检索结果-内蒙古大学图书馆

20th European Conference on Computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Wu, Haonan Xu, Erci Wang, Ligang Hong, Yuandong Niu, Changsheng Shi, Bo Zhu, Lingjun He, Jinnian Wu, Dong Zhang, Weidong Wang, Qiuping Wang, Changhong Chen, Xinqi Xue, Guangtao Chen, Yi-Chao Ding, Dian Shanghai Jiao Tong University Shanghai China Alibaba Cloud Hangzhou China Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3 Shanghai China

ISBN: (纸本)9798400711961

Elastic Block Storage (EBS) has a pivotal role in modern data center infrastructure, providing reliable, high-performance and flexible block storage service to users. In Alibaba Cloud, EBS is the most widely used service and has been supporting the operation of millions of virtual disks. However, even with layers of load balancing and caching, we still observe significant traffic skewness across the EBS stack. This motivates us to comprehensively investigate symptoms and root causes behind the traffic patterns and, more importantly, explore the fixes for the identified issues. In this paper, we collect 310 million IO traces from approximately 60k virtual machines and 140k virtual disks deployed in our EBS. Based on extensive statistical analysis, we examine the traffic skewness across multiple components of the EBS stack. We identify four typical symptoms related to the IO virtualization framework, traffic throttle, storage cluster management and cache. For each symptom, we further explore the potential solutions along with the challenges. © 2025 Copyright held by the owner/author(s).

关键词： Virtual storage

来源：评论

学校读者我要写书评

暂无评论

Generative Diffusion Model-based Energy Management in Networked Energy Systems

Generative Diffusion Model-based Energy Management in Networ...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lu, Xinyu Feng, Zhanbo Sun, Jiawei Lou, Jiong Wu, Chentao Bao, Wugedele Li, Jie Shanghai Jiao Tong University China Yancheng Blockchain Research Institute China Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3 China Hohhot Minzu College China

ISBN: (纸本)9798350368741

In recent years, the proliferation of renewable energy sources has heightened the focus on networked energy systems. These systems face significant challenges due to the unpredictable nature of energy generation and consumption, as well as the complexity of managing numerous components and parameters. To address the challenges associated with the time-consuming nature of optimization problems and the expansive solution space, we propose an innovative energy management method based on a generative diffusion model applicable to general networked energy systems. This approach aims to balance energy supply and demand while minimizing transmission costs. The efficacy of this method is validated through evaluations on real-world datasets and simulations, demonstrating a 26.6% cost reduction compared to the state-of-the-art model and a 62.8% decrease in execution time compared to existing optimizers. This research highlights the potential of generative diffusion techniques in networked energy management. Code: https://***/gale13/GEM. © 2025 IEEE.

关键词： energy management Generative diffusion model renewable energy sources scheduling

来源：评论

学校读者我要写书评

暂无评论

SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning 27th

SecureCut: Federated Gradient Boosting Decision Trees with ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Bowen Zhang, Jian Li, Jie Wu, Chentao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Yancheng Blockchain Research Institute Yancheng China Blockchain Advanced Research Center Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai China

ISBN: (纸本)9783031781681

In response to legislation, companies are now mandated to honor the right to be forgotten by erasing user data. Consequently, it has become imperative to enable data removal in Vertical Federated Learning (VFL), where multiple parties provide private features for model training. In VFL, data removal, i.e., machine unlearning, requires removing specific features across all samples, ensuring privacy in federated learning. To address this challenge, we propose SecureCut, a novel Gradient Boosting Decision Tree (GBDT) framework that effectively enables both instance unlearning and feature unlearning without the need for retraining from scratch. Leveraging a robust GBDT structure, we enable effective data deletion while reducing degradation of model performance. Extensive experimental results on popular datasets demonstrate that our method achieves superior model utility and forgetfulness compared to the state-of-the-art methods. To the best of our knowledge, this is the first work that investigates machine unlearning in VFL scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

On Strong Secrecy for Multiple Access Channels With States and Causal CSI

引用

IEEE Transactions on Information Theory 2025年第4期71卷 3070-3099页

作者： Chen, Yiqi Oechtering, Tobias J. Skoglund, Mikael Luo, Yuan Technical University of Munich Chair of Theoretical Information Technology Munich80333 Germany Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai200240 China

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases. © 1963-2012 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

AMSER: Accelerate Mobile Speech Emotion Recognition with Signal Compression

AMSER: Accelerate Mobile Speech Emotion Recognition with Sig...

引用

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

作者： Yu Lu Ran Wang Dian Ding Han Zhang Liyun Zhang Lanqing Yang Yi-Chao Chen Guangtao Xue Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech-based interaction systems are widely used in mobile devices like smartphones. With advances in deep neural networks, tasks such as speech emotion recognition (SER) enhance these systems’ user-friendliness. However, deploying SER models on mobile devices is challenging due to their complexity and computational demands. While pruning can reduce complexity, it often compromises accuracy, and hardware accelerators like FPGAs are difficult to integrate into mobile devices. This paper proposes AMSER, a real-time speech emotion recognition framework using signal compression and task offloading. AMSER utilizes logarithmic Mel-filter bank coefficients (Fbank) and singular value decomposition (SVD) for feature extraction and compression. The compressed signal is only 6.25% of the original size, achieving 2.24x faster transfer rates and 55.35% energy savings compared to raw audio transmission. Despite the compression, the features preserve key audio information for text and emotion recognition, performed server-side. Experiments show a WER of 4.68% (Librispeech), 10.69% (CommonVoice), and 69.83% emotion recognition accuracy (IEMOCAP).

关键词： Emotion recognition Accuracy Computational modeling Speech recognition Artificial neural networks Speech enhancement Real-time systems Complexity theory Smart phones Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Generative Diffusion Model-based Energy Management in Networked Energy Systems

Generative Diffusion Model-based Energy Management in Networ...

引用

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

作者： Xinyu Lu Zhanbo Feng Jiawei Sun Jiong Lou Chentao Wu Wugedele Bao Jie Li Shanghai Jiao Tong University Yancheng Blockchain Research Institute Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3 Hohhot Minzu College

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Renewable energy sources Costs Supply and demand Signal processing Diffusion models Complexity theory Energy management Speech processing Optimization Faces

来源：评论

学校读者我要写书评

暂无评论

Cheetah: Metadata Aggregation for Fast Object Storage without Distributed Ordering 25

Cheetah: Metadata Aggregation for Fast Object Storage withou...

引用

20th European Conference on Computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Zhang, Yiming Wang, Li Liu, Shengyun Gai, Shun Wang, Haonan Yao, Xin Wang, Meiling Chen, Kai Li, Dongsheng Shu, Jiwu Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3 China NICE XLab XMU China China KylinSoft Hunan Changsha China SJTU Shanghai China NICE XLab Fujian Xiamen China Huawei Theory Lab Hong Kong Hong Kong HKUST Hong Kong Hong Kong NUDT Hunan Changsha China Tsinghua University Beijing China

ISBN: (纸本)9798400711961

Object stores usually maintain the mapping of objects to data servers’ disk volumes (referred to as volume metadata) in a central directory, while storing the object data’s in-volume offsets (referred to as offset metadata) together with the data on data servers. Unfortunately, the separation between volume/offset metadata complicates the processing of an object put: to ensure consistency, the multiple writes of the object’s volume/offset metadata and object data have to be orchestrated in a particular order, which severely lowers object I/O performance. We propose a write-optimal structure called MetaX that aggregates all metadata of a put, including both volume and offset metadata as well as other meta information such as data checksum and temporary meta-log. Based on MetaX, we design the Cheetah object store, which organizes object storage into rich metadata storage (on meta servers) and raw data storage (on data servers). Cheetah removes the distributed ordering constraint on the multiple metadata/data writes by enforcing local atomicity of writing MetaX, while still ensuring consistency. Evaluation shows that Cheetah significantly outperforms existing object stores. © 2025 Copyright held by the owner/author(s).

关键词： data consistency

来源：评论

学校读者我要写书评

暂无评论

Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

arXiv

引用

arXiv 2025年

作者： Wu, Weiqi Huang, Shen Jiang, Yong Xie, Pengjun Huang, Fei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongyi Lab Alibaba Group China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Efficient Resource Allocation in Computing Power Networks Considering Similar Task Merging: A Lyapunov Optimization-Based DRL Approach

引用

IEEE Internet of Things Journal 2025年

作者： Jia, Zhonghai Xue, Junxiao Shi, Lei Li, Jie He, Mengyang Zhejiang Lab Research Center for Space Computing System Hangzhou310000 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China SongShan Laboratory Zhengzhou450040 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 Shanghai China

The cloud-edge-terminal architecture relies on hierarchy for resource allocation but lacks global optimization. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. However, most CPN research focuses on task optimization during resource allocation, while neglecting the similarity of random tasks before the allocation stage. Additionally, fragmented CPN resources and complex task demands pose challenges to global load balancing. This paper proposes a deep reinforcement learning framework with task merging and congestion avoidance for on-demand resource allocation. Specifically, a low-complexity similar task merging algorithm reduces redundant resource consumption during task preprocessing. In task offloading, the principal neighborhood aggregated graph neural network captures CPN's intricate features. Lyapunov optimization, integrated into a multi-threaded training framework, minimizes resource backlog congestion. A carefully designed reward function balances multiple objectives, enhancing computing resource utilization efficiency and ensuring system stability. Theoretical analysis shows that with control parameter V, the trade-off between resource utilization efficiency and system stability follows the relationship O(1/V), O(V). Extensive experiments demonstrate a 33.5% improvement in resource utilization efficiency and a 62.7% increase in task offloading success rates with respect to those in state-of-the-art algorithms. The proposed algorithm exhibits robustness and effectiveness, particularly in high-load and real network topologies. © 2014 IEEE.

关键词： Computing power

来源：评论

学校读者我要写书评

暂无评论

LESA: Learnable LLM Layer Scaling-Up

arXiv

引用

arXiv 2025年

作者： Yang, Yifei Cao, Zouying Ma, Xinbei Yao, Yao Qin, Libo Chen, Zhi Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China School of Computer Science and Engineering Central South University China ByteDance China

Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leveraging the parameters of smaller models to create larger ones. However, existing depth scaling-up methods rely on empirical heuristic rules for layer duplication, which result in poorer initialization and slower convergence during continual pre-training. We propose LESA, a novel learnable method for depth scaling-up. By concatenating parameters from each layer and applying Singular Value Decomposition, we uncover latent patterns between layers, suggesting that inter-layer parameters can be learned. LESA uses a neural network to predict the parameters inserted between adjacent layers, enabling better initialization and faster training. Experiments show that LESA outperforms existing baselines, achieving superior performance with less than half the computational cost during continual pre-training. Extensive analyses demonstrate its effectiveness across different model sizes and tasks.1 Copyright © 2025, The Authors. All rights reserved.

关键词： Multilayer neural networks

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：