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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： Siva Rama Lingham N M Parameswari Karthick P T. Subha Rathi Priya G Rajesh J. Jagannathan Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India School of Engineering and Technology Jeppiaar University Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events.

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Empowering Adaptive Endogenous Security Trend Prediction Detection for IoT Sensor Nodes

Empowering Adaptive Endogenous Security Trend Prediction Det...

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Advances in Electrical, Electronics and Computing technology (EECT), International Conference on

作者： Lihu Zhou Xiuying Dong Enli Zhang Ting Wang Xiao Zhang Chong Zhang School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Exploration and Development Research Institute of PetroChina Southwest Oil & Gasfield Company Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Massively deployed IoT devices require a lightweight design to reduce costs. However, this architecture inherently limits their security, increasing the risk of data breaches and tampering on a large scale. In this paper, we propose CGAD, a model that leverages one-dimensional convolution (Conv1D), gated recurrent units (GRU), and the attention mechanism to analyze multi-sensor time series data at the data gateway from sensing terminals. To achieve this, we generate time series predictions and detect anomalies by comparing predicted values with real data using a threshold-based approach, thereby enhancing data security. To address the labeling problem of massive data, we employ unsupervised learning and design a dynamic sliding window threshold to improve the judgment of time series with varying characteristics. We also used a number of datasets, such as the KW51 railroad bridge dataset and the self-picked temperature and humidity dataset, to evaluate our system. The results demonstrate that our CGAD model achieves recall rates of 98 % and 99 % for anomaly detection on the two datasets, respectively. Additionally, the precision and F1 scores surpass other methods, ensuring cost-effectiveness for massive terminals and addressing the security issues of sensitive data. These advantages contribute to realizing the Internet of Everything.

关键词： Adaptation models Data security Time series analysis Humidity Predictive models Logic gates Data models Sensors Internet of Things Anomaly detection

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General Dynamic Regularization Federated Learning with Hybrid Sharpness-Aware Minimization

General Dynamic Regularization Federated Learning with Hybri...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fengchun Zhang Dongfen Li Jinshan Lai Yang Zhang Fengli Zhang Ruijin Wang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Network Security Chengdu University of Technology Chengdu China Science Research Centre The Second Research Institute of CAAC Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

One of the main challenges in federated learning is its non-independent and identically distributed (non-IID) nature, where independent client training leads to overfitting and model deviations, negatively impacting overall performance. To address this, most research focuses on aligning local and global models to reduce client drift. However, existing algorithms use Empirical Risk Minimization (ERM) as the local optimizer, leading the global model to sharp minima, increasing bias in some clients and lowering generalization. To address these challenges, we propose GFed-HSAM, a general federated learning method that improves both local and global model generalization. GFed-HSAM uses Hybrid Sharpness-Aware Minimization (HSAM) as the local optimizer to smooth gradients with zeroth-order and first-order sharpness measures. It also includes a dynamic regularizer (DR) to align global and local models at the parameter level. Experiments show that GFed-HSAM outperforms state-of-theart methods in accuracy and generalization across different data heterogeneity settings on CIFAR10/CIFAR100.

关键词： Training Accuracy Risk minimization Federated learning Heuristic algorithms Perturbation methods Signal processing algorithms Signal processing Minimization Speech processing

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An efficient ring signcryption scheme for wireless sensor networks

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Wireless Networks 2025年

作者： Jin, Chunhua Cui, Yusheng Qin, Wenyu Chen, Zhiwei Chen, Guanhua Shan, Jinsong Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai’an233003 China

In the context of big data and cloud computing, data security is facing unprecedented challenges. In order to ensure data security, this article proposes an efficient ring signature encryption scheme. This scheme allows one member of the ring to use their own private key and the public keys of other members to sign the entire ring. The receiver only knows that the signature comes from a certain member of the ring, but does not know who that member is, thus achieving anonymity. We have also demonstrated that this scheme satisfies confidentiality, authentication, non repudiation, and integrity. Our proposed scheme can provide security proof in the random oracle model. Performance analysis shows that the proposed scheme not only achieves great optimization in calculation cost and communication consumption but also makes great progress in total energy consumption. In addition, we illustrate the practical applicability of our proposed scheme by providing a realistic use case scenario. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wireless sensor networks

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VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models

arXiv

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arXiv 2024年

作者： Liu, Yu Gao, Lang Yang, Mingxin Xie, Yu Chen, Ping Zhang, Xiaojin Chen, Wei Institute of BigData Fudan University Shanghai China School of Computer Science Huazhong University of Science and Technology China Purple Mountain Laboratories Nanjing China School of Computer Science Fudan University Shanghai China School of Software Engineering Huazhong University of Science and Technology China

Large Language Models (LLMs) have training corpora containing large amounts of program code, greatly improving the model’s code comprehension and generation capabilities. However, sound comprehensive research on detecting program vulnerabilities, a more specific task related to code, and evaluating the performance of LLMs in this more specialized scenario is still lacking. To address common challenges in vulnerability analysis, our study introduces a new benchmark, VulDetectBench, specifically designed to assess the vulnerability detection capabilities of LLMs. The benchmark comprehensively evaluates LLM’s ability to identify, classify, and locate vulnerabilities through five tasks of increasing difficulty. We evaluate the performance of 17 models (both open- and closed-source) and find that while existing models can achieve over 80% accuracy on tasks related to vulnerability identification and classification, they still fall short on specific, more detailed vulnerability analysis tasks, with less than 30% accuracy, making it difficult to provide valuable auxiliary information for professional vulnerability mining. Our benchmark effectively evaluates the capabilities of various LLMs at different levels in the specific task of vulnerability detection, providing a foundation for future research and improvements in this critical area of code security. VulDetectBench is publicly available at https://***/Sweetaroo/VulDetectBench. © 2024, CC BY-NC-SA.

关键词： Classification (of information)

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Empowering Adaptive Endogenous Security Trend Prediction Detection for IoT Sensor Nodes 5

Empowering Adaptive Endogenous Security Trend Prediction Det...

引用

5th International Conference on Advances in Electrical, Electronics and Computing technology, EECT 2025

作者： Zhou, Lihu Dong, Xiuying Zhang, Enli Wang, Ting Zhang, Xiao Zhang, Chong School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Exploration and Development Research Institute of PetroChina Southwest Oil & Gasfield Company Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541545

Massively deployed IoT devices require a lightweight design to reduce costs. However, this architecture inherently limits their security, increasing the risk of data breaches and tampering on a large scale. In this paper, we propose CGAD, a model that leverages one-dimensional convolution (Conv1D), gated recurrent units (GRU), and the attention mechanism to analyze multi-sensor time series data at the data gateway from sensing terminals. To achieve this, we generate time series predictions and detect anomalies by comparing predicted values with real data using a threshold-based approach, thereby enhancing data security. To address the labeling problem of massive data, we employ unsupervised learning and design a dynamic sliding window threshold to improve the judgment of time series with varying characteristics. We also used a number of datasets, such as the KW51 railroad bridge dataset and the self-picked temperature and humidity dataset, to evaluate our system. The results demonstrate that our CGAD model achieves recall rates of 98% and 99% for anomaly detection on the two datasets, respectively. Additionally, the precision and F1 scores surpass other methods, ensuring cost-effectiveness for massive terminals and addressing the security issues of sensitive data. These advantages contribute to realizing the Internet of Everything. © 2025 IEEE.

关键词： Gateways (computer networks)

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module

Improving Event Coreference Resolution Using Prompt Learning...

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computer Science and Blockchain (CCSB), International Conference on

作者： Mengyao Zhang Jun Long Wenti Huang School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments.

关键词： computer science Tensors Computational modeling Semantics Vectors Encoding Blockchains

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Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction

arXiv

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arXiv 2023年

作者： Lei, Pengcheng Fang, Faming Zhang, Guixu Xu, Ming School of Computer Science and Technology East China Normal University China Software Engineering Institute East China Normal University China

Magnetic resonance imaging (MRI) tasks often involve multiple contrasts. Recently, numerous deep learning-based multi-contrast MRI super-resolution (SR) and reconstruction methods have been proposed to explore the complementary information from the multi-contrast images. However, these methods either construct parameter-sharing networks or manually design fusion rules, failing to accurately model the correlations between multi-contrast images and lacking certain interpretations. In this paper, we propose a multi-contrast convolutional dictionary (MC-CDic) model under the guidance of the optimization algorithm with a well-designed data fidelity term. Specifically, we built an observation model for the multi-contrast MR images to explicitly model the multi-contrast images as common features and unique features. In this way, only the useful information in the reference image can be transferred to the target image, while the inconsistent information will be ignored. We employ the proximal gradient algorithm to optimize the model and unroll the iterative steps into a deep CDic model. Especially, the proximal operators are replaced by learnable ResNet. In addition, multi-scale dictionaries are introduced to further improve the model performance. We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks. Experimental results demonstrate the superior performance of the proposed MC-CDic model against existing SOTA methods. Code is available at https://***/lpcccc-cv/MC-CDic. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Existence of strongly nonlocal sets of three states in any N-partite system

arXiv

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arXiv 2024年

作者： Xiong, Zong-Xing Li, Mao-Sheng Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China School of Mathematics South China University of Technology Guangzhou510641 China

The notion of strong nonlocality, which refers to local irreducibility of a set of orthogonal multipartite quantum states across each bipartition of the subsystems, was put forward by Halder et al. in [Phys. Rev. Lett. 122, 040403 (2019)]. Here, we show the existence of three orthogonal quantum states in (C2)⊗N that cannot be perfectly distinguished locally across any bipartition of the subsystems. Specifically, all these three states are genuinely entangled, among which two are the N-qubit GHZ pairs. Since any three locally indistinguishable states are always locally irreducible, the three N-partite orthogonal states we present are strongly nonlocal. Thus, the caridnality of strongly nonlocal sets here is dramatically smaller than all known ones. © 2024, CC BY.

关键词： Quantum entanglement

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