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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fan, Shengda Wang, Yanting Mo, Shasha Niu, Jianwei School of Cyber Science and Technology Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798891761643

Document-level relation extraction (DocRE) aims to identify relationships between entities within a document. Due to the vast number of entity pairs, fully annotating all fact triplets is challenging, resulting in datasets with numerous false negative samples. Recently, self-training-based methods have been introduced to address this issue. However, these methods are purely black-box and sub-symbolic, making them difficult to interpret and prone to overlooking symbolic interdependencies between relations. To remedy this deficiency, our insight is that symbolic knowledge, such as logical rules, can be used as diagnostic tools to identify conflicts between pseudo-labels. By resolving these conflicts through logical diagnoses, we can correct erroneous pseudo-labels, thus enhancing the training of neural models. To achieve this, we propose LogicST, a neural-logic self-training framework that iteratively resolves conflicts and constructs the minimal diagnostic set for updating models. Extensive experiments demonstrate that LogicST significantly improves performance and outperforms previous state-of-the-art methods. For instance, LogicST achieves an increase of 7.94% in F1 score compared to CAST (Tan et al., 2023a) on the DocRED benchmark (Yao et al., 2019). Additionally, LogicST is more time-efficient than its self-training counterparts, requiring only 10% of the training time of CAST. Code is available at https://***/XingYing-stack/LogicST. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Comprehensive Review of Redirected Walking Techniques:Taxonomy,Methods,and Future Directions

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Journal of computer Science & technology 2022年第3期37卷 561-583页

作者： Yi-Jun Li Frank Steinicke Miao Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Department of Informatics Universität HamburgHamburg 20146Germany Peng Cheng Laboratory Shenzhen 518055China

virtual reality(VR)allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual ***,the limited physical tracking space usually restricts the exploration of large virtual spaces,and VR users have to use special locomotion techniques to move from one location to *** these techniques,redirected walking(RDW)is one of the most natural locomotion techniques to solve the problem based on near-natural walking *** core idea of the RDW technique is to imperceptibly guide users on virtual paths,which might vary from the paths they physically walk in the real *** a similar way,some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking *** this survey,we first present a taxonomy of existing RDW *** on this taxonomy,we compare and analyze both contributions and shortcomings of the existing methods in detail,and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries,while virtual environment manipulation methods can provide users with consistent movement but have limited application ***,we discuss possible future research directions,indicating combining artificial intelligence with this area will be effective and intriguing.

关键词： virtual reality locomotion redirected walking

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Dual Adaptive Representation Alignment for Cross-Domain Few-Shot Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11720-11732页

作者： Zhao, Yifan Zhang, Tong Li, Jia Tian, Yonghong Peking University The School of Computer Science Beijing100871 China Beihang University The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China The Pengcheng Laboratory Guangdong Shenzhen518055 China Peking University Shenzhen Granduate School The School of Electronic and Computer Engineering Guangdong Shenzhen518055 China

Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications. Toward this issue, we propose to address the cross-domain few-shot learning problem where only extremely few samples are available in target domains. Under this realistic setting, we focus on the fast adaptation capability of meta-learners by proposing an effective dual adaptive representation alignment approach. In our approach, a prototypical feature alignment is first proposed to recalibrate support instances as prototypes and reproject these prototypes with a differentiable closed-form solution. Therefore feature spaces of learned knowledge can be adaptively transformed to query spaces by the cross-instance and cross-prototype relations. Besides the feature alignment, we further present a normalized distribution alignment module, which exploits prior statistics of query samples for solving the covariant shifts among the support and query samples. With these two modules, a progressive meta-learning framework is constructed to perform the fast adaptation with extremely few-shot samples while maintaining its generalization capabilities. Experimental evidence demonstrates our approach achieves new state-of-the-art results on 4 CDFSL benchmarks and 4 fine-grained cross-domain benchmarks. © 1979-2012 IEEE.

关键词： Alignment

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Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Uncertainty-Estimation With Normalized Logits For Out-of-distribution Detection

Uncertainty-Estimation With Normalized Logits For Out-of-dis...

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2023 International Conference on computer, Artificial Intelligence, and Control Engineering, CAICE 2023

作者： Huang, Mouxiao Qiao, Yu The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781510665040

Out-of-distribution (OOD) detection is critical for preventing deep learning models from making incorrect predictions, especially in safety-critical applications such as medical diagnosis and autonomous driving. However, neural networks often suffer from overconfidence, making high confidence predictions for OOD data that are never seen during training and may be irrelevant to training data. Determining the reliability of the prediction is still a difficult and challenging task. To address this challenge, we propose a new method called Uncertainty-Estimation with Normalized Logits (UE-NL) for robust learning and OOD detection. The method has three main benefits: (1) Neural networks with UE-NL treat every in-distribution (ID) sample equally by predicting the uncertainty score of input data and the uncertainty is added into softmax function to adjust the learning strength of easy and hard samples during training phase, making the model learn robustly and accurately. (2) UE-NL enforces a constant vector norm on the logits to decouple the effect of the increasing output’s norm from optimization process, which causes the overconfidence issue to some extent. (3) UE-NL provides a new metric, the magnitude of uncertainty score, to detect OOD data. Experiments demonstrate that UE-NL outperforms existing methods on common OOD benchmarks and is more robust to noisy ID data that may be misjudged as OOD data by other methods. © 2023 SPIE.

关键词： Forecasting

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A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

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Frontiers of Information technology & Electronic Engineering 2024年第5期25卷 664-684页

作者： Xiaojun BAI Yang ZHANG Haixing WU Yuting WANG Shunfu JIN School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research *** this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance *** to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant *** meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,*** establish a four-dimensional continuous-time Markov chain as the system *** using the Gauss-Seidel method,we derive the stationary probability distribution of the system ***,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of *** experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading ***,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

关键词： Edge computing Ofloading scheme Cloud-edge device collaboration Markov chain Cost function

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Learning to Cluster Person in the Video via Graph Convolution Network 13

Learning to Cluster Person in the Video via Graph Convolutio...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Li, Wei Liang, Sixin Bian, Ang Du, Guodong College of Computer Science Sichuan University Chengdu China School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350319156

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module;(ii) a pose-guided embedding module;(iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior. © 2023 IEEE.

关键词： Convolution

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Attention-Based Deep Learning Model for Image Desaturation of SDO/AIA

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Research in Astronomy and Astrophysics 2023年第8期23卷 92-102页

作者： Xinze Zhang Long Xu Zhixiang Ren Xuexin Yu Jia Li State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518000China Department of Automation Tsinghua UniversityBeijing 100084China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)captures full-disk solar images in seven extreme ultraviolet wave *** a violent solar flare occurs,incoming photoflux may exceed the threshold of an optical imaging system,resulting in regional saturation/overexposure of ***,the lost signal can be partially retrieved from non-local unsaturated regions of an image according to scattering and diffraction principle,which is well consistent with the attention mechanism in deep ***,an attention augmented convolutional neural network(AANet)is proposed to perform image desaturation of SDO/AIA in this *** is built on a U-Net backbone network with partial convolution and adversarial *** addition,a lightweight attention model,namely criss-cross attention,is embedded between each two convolution layers to enhance the backbone *** results validate the superiority of the proposed AANet beyond state-of-the-arts from both quantitative and qualitative comparisons.

关键词： techniques image processing-Sun atmosphere-Sun flares

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DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations 24

DualFed: Enjoying both Generalization and Personalization in...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Guogang Liu, Xuefeng Niu, Jianwei Tang, Shaojie Wu, Xinghao Zhang, Jiayuan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States

ISBN: (纸本)9798400706868

In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to their conflicting nature. As a result, existing PFL methods can only manage a trade-off between these two objectives. This raises an interesting question: Is it feasible to develop a model capable of achieving both objectives simultaneously? Our paper presents an affirmative answer, and the key lies in the observation that deep models inherently exhibit hierarchical architectures, which produce representations with various levels of generalization and personalization at different stages. A straightforward approach stemming from this observation is to select multiple representations from these layers and combine them to concurrently achieve generalization and personalization. However, the number of candidate representations is commonly huge, which makes this method infeasible due to high computational costs. To address this problem, we propose DualFed, a new method that can directly yield dual representations correspond to generalization and personalization respectively, thereby simplifying the optimization task. Specifically, DualFed inserts a personalized projection network between the encoder and classifier. The pre-projection representations are able to capture generalized information shareable across clients, and the post-projection representations are effective to capture task-specific information on local clients. This design minimizes the mutual interference between generalization and personalization, thereby achieving a win-win situation. Extensive experiments show that DualFed can outperform other FL methods. Code is available at https://***/GuogangZhu/DualFed. © 2024 ACM.

关键词： Federated learning

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Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

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作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

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