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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

science China(Information sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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3D AIR-UNet: attention–inception–residual-based U-Net for brain tumor segmentation from multimodal MRI

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Neural Computing and Applications 2025年 1-22页

作者： Sharma, Vani Kumar, Mohit Yadav, Arun Kumar Department of Computer Science & Engineering NIT Hamirpur Hamirpur India

Brain tumors are ranked highly among the leading causes of cancer-related fatalities. Precise segmentation and quantitative assessment of brain tumors are crucial for effective diagnosis and treatment planning. However, manual segmentation is often laborious, challenging, and prone to errors, necessitating the creation of a fully automated brain tumor segmentation approach. This article introduces "3D AIR-UNet," an end-to-end architecture aiming to automate the segmentation of brain tumors from MRI data. The presented model employs an encoder–decoder architecture, with carefully constructed inception–residual units replacing the usual convolution layers used in UNet. The inception–residual block combines the advantages of inception modules and residual connections to provide a powerful feature extraction mechanism. It captures extensive multi-scale information by combining different filter sizes. This block’s design is effective at handling complex 3D data patterns, making it a vital component of sophisticated neural network architecture. Moreover, an attention mechanism further boosts the capability of the model to differentiate between tumor and non-tumor regions, leading to improved localization and contextual understanding. Additionally, skip connections are employed between the encoder and decoder at each level to speed up the training process. The proposed 3D AIR-UNet architecture demonstrated encouraging outcomes, attaining dice scores of 0.9218 for the whole tumor, 0.9019 for the tumor core, and 0.8788 for the enhancing tumor when evaluated on the BraTS 2020 dataset. Comparative analysis with contemporary methods suggests that 3D AIR-UNet notably enhances the segmentation accuracy of brain tumor subregions. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Decoding

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COVID-19 emergency decision-making using q-rung linear diophantine fuzzy set,differential evolutionary and evidential reasoning techniques

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Applied Mathematics(A Journal of Chinese Universities) 2025年第1期40卷 182-206页

作者： G Punnam Chander Sujit Das Department of Computer science and Engineering National Institute of TechnologyWarangal 506004India

In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)*** proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the *** optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each *** the score values of alternatives are computed based on the aggregated *** alternative with the maximum score value is selected as a better *** applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning ***,we have validated the proposed approach with a numerical ***,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.

关键词： COVID-19 q-rung linear diophantine fuzzy set differential evolutionary evidential reasoning decision-making

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VAT: Visibility Aware Transformer for Fine-Grained Clothed Human Reconstruction

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2277-2287页

作者： Zhang, Xiaoyan Zhu, Zibin Xie, Hong Ren, Sisi Shenzhen University College of Computer Science and Software Engineering Shenzhen518000 China

In order to reconstruct 3D clothed human with accurate fine-grained details from sparse views, we propose a deep cooperating two-level global to fine-grained reconstruction framework that constructs robust global geometry to guide fine-grained geometry learning. The core of the framework is a novel visibility aware Transformer VAT, which bridges the two-level reconstruction architecture by connecting its global encoder and fine-grained decoder with two pixel-aligned implicit functions, respectively. The global encoder fuses semantic features of multiple views to integrate global geometric features. In the fine-grained decoder, visibility aware attention mechanism is designed to efficiently fuse multi-view and multi-scale features for mining fine-grained geometric features. The global encoder and fine-grained decoder are connected by a global embeding module to form a deep cooperation in the two-level framework, which provides global geometric embedding as a query guidance for calculating visibility aware attention in the fine-grained decoder. In addition, to extract highly aligned multi-scale features for the two-level reconstruction architecture, we design an image feature extractor MSUNet, which establishes strong semantic connections between different scales at minimal cost. Our proposed framework is end-to-end trainable, with all modules jointly optimized. We validate the effectiveness of our framework on public benchmarks, and experimental results demonstrate that our method has significant advantages over state-of-the-art methods in terms of both fine-grained performance and generalization. © 2025 IEEE.

关键词： Decoding

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Image-based rice leaf disease detection using CNN and generative adversarial network

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Neural Computing and Applications 2025年第1期37卷 439-456页

作者： Ramadan, Syed Taha Yeasin Islam, Md Shafiqul Sakib, Tanjim Sharmin, Nusrat Rahman, Md. Mokhlesur Rahman, Md. Mahbubur Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma

关键词： Generative adversarial networks

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ConSCompF: Consistency-focused Similarity Comparison Framework for Generative Large Language Models

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Journal of Artificial Intelligence Research 2025年 82卷 1325-1347页

作者： Karev, Alexey Xu, Dong School of Computer Engineering and Science Shanghai University Shanghai China

Large language models (LLMs) have been one of the most important discoveries in machine learning in recent years. LLM-based artificial intelligence (AI) assistants, such as ChatGPT, have consistently attracted the attention from researchers, investors, and the general public, driving the rapid growth of this industry. With the frequent introduction of new LLMs to the market, it becomes increasingly difficult to differentiate between them, creating a demand for new LLM comparison methods. In this research, the Consistency-focused Similarity Comparison Framework (ConSCompF) for generative large language models is proposed. It compares texts generated by two LLMs and produces a similarity score, indicating the overall degree of similarity between their responses. The main advantage of this framework is that it can operate on a small number of unlabeled data, such as chatbot instruction prompts, and does not require LLM developers to disclose any information about their product. To evaluate the efficacy of ConSCompF, two experiments aimed at identifying similarities between multiple LLMs are conducted. Additionally, these experiments examine the correlation between the similarity scores generated by ConSCompF and the differences in the outputs produced by other benchmarking techniques, such as ROUGE-L. Finally, a series of few-shot LLM comparison experiments is conducted to evaluate the performance of ConSCompF in a few-shot LLM comparison scenario. The proposed framework can be used for calculating similarity matrices of multiple LLMs, which can be effectively visualized using principal component analysis (PCA). The ConSCompF output may provide useful insights into data that might have been used during LLM training and help detect possible investment fraud attempts. ©2025 The Authors.

关键词： Contrastive Learning

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LoRa Sense:Sensing and Optimization of LoRa Link Behavior Using Path-Loss Models in Open-Cast Mines

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computer Modeling in engineering & sciences 2025年第1期142卷 425-466页

作者： Bhanu Pratap Reddy Bhavanam Prashanth Ragam School of Computer Science and Engineering VIT-AP UniversityAmaravati52237India

The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic *** study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining *** IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable *** advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and *** field tests are conducted in various opencast mines to investigate coverage potential and signal *** is tested in the Tadicherla open-cast coal mine in ***,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal *** field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of *** the O2O scenario,the Ericsson model

关键词： Internet of things long range wireless area network ZigBee mining environments path-loss models coefficient of determination mean square error

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Why Smart Contracts Reported as Vulnerable Were Not Exploited?

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2579-2596页

作者： Hu, Tianyuan Li, Jingyue Li, Bixin Storhaug, André Southeast University School of Computer Science and Engineering Nanjing211189 China Norwegian University of Science and Technology Department of Computer Science Trondheim7034 Norway

Smart contract security is crucial for blockchain applications. While studies suggest that only a small fraction of reported vulnerabilities are exploited, no follow-up research has investigated the reasons behind this. Our goal is to understand the factors contributing to the low exploitation rate to improve vulnerability detection and defense mechanisms. We collected 136,969 real-world smart contracts and analyzed them using seven vulnerability detectors. We applied Strauss’ grounded theory to gain insights into exploitability and analyzed transaction logs to trace the historic exploitations. Among the 4,364 smart contracts flagged as vulnerable, a significant 75.25% were found to be unexploitable, meaning they were either false positives or posed no security risk. We identified ten reasons for reporting unexploitable vulnerabilities. Furthermore, we found that only 66 out of 1,080 (6%) exploitable contracts had been exploited. We compared the characteristics of exploited versus non-exploited vulnerabilities and identified five factors that may reduce the likelihood of exploitation. Our findings highlight the importance of not treating smart contracts as conventional object-oriented (OO) applications. Researchers must account for the unique features of Solidity, smart contract design principles, and execution environments. Based on these insights, we propose six recommendations to improve smart contract vulnerability detection, prioritization, and mitigation. © 2004-2012 IEEE.

关键词： Smart contract

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Hybrid Deep Learning Approach for Sentiment Analysis on Twitter Data

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Multimedia Tools and Applications 2025年第15期84卷 15271-15292页

作者： Rakshit, Pranati Sarkar, Pronit Roy, Shubhankar Department of Computer Science and Engineering JIS College of Engineering Nadia Kalyani India

Social media is nowadays a vital platform where people can share their feelings about any incident, product, or any issue. Twitter is one of those platforms which are very popular. If we must make use of this to extract some information and to use that to make recommendation systems, to know the mental status of any person or to find the review, we have to perform the sentiment analysis of those tweets. Recently, deep learning approaches emerged as powerful computational models that discover intricate semantic representations of texts automatically from data without feature engineering. This present work discusses how to find the sentiments from a stream of tweets from the twitter, blogging site subjected to pre-processing and classification supported on emotion of the subject matter as "POSITIVE" or "NEGATIVE". We have used a twitter dataset of 1.6 million data consisting of general tweets on various domains with 800,000 POSITIVE and 800,000 NEGATIVE sentiments. We have developed 3 models with Recurrent Neural Network (RNN), Bi-directional RNN, Long Short-Term Memory (LSTM). Our work focuses on the way which will help us to understand the basic sentiment of people which they want to express by their tweets, comment, or blogs. We have obtained highest accuracy of 83.50%, F1 score of 84.60% from our model with LSTM architecture in analysing the sentiment. LSTMs are specifically designed to address the vanishing gradient problem that occurs in traditional RNNs when learning long-term dependencies. In sentiment analysis, understanding the sentiment of a tweet may require capturing dependencies between words that are spread across a potentially long sequence. LSTMs are better at retaining and utilizing information over extended sequences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Boosting cervical cancer detection with a multi-stage architecture and complementary information fusion

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Soft Computing 2025年第2期29卷 1191-1206页

作者： Sahoo, Pranab Saha, Sriparna Sharma, Saksham Kumar Mondal, Samrat Computer Science and Engineering Indian Institute of Technology Patna India Computer Science University of Maryland Baltimore CountyMD United States

Cervical cancer is one of the most fatal and prevalent illnesses affecting women globally. Early detection of cervical cancer is crucial for effective treatment. Pap smear tests are commonly used, but population-based screening is time-consuming, expensive, and requires expert physicians. computer-Aided Diagnosis (CAD) has shown promise in addressing this challenge. However, accurately predicting the disease using a single model can be difficult due to the complex data patterns involved. This research proposes a multi-stage architecture to improve cervical cancer screening. Initially, three pre-trained models are employed for image classification, after which the proposed advanced fusion technique is applied to combine the predictions. Additionally, we introduce a filtering approach in the third stage to refine the predictions. Unlike traditional fusion methods, the proposed architecture considers the confidence score of the base classifiers in making the final predictions on test samples. To enhance the performance of the models, we incorporate advanced augmentation techniques, including CutMix, CutOut, and MixUp. We assessed the performance of the proposed framework using a 5-fold cross-validation technique on two benchmark datasets. We evaluated the performance of the proposed framework through 5-fold cross-validation on two benchmark datasets. Remarkably, our framework achieved a classification accuracy of 97.62% and an F1-score of 97.64% on the SIPaKMeD dataset, demonstrating its effectiveness in accurately categorizing various cell types in the dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cervical cancer computer-aided detection Deep learning Ensemble learning

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