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arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified. Copyright © 2025, The Authors. All rights reserved.

关键词： Digital arithmetic

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Semantic enrichment of pretrained embedding output for unsupervised IR

Semantic enrichment of pretrained embedding output for unsup...

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2021 AAAI Spring Symposium on Combining Machine Learning and Knowledge engineering, AAAI-MAKE 2021

作者： Dervakos, Edmund Filandrianos, Giorgos Thomas, Konstantinos Mandalios, Alexios Zerva, Chrysoula Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece National Centre for Text Mining School of Computer Science University of Manchester Manchester United Kingdom

The rapid growth of scientific literature in the biomedical and clinical domain has significantly complicated the identification of information of interest by researchers as well as other practitioners. More importantly, the rapid emergence of new topics and findings, often hinders the performance of supervised approaches, due to the lack of relevant annotated data. The global COVID-19 pandemic further highlighted the need to query and navigate uncharted ground in the scientific literature in a prompt and efficient way. In this paper we investigate the potential of semantically enhancing deep transformer architectures using SNOMED-CT in order to answer user queries in an unsupervised manner. Our proposed system attempts to filter and re-rank documents related to a query that were initially retrieved using BERT models. To achieve that, we enhance queries and documents with SNOMED-CT concepts and then impose filters on concept co-occurrence between them. We evaluate this approach on OHSUMED dataset and show competitive performance and we also present our approach for adapting such an approach to full papers, such as kaggle's CORD-19 full-text dataset challenge. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Query processing

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Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects

arXiv

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arXiv 2024年

作者： Jiang, Xiaoheng Yan, Feng Lu, Yang Wang, Ke Guo, Shuai Zhang, Tianzhu Pang, Yanwei Niu, Jianwei Xu, Mingliang The School of Computer and Artificial Intelligence Zhengzhou University Engineering Research Center of Intelligent Swarm Systems Ministry of Education National Supercomputing Center in Zhengzhou Zhengzhou450001 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China The School of Information Science and Technology University of Science and Technology of China Hefei230026 China The School of Electrical Automation and Information Engineering Tianjin University Tianjin300072 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Hangzhou Innovation Institute of Beihang University Hangzhou310051 China

Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods have made huge leaps, they still confront a lot of challenges such as defect scale variation, complex background, low contrast, and so on. To address these issues, we propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder-decoder network. JAFFNet mainly incorporates a joint attention-guided feature fusion (JAFF) module into decoding stages to adaptively fuse low-level and high-level features. The JAFF module learns to emphasize defect features and suppress background noise during feature fusion, which is beneficial for detecting low-contrast defects. In addition, JAFFNet introduces a dense receptive field (DRF) module following the encoder to capture features with rich context information, which helps detect defects of different scales. The JAFF module mainly utilizes a learned joint channel-spatial attention map provided by high-level semantic features to guide feature fusion. The attention map makes the model pay more attention to defect features. The DRF module utilizes a sequence of multi-receptive-field (MRF) units with each taking as inputs all the preceding MRF feature maps and the original input. The obtained DRF features capture rich context information with a large range of receptive fields. Extensive experiments conducted on SD-saliency-900, Magnetic tile, and DAGM 2007 indicate that our method achieves promising performance in comparison with other state-of-the-art methods. Meanwhile, our method reaches a real-time defect detection speed of 66 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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FedMDC: Enabling Communication-Efficient Federated Learning over Packet Lossy Networks via Multiple Description Coding

FedMDC: Enabling Communication-Efficient Federated Learning ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yixuan Guan Xuefeng Liu Tao Ren Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Federated learning (FL) generally suffers significant communication overhead from high-traffic gradient synchronization. The majority of existing studies on this problem aim at compressing gradients under the premise of reliable transmission. While transmission reliability can be ensured via TCP by default, the notably increased latency and retransmitted packets are prohibitive for most clients in FL. To tackle this issue, we propose FedMDC, a retransmission-free compression framework for FL over packet lossy networks. Given clients’ limited resources, FedMDC adopts multiple description coding to encode gradients into redundant descriptions for erasure resilience simply through multiplying an overcomplete matrix; and then quantizes these descriptions for compression. To further reduce quantization distortion and computational overhead, a reduced decoding algorithm is developed by decoding the aggregation of all clients’ encodings in conjunction with a customized dither quantization design. Besides, FedMDC explicitly supports adaptive bitrates subject to clients’ heterogeneous communication budgets, which maximize resource utilization to facilitate distortion reduction and accelerate model convergence. Theoretical analysis and experimental results both demonstrate the effectiveness of our scheme.

关键词： Quantization (signal) Federated learning Distortion Propagation losses Encoding Decoding Reliability Synchronization Resource management Resilience

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Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation

Combining Implicit-Explicit View Correlation for Light Field...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Ruixuan Cong Da Yang Rongshan Chen Sizhe Wang Zhenglong Cui Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beihang Hangzhou Innovation Institute Yuhang Faculty of Applied Sciences Macao Polytechnic University

Since light field simultaneously records spatial information and angular information of light rays, it is considered to be beneficial for many potential applications, and semantic segmentation is one of them. The regular variation of image information across views facilitates a comprehensive scene understanding. However, in the case of limited memory, the high-dimensional property of light field makes the problem more intractable than generic semantic segmentation, manifested in the difficulty of fully exploiting the relationships among views while maintaining contextual information in single view. In this paper, we propose a novel network called LF-IENet for light field semantic segmentation. It contains two different manners to mine complementary information from surrounding views to segment central view. One is implicit feature integration that leverages attention mechanism to compute inter-view and intra-view similarity to modulate features of central view. The other is explicit feature propagation that directly warps features of other views to central view under the guidance of disparity. They complement each other and jointly realize complementary information fusion across views in light field. The proposed method achieves outperforming performance on both real-world and synthetic light field datasets, demonstrating the effectiveness of this new architecture.

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Naval Cybersecurity in the Age of AI: deceptive ISAR Images Generation with GANs

Naval Cybersecurity in the Age of AI: deceptive ISAR Images ...

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Conference on Local computer Networks (LCN)

作者： Giulio Meucci Bertan Karahoda Amir Hosein Oveis Francesco Mancuso Edmond Jajaga Alessandro Cantelli-Forti Department of Information Engineering University of Pisa Pisa Italy Radar and Surveillance Systems (RaSS) National Laboratory - CNIT Pisa Italy Department of Computer Science University for Business and Technology Prishtina Kosovo

Navigational systems, the heart of maritime operations, face escalating cybersecurity risks due to their system of systems nature and reliance on diverse suppliers. Amid concerns about supply chain attacks and insider threats, the potential for malicious radar image injections, including Inverse Synthetic Aperture Radar (ISAR) images, is emerging. Such manipulations can critically undermine navigational integrity through the generation of decoy targets, the strategic relocation of existing ones, and the effective concealment of additional targets. We have identified where an Advanced Persistent Threat (APT) could be concealed within the processing chain of a modern radar system. This study demonstrates the potential of APTs to exploit Generative Adversarial Networks (GANs) for the creation of deceptive ISAR images, thereby spotlighting this previously unexplored threat vector. Our findings offer novel insights into bolstering maritime cybersecurity in an increasingly AI-dominated landscape.

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Large Language Models (LLMs) Inference Offloading and Resource Allocation in Cloud-Edge Networks: An Active Inference Approach

Large Language Models (LLMs) Inference Offloading and Resour...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jingcheng Fang Ying He F. Richard Yu Jianqiang Li Victor C. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

As the research and applications of large language model (LLM) become increasingly sophisticated, it is difficult for resource-limited mobile terminals to run large-model inference tasks efficiently. Traditional deep reinforcement learning (DRL) based approaches have been used to offload LLM inference tasks to servers. However, existing solutions suffer from data inefficiency, insensitivity to latency requirements, and non-adaptability to task load variations. In this paper, we propose an active inference with rewardless guidance algorithm using expected future free energy for offloading decisions and allocating resources for the LLM inference task offloading and resource allocation problem of cloud-edge networks systems. Experimental results show that our proposed method has superior performance over mainstream DRLs, improves in data utilization efficiency, and is more adaptable to changing task load scenarios.

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Early Prediction of Student Performance with LSTM-Based Deep Neural Network

Early Prediction of Student Performance with LSTM-Based Deep...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Han Wan Mengying Li Zihao Zhong Xiaoyan Luo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

The shift to hybrid teaching during the COVID-19 pandemic brought about a real challenge to predict student performance and conduct timely interventions on at-risk students. This study proposed a deep neural network supporting the early prediction of student performance. Bidirectional LSTM, Global Average Pooling, and TIME MASK structure were utilized in the improved GritNet model. Subsequently, this study optimized the hyperparameters with the aid of the hill-climbing algorithm. Finally, on-campus data sets were used in experiments to evaluate the model's performance. Data were collected from a course that carried out multiple iterations from Fall 2021 to Fall 2022. In Fall 2022, the proposed model achieved a ROC-AUC value of 95.47% in the 8th week, while the baseline model only achieved 91.44% in the same week. Besides, the proposed model achieved a ROC-AUC value of 89.67% in the 4th week, which meant it had acceptable prediction performance in the early stage. The experimental findings demonstrated that the model was capable of predicting the academic performance of students in hybrid courses early on.

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Continuous-Time LiDAR-Inertial-Vehicle Odometry Method with Lateral Acceleration Constraint

Continuous-Time LiDAR-Inertial-Vehicle Odometry Method with ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bin He Weichen Dai Zeyu Wan Hong Zhang Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Computer Science Hangzhou Dianzi University Hangzhou China Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

In this paper, we propose a continuous-time-based LiDAR-inertial-vehicle odometry method, which can tightly fuse the data from Light Detection And Ranging (LiDAR), inertial measurement units (IMU), and vehicle measurements. The lateral acceleration constraint is further added to trajectory estimation to make the estimated trajectory follow the motion characteristics of vehicles. In addition, since vehicle model parameters vary with different motion conditions and tyre pressure, we estimate vehicle correction factors that rectify changes in vehicle model parameters online, and also analyze the observability of these vehicle correction factors. In experiments, the proposed method is evaluated and compared with state-of-the-art methods in the public dataset. The experimental results show that the proposed method achieves more accurate results in all sequences since we add additional sensor measurements and utilize the characteristic of vehicle motion to restrict the trajectory estimation. The ablation study also proved the effectiveness of continuous-time representation, online correction factor estimation, and incorporation of lateral acceleration constraint.

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Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models

Real-Time Adaptive Safety-Critical Control with Gaussian Pro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yu Zhang Long Wen Xiangtong Yao Zhenshan Bing Linghuan Kong Wei He Alois Knoll Department of Informatics Technical University of Munich Germany Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model’s online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from newly samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both simulation platform and a real-world 7-DOF robot.

关键词： Adaptation models Adaptive systems Uncertainty Computational modeling Gaussian processes Real-time systems Safety

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