检索结果-内蒙古大学图书馆

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shang, Xiaojun Mao, Yingling Liu, Yu Huang, Yaodong Liu, Zhenhua Yang, Yuanyuan Stony Brook University Department of Electrical and Computer Engineering United States Shenzhen University College of Computer Science and Software Engineering Shenzhen China Stony Brook University Department of Applied Mathematics and Statistics United States

ISBN: (纸本)9798350334142

Introducing the emerging serverless paradigm into edge computing could avoid over- and under-provisioning of limited edge resources and make complex edge resource management transparent to application developers, which largely facilitates the cost-effectiveness, portability, and short time-to-market of edge applications. However, the computation/data dispersion and device/network heterogeneity of edge environments prevent current serverless computing platforms from acclimating to the network edge. In this paper, we address such challenges by formulating a container placement and data flow routing problem, which fully considers the heterogeneity of edge networks and the overhead of operating serverless platforms on resource-limited edge servers. We design an online algorithm to solve the problem. We further show its local optimum for each arriving container and prove its theoretical guarantee to the optimal offline solution. We also conduct extensive simulations based on practical experiment results to show the advantages of the proposed algorithm over existing baselines. © 2023 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Analysis of software Testing Automation Challenges Using Structural Equation Modeling (SEM) Approach

引用

IEEE Access 2025年 13卷 96159-96181页

作者： Muhammad Faisal Abrar Muhammad Faran Majeed Muhammad Saqib Ali Alferaidi Razauddin College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Computer Science Kohsar University at Murree Murree Punjab Pakistan Department of Software Engineering University of Engineering and Technology at Peshawar Peshawar Pakistan

The adoption of automation in software testing presents challenges that can hinder its effectiveness and scalability. This study systematically investigates these challenges using a multi-phase research approach. First, a Systematic Literature Review (SLR) was conducted to identify 14 Critical Challenges (CCs) in automation adoption. Second, a questionnaire survey of 50 industry experts validated these challenges and examined their interrelationships. Finally, Structural Equation Modeling (SEM) a mathematical and statistical approach was employed to analyze correlations and uncover structural dependencies among the challenges. The SEM analysis identified five latent variables: Human Resource Constraints (HRC), Technological & Process Challenges (TPC), Financial & Resource Constraints (FRC), Security & Reliability Issues (SRI), and Future Adaptability & Scalability (FAS) that significantly influence automation adoption. Hypothesis testing revealed that HRC (-0.32), TPC (-0.45), FRC (−0.41), and SRI (−0.38) negatively impact automation success, whereas FAS (+0.51) plays a pivotal role in enabling successful adoption. Model validation through Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) confirmed strong construct reliability and fit indices (RMSEA =0.043, CFI =0.95, TLI =0.92, SRMR =0.037). The study highlights the need for workforce training, standardized automation processes, cost-effective solutions, and security enhancements. By providing an empirically validated framework, this research contributes to both academia and industry, guiding decision-makers in optimizing automation strategies and improving software testing efficiency.

关键词： Automation Numerical analysis Mathematical models software testing Organizations Systematic literature review Surveys Industries Analytical models Systematics

来源：评论

学校读者我要写书评

暂无评论

LeaderKV: Improving Read Performance of KV Stores via Learned Index and Decoupled KV Table

LeaderKV: Improving Read Performance of KV Stores via Learne...

引用

International Conference on Data engineering

作者： Yi Wang Jianan Yuan Shangyu Wu Huan Liu Jiaxian Chen Chenlin Ma Jianbin Qin College of Computer Science and Software Engineering Shenzhen University Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Log-structured merge-tree (LSM-tree) is a storage architecture widely used in key-value (KV) stores. To enhance the read efficiency of LSM-tree, recent works utilize the learned index to learn the mapping between keys and locations. However, in existing learned-index-aided KV stores, inefficient design of the learned index and disk access significantly impact the read performance. How to design a learned KV store to improve index efficiency and minimize disk access remains a critical problem. This paper presents LeaderKV, a read-optimized LSM-tree-based KV store. LeaderKV employs decoupled KV tables (DK-Table) and efficient learned indexes for data retrieval. DKTables are storage files in Leader Kvbecause they avoid reading irrelevant data in collaboration with learned indexes during queries. A learned index called Leader is proposed to accelerate data retrieval within DKTable. Leader is composed of precise models and approximate models. A redirect mechanism is designed to reduce the cost of mispredictions in Leader. We integrate DKTable and Leader into LeaderKV and demonstrate its effectiveness using a variety of datasets and workloads. Experimental results show that LeaderKV significantly improves the read performance compared to representative schemes.

关键词： Training Lead acid batteries Costs Graphics processing units Collaboration Bandwidth Data engineering

来源：评论

学校读者我要写书评

暂无评论

TransRec++:Translation-based sequential recommendation with heterogeneous feedback

引用

Frontiers of computer science 2022年第2期16卷 190-192页

作者： Zhuo-Xin ZHAN Ming-Kai HE Wei-Ke PAN Zhong MING National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China

1 Introduction Nowadays,recommender systems are widely used because of the information overload problem on the *** are a variety of feedback in recommender systems,such as explicit feedback,implicit feedback,sequential feedback,*** them,modeling of heterogeneous sequential feedback,which contains not only different types of feedback such as examinations and purchases but also the sequential information,is an emerging and important problem receiving more and more *** sequential feedback is relatively easy to be collected in a deployed system and is also able to provide more information than the homogeneous feedback,which is thus expected to be helpful in improving the recommendation accuracy.

关键词： feedback recommendation sequential

来源：评论

学校读者我要写书评

暂无评论

SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM Adaptive Head 13

SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM...

引用

13th International Conference on Information science and Technology, ICIST 2023

作者： Liu, Zhitao Wang, Dong Luo, Xiaonan Li, Fang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Mathematics and Informatics College of Software Engineering South China Agricultural Univerrsity Guangzhou China

ISBN: (纸本)9798350313925

In the field of single object tracking, conventional methods often rely on correlation filters or visual image processing. However, these approaches typically focus solely on extracting target object features and consider only the position information in the current frame. They lack integration of contextual and spatial-temporal information, which can limit tracking *** method takes inspiration from single object detection and tracking techniques. It combines efficient model-based object detection with the spatial position of the final target in the image. This approach offers significant advantages in reducing tracking drift (deviation from the target object) and improving tracking accuracy (predicting bounding boxes that closely follow the target).To extract spatial information, we utilize a three-layer ResNet and Convolutional Block Attention Module (CBAM) in conjunction with a siamese network. This combination effectively captures the spatial characteristics of the target object. Additionally, we employ an adaptive processing head with an internal structure of Long Short Term Memory (LSTM) to capture temporal information. By integrating spatial and temporal cues, our method achieves more robust and accurate *** comprehensive comparison and ablation experiments across multiple datasets, we have demonstrated notable improvements with our method. It tightly connects the predicted bounding box with the tracking target and effectively combines spatial and temporal information for precise object tracking. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Boosting Write Performance of KV Stores: An NVM - Enabled Storage Collaboration Approach

Boosting Write Performance of KV Stores: An NVM - Enabled St...

引用

International Conference on Data engineering

作者： Yi Wang Jiajian He Kaoyi Sun Yunhao Dong Jiaxian Chen Chenlin Ma Amelie Chi Zhou Rui Mao College of Computer Science and Software Engineering Shenzhen University Department of Computer Science Hong Kong Baptist University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

As the most common data structure for key-value stores, LogStructured Merge Tree (LSM-tree) can eliminate random write operations and keep acceptable read performance. However, write stall and write amplification introduced by the leveled compaction of LSM-tree significantly degrade the system performance. The emerging non-volatile memory (NVM) provides byte-addressable access and low-latency data persistence. Integrating DIMM-interface NVM in the design of the LSM-tree can potentially alleviate the write stall and write amplification issue, as the access speed of NVM is several orders of magnitude faster than hard disk drives or flash memory-based solid-state drives. This hybrid storage should be carefully designed, requiring new architectural and key-value structural support. This paper presents ZigZagDB, an NVM-enabled data man-agement scheme for LSM-tree-based key-value stores. ZigZagDB adds additional layers of key-value stores and uses non-volatile memory as the storage media to hold these additional layers of data. The newly designed key-value stores alternately access the data from either SSD or NVM. This ‘ZigZag’ shape of storage collaboration and synchronization can benefit write efficiency and space utilization. By utilizing the NVM with very limited capacity, the redesigned organization of LSM-tree can effectively solve the write stall and write amplification issue. We demonstrate the viability of the proposed ZigZagDB using a set of extensive experiments. Experimental results show that ZigZagDB can significantly reduce the write amplification and boost the throughput in comparison with representative schemes.

关键词： Nonvolatile memory Solid state drives Shape System performance Collaboration Tail Throughput

来源：评论

学校读者我要写书评

暂无评论

Towards Efficient and Diverse Generative Model for Unconditional Human Motion Synthesis 24

Towards Efficient and Diverse Generative Model for Unconditi...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Yu, Hua Liu, Weiming Bai, Jiapeng Gui, Xu Hou, Yaqing Ong, Yewsoon Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science Zhejiang University Hangzhou China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China College of Computing and Data Science Nanyang Technological University Singapore Singapore Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Recent generative methods have revolutionized the way of human motion synthesis, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields. However, there are still challenges in unconditionally generating highly diverse human motions from a given distribution. To enhance the diversity of synthesized human motions, previous methods usually employ deep neural networks (DNNs) to train a transport map that transforms Gaussian noise distribution into real human motion distribution. According to Figalli's regularity theory, the optimal transport map computed by DNNs frequently exhibits discontinuities. This is due to the inherent limitation of DNNs in representing only continuous maps. Consequently, the generated human motions tend to heavily concentrate on densely populated regions of the data distribution, resulting in mode collapse or mode mixture. To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis. First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex optimization to match the noise distribution with the latent space distribution of human motions through the Optimal Transport (OT) map. Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion. Through qualitative and quantitative experiments, MOOT achieves state-of-the-art results surpassing the latest methods, validating its superiority in unconditional human motion generation. © 2024 ACM.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Near-Linear Time Approximation Algorithms for k-means with Outliers 41

Near-Linear Time Approximation Algorithms for k-means with O...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Huang, Junyu Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio ∆ (the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time O(ndk log log n/∊2) with O(k/∊) centers opened and (1 + ∊)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/∊)-approximate solution can be obtained in time O(ndk log log n/∊2 + dpoly(k, 1/∊) log(n∆)) with (1 + ∊)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem. Copyright 2024 by the author(s)

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

A Review of Link Prediction on Heterogeneous Networks 8th

A Review of Link Prediction on Heterogeneous Networks

引用

8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Mi, Rui Wang, Changbo Zhang, Liang-Jie Wu, Yirui Chen, Junyang Wang, Huan College of Informatics Huazhong Agricultural University Wuhan China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hohai University Nanjing China

ISBN: (纸本)9783031779534

Link prediction is a central downstream assignment in network analysis, which denotes an attempt to assess the probability of a connection between two nodes based on observed link and node properties, which can be utilized across diverse categories of graph system, including knowledge graphs, network traffic, social networks, biological networks, etc. Heterogeneous networks usually contain diverse nodes and a variety of edges’ type, which can preserve more information than homogeneous information networks. However, the majority of researches focus on graph representation analysis of homogeneous networks. Limited research has systematically reviewed advanced techniques for link prediction and applications on Heterogeneous Information Networks (HIN). Building upon this, we propose a classification summary for link prediction methods in heterogeneous information graphs. We will introduce some classical methods and some newly proposed methods for link prediction on heterogeneous graphs. We provide general technical ideas behind each method in classes and analysis performance of models. We further discuss the applications of link prediction on complex heterogeneous graphs across various domains. Additionally, the research challenges are discussed, and several possible research avenues for link prediction on heterogeneous network are pointed out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Multi-Agent Spatial Logic for Scenario-Based Decision Modeling and Verification in Platoon Systems

引用

Journal of computer science & Technology 2021年第6期36卷 1231-1247页

作者： Jingwen Xu Yanhong Huang Jianqi Shi Shengchao Qin National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai 200062China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518061China

To cater for the scenario of coordinated transportation of multiple trucks on the highway,a platoon system for autonomous driving has been extensively explored in the *** such a platoon is deployed,it is necessary to ensure the safety of its driving behavior,whereby each vehicle’s behavior is commanded by the decision-making function whose decision is based on the observed driving ***,there is currently a lack of verification methods to ensure the reliability of the scenario-based decision-making process in the platoon *** this paper,we focus on the platoon driving scenario,whereby the platoon is composed of intelligent heavy trucks driving on cross-sea *** propose a formal modeling and verification approach to provide safety assurance for platoon vehicles’cooperative driving *** existing Multi-Lane Spatial Logic(MLSL)with a dedicated abstract model can express driving scene spatial properties and prove the safety of multi-lane traffic maneuvers under the single-vehicle *** cater for the platoon system’s multi-vehicle perspective,we modify the existing abstract model and propose a Multi-Agent Spatial Logic(MASL)that extends MLSL by relative orientation and multi-agent *** then utilize a timed automata type supporting MASL formulas to model vehicles’decision controllers for platoon *** the behavior of a human-driven vehicle(HDV)joining the platoon as a case study,we have implemented the model and verified safety properties on the UPPAAL tool to illustrate the viability of our framework.

关键词： autonomous driving decision-making model platoon system safety verification timed automaton

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：