检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Jeong, Youngjoon Chun, Junha Cha, Soonwoo Kim, Taesup Graduate School of Data Science Department of Electrical and Computer Engineering

A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on video generation, which has gained significant attention due to the impressive success of diffusion models. However, these models require substantial computational resources. To address these challenges, we propose a world model leveraging object-centric representation space using slot attention, guided by language instructions. Our model perceives the current state as an object-centric representation and predicts future states in this representation space conditioned on natural language instructions. This approach results in a more compact and computationally efficient model compared to diffusion-based generative alternatives. Furthermore, it flexibly predicts future states based on language instructions, and offers a significant advantage in manipulation tasks where object recognition is crucial. In this paper, we demonstrate that our latent predictive world model surpasses generative world models in visuo-linguo-motor control tasks, achieving superior sample and computation efficiency. We also investigate the generalization performance of the proposed method and explore various strategies for predicting actions using object-centric representations. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot programming

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding 30

Hierarchical Topic Modeling via Contrastive Learning and Hyp...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lin, Zhicheng Chen, Hegang Lu, Yuyin Rao, Yanghui Xu, Hao Lai, Hanjiang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9782493814104

Hierarchical topic modeling, which can mine implicit semantics in the corpus and automatically construct topic hierarchical relationships, has received considerable attention recently. However, the current hierarchical topic models are mainly based on Euclidean space, which cannot well retain the implicit hierarchical semantic information in the corpus, leading to irrational structure of the generated topics. On the other hand, the existing Generative Adversarial Network (GAN) based neural topic models perform satisfactorily, but they remain constrained by pattern collapse due to the discontinuity of latent space. To solve the above problems, with the hypothesis of hyperbolic space, we propose a novel GAN-based hierarchical topic model to mine high-quality topics by introducing contrastive learning to capture information from documents. Furthermore, the distinct tree-like property of hyperbolic space preserves the implicit hierarchical semantics of documents in topic embeddings, which are projected into the hyperbolic space. Finally, we use a multi-head self-attention mechanism to learn implicit hierarchical semantics of topics and mine topic structure information. Experiments on real-world corpora demonstrate the remarkable performance of our model on topic coherence and topic diversity, as well as the rationality of the topic hierarchy. Our code is available at https://***/Adrian-LZC/hHTM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Road Detection Based on Fusion of LiDAR data and Visual data 1

Road Detection Based on Fusion of LiDAR Data and Visual Data

引用

1st International Conference on Intelligent Transportation and Smart Cities, ICITSC 2024

作者： Yang, Chaoju Rong, Jian Chin, Soon Ku School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

ISBN: (纸本)9781643685205

Smart transportation is a new development model for intelligent traffic based on the combination of the Internet, the Internet of Things, and other networks. Road detection is one of the foundational and core technologies in smart transportation. It provides accurate, reliable, and real-time road information for applications such as autonomous driving, intelligent traffic management, and urban planning. This contributes to the improvement of traffic safety, efficiency, and sustainability. However, relying solely on visual images for road detection still faces numerous challenges, such as changes in lighting conditions, image blurring, and occlusions. To enhance the robustness and accuracy of road detection, leveraging LiDAR (Light Detection and Ranging) data as supplementary information is considered. LiDAR data is not affected by visual noise and can offer more precise depth and height information. Nevertheless, effectively integrating and adapting to different data and feature spaces remain crucial challenges. This paper proposes an improved road detection method based on the PLARD (Progressive LiDAR Adaptation for Road Detection) network. It introduces the U-Net network as an additional branch, collaborating with the image and point cloud branches for feature fusion and road detection. The U-Net network utilizes information from the images for initial segmentation, providing a better prior, and enhances the feature fusion capability of the PLARD network, thereby improving segmentation accuracy and robustness. Experiments conducted on the KITTI Road dataset demonstrate that the proposed method outperforms the PLARD network and other baseline methods in terms of the Dice coefficient. Particularly, it excels in complex urban scenarios and exhibits strong generalization capabilities. The introduced road detection approach, combining LiDAR and visual data, provides an efficient, robust, and versatile solution for smart transportation. It adapts to different scenarios

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Hypergraph-enhanced Dual Semi-supervised Graph Classification 41

Hypergraph-enhanced Dual Semi-supervised Graph Classificatio...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles United States School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods. Copyright 2024 by the author(s)

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

引用

5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

The 32nd AIAI Irish Conference on Artificial Intelligence and Cognitive science 32

The 32nd AIAI Irish Conference on Artificial Intelligence an...

引用

32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Pakrashi, Arjun Rushe, Ellen Namee, Brian Mac Brennan, Rob Yaman, Bezya School of Computer Science University College Dublin BelfieldDublin 4 Ireland School of Computer Science Dublin City University Collins Ave Ext Whitehall Dublin 9 Ireland The Insight Centre for Data Analytics Ireland SFI Centre for Research Training in Machine Learning Ireland ADAPT Centre Ireland School of Computer Science Trinity College Dublin College Green Dublin 2 Ireland

The 32nd Irish Conference on Artificial Intelligence and Cognitive science (AICS 2024), hosted by University College Dublin (UCD) in collaboration with Dublin City University (DCU), featured high-quality research in AI and cognitive science, with a focus on trustworthy AI, fairness, and bias mitigation. Of 82 submissions, 44 papers were accepted, covering applications in healthcare, smart mobility, financial forecasting, anomaly detection, and sports analytics, alongside fundamental machine learning advancements. Keynote speakers Dr. Susan Leavy (UCD) discussed AI’s societal impact, while Prof. Eamonn Keogh (UC Riverside) critiqued research in time series anomaly detection. AICS 2024 reaffirmed its role as Ireland’s leading AI research forum, fostering discussions on cutting-edge advancements and their societal implications. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction 30

A Semantic Mention Graph Augmented Model for Document-Level ...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an China National University of Singapore Singapore

ISBN: (纸本)9782493814104

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Plausible Heterogeneous Graph k-Anonymization for Social Networks

引用

Tsinghua science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

来源：评论

学校读者我要写书评

暂无评论

Futures Quantitative Investment With Heterogeneous Continual Graph Neural Network 24

Futures Quantitative Investment With Heterogeneous Continual...

引用

24th IEEE International Conference on data Mining, ICDM 2024

作者： Tan, Zhizhong Hu, Min Liu, Bin Yin, Guosheng School of Computer Science and Engineering Macau University of Science and Technolog China School of Finance Southwestern University of Finance and Economics China Center of Statistical Research School of Statistics Southwestern University of Finance and Economics China School of Computing and Data Science The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798331506681

It is challenging to predict futures prices with traditional econometric models as it necessitates a comprehensive consideration of both historical observations and correlations among various futures. Spatial-temporal graph neural networks (STGNNs) offer a promising approach for modeling such complex spatio-temporal data. Nevertheless, the direct application of STGNNs to high-frequency futures data remains a challenge, as traders must account for both short- and long-term characteristics when making decisions. To capture these distinct timeframes, we leverage additional label information by devising four heterogeneous tasks: price regression, price gap regression, price moving average regression, and change-point detection. To make full use of these labels, we train the model in a continual manner. Traditional continual GNNs define the gradient of losses as parameter importance to overcome the catastrophic forgetting (CF) issue, while this is unsuitable for our heterogeneous tasks with losses in distinct spaces. Therefore, we propose to calculate the parameter importance with mutual information between the original observations and the extracted features. The empirical results based on 49 varieties of commodity futures demonstrate that our model achieves superior performance compared to the leading approaches, highlighting the effectiveness of our approach in modeling the intricate dynamics of the futures market. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Global and Local Mixer for Micro-Expression Recognition 6

Global and Local Mixer for Micro-Expression Recognition

引用

6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Huaying Zhang, Xiaorong Mao, Xinglong Liu, Shifeng Zhao, Sirui Chen, Enhong University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology Hefei China Southwest University of Science and Technology School of Computer Science and Technology Mianyang China University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence School of Data Science Hefei China University of Science and Technology of China & Southwest University of Science and Technology School of Computer Science and Technology Hefei Mianyang China

ISBN: (纸本)9798350325485

Micro-expressions (MEs) refer to spontaneous, subtle, and fleeting facial movements that usually only occur in local facial areas, which can effectively reveal genuine emotions that humans try to hide. However, micro-expression recognition (MER) is still a difficult task due to its low intensity and local movements, and existing datasets are usually small-scale with class imbalance problem. To address these problems, we propose a novel MER framework named Global and Local Mixer (GL-Mixer) based on MLP-Mixer, which can focus on local regions where MEs appear and reduce the influence of ME-irrelevant regions. Specifically, the GL-Mixer mainly consists of three parts: Global Feature Learning (GFL) module, Local Feature Learning (LFL) module, and Feature Refinement (FR) module. The GFL module is designed to learn the global ME features from the whole face region. Meanwhile, the LFL module is composed of multiple parallel local mixers, and each local mixer extracts fine-grained features in a specific facial region. Then, Feature Refinement (FR) module aims to refine the fin e-grained ME features by focusing more on the local features of the effective ME regions and suppressing the features of the irrelevant region under the guidance of the global feature. Last, the model fuses the global and fine-grained ME features for classification. In addition, to solve the problem of insufficient data and class imbalance in ME datasets, we introduce class-balanced implicit semantic data augmentation. Extensive experiments are conducted on CASMEII, SAMM, and SMIC datasets, and the experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Semantics

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：