检索结果-内蒙古大学图书馆

12th International Conference on Computational Visual Media, CVM 2024

作者： Fu, Yiyu Zhao, Baoquan Lv, Chenlei Yue, Guanghui Wang, Ruomei Zhou, Fan School of Artificial Intelligence Sun Yat-sen University Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789819720941

Text-driven human motion generation is gaining momentum lately thanks to its great potential in shaping the new pathway of interactive computer graphics in the era of AI. Despite the enormous efforts made so far, existing methods still struggle to ensure fluidity and body coordination when generating motions, which seriously hinders its application in a wide spectrum of areas such as gaming, animation, and the emerging metaverse. One of the many causes is, that learning directly from motion data is prone to interference from noise within the data, resulting in reduced quality of the generated motions. In this study, we for the first time propose to promote text-to-motion generation via out-of-distribution detection in the embedding space. Leveraging the Z-score-based outlier detection algorithm, we apply masking to motion data within the motion encoder and replace target data with means, ensuring the consistency of data distribution. To verify the effectiveness of the proposed method, we have conducted extensive experiments on the widely used KIT-ML dataset. Experimental results indicate that compared to previous frameworks, our solution significantly improves the quality of text-driven human motion generation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Interactive computer graphics

来源：评论

学校读者我要写书评

暂无评论

Cost-Effective Clustering by Aggregating Local Density Peaks 28th

Cost-Effective Clustering by Aggregating Local Density Peak...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xie, Wen-Bo Chen, Bin Shi, Jun-Hao Lee, Yan-Li Wang, Xin Fu, Xun School of Computer Science Southwest Petroleum University Chengdu610500 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9783031306778

Hierarchical clustering algorithms that provide tree-shaped results can be regarded as data summarization and thus play an important role in the application of knowledge discovery and data mining. However, such structured result also brings a challenge, i.e., a difficult trade-off between complexity (time and space) and quality. To tackle of this issue, we propose a newly designed agglomerative algorithm for hierarchical clustering in this paper, which merges data points into tree-shaped sub-clusters via the operations of nearest-neighbor chain searching and determines the proxy of each sub-cluster by the process of local density peak detection. Extensive experimental studies on real-world and synthetic datasets show that our method performs well by outperforming other baselines in accuracy, response time, and memory footprint. Meanwhile, our method can scale to half a million data points on a personal computer, further verifying its cost-effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Personal computers

来源：评论

学校读者我要写书评

暂无评论

Driving Scene Understanding with Traffic Scene-Assisted Topology Graph Transformer 24

Driving Scene Understanding with Traffic Scene-Assisted Topo...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Rong, Fu Peng, Wenjin Lan, Meng Zhang, Qian Zhang, Lefei National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Hong Kong University of Science and Technology Hong Kong Hong Kong Horizon Robotics Beijing China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798400706868

Driving scene topology reasoning aims to understand the objects present in the current road scene and model their topology relationships to provide guidance information for downstream tasks. Previous approaches fail to adequately facilitate interactions among traffic objects and neglect to incorporate scene information into topology reasoning, thus limiting the comprehensive exploration of potential correlations among objects and diminishing the practical significance of the reasoning results. Besides, the lack of constraints on lane direction may introduce erroneous guidance information and lead to a decrease in topology prediction accuracy. In this paper, we propose a novel topology reasoning framework, dubbed TSTGT, to address these issues. Specifically, we design a divide-and-conquer topology graph Transformer to respectively infer the lane-lane and lane-traffic topology relationships, which can effectively aggregate the local and global object information in the driving scene and facilitate the topology relationship learning. Additionally, a traffic scene-assisted reasoning module is devised and combined with the topology graph Transformer to enhance the practical significance of lane-traffic topology. In terms of lane detection, we develop a point-wise matching strategy to infer lane centerlines with correct directions, thereby improving the topology reasoning accuracy. Extensive experimental results on Openlane-V2 benchmark validate the superiority of our TSTGT over state-of-the-art methods and the effectiveness of our proposed modules. The code is available at https://***/rongfu-dsb/TSTGT. © 2024 ACM.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

FedTop:a constraint-loosed federated learning aggregation method against poisoning attack

引用

Frontiers of computer science 2024年第5期18卷 233-235页

作者： Che WANG Zhenhao WU Jianbo GAO Jiashuo ZHANG Junjie XIA Feng GAO Zhi GUAN Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China China Unicom Beijing 100033China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].

关键词： constraint loose aggregation

来源：评论

学校读者我要写书评

暂无评论

UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

来源：评论

学校读者我要写书评

暂无评论

Eight-Partitioning Points in 3D, and Efficiently Too 40

Eight-Partitioning Points in 3D, and Efficiently Too

引用

40th International Symposium on Computational Geometry, SoCG 2024

作者： Aronov, Boris Basit, Abdul Ramesh, Indu Tasinato, Gianluca Wagner, Uli Department of Computer Science and Engineering Tandon School of Engineering New York University BrooklynNY United States School of Mathematics Monash University Clayton Australia Institute of Science and Technology Austria Klosterneuburg Austria

ISBN: (纸本)9783959773164

An eight-partition of a finite set of points (respectively, of a continuous mass distribution) in R3 consists of three planes that divide the space into 8 octants, such that each open octant contains at most 1/8 of the points (respectively, of the mass). In 1966, Hadwiger showed that any mass distribution in R3 admits an eight-partition;moreover, one can prescribe the normal direction of one of the three planes. The analogous result for finite point sets follows by a standard limit argument. We prove the following variant of this result: Any mass distribution (or point set) in R3 admits an eight-partition for which the intersection of two of the planes is a line with a prescribed direction. Moreover, we present an efficient algorithm for calculating an eight-partition of a set of n points in R3 (with prescribed normal direction of one of the planes) in time O∗(n5/2). © Boris Aronov, Abdul Basit, Indu Ramesh, Gianluca Tasinato, and Uli Wagner.

关键词： Borsuk-Ulam Theorem Ham-Sandwich Theorem Mass partitions partitions of points in three dimensions

来源：评论

学校读者我要写书评

暂无评论

An Extensive Analysis of Data Clumps in UML Class Diagrams 19

An Extensive Analysis of Data Clumps in UML Class Diagrams

引用

19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Baumgartner, Nils Pulvermüller, Elke Software Engineering Research Group School of Mathematics/Computer Science/Physics Osnabrück University Osnabrück49090 Germany

ISBN: (纸本)9789897586965

This study investigated the characteristics of data clumps in UML class diagrams. Data clumps are group of variables which appear together in multiple locations. In this study we compared the data clumps characteristics in UML class diagrams with them of source code projects. By analyzing the extensive Lindholmen and GenMyModel datasets, known for their real–world applicability, diversity, and containing more than 100,000 class diagrams in total, significant differences in the distribution and nature of data clumps were revealed. Approximately 19 % of the analyzed class diagrams contained data clumps. It was observed that field–field data clumps predominated in UML class diagrams, particularly in the GenMyModel dataset, while parameter–parameter data clumps were less frequent. Moreover, in contrast to the distribution in source code projects, data clumps in UML class diagrams were typically distributed across multiple classes or interfaces, forming larger chains. parameter–parameter data clumps were predominant in source code projects, indicating more detailed implementation of methods in these projects. These findings reflect different modeling approaches and paradigms among the respective user groups. This study has provided important insights regarding the development of UML modeling tools, teaching methods, and design practices in software development. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Design

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model

引用

computer Modeling in engineering & sciences 2023年第10期137卷 903-922页

作者： Guangxia Xu Lei Liu Jingnan Dong Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code ***,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at *** this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent *** downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their *** experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.

关键词： Smart contract pre-trained language model deep learning recurrent neural network blockchain security

来源：评论

学校读者我要写书评

暂无评论

Neural Visual-Inertial Odometry with Integrated Mechanical Choreography 5

Neural Visual-Inertial Odometry with Integrated Mechanical C...

引用

5th International Academic Exchange Conference on science and Technology Innovation, IAECST 2023

作者： Liu, Jiaxing Liu, Chengyu Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350357738

With the extensive application of deep learning, neural network-based Visual Inertial Odometry (VIO) has demonstrated significantly better performance than traditional geometric VIO. Deep learning-based VIO fuses information from vision and inertial measurement unit (IMU) to learn positional changes in an end-to-end data-driven manner. However, existing studies have generally focused only on the extraction of representations from raw data, ignoring the correlations and interactions between modalities. Moreover, these end-to-end networks are often criticized for their limited interpretability. This study proposes an innovative deep learning VIO model that incorporates mechanical choreography. The model enhances the network with prior positional information through the integration of results from IMU mechanical choreography. It leverages multimodal feature learning concepts and employs a cross-modal co-attention mechanism to effectively merge features extracted from images and IMUs. This approach aims to achieve highly accurate, efficient, and interpretable position estimation tasks. Finally, the trained model is tested on a public dataset and the accuracy is compared with traditional methods and classical neural VIO to verify the effectiveness and feasibility of the proposed method in this study. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

引用

computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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：