检索结果-内蒙古大学图书馆

RLDR: Reinforcement Learning-Based Fast data Recovery in Cloud-of-Clouds Storage Systems

IEEE Transactions on Cloud Computing 2025年

作者： Shen, Jiajie Wu, Bochun Wang, Maoyi Zou, Sai Cui, Laizhong Ni, Wei Fudan University Shanghai200433 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Shenzhen University College of Computer Science and Software Engieering Shenzhen518060 China Data61 Commonwealth Scientific and Industrial Research Organization NSW2122 Australia

Cloud-of-clouds storage systems are widely used in online applications, where user data are encrypted, encoded, and stored in multiple clouds. When some cloud nodes fail, the storage systems can reconstruct the lost data and store it in the substitute nodes. It is a challenge to reduce the latency of data recovery to ensure data reliability. In this paper, we adopt a Reinforcement Learning-based data Recovery (RLDR) approach to reduce the regeneration time. By employing the Monte-Carlo method, our approach can construct the tree-topology-based regeneration process, a.k.a. regeneration tree, to effectively reduce the regeneration time. Through rigorous analysis, we apply the information flow graph to optimize the inter-cloud traffic for a given regeneration tree. To verify the merit of RLDR, We conduct extensive experiments on real-world traces. Experiments demonstrate that RLDR can significantly accelerate the regeneration process. Specifically, RLDR can reduce the regeneration time by up to 92% and increase the throughput by up to twelve-fold, compared to the prior art. © 2013 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

引用

Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Transforming 6G Mobile Edge Intelligence with Large Models

引用

IEEE Network 2025年

作者： He, Xiaoming Jiang, Yunzhe Liu, Yinqiu Cui, Huajun Pan, Heng Mao, Yingchi Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing China University of Electronic Science and Technology of China College of Communication and Information Engineering Chengdu China Nanyang Technological University College of Computing and Data Science Singapore PowerChina Beijing Engineering Corporation Limited Digital Intelligence Research Institute Beijing China Zhongyuan University of Technology Zhengzhou China Hohai University College of Computer Science and Software Engineering Nanjing China

Large models (LMs) exhibit transformative power in 6G Mobile-Edge Intelligence (MEI) scenarios, which can effectively address key challenges regarding network complexity, resource optimization, intelligent decision-making, and user experience. In this article, we first introduce the basics of LM and explain the potential applications and existing solutions for integrating LMs into 6G MEI. Afterward, we conduct a case study oriented to LM-assisted 6G MEI scenarios, focusing on how to use LMs to predict the size of traffic. Specifically, we employ two attention modules named Dartboard Spatial Multi-head Self-Attention (DS-MSA) and Causal Temporal Multi-head Self-Attention (CT-MSA) to extract spatio-temporal features. A Large Language Model (LLM) named GPT-2 is utilized as the backbone model. To fit time-series data, the original token encoding layer is replaced by the convolutional encoding layer. Additionally, to handle multi-scale temporal information, we adopt an additional temporal encoding layer. During training, the majority of the pre-trained parameters are frozen, with only the input/output layers and the fully connected layer involved in parameter updates. The simulation results demonstrate that, compared to the traditional AI-based baselines, the proposed LM-based framework acquires a lower training loss and converges faster. Moreover, it achieves better prediction accuracy with less resource consumption. © 1986-2012 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

MA-Det: A Discriminative Morphology-Aware Detector for Cervical Lesion Cell Clumps

MA-Det: A Discriminative Morphology-Aware Detector for Cervi...

引用

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

作者： Ziyang Yin Qian Huang Yulin Chen Hao Lu College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automated detection of cervical lesion cell clumps is crucial for cervical cancer screening. However, the dense packing and overlap of cells, caused by adhesion molecules, make detection challenging. To address this issue, we propose the Morphology-Aware Detector (MA-Det). Specifically, by innovatively employing Deformable Convolution, we propose the Dynamic Context Aggregation Module (DCAM) which dynamically captures contextual information, improving the feature representation of lesion cells. To enhance discrimination in high-density regions, we design the Distance-Weighted Interaction Module (DWIM) and Adaptive Morphology-Aware (AMA) loss into the detection head. These components improve spatial awareness by utilizing interactions between cell features and proposals. In particular, our method achieves state-of-the-art performance on the CDetector and CRIC datasets while significantly reducing parameters.

关键词： Convolution Adhesives Detectors Medical services Feature extraction Complexity theory Lesions Proposals Speech processing Cervical cancer

来源：评论

学校读者我要写书评

暂无评论

Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

引用

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

作者： Qian Huang Wenting Liu Xin Li Yiming Wang College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR.

关键词： Interpolation Adaptive systems Costs Acoustic distortion Accuracy Motion estimation Video compression Feature extraction Motion compensation Speech processing

来源：评论

学校读者我要写书评

暂无评论

An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

NucleiFormer: A Nuclei Segmentation Model Optimized by Joint Haar Wavelet and Adaptive Feature Calibration

NucleiFormer: A Nuclei Segmentation Model Optimized by Joint...

引用

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

作者： Yulin Chen Qian Huang Zhijian Wang Ziyang Yin Meng Geng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Nucleus segmentation plays a vital role in medical image analysis. However, existing segmentation methods frequently encounter hurdles, such as the loss of crucial image details during downsampling and issues like noise and spatial displacement. In this study, we propose NucleiFormer, where Haar wavelet transforms are employed in the encoder to replace conventional downsampling techniques. Additionally, we utilize an adaptive feature calibration module to align and calibrate features of different scales, reduce feature redundancy, suppress noise, and enhance the spatial awareness of the model. Extensive experiments validate our model’s superior segmentation performance in accurately delineating nucleus boundaries and minimizing errors.

关键词： Wavelet transforms Adaptation models Image segmentation Image analysis Noise Termination of employment Calibration Speech processing Optimization Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Improving Multi-Label Contrastive Learning by Leveraging Label Distribution

arXiv

引用

arXiv 2025年

作者： Chen, Ning Lyu, Shen-Huan Wu, Tian-Shuang Wang, Yanyan Tang, Bin Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing label information. Previous studies selected positive and negative samples based on the overlap between labels and used them for label-wise loss balancing. However, these methods suffer from a complex selection process and fail to account for the varying importance of different labels. To address these problems, we propose a novel method that improves multi-label contrastive learning through label distribution. Specifically, when selecting positive and negative samples, we only need to consider whether there is an intersection between labels. To model the relationships between labels, we introduce two methods to recover label distributions from logical labels, based on Radial Basis Function (RBF) and contrastive loss, respectively. We evaluate our method on nine widely used multi-label datasets, including image and vector datasets. The results demonstrate that our method outperforms state-of-the-art methods in six evaluation metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Compressing Model with Few Class-Imbalance Samples: An Out-of-Distribution Expedition

arXiv

引用

arXiv 2025年

作者： Wu, Tian-Shuang Lyu, Shen-Huan Chen, Ning Qu, Zhihao Ye, Baoliu Key Laboratory of Water Big Data Technology Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In recent years, as a compromise between privacy and performance, few-sample model compression has been widely adopted to deal with limited data resulting from privacy and security concerns. However, when the number of available samples is extremely limited, class imbalance becomes a common and tricky problem. Achieving an equal number of samples across all classes is often costly and impractical in real-world applications, and previous studies on few-sample model compression have mostly ignored this significant issue. Our experiments comprehensively demonstrate that class imbalance negatively affects the overall performance of few-sample model compression methods. To address this problem, we propose a novel and adaptive framework named OOD-Enhanced Few-Sample Model Compression (OE-FSMC). This framework integrates easily accessible out-of-distribution (OOD) data into both the compression and fine-tuning processes, effectively rebalancing the training distribution. We also incorporate a joint distillation loss and a regularization term to reduce the risk of the model overfitting to the OOD data. Extensive experiments on multiple benchmark datasets show that our framework can be seamlessly incorporated into existing few-sample model compression methods, effectively mitigating the accuracy degradation caused by class imbalance. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ma, Long Hou, Junhui Liu, Yuan Wang, Wenping The School of Software Shandong University Jinan250100 China The Department of Computer Science City University of Hong Kong Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore The Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States

As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero-level set, would result in sub-optimal reconstruction quality. In this paper, we propose the scaled-squared distance function (S2DF), a novel implicit surface representation for modeling arbitrary surface types. S2DF does not distinguish between inside and outside regions while effectively addressing the non-differentiability issue of UDF at the zero-level set. We demonstrate that S2DF satisfies a second-order partial differential equation of Monge-Ampere-type, allowing us to develop a learning pipeline that leverages a novel Monge-Ampere regularization to directly learn S2DF from raw unoriented point clouds without supervision from ground-truth S2DF values. Extensive experiments across multiple datasets show that our method significantly outperforms state-of-the-art supervised approaches that require ground-truth surface information as supervision for training. The code will be publicly available at https://***/chuanxiang-yang/S2DF. © 2025 IEEE. All rights reserved.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：