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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Mean-field games among teams

arXiv

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arXiv 2023年

作者： Subramanian, Jayakumar Kumar, Akshat Mahajan, Aditya Media and Data Science Research Lab Digital Experience Cloud Adobe Inc. Uttar Pradesh Noida India School of Computing and Information Systems The Singapore Management University Singapore Department of Electrical and Computer Engineering McGill University Montreal Canada

In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with other teams. The dynamics and the costs are coupled through the empirical distribution (or the mean field) of the state of agents in each team. This mean-field is assumed to be observed by all agents. Agents have asymmetric information (also called a non-classical information structure). We propose a mean-field based refinement of the Team-Nash equilibrium of the game, which we call mean-field Markov perfect equilibrium (MF-MPE). We identify a dynamic programming decomposition to characterize MF-MPE. We then consider the case where each team has a large number of players and present a mean-field approximation which approximates the game among large-population teams as a game among infinite-population teams. We show that MF-MPE of the game among teams of infinite population is easier to compute and is an Ε-approximate MF-MPE of the game among teams of finite population. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

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Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge

arXiv

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arXiv 2024年

作者： Guo, Longkun Jia, Chaoqi Liao, Kewen Lu, Zhigang Xue, Minhui School of Mathematics and Statistics Fuzhou University China School of Accounting Information Systems and Supply Chain RMIT University Australia HilstLab Peter Faber Business School Australian Catholic University Australia College of Science and Engineering James Cook University Australia CSIRO’s Data61 Australia

Many practical applications impose a new challenge of utilizing instance-level background knowledge (e.g., subsets of similar or dissimilar data points) within their input data to improve clustering results. In this work, we build on the widely adopted k-center clustering, modeling its input instance-level background knowledge as must-link and cannot-link constraint sets, and formulate the constrained k-center problem. Given the long-standing challenge of developing efficient algorithms for constrained clustering problems, we first derive an efficient approximation algorithm for constrained k-center at the best possible approximation ratio of 2 with LP-rounding technology. Recognizing the limitations of LP-rounding algorithms including high runtime complexity and challenges in parallelization, we subsequently develop a greedy algorithm that does not rely on the LP and can be efficiently parallelized. This algorithm also achieves the same approximation ratio 2 but with lower runtime complexity. Lastly, we empirically evaluate our approximation algorithm against baselines on various real datasets, validating our theoretical findings and demonstrating significant advantages of our algorithm in terms of clustering cost, quality, and runtime complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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$\mathsf{PF\text{-}HIN}$:Pre-Training for Heterogeneous information Networks

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IEEE Transactions on Knowledge and data engineering 2022年第8期35卷 8372-8385页

作者： Yang Fang Xiang Zhao Yifan Chen Weidong Xiao Maarten de Rijke Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha Hunan China Laboratory for Big Data and Decision National University of Defense Technology Changsha Hunan China University of Amsterdam Amsterdam WX Netherlands

In network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework, $\mathsf{PF\text{-}HIN}$ , to capture the features of a heterogeneous information network. Unlike traditional network representation learning models that have to train the entire model all over again for every downstream task and dataset, $\mathsf{PF\text{-}HIN}$ only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving model efficiency and effectiveness. During pre-training, we first transform the neighborhood of a given node into a sequence. $\mathsf{PF\text{-}HIN}$ is pre-trained based on two self-supervised tasks, masked node modeling and adjacent node prediction. We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In the fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification, and node clustering. $\mathsf{PF\text{-}HIN}$ outperforms state-of-the-art alternatives on each of these tasks, on four datasets.

关键词： Task analysis Representation learning Computational modeling Transformers Training Predictive models Transforms

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Evaluation of Wireless Sensor Networks Module using IoT Approach

Evaluation of Wireless Sensor Networks Module using IoT Appr...

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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： L. Anand Padmalal S J. Seetha R. Juliana PS Naveen Kumar Gayatri Parasa Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattanguluthur Department of Computer Science and Engineering Mangalam College of Engg Kerala Department of Computer Science and Business Systems Panimalar Engineering College Chennai Department of Information Technology Loyola-ICAM College of Engineering and Technology Chennai India Department of CSE-DATA SCIENCE St. Ann's College Of Engineering And Technology Chirala AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.

关键词： Temperature sensors Wide area networks Vibrations Temperature measurement Wireless sensor networks Wireless networks Transceivers

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Menu Creation Support System Considering User eXperience Based on Multiobjective Optimization

Menu Creation Support System Considering User eXperience Bas...

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IEEE International Symposium on Intelligent Signal Processing and Communication systems (ISPACS)

作者： Yurie Okuhara António Oliveira Nzinga René Kazuhide Mizukami Department of Liberal Arts Informatics The Open University of Japan 2-11 WakabaMihama-ku Chiba Japan Department of Data Science Toyama Prefectural University 5180 KurokawaImizu Toyama Japan Department of Information Systems Engineering Toyama Prefectural University 5180 KurokawaImizu Toyama Japan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

Eating food is important for not only physical health but also, mental health. meals are essential for maintaining and improving health moreover right amount of nutrients also essential for preventing and treating disease. The sufficient meal provides spiritual enrichment and contributes to communication. However, planning a satisfying menu may be a burden when time is limited. In this research, we formulate menu creation as a combinatorial multiobjective optimization and develop an automatic menu creation support system incorporating User eXperience design.

关键词： Distributed processing Prevention and mitigation Mental health Signal processing Nutrients Linear programming User experience Planning Optimization Diseases

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Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection

arXiv

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arXiv 2023年

作者： Min, Wenwen Xu, Taosheng Ding, Chris The Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University Yunnan Kunming650091 China The Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei230031 China The School of Data Science The Chinese University of Hong Kong Shenzhen China

Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a small number of variables with the maximum variance. However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples. To extend the application of sPLS by identifying a specific subset of samples and remove outliers, we propose an ∞/0-norm constrained weighted sparse PLS (∞/0-wsPLS) method for joint sample and feature selection, where the ∞/0-norm constrains are used to select a subset of samples. We prove that the ∞/0-norm constrains have the Kurdyka-Lojasiewicz property so that a globally convergent algorithm is developed to solve it. Moreover, multi-view data with a same set of samples can be available in various real problems. To this end, we extend the ∞/0-wsPLS model and propose two multi-view wsPLS models for multi-view data fusion. We develop an efficient iterative algorithm for each multi-view wsPLS model and show its convergence property. As well as numerical and biomedical data experiments demonstrate the efficiency of the proposed methods. © 2023, CC BY.

关键词： data fusion

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Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Guo, Tong Mei, Yi Du, Wenbo Lv, Yisheng Li, Yumeng Song, Tao Beihang University State Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beijing100191 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Wellington New Zealand Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Jinan250353 China University of the Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Beijing Institute of Technology School of Aerospace Engineering Beijing China

The thriving advances in autonomous vehicles and aviation have enabled the efficient implementation of aerial last-mile delivery services to meet the pressing demand for urgent relief supply distribution. Variable Neighborhood Search (VNS) is a promising technique for aerial emergency scheduling. However, the existing VNS methods usually exhaustively explore all considered neighborhoods with a prefixed order, leading to an inefficient search process and slow convergence speed. To address this issue, this paper proposes a novel graph neural neighborhood search algorithm, which includes an online reinforcement learning (RL) agent that guides the search process by selecting the most appropriate low-level local search operators based on the search state. We develop a dual-graph neural representation learning method to extract comprehensive and informative feature representations from the search state. Besides, we propose a reward-shaping policy learning method to address the decaying reward issue along the search process. Extensive experiments conducted across various benchmark instances demonstrate that the proposed algorithm significantly outperforms the state-of-the-art approaches. Further investigations validate the effectiveness of the newly designed knowledge guidance scheme and the learned feature representations. © 2020 IEEE.

关键词： Reinforcement learning

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Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs

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Journal of Biomedical Informatics 2022年 134卷 104198页

作者： Novitski, Pavel Cohen, Cheli Melzer Karasik, Avraham Hodik, Gabriel Moskovitch, Robert Software and Information Systems Engineering Ben Gurion University Beer-Sheva Israel Maccabi Data Science Institute Maccabi Healthcare Services Tel-Aviv Israel Population Health and Science Ichan Medical School at Mount Sinai NYC United States

Mortality prevention in T2D elderly population having Chronic Kidney Disease (CKD) may be possible thorough risk assessment and predictive modeling. In this study we investigate the ability to predict mortality using heterogeneous Electronic Health Records data. Temporal abstraction is employed to transform the heterogeneous multivariate temporal data into a uniform representation of symbolic time intervals, from which then frequent Time Intervals Related Patterns (TIRPs) are discovered. However, in this study a novel representation of the TIRPs is introduced, which enables to incorporate them in Deep Learning Networks. We describe here the use of iTirps and bTirps, in which the TIRPs are represented by a integer and binary vector representing the time respectively. While bTirp represents whether a TIRP's instance was present, iTirp represents whether multiple instances were present. While the framework showed encouraging results, a major challenge is often the large number of TIRPs, which may cause the models to under-perform. We introduce a novel method for TIRPs’ selection method, called TIRP Ranking Criteria (TRC), which is consists on the TIRP's metrics, such as the differences in its recurrences, its frequencies, and the average duration difference between the classes. Additionally, we introduce an advanced version, called TRC Redundant TIRP Removal (TRC-RTR), TIRPs that highly correlate are candidates for removal. Then the selected subset of iTirp/bTirps is fed into a Deep Learning architecture like a Recurrent Neural Network or a Convolutional Neural Network. Furthermore, a predictive committee is utilized in which raw data and iTirp data are both used as input. Our results show that iTirps-based models that use a subset of iTirps based on the TRC-RTR method outperform models that use raw data or models that use full set of discovered iTirps. © 2022 Elsevier Inc.

关键词： Recurrent neural networks

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E-commerce customer churn prevention using machine learning-based business intelligence strategy

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Measurement: Sensors 2023年 27卷

作者： J, Shobana Gangadhar, Ch. Arora, Rakesh Kumar Renjith, P.N. Bamini, J. Chincholkar, Yugendra devidas Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Electronics & Communication Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada520007 India Department of Information Technology Krishna Engineering College Mohan Nagar UP Ghaziabad India Computer Science and Engineering Department Vellore Institute of Technology Chennai India Sri Ramakrishna College of Arts & Science Tamil Nadu Coimbatore India Department of Electronic & Telecommunication Sinhgad College of Engineering Pune India

Businesses in the E-Commerce sector, especially those in the business-to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. The best course of action in this circumstance is to detect prospective churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Each customer's information, including searches made, purchases made, frequency of purchases, reviews left, feedback is given, and other data, is kept on file by the e-commerce company. Machine learning and data mining may be aided by examining this enormous quantity of data, analysing customer behaviour, and seeing potential attrition opportunities. The support vector machine is a popular supervised learning method in machine learning applications. Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-Commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle. © 2023 The Authors

关键词： Support vector machines

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