检索结果-内蒙古大学图书馆

IEEE Conference on Vehicular Technology (VTC)

作者： Siyao Li Chuhuan Liu Yifan Chen Liang Xiao Helin Yang Dept. of Information and Communication Engineering Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Reliable port communications support maritime applications such as vessel navigation and cargo tracking for a large number of mobile users on ships, but the quality of services (QoS) such as data rate and energy consumption is severely degraded by inter-cell interference. In this paper, we propose a deep reinforcement learning (RL)-based interference coordination scheme for port communications to reduce the transmission latency and energy consumption, and improve the data rate. Based on the signal-to-interference plus noise ratio, the channel gains, the estimated interference levels and the transmission latency, the base station chooses the transmit power and downlink bandwidth constraint to avoid choosing risk policies that cause the communication performance degradation. In addition, a two-level hierarchical structure with two convolution networks and four fully connected layers is designed to reduce the algorithm complexity and enhance the convergence speed. Simulation results verify the performance gain of the proposed scheme in terms of the data rate, the transmission latency, and the energy consumption compared with the benchmark.

关键词： Energy consumption Navigation Convolution Channel estimation Interference Quality of service Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Grasp as You Say: Language-guided Dexterous Grasp Generation

arXiv

引用

arXiv 2024年

作者： Wei, Yi-Lin Jiang, Jian-Jian Xing, Chengyi Tan, Xian-Tuo Wu, Xiao-Ming Li, Hao Cutkosky, Mark Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Stanford University United States Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However, the development of this field is hindered by the lack of datasets with natural human guidance;thus, we propose a language-guided dexterous grasp dataset, named DexGYSNet, offering high-quality dexterous grasp annotations along with flexible and fine-grained human language guidance. Our dataset construction is cost-efficient, with the carefully-design hand-object interaction retargeting strategy, and the LLM-assisted language guidance annotation system. Equipped with this dataset, we introduce the DexGYSGrasp framework for generating dexterous grasps based on human language instructions, with the capability of producing grasps that are intent-aligned, high quality and diversity. To achieve this capability, our framework decomposes the complex learning process into two manageable progressive objectives and introduce two components to realize them. The first component learns the grasp distribution focusing on intention alignment and generation diversity. And the second component refines the grasp quality while maintaining intention consistency. Extensive experiments are conducted on DexGYSNet and real world environments for validation. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

引用

36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection

arXiv

引用

arXiv 2024年

作者： Liang, Ziyou Liu, Weifeng Wang, Run Wu, Mengjie Li, Boheng Zhang, Yuyang Wang, Lina Yang, Xinyi Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Nanyang Technological University Singapore NSFOCUS China

In the last few years, the artifact patterns in fake images synthesized by different generative models have been inconsistent, leading to the failure of previous research that relied on spotting subtle differences between real and fake. In our preliminary experiments, we find that the artifacts in fake images always change with the development of the generative model, while natural images exhibit stable statistical properties. In this paper, we employ natural traces shared only by real images as an additional target for a classifier. Specifically, we introduce a self-supervised feature mapping process for natural trace extraction and develop a transfer learning based on soft contrastive loss to bring them closer to real images and further away from fake ones. This motivates the detector to make decisions based on the proximity of images to the natural traces. To conduct a comprehensive experiment, we built a high-quality and diverse dataset that includes generative models comprising GANs and diffusion models, to evaluate the effectiveness in generalizing unknown forgery techniques and robustness in surviving different transformations. Experimental results show that our proposed method gives 96.2% mAP significantly outperforms the baselines. Extensive experiments conducted on popular commercial platforms reveal that our proposed method achieves an accuracy exceeding 78.4%, underscoring its practicality for real-world application deployment. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning Based QoS-Aware Anti-Jamming Underwater Video Transmission

Reinforcement Learning Based QoS-Aware Anti-Jamming Underwat...

引用

IEEE Conference on Wireless Communications and Networking

作者： Shiyu Sun Shun Chen Shaoxuan Li Zefang Lv Liang Xiao Wei Su Dept. of Information and Communication Engineering Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Underwater video transmission has to ensure quality-of-service (QoS) against jamming with severe multipath effect and narrow bandwidth limitation that degrade the communication performance under variable channel state. In this paper, we propose a reinforcement learning (RL)-based QoS-aware underwater video transmission scheme to optimize the video compression ratio, modulation format and transmit power based on the state consisting of the channel gain and previous transmission performance. This scheme evaluates the risk level that indicates the probability of failing the QoS and the long-term expected utility of each transmission policy under the current state to improve the anti-jamming communication performance. We derive the performance bound of the utility and analyze its relationship with transmission policy. Simulation results illustrate that our scheme improves the QoS by reducing the frame loss rate (FLR), transmission delay and increasing spatial-spectral entropy-based quality (SSEQ) compared with the benchmark.

关键词： Transmitters Simulation Modulation Quality of service Reinforcement learning Benchmark testing Video compression

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions 22

A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

引用

22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Yuan, Ye Yang, Mingxing Xiong, Ruoting Sun, Yani Zhang, Xianchao Ren, Wei HuBei Land Resources Vocational College China China University of Geosciences Gemological Institute Wuhan China University of East Anglia School of Computing Science United Kingdom Yunnan Innovation Institute Beihang University Yunnan Key Laboratory of Blockchain Application Technology Kunming China Jiaxing University Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing China State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology Mnr Beijing China Ministry of Education China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798331509712

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens;but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient. © 2024 IEEE.

关键词： Data Provenance Data Tracing Hybrid Blockchain Token Handover

来源：评论

学校读者我要写书评

暂无评论

FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

引用

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

作者： Yihuan Huang Yuanzhe Li Yanzhen Ren Weiping Tu Yuhong Yang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds.

关键词： Time-frequency analysis Accuracy Noise Speech recognition Feature extraction Acoustics Speaker recognition Low latency communication Speech processing

来源：评论

学校读者我要写书评

暂无评论

A Description Model of Multi-Step Attack Planning Domain Based on Knowledge Representation

引用

Chinese Journal of Electronics 2023年第3期22卷 437-441页

作者： HU Liang XIE Nannan CHAI Sheng Nurbol Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Computer Science and Technology College Jilin University Changchun China Information Science and Engineering College Xinjiang University Urumqi China

Alerts of intrusion detection system are numerous, complex, and difficult to analyze. Alert correlation of multi-step attack is one of the solutions to this problem. Intelligence planning is an important research area of artificial intelligence, and always used in fields problems. Intelligence planning is used to deal with multi-step attack recognition in this work. A multi-step attack planning domain description model is proposed, in order to describe the attack knowledge base, and based on knowledge representation. The model is described with PDDL (Planning domain definition language). Experiments with DARPA 2000 dataset showed the model proposed can recognize multi-step attacks effectively, and is available and practical.

关键词： Correlation Knowledge based systems Intrusion detection Knowledge representation Medical services Planning Network intrusion

来源：评论

学校读者我要写书评

暂无评论

FCC-MF: Detecting Violence in Audio-Visual Context with Frame-Wise Cluster Contrast and Modality-Stage Flooding

FCC-MF: Detecting Violence in Audio-Visual Context with Fram...

引用

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

作者： Jiaqing He Yanzhen Ren Liming Zhai Wuyang Liu School of Cyber Science and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Computer Science Central China Normal University

This paper explores the detection of frame-wise instances of violence in both audio and visual modalities, where only clip-level labels are available. Previous works selected fixed value of frames for objective optimization to model frame-level features, and applied straightforward fusion strategy to aggregate audio and visual information. However, these two issues, namely Constant Frames Selection and Vulnerable Fusion, significantly impair the network’s detection performance. To address these issues, we present a novel framework called Frame-wise Cluster Contrast with Modality-stage Flooding (FCC-MF). Our contributions include: 1) We propose Frame-wise Cluster Contrast, which leverages unsupervised clustering for pseudo-labeling frames and triplet loss for contrastive learning to allow for dynamic frame-wise discrimination. 2) We propose Modality-stage Flooding, a two-stage flooding approach with the higher loss flooding level assigned to uni-modal features, which prevents over-memorization of redundant uni-modal data and promotes effective aggregation of multi-modal information. Our FCC-MF framework yields a promising average precision of 84.24% on the XD-Violence dataset, which performs favorably against previous SOTA methods. Extensive ablation studies exhibit that our FCC-MF framework produces finer frame-level violence discrimination ability and generalizable audio-visual fusion features.

关键词：

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：