检索结果-内蒙古大学图书馆

Exploring & exploiting high-order graph structure for sparse knowledge graph completion

Frontiers of Computer Science 2025年第2期19卷 31-42页

作者： Tao HE Ming LIU Yixin CAO Zekun WANG Zihao ZHENG Bing QIN Research Center for Social Computing and Information Retrieval Harbin Institute of TechnologyHarbin 150001China Peng Cheng Laboratory Shenzhen 518000China SMU School of Computing and Information Systems Singapore Management UniversitySingapore 178902Singapore

Sparse Knowledge Graph(KG)scenarios pose a challenge for previous Knowledge Graph Completion(KGC)methods,that is,the completion performance decreases rapidly with the increase of graph *** problem is also exacerbated because of the widespread existence of sparse KGs in practical *** alleviate this challenge,we present a novel framework,LR-GCN,that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse *** proposed approach comprises two main components:a GNN-based predictor and a reasoning path *** reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges,explicitly compositing long-range dependencies into the *** step also plays an essential role in densifying KGs,effectively alleviating the sparse ***,the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the *** two components are jointly optimized using a well-designed variational EM *** experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.

关键词： knowledge graph completion graph neural networks reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

TrustMesh: A Blockchain-Enabled Trusted distributed computing Framework for Open Heterogeneous IoT Environments 22

TrustMesh: A Blockchain-Enabled Trusted Distributed Computin...

引用

22nd IEEE International Conference on Software Architecture, ICSA 2025

作者： Rangwala, Murtaza Buyya, Rajkumar Laboratory School of Computing and Information Systems Australia

ISBN: (纸本)9798331520908

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol. The key innovation lies in TrustMesh's ability to support non-deterministic scheduling algorithms while maintaining Byzantine fault tolerance, features traditionally considered mutually exclusive in blockchain systems. The framework supports a sophisticated resource management approach that enables flexible scheduling decisions while preserving the security guarantees of blockchain-based verification. Our experimental evaluation using a real-world cold chain monitoring scenario demonstrates that TrustMesh successfully maintains Byzantine fault tolerance with fault detection latencies under 150 milliseconds, while maintaining consistent framework overhead across varying computational workloads even with network scaling. These results establish TrustMesh's effectiveness in balancing security, performance, and flexibility requirements in trustless IoT environments, advancing the state-of-the-art in secure distributed computing frameworks. © 2025 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

TrustMesh: A Blockchain-Enabled Trusted distributed computing Framework for Open Heterogeneous IoT Environments

TrustMesh: A Blockchain-Enabled Trusted Distributed Computin...

引用

Working IEEE/IFIP Conference on Software Architecture

作者： Murtaza Rangwala Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9798331520908

ISBN: (纸本)9798331520915

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol. The key innovation lies in TrustMesh’s ability to support non-deterministic scheduling algorithms while maintaining Byzantine fault tolerance, features traditionally considered mutually exclusive in blockchain systems. The framework supports a sophisticated resource management approach that enables flexible scheduling decisions while preserving the security guarantees of blockchain-based verification. Our experimental evaluation using a real-world cold chain monitoring scenario demonstrates that TrustMesh successfully maintains Byzantine fault tolerance with fault detection latencies under 150 milliseconds, while maintaining consistent framework overhead across varying computational workloads even with network scaling. These results establish TrustMesh’s effectiveness in balancing security, performance, and flexibility requirements in trustless IoT environments, advancing the state-of-the-art in secure distributed computing frameworks.

关键词： Fault tolerance Trusted computing Technological innovation Software architecture Scheduling algorithms Fault tolerant systems Trustless services Internet of Things Security Resource management

来源：评论

学校读者我要写书评

暂无评论

A Forced Decoding-based Approach for Enhancing Low-resource ASR

引用

Neural Processing Letters 2025年第3期57卷 1-10页

作者： Liu, Yunpeng Yang, Xukui Qu, Dan School of Information Systems Engineering University of Information Engineering Henan Zhengzhou450001 China The Laboratory for Advanced Computing and Intelligence Engineering Jiangsu Wuxi214001 China

Multilingual automatic speech recognition represents a crucial research direction in tackling the challenges associated with low-resource scenarios. To effectively incorporate language-specific information in the joint training of a model across multiple languages and leverage linguistic similarities to enhance performance on the target low-resource language, this paper introduces a language similarity evaluation approach based on forced decoding. Specifically, when the target language is specified, the speeches of the source language are decoded into transcription in the target language, and the normalized posterior is utilized as the foundation for evaluating language similarity. Comprehensive experiments and analyses conducted on six low-resource languages reveal that the proposed approach achieves an average word error rate relative reduction of 21.74, 7.68, and 3.45% compared to three widely used benchmark methods, respectively, thereby validating the effectiveness of our approach. © The Author(s) 2025.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

A Utility-Adjustable Reverse Auction Mechanism for UAV Data Collection Task Allocation 20th

A Utility-Adjustable Reverse Auction Mechanism for UAV Data...

引用

20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Chen, Peng Zhang, Jixian School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9789819628636

Due to their low cost, easy deployment, and high flexibility, unmanned aerial vehicles(UAV) have become widely utilized for various data collection tasks across different fields. In particular, virtual service providers(VSP) recruit UAVs to collect data in their regions of interest(ROI), and leverage this data to offer a range of services, such as metaverse, traffic management, etc. To attract UAV owners to participate in the recruitment activities, VSP need to provide participating users with payments as rewards to motivate their participation. However, the recruitment demands of different VSPs for UAVs can vary significantly. For instance, time-sensitive tasks require timely recruitment of sufficient UAVs to ensure prompt completion of data collection tasks, necessitating higher payments to increase user enthusiasm. Conversely, in other types of tasks, the VSP’s objective might be to maximize its own utility. However, designing a general incentive mechanism for VSPs with different tasks that satisfies incentive compatibility(IC) and individual rationality(IR) to address the issues of UAV task allocation and payment is challenging. If VSP offer insufficient payments to users, it will be difficult to attract enough users to share their UAVs;conversely, offering excessive payments to users will harm the VSP’ profits. To address this problem, we propose a utility-adjustable reverse auction mechanism based on reverse Vickrey-Clarke-Groves(VCG) auction and affine maximizer auction(AMA) theory to cope with different task scenarios. This incentive mechanism can maximize social welfare while flexibly adjusting the utility allocation between VSP and users, ensuring non-negative utility for both parties. Experimental results show that the mechanism has excellent performance. © IFIP International Federation for information Processing 2025.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

A deep reinforcement learning based algorithm for time and cost optimized scaling of serverless applications

引用

Future Generation Computer systems 2025年 173卷

作者： Mampage, Anupama Karunasekera, Shanika Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Australia

Serverless computing has gained a strong traction in the cloud computing community in recent years. Among the many benefits of this novel computing model, the rapid auto-scaling capability of user applications takes prominence. However, the offer of adhoc scaling of user deployments at function level introduces many complications to serverless systems. The added delay and failures in function request executions caused by the time consumed for dynamically creating new resources to suit function workloads, known as the cold-start delay, is one such very prevalent shortcoming. Maintaining idle resource pools to alleviate this issue often results in wasted resources from the cloud provider perspective. Existing solutions to address this limitation mostly focus on predicting and understanding function load levels in order to proactively create required resources. Although these solutions improve function performance, the lack of understanding on the overall system characteristics in making these scaling decisions often leads to the sub-optimal usage of system resources. Further, the multi-tenant nature of serverless systems requires a scalable solution adaptable for multiple co-existing applications, a limitation seen in most current solutions. In this paper, we introduce a novel multi-agent Deep Reinforcement Learning based intelligent solution for both horizontal and vertical scaling of function resources, based on a comprehensive understanding on both function and system requirements. Our solution elevates function performance reducing cold starts, while also offering the flexibility for optimizing resource maintenance cost to the service providers. Experiments conducted considering varying workload scenarios show improvements of up to 23% and 34% in terms of application latency and request failures, or alternatively saving up to 45% in infrastructure cost for the service providers. © 2025

关键词： Computer aided instruction

来源：评论

学校读者我要写书评

暂无评论

FlexFL: Flexible and Effective Fault Localization With Open-Source Large Language Models

引用

IEEE Transactions on Software Engineering 2025年第5期51卷 1455-1471页

作者： Xu, Chuyang Liu, Zhongxin Ren, Xiaoxue Zhang, Gehao Liang, Ming Lo, David Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security Hangzhou310052 China Ant Group Hangzhou310013 China Singapore Management University School of Computing and Information Systems 188065 Singapore

Fault localization (FL) targets identifying bug locations within a software system, which can enhance debugging efficiency and improve software quality. Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, we propose a novel LLM-based FL framework named FlexFL, which can flexibly leverage different types of bug-related information and effectively work with open-source LLMs. FlexFL is composed of two stages. In the first stage, FlexFL reduces the search space of buggy code using state-of-the-art FL techniques of different families and provides a candidate list of bug-related methods. In the second stage, FlexFL leverages LLMs to delve deeper to double-check the code snippets of methods suggested by the first stage and refine fault localization results. In each stage, FlexFL constructs agents based on open-source LLMs, which share the same pipeline that does not postulate any type of bug-related information and can interact with function calls without the out-of-the-box capability. Extensive experimental results on Defects4J demonstrate that FlexFL outperforms the baselines and can work with different open-source LLMs. Specifically, FlexFL with a lightweight open-source LLM Llama3-8B can locate 42 and 63 more bugs than two state-of-the-art LLM-based FL approaches AutoFL and AgentFL that both use GPT-3.5. In addition, FlexFL can localize 93 bugs that cannot be localized by non-LLM-based FL techniques at the top 1. Furthermore, to mitigate potential data contamination, we conduct experiment

关键词： Program debugging

来源：评论

学校读者我要写书评

暂无评论

OFDM-structure based waveform designs for integrated sensing and communication

引用

Science China(information Sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study of OOP-Related Bugs in C++ Compilers

引用

IEEE Transactions on Software Engineering 2025年 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

来源：评论

学校读者我要写书评

暂无评论

Collaborative Pushing and Grasping in Complex Scenarios via a Generator-Evaluator Network with Multiple Action Primitives 17th

Collaborative Pushing and Grasping in Complex Scenarios vi...

引用

17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. This paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. This strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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：