检索结果-内蒙古大学图书馆

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhai, Yuanzhao Xu, Kele Ding, Bo Feng, Dawei Gao, Zijian Wang, Huaimin National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9781728163277

The use of graph attention networks (GAT) in communication-enhanced multi-agent reinforcement learning (Comm-MARL) has become prevalent. While successful, GAT can lead to homogeneity in the strategies of message aggregation, which can severely limit multi-agent coordination. To address this challenge, we study the adjacency tensor of the communication graph. Then we define a new nuclear tensor rank and its convex surrogate, the normalized tensor nuclear norm to measure the homogeneity of message aggregation. Leveraging the norm, we further propose a plug-and-play regularizer on the adjacency tensor, named Normalized Tensor Nuclear Norm Regularization (NTNNR), to actively enrich the diversity of message aggregation during the training stage. NTNNR is agnostic to specific Comm-MARL algorithms and can be flexibly integrated with different graph-attention methods. Empirical results demonstrate that aggregating messages using NTNNR-enhanced GAT can improve the efficiency of the training and achieve higher asymptotic performance than existing message aggregation methods. © 2023 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

引用

Acta Oceanologica Sinica 2022年第2期41卷 26-40页

作者： Meng Shen Yan Chen Pinqiang Wang Weimin Zhang College of Meteorology and Oceanology National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems Changsha 410073China

The Localized Weighted Ensemble Kalman Filter(LWEnKF)is a new nonlinear/non-Gaussian data assimilation(DA)method that can effectively alleviate the filter degradation problem faced by particle filtering,and it has great prospects for applications in geophysical *** terms of operational applications,along-track sea surface height(AT-SSH),swath sea surface temperature(S-SST)and in-situ temperature and salinity(T/S)profiles are assimilated using the LWEnKF in the northern South China Sea(SCS).To adapt to the vertical S-coordinates of the Regional Ocean Modelling System(ROMS),a vertical localization radius function is designed for T/S profiles assimilation using the *** results show that the LWEnKF outperforms the local particle filter(LPF)due to the introduction of the Ensemble Kalman Filter(EnKF)as a proposal density;the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF,but the RMSEs of T/S profiles reduce significantly by approximately 55%for the T profile and 35%for the S profile(relative to the EnKF).As a result,the LWEnKF makes more reasonable predictions of the internal ocean temperature *** addition,the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF.

关键词： data assimilation Localized Weighted Ensemble Kalman Filter northern South China Sea sea surface height sea surface temperature temperature and salinity profiles mesoscale eddy

来源：评论

学校读者我要写书评

暂无评论

Progressive Diversifying Policy for Multi-Agent Reinforcement Learning 48

Progressive Diversifying Policy for Multi-Agent Reinforcemen...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sun, Shaoqi Zhai, Yuanzhao Xu, Kele Feng, Dawei Ding, Bo National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9781728163277

Multi-Agent Reinforcement Learning (MARL) has recently achieved promising performance in many collaborative decision making tasks. However, one of the main bottleneck challenges for MARL is the sparsity of the team reward, which can lead to the homogenization of agents' behaviors. To address these issues, we propose a Progressive Diversifying Policy (PDP) algorithm in this paper. Specifically, we propose to actively amplify the diversity between agents' policies during learning and exploit diversity as an additional intrinsic reward for MARL. Furthermore, we propose a progressive diversity boosting policy to find a better team policy. Leveraging the aforementioned improvements, our method can handle sparse team rewards and alleviate the homogeneous behaviors of agents. We conduct experiments on widely-used MARL environments and the results show that PDP can provide state-of-the-art performance while maintaining a competitive convergence speed. © 2023 IEEE.

关键词： Homogeneous behavior Multi-agent reinforcement learning Policy diversity Sparse reward

来源：评论

学校读者我要写书评

暂无评论

EPipe: Pipeline Inference Framework with High-quality Offline Parallelism Planning for Heterogeneous Edge Devices 24

EPipe: Pipeline Inference Framework with High-quality Offlin...

引用

43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Xiong, Yi Liu, Weihong Zhang, Rui Zu, Yulong Zhu, Zongwei Zhou, Xuehai School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China School of Software Engineering University of Science and Technology of China China

ISBN: (纸本)9798400710773

Pipeline parallelism is essential for edge computing as it effectively consolidates the limited resources of edge devices, enabling the deployment of large Deep Neural Network (DNN) models and accelerating inference processes without compromising the performance of models. Accurate computation and communication latency estimation on heterogeneous edge devices is essential for searching for a superior parallelism plan. However, existing heterogeneous pipeline inference approaches either incur substantial resource wastage during online parallelism planning, as they utilize profiling strategies that occupy physical devices;or rely on cost models with inadequate representational capabilities, leading to inaccurate predictions, thereby harming the result of pipeline planning. This paper proposes EPipe, a novel pipeline inference framework that supports high-quality offline planning in heterogeneous edge environments. EPipe integrates two core components: the Task-Device Co-analyzer (TDC) and the Multi-pipeline Parallelism Planner (MPP). TDC utilizes an undirected connected graph to depict the compatibility of DNNs across device groups and precisely estimates inference and communication latencies through fine-grained modeling. Based on TDC, MPP utilizes a dynamic programming-based genetic algorithm to explore multi-pipeline solutions, extending beyond traditional single-pipeline methods. A comprehensive experimental evaluation on an edge testbed confirms the effectiveness of EPipe, demonstrating significant speedups in inference tasks for both task streams and single tasks. © 2024 Copyright is held by the owner/author(s).

关键词： Edge detection

来源：评论

学校读者我要写书评

暂无评论

WASMDYPA: Effectively Detecting WebAssembly Bugs via Dynamic Program Analysis 23

WASMDYPA: Effectively Detecting WebAssembly Bugs via Dynamic...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Zheng, Wenlong Hua, Baojian Jiang, Zhuochen School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China

ISBN: (纸本)9798350359398

Safe binary execution is a crucial requirement in to-day's security-critical computing infrastructures. WebAssem-bly is an emerging language designed for safe binary execution that has been deployed in many security-critical domains, such as blockchain, edge computing, and clouds. However, WebAssembly's security guarantee is not a panacea, and recent studies have revealed a large spectrum of security issues such as integer overflows and memory vulnerabilities, leading to serious security hazards to WebAssembly applications. In this paper, we present Wasmdypa, the first automated bug detection framework for WebAssembly programs based on dynamic program analysis with three primary components: 1) an input generator for WebAssembly binaries;2) static instrumentation hooks with extensible interfaces to record runtime information;and 3) dynamic program analysis al-gorithms as security plugins to detect vulnerabilities. We have implemented a software prototype for Wasmdypa, and have conducted experiments to evaluate the effectiveness, usefulness, performance of our approach. The experimental results demonstrated that WASMDYPA can accurately detect vulnerabilities with an 88.24% precision and a 93.75% recall. Furthermore, WASMDYPA detected 56 bugs in real-world WebAssembly programs, including 2 integer overflows and 54 memory bugs. © 2023 IEEE.

关键词： software prototyping

来源：评论

学校读者我要写书评

暂无评论

Soft Tissue Sarcoma Segmentation Network Based on Self-supervised Learning

Soft Tissue Sarcoma Segmentation Network Based on Self-super...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Liang Shao, Yu Gang, Zhanxin Jia, Chaoran Chen, Yihan Yang, Yi Dalian University of Technology School of Software Technology Dalian China Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (纸本)9798350337488

Soft tissues sarcomas include striated muscle, fibrous tissue, fat, and other soft tissues. Simultaneously, their mortality rates are comparable to those of esophageal cancer, cervical cancer, and other cancers. Prior to surgical resection of patients, it is frequently necessary to study and diagnose the sarcoma area using MRI images in order to design a better surgical plan. However, artificial approaches for diagnosing the sarcoma region are time-consuming and error-prone. While the advent of artificial intelligence allows for computer-assisted diagnosis of the sarcoma region. Nevertheless, there is currently a scarcity of high-quality soft tissue sarcoma imaging data sets in relevant sectors. Thus, with the aim to investigate how to use multi-modal MRI images of patients with soft tissue sarcomas to segment the sarcoma area, we collect and process 15372 multi-modal MRI images in coronal of 40 patients with soft tissue sarcomas found in the thigh, which we subsequently combine with the help of several clinicians to mark the sarcoma area. The multi-modal MRI imaging data set of soft tissue sarcoma is therefore acquired by a number of preprocessing techniques. The sarcoma area is then segmented using a multi-encoder and single-decoder network that adapts to multiple input modalities. For motivating the network to learn the important semantic features of different modalities, we design a feature fusion strategy mechanism that is applied to the skip connection. Additionally, self-supervised learning is being investigated to address the issue of a small number of data points in the data set. Experiments show that our network can achieve the highest Dice score of 57.76% on our data set. Our code and the dataset are available at https://***/syaxx0819/The-Multimodal-Soft-Tissue-Sarcoma-Image-Dataset. © 2023 IEEE.

关键词： Latent Correlation Representation Medical Image Segmentation Multi-modal Self-supervised Learning Soft Tissue Sarcoma

来源：评论

学校读者我要写书评

暂无评论

VMCanary: Effective Memory Protection for WebAssembly via Virtual Machine-assisted Approach 23

VMCanary: Effective Memory Protection for WebAssembly via Vi...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Zhang, Ziyao Zheng, Wenlong Hua, Baojian Fan, Qiliang Pan, Zhizhong University of Science and Technology of China School of Software Engineering China Suzhou Institute for Advanced Research University of Science and Technology of China China

ISBN: (纸本)9798350319583

WebAssembly is an emerging secure programming language and portable instruction set architecture, and has been deployed in diverse security-critical scenarios due to its safety advantages. However, WebAssembly's linear memory is still vulnerable to buffer overflows due to the lack of effective protection mechanism, defeating its security guarantees. In this paper, we present VMCanary, the first framework for effective WebAssembly memory protection, by leveraging a canary approach but with the aid from WebAssembly virtual machines (VMs). Our key idea is that, due to the fact that WebAssembly is a managed programming language to be executed by underlying WebAssembly VMs, the VMs must understand any protection mechanisms already enforced in programs. With this key idea, we first propose the concept of canary in code, which is like a traditional canary in data but whose semantics is understandable by underlying WebAssembly VMs. To realize this kind of canary, we introduced two novel WebAssembly instructions by defining their semantics. Furthermore, we designed an instrumentation for WebAssembly binaries to instrument these two instructions automatically, hence no sources and compiler toolchain modifications are required. We have implemented a software prototype for VMCanary, and have conducted extensive experiment to evaluate it on micro benchmarks and 59 real-world CWEs. Experimental results demonstrated that VMCanary is effective in protecting Wasm memory with negligible overhead (3% on average). © 2023 IEEE.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

RUSPATCH: Towards Timely and Effectively Patching Rust Applications 23

RUSPATCH: Towards Timely and Effectively Patching Rust Appli...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Wu, Yufei Hua, Baojian University of Science and Technology of China School of Software Engineering China University of Science and Technology of China Suzhou Institute for Advanced Research China

ISBN: (纸本)9798350319583

Despite the fact that Rust is designed to be a secure programming language for system programming, it is still vulnerable and exploitable due to its inclusion of an unsafe sub-language. However, existing studies on Rust security only focus on static detection or rectification of vulnerability, but ignore the problem of timely and effective rectifications of vulnerabilities dynamically. In this paper, to fill this gap, we present RUSPATCH, the first infrastructure to timely and effectively patch vulnerable Rust applications. RUSPATCH consists of two main phases: static partitioning and dynamic patching. In the static partitioning phase, RUSPATCH divides the candidate program into target code and patch candidates via a customized compiler. During the patching phase, RUSPATCH dynamically validates and applies the security patch once a vulnerability is detected. To realize the whole process, we tackled three technical challenges of language discrepancy, efficiency issues, and security threats. We have designed and implemented a software prototype for RUSPATCH, and have conducted extensive experiments to evaluate its effectiveness, performance, overhead, and usefulness. Experimental results demonstrated that RusPATCH is effective in patching off-the-shelf Rust applications including real-world Rust CVEs, and the extra overhead RusPATCH introduced is less than 3.28% and thus insignificant. Furthermore, RUSPATCH is easy to incorporate into existing Rust applications without any manual interventions. © 2023 IEEE.

关键词： software prototyping

来源：评论

学校读者我要写书评

暂无评论

AttenTPU: Tensor Processor for Attention Mechanism with Fine-Grained Padding

AttenTPU: Tensor Processor for Attention Mechanism with Fine...

引用

2023 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2023

作者： Du, Zhihao Li, Yike Chen, Chao Wang, Zheng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Software Engineering University of Science and Technology of China China

ISBN: (纸本)9798350344288

Transformer-based models have achieved state-of-the art performance in many Artificial Intelligence (AI) tasks. The core component of the transformer is the attention mechanism, which includes computation-intensive operators-such as MatMul and Linear-raising demand for hardware support. Many Institute, and researchers have proposed their hardware acceleration methods in recent years. However, most of these works mainly focus on algorithm optimization and hardware performance. Little attention has been paid to the variable sequence lengths in the attention mechanism. In the hardware design, the variability of sequence length brings a challenge to the load-store unit and on-chip data movement. In this work, we first propose a hardware-friendly fine-grained padding method aiming to handle the variability of sequence length in scaled dot-product attention. Then, a hardware architecture named AttenTPU is proposed using the padding method. The experimental results indicate that compared to the CPU and GPU platform, our accelerator achieves speed-ups of 3.43× and 1.41×, respectively. © 2023 IEEE.

关键词： Acceleration

来源：评论

学校读者我要写书评

暂无评论

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

引用

Frontiers of Environmental Science & engineering 2023年第5期17卷 27-40页

作者： Yirong Hu Wenjie Du Cheng Yang Yang Wang Tianyin Huang Xiaoyi Xu Wenwei Li CAS Key Laboratory of Urban Pollutant Conversion Department of Environmental Science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China School of Software Engineering University of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou Institute for Advanced Research of USTCSuzhou 215123China National and Local Joint Engineering Laboratory for Municipal Sewage Resource Utilization Technology Suzhou University of Science and TechnologySuzhou 215009China

Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources,for which mathematical modeling is commonly *** contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling,we employed an ensemble machine learning(ML)model to identify the key nitrogen and phosphorus sources of *** ML models were developed based on 13 years of historical data of Lake Taihu’s water quality,environmental input,and meteorological conditions,among which the XGBoost model stood out as the best model for total nitrogen(TN)and total phosphorus(TP)*** results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality,while the lake TP is predominantly from endogenous *** prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication ***,one-month-ahead prediction of lake TN and TP concentrations(R2 of 0.85 and 0.95,respectively)was achieved based on this model with sliding time window lengths of 9 and 6 months,*** work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction,which may provide valuable references for early warning and rational control of lake eutrophication.

关键词： Eutrophication Machine learning Water quality Nutrients Prediction

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：