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共 68658 条记录，以下是251-260 订阅

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Adaptive Filtering for Low-Energy Data Aggregation 5th

Adaptive Filtering for Low-Energy Data Aggregation

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Panwar, Govind Singh Mrutyunjaya, M.S. Agarwal, Trapty Kamalraj, R. Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India

ISBN: (纸本)9789819780426

Hyper Spectral Imaging (HSI) is a powerful approach used in far flung sensing packages that captures spatially contiguous set of spectrum statistics from a scene. HSI information incorporates beneficial facts about the goals within the scene;however, due to noise and various distortions, it can be tough to discover those objectives. Spatial filtering is a method used to reduce undesirable noise in images if you want to make goal identity easier and extra accurate. On this paper, we endorse a brand new technique evolved for applying spatial filters on HSI pics that utilizes a spatially adaptive sigma clear out to successfully lessen noise and improve target identity accuracy. The advanced method correctly carries spatial variability within the filter length and clear out parameters to obtains progressed goal discrimination overall performance. The proposed technique is evaluated the use of both simulated and actual HSI records sets and effects display progressed target accuracy when as compared to traditional spatial filters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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FSMM: An Efficient Matrix Multiplication Accelerator Supporting Flexible Sparsity 24

FSMM: An Efficient Matrix Multiplication Accelerator Support...

引用

43rd international conference on computer-Aided Design, ICCAD 2024

作者： Qiao, Yuxuan Yang, Fan Zhang, Yecheng Xiong, Xiankui Yao, Xiao Yao, Haidong State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China School of Microelectronics Fudan University Shanghai China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation Shenzhen China

ISBN: (纸本)9798400710773

Sparse matrix multiplication is a critical operation in deep learning. However, matrix sparsity leads to irregular data flow, which would degrade the efficiency of matrix multiplication. Traditional accelerators, equipped with additional hardware units to address this issue, often experience the issue of low hardware utilization. Furthermore, N : M structured sparsity and corresponding hardware architectures face challenges such as accuracy degradation, limited flexibility, and restricted applicability. In this paper, we propose a Flexible Sparse Matrix Multiplication Accelerator (FSMM), which can improve the efficiency of sparse matrix multiplication through both algorithmic-level and hardware-level optimizations. At the algorithmic level, we propose the matrix-matrix multiplication with block-level outer production and fine-grained matrix reordering algorithm. The algorithm balances the sparsity of each column of a matrix block, which improves matrix compression, balances the load, and speeds up computation. This algorithm reduces storage by 8.2% ~ 85.9%. At the hardware-level, we introduce a flexible architecture for matrix multiplication. It selects the most suitable data path to complete matrix multiplication based on the sparsity of the reordered matrix. FSMM achieves a speedup of 1.90× ~ 16.18× over Systolic Array and 1.70× ~ 2.87× over the existing TSTC approach. © 2024 Copyright is held by the owner/author(s).

关键词： Fast response computer systems

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The Smart and Secured Communication Using Applied Cryptography Algorithms 5th

The Smart and Secured Communication Using Applied Cryptograp...

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Jebakumari, S. Adlin Hemlata Ayesha, Amreen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper describes the implementation of a at ease communication device the usage of an implemented cryptography algorithm. The gadget is designed to provide comfy statistics and voice communique between endpoints using techniques consisting of encryption and digital signature. The applied cryptography set of rules includes primary components, the symmetric and the uneven algorithms. The symmetric algorithms, along with AES and DES, are used for statistics encryption, even as the uneven algorithms, which include RSA and DSA, are used for virtual signature. The proposed gadget additionally focuses on security of physical places to defend the important thing exchange by means of the use of physical access control measures which include biometric identification. The system additionally employs advanced safety strategies together with message authentication codes and protection protocols like SSL/TLS. The device is examined for robustness and safety using real communique experiments. The results confirm that the proposed machine is cozy and green in supplying comfortable communication among endpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Data Augmentation for Messenger Phishing Detection Using Large Language Models

Data Augmentation for Messenger Phishing Detection Using Lar...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Noh, Keonwoong Oh, Seokjin Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

In this study, we introduce a new dataset specifically designed for detecting messenger phishing, an increasingly significant issue in cybercrime. To overcome the scarcity of labeled phishing data, we employ large language models (LLMs) to generate synthetic data, thereby expanding the dataset and improving detection capabilities. Our experimental results show that a model trained exclusively on synthetic data performs comparably to those trained with labeled data. Furthermore, combining synthetic data with labeled data achieves superior F1 and accuracy scores compared to using labeled data only while reducing misclassification errors. © 2025 IEEE.

关键词： Phishing

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Decentralized Continuous Group Key Agreement for UAV Ad-Hoc Network 6th

Decentralized Continuous Group Key Agreement for UAV Ad-Hoc...

引用

6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Hong, Shijia Zhou, Tianqi Yang, Huijie Shen, Mingdi Zheng, Wenying School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

With the rise of the Industrial Internet of Things (IIoT), Unmanned Aerial Vehicles (UAVs) have emerged as the preferred sensing tools for data collection. However, resource-constrained UAVs often face challenges in handling complex group key agreement processes, while key management in the flying ad hoc network (FANET) is also susceptible to single points of failure. To address this issue, this paper proposes a blockchain-based continuous group key agreement protocol for FANET. The blockchain is collectively maintained by UAVs and serves to record dynamic changes within the FANET. Additionally, it employs a ratchet tree to integrate a pseudo-random function generator with an updatable public key encryption scheme, effectively mitigating the risk of single points of failure, reducing communication overhead, and enhancing security and self-healing capabilities. Simulation experiments demonstrate that this protocol outperforms existing key agreement schemes in terms of reduced delay and improved security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain

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An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features 3rd

An Ensemble Machine Learning-Based Approach for Detecting Ma...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Islam, Md. Ashraful Hasan, Md. Nahid Ahmad, Kawsar Haque, Mir Aminul Department of Computer Science andEngineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Department of Computer Science and Engineering Jahangirnagar Univerisity Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The Internet has transformed into a hub for a wide array of illegal activities, ranging from annoying spam ads to financial scams, all thanks to advancements in modern technology. With the constant enhancements in network technology, data traffic on the Internet is ballooning in both volume and scope. Consequently, the complexity and prevalence of cyber threats and attacks are on the rise, with websites becoming the prime targets for hackers. These intruders, or hackers, embed harmful content into web pages with the intent of carrying out nefarious activities. They employ various tactics, such as hacking into computers or trying to harvest information through compromised or malicious websites. Some of these tactics involve injecting malicious software into web links. Therefore, it has become more critical than ever to identify and block such websites before the average user can stumble upon them. In this paper, we propose a method that utilizes machine learning techniques to detect fraudulent websites. We evaluate the prediction accuracy of different machine learning classification methods in this study. We trained our model using various algorithms, including Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), Decision Trees, Random Forests, and AdaBoost. Ultimately, we employed an ensemble approach and achieved an outstanding accuracy rate of 99.98%, surpassing other methods in effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Design and Implementation of Smart Home Control System Based on STM32 7th

Design and Implementation of Smart Home Control System Based...

引用

7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Gao, Lang Li, Guo College of Computer Science and Engineering Guangxi Normal University Guilin541004 China

ISBN: (纸本)9789819624089

With the rapid development of Internet of Things (IoT) technology, smart home control has become an indispensable part of modern life. This study designs a smart home control system based on the STM32 microcontroller, combined with Tencent CloudBase and WiFi communication technology, to achieve remote control of home devices and real-time monitoring of home environments. The system uses the MQTT protocol to transmit data and send control commands. At the same time, a WeChat mini-program is developed as the client for controlling devices. The feasibility of the system has been verified through testing. © The Author(s) 2025.

关键词： Smart homes

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MiDedup: A Restore-Friendly Deduplication Method on Docker Image Storage Systems 20th

MiDedup: A Restore-Friendly Deduplication Method on Docker ...

引用

20th IFIP WG 10.3 international conference on Network and Parallel Computing, NPC 2024

作者： Qin, Lisha Tan, Haoliang Zou, Xiangyu Ou, Wenhao Wei, Yan Huang, Rubing Xia, Wen Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Zhongruan International Technology Service Co. Ltd. Shenzhen China Macau University of Science and Technology Macao China

ISBN: (纸本)9789819628292

Docker image storage systems, like Docker registry, often employ deduplication to reduce storage overhead. Existing deduplication methods for these systems detect redundancy at either layer or file level, each with its own advantages and limitations. Layer-level deduplication offers much faster restoration but lower deduplication ratio, as it eliminates duplicate layers while ignoring duplicate files across layers. Conversely, file-level deduplication offers a higher deduplication ratio but slower restoration, as removing duplicate files scatters the files of a layer, leading to multiple disk read I/O during restoration. Our analysis reveals that the slow restoration in file-level deduplication partly stems from ignoring file-layer relationships. In this paper, we propose MiDedup, a new Docker deduplication method. MiDedup detects redundancy at file-level and organizes files into a restore-friendly layout by considering file-layer relationships to enhance restore speed. Our studies also show that duplicated files of one layer are not uniformly distributed across different layers. MiDedup uses this insight to skip deduplicating scattered files to further optimize the file layout for faster restoration. Evaluations indicate that MiDedup achieves restore speeds that are 8–19× faster than file-level deduplication (slightly slower than layer-level one), while maintaining 9–47% higher deduplication ratios than layer-level deduplication. © IFIP international Federation for Information Processing 2025.

关键词： Redundancy

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Improving Messenger Phishing Detection Using Heterogeneous Phishing Data

Improving Messenger Phishing Detection Using Heterogeneous P...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Oh, Seokjin Noh, Keonwoong Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

Recently, as messenger phishing has been occurring more frequently, the need for its detection has increased;however, datasets for messenger phishing detection are publicly unavailable. In this paper, we address the data scarcity problem of the newly collected messenger phishing dataset by leveraging various types of pre-existing auxiliary phishing data. Experimental results demonstrate that the error rate decreased by up to 1.81 % and the F1 score improved by 7.35% when smishing and voice phishing data are used. These findings confirm that integrating heterogeneous phishing data can mitigate the data scarcity problem and enhance messenger phishing detection performance. © 2025 IEEE.

关键词： Phishing

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SACR: Self-training with Saliency-Augmented Consistency Regularization for Few-Shot Learners

SACR: Self-training with Saliency-Augmented Consistency Regu...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Yanyan Zhou, Yue Xue, Yun Li, Fenghuan Lin, Zehong School of Electronics and Information Engineering South China Normal University Foshan China Department of Artiffcial Intelligence School of Informatics Xiamen University Xiamen China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Pre-trained language models have made significant strides in natural language processing tasks, enabling flexible fine-tuning for downstream applications. However, in few-shot learning scenarios, pre-trained models face challenges related to overfitting due to limited training samples, which hinders their ability to capture data diversity and robustly handle input variations. To overcome these limitations, we propose the Saliency-Augmented Consistency Regularization (SACR) framework, a novel self-training strategy designed to improve few-shot learning performance and robustness. SACR consists of three key components: (1) Saliency-guided Data Perturbation, which uses saliency analysis to identify and perturb words that significantly influence model predictions, generating semantically consistent pseudo-samples;(2) Semantic-equivalent Sample Mining, which employs K-means clustering to select semantically similar samples and prevent semantic shift;and (3) Consistency Training, where regularization ensures consistency between the semantics and prediction distributions of the original and perturbed samples. We conducted extensive experiments on 15 public datasets to evaluate our approach. SACR significantly outperforms strong baseline models and demonstrates superior generalization capabilities compared to state-of-the-art methods, achieving an average 1.0% improvement in classification accuracy. © 2025 IEEE.

关键词： consistency regularization few-shot learning saliency self-training

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