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Hidden Markov Models:Inverse Filtering,Belief Estimation and Privacy Protection

Journal of systems science & Complexity 2021年第5期34卷 1801-1820页

作者： LOURENCO Ines MATTILA Robert ROJAS Cristian R. HU Xiaoming WAHLBERG Bo Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Division of Optimizations and Systems Theory School of Engineering SciencesKTH Royal Institute of TechnologyStockholmSweden

A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy *** paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from actions,and privacy enforcement in such a ***,the authors discuss how HMM parameters and sensor measurements can be reconstructed from posterior distributions of an HMM ***,the authors consider a rational decision-maker that forms a private belief(posterior distribution)on the state of the world by filtering private *** authors show how to estimate such posterior distributions from observed optimal actions taken by the *** the setting of adversarial systems,the authors finally show how the decision-maker can protect its private belief by confusing the adversary using slightly sub-optimal *** range from financial portfolio investments to life science decision systems.

关键词： Belief estimation counter-adversarial systems hidden Markov models inverse decision making inverse filtering

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and systems for Video Technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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An AoI-Guaranteed Sensor Data Collection Strategy for RIS-assisted UAV Communication System

An AoI-Guaranteed Sensor Data Collection Strategy for RIS-as...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Yang, Bowen Yu, Yao Li, Jianqi Xu, Tongze Deng, Der-Jiunn Chin, Hui-Hsin Li, Yonghui Northeastern University School of Computer Science and Engineering Shenyang China National Changhua University of Education Department of Computer Science and Information Engineering China Overseas Chinese University Department of Information Technology China University of Sydney School of Electrical and Information Engineering Sydney Australia Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350345384

The recently proposed concept of Age of information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data's AoI due to its high maneuverability. However, the UAV's high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Maneuverability

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BTDFS: B+ Tree-Based Distributed File Storing Mechanism for Improving Small File Storage 24

BTDFS: B+ Tree-Based Distributed File Storing Mechanism for ...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Jingyu Tian, Jiachen Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Systems Engineering National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350319934

Hadoop is currently the most popular big data processing architecture, which provides a processing framework for managing and analyzing massive data. Hadoop Distributed File System (HDFS) is the core component for storing data in the Hadoop systems. It performs well when storing and managing large-sized files. However, it shows extremely bad performance when dealing with a large number of small files, which is reflected in the consumption of a large amount of memory space of the NameNode node and the low efficiency of accessing small files. Archive file is proposed to solve this problem. It combines small files into larger merged files for storage, and uses index files to record the relevant information of small files in the merged files. However, archive files (e.g., Hadoop Archive, MapFile) all require additional processing and multiple I/O operations to obtain index information before actually accessing the file content, which reduces the efficiency of file access. This paper proposes a novel distributed file storing mechanism called B+ Tree-Based Distributed File Storing mechanism (BTDFS), which combines a large number of small files into a merged file, records the index information of the small files in the merged file, and organizes the index information into a B+ tree for storage. Using BTDFS, while reducing the metadata consumption of the NameNode node, improves the access performance of a single small file with additional abilities, which provides convenience for analyzing a large number of small files. Finally, this paper designs corresponding experiments for the proposed BTDFS, and tests and analyzes the memory usage in N ameN ode and the access performance of small files. The experimental results show that the proposed BTDFS can effectively reduce the memory usage of the NameNode, and the efficiency of accessing small files is improved compared with state-of-the-art methods. © 2022 IEEE.

关键词： Digital storage

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Liufu, Ying Liu, Mei Jin, Long Wang, Feiyue School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems. © 2023 IEEE.

关键词： ACP constraints differential neural solution numerical simulation Quadratic optimization with quadratic

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State estimation for delayed switched positive systems: delayed radius approach

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science China(information sciences) 2024年第8期67卷 190-205页

作者： Weizhong CHEN Zhongyang FEI Xudong ZHAO Zheng-Guang WU School of Electronics and Information Xi'an Polytechnic University Research Institute of Intelligent Control and Systems Harbin Institute of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology School of Control Science and Engineering Dalian University of Technology State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University

In this paper, an interval estimation scheme is developed for delayed switched positive systems（DSPS） with mode-dependent average dwell time switching. A lossless zonotopic estimation approach is proposed for the delayed intersection zonotope with the positive generator matrix. First, considering the existence of asynchronism between the system mode and the correction matrix mode, the mismatched intersection zonotope is constructed for DSPS to verify the consistency between the system model and ***, by utilizing the introduced radius definitions, the ?∞performance is addressed to optimize the size of delayed intersection zonotopes. Subsequently, we present a joint-design approach of switching signals and the mode-dependent correction matrix by constructing positive generator matrix-based delayed radius functions. Furthermore, guaranteed nonnegative state bounds are derived for the considered DSPS based on the proposed lossless zonotopic estimation criteria. Finally, detailed simulations are conducted to validate the feasibility and superiority of the developed methods.

关键词： delayed switched positive systems interval estimation asynchronous correction matrix zonotopes mode-dependent average dwell time

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年第6期14卷 1733-1737页

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

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Chinese Physics C 2024年第9期48卷 176-187页

作者： Ming-Xiang Fu Yu Song Jia-Meng Lv Liang Cao Peng Jia Nan Li Xiang-Ru Li Ji-Feng Liu A-Li Luo Bo Qiu Shi-Yin Shen Liang-Ping Tu Li-Li Wang Shou-Lin Wei Hai-Feng Yang Zhen-Ping Yi Zhi-Qiang Zou National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Key lab of Space Astronomy and Technology National Astronomical ObservatoriesBeijing 100101China School of Computer Science South China Normal UniversityGuangzhou 510631China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesBeijing 100101China University of Chinese Academy of Sciences NanjingNanjing 211135China University of Science and Technology Beijing Beijing 100083China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China School of Computer and Information Dezhou UniversityDezhou 253023China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 30024China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Nanjing University of Posts&Telecommunications Nanjing 210023China School of Science University of Science and Technology LiaoningAnshan 114051China

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the ***,effectively analyzing this vast amount of data poses a significant *** response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate *** overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and *** the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images *** proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging *** particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 *** addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.

关键词： artificial intelligence large vision model human-in-the-loop astronomy galaxies

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