dalian Maritime University School of Information Science and Technology Liaoning Dalian116026 China Xidian University School of Computer Science and Technology Shanxi Xi’an710071 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guangxi Guilin541004 China University of Macau Faculty of Science and Technology Department of Computer and Information Science Macau China

Semantically guided skeleton-based action recognition is a crucial but challenging task in the data processing with RGB and depth sensors. The generation of action descriptions is facilitated by the pretrained language model, thereby imbuing the skeleton with essential contextual semantics. However, it also presents two challenges that require careful consideration: 1) efficiently and automatically generating the core semantics of actions and 2) extending the applicability of supervised learning when only partial labels are available. Therefore, we propose critical elements prompts for partial supervision in skeleton-based action contrastive learning (CEPCLR) as a significant attempt to break through the existing limitations of skeleton and semantic multimodal fusion. First, we design action-specific prompts that enable large language models (LLMs) to generate general semantic descriptions both efficiently and accurately, followed by iterative prompting that mines discriminative descriptions through controlled, rule-based emergence. Second, we adopt language-skeleton contrastive learning for multimodal fusion, which significantly enhances the integration of positive and negative action pairs. Notably, we introduce a novel partially supervised learning task for skeleton data, where labels are used selectively for semantic generation but are not involved in the classification tasks. The proposed CEPCLR achieves strong results on standard datasets like NTU RGB+D, NTU RGB+D 120, and PKU-MMD, effectively addressing missing contextual information in human skeleton and action expressions. © 2001-2012 IEEE.

关键词： Musculoskeletal system

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An Automated Contrast Enhancement Approach for Aerial Images

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Traitement du Signal 2025年第2期42卷 607-615页

作者： Elaraby, Ahmed Elmannai, Hela Alkanhel, Reem Ibrahim Basaqr, Ahad Fahd Department of Computer Science Faculty of Computers and Information South Valley University Qena83523 Egypt Department of Information Technology College of Computer and Information Science Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Real-world applications benefit greatly from aerial imagery. Various modern applications utilizing aerial images;however, these images are often low-contrast due to imperfect atmospheric conditions and limitations in the imaging systems. Many methods exist to enhance the quality of aerial images, yet not all of them capable of producing desired results. Some may have high complexity, and others may require numerous inputs. On the other hand, it is observed that a low-contrast impact that is difficult to prevent throughout the data collection process degrades the quality of aerial images a lot. As a result, in this paper a novel method for improving aerial image contrast has been presented. Hence a two-phase approach for increasing contrast and remapping the intensities of an aerial image to its native dynamic range has been presented in this paper. Additionally, a regularization technique is provided using the two-step regularization and mapping procedures. For image quality assessment (IQA), two performance assessment metrics;measure of enhancement (EME) and Structural SIMilarity (SSIM) have been suggested to measure the quality of the results of the proposed algorithm. The experimental results indicate that the proposed approach increases the contrast of aerial image substantially as compared with other widely contrast enhancement methods. ©2025 The authors.

关键词： Aerial photography

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