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arXiv 2024年

作者： Zhang, Chunxu Long, Guodong Guo, Hongkuan Fang, Xiao Song, Yang Liu, Zhaojie Zhou, Guorui Zhang, Zijian Liu, Yang Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Computer Science and Technology Jilin University China Australian Artificial Intelligence Institute FEIT University of Technology Sydney Australia Kuaishou Technology China Institute for AI Industry Research Tsinghua University China

With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradigm to improve existing recommendation systems. It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy. This paper proposes a novel federated adaptation mechanism to enhance the foundation model-based recommendation system in a privacy-preserving manner. Specifically, each client will learn a lightweight personalized adapter using its private data. The adapter then collaborates with pre-trained foundation models to provide recommendation service efficiently with fine-grained manners. Importantly, users’ private behavioral data remains secure as it is not shared with the server. This data localization-based privacy preservation is embodied via the federated learning framework. The model can ensure that shared knowledge is incorporated into all adapters while simultaneously preserving each user’s personal preferences. Experimental results on four benchmark datasets demonstrate our method’s superior performance. Implementation code is available to ease reproducibility1 Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Color Image Super-Resolution and Enhancement with Inter-Channel Details at Trivial Cost

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Journal of Computer Science & Technology 2020年第4期35卷 889-899页

作者： Chuang-Ye Zhang Yan Niu Tie-Ru Wu Xi-Ming Li College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computer and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China School of Mathematics Jilin UniversityChangchun 130012China

Image super-resolution is essential for a variety of applications such as medical imaging,surveillance imaging,and satellite imaging,among ***,the most popular color image super-resolution is performed in each color channel *** this paper,we show that the super-resolution quality can be further enhanced by exploiting the cross-channel *** by the High-Quality Linear Interpolation(HQLI)demosaicking algorithm by Malvar et al.,we design an image super-resolution scheme that integrates intra-channel interpolation with cross-channel details by isotropic linear *** its simplicity,our super-resolution method achieves the accuracy comparable with the existing fastest state-of-the-art super-resolution algorithm at 20 times faster *** is well applicable to applications that adopt traditional interpolations,for improved visual quality at trivial computation *** comparative study verifies the effectiveness and efficiency of the proposed super-resolution algorithm.

关键词： image super-resolution isotropic linear interpolation inter-channel detail enhancement low cost

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Vector Quantization: A Case Study on Robust Medical Semantic Segmentation

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SSRN 2024年

作者： Lang, Yijun Liu, Pingping Zhao, Hongwei Zhou, Qiuzhan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Mechanical Science and Engineering Jilin University Changchun130025 China College of Communication Engineering Jilin University Changchun130022 China

Pixel-wise medical image segmentation is essential for accurate disease diagnosis and analysis. Although these models exhibit high accuracy and impressive performance, they often face issues of trustworthiness due to their sensitivity to minor perturbations in the original images, which can cause significant deviations in segmentation tasks. To address these challenges, we focus on enhancing both the accuracy and the robustness of these systems against adversarial perturbations. We introduce a robust projection-head that utilizes a non-parametric method, Vector Quantization Momentum (VQM), to increase the hypothesis margin in the input space and reduce disturbances in the feature space. Additionally, we propose a new loss function, Anchor-based Vector Quantization (AVQ), designed to achieve denser pixel-prototype embedding. Our evaluation, using two white-box adversarial attack algorithms across two datasets, demonstrates a defense improvement of up to 14.46%. © 2024, The Authors. All rights reserved.

关键词： Pixels

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model 1

Elucidating Spatial Complex Structures from Mass Spectrometr...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Cui, Jiangnan Xia, Junjie Li, Xuan Wang, Yan Qiu, Fufang Xu, Yaochen Xu, Keren Zuo, Chunman School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Huashan Hospital Department of Neurosurgery Shanghai China Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Cas Key Laboratory of Systems Biology Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms. © 2023 IEEE.

关键词： Data integration

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A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-Aware POI Recommendation 3

A Fuzzy Clustering Based Collaborative Filtering Algorithm f...

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Yin, Minghao Liu, Yanheng Zhou, Xu Sun, Geng College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China Center for Computer Fundamental Education Jilin University Changchun Jilin China

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-Aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users' check-in behaviours. Individual's check-in behaviour can be under the influence of location region and time slots, so user's feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy clustering

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Nighttime traffic object detection via adaptively integrating event and frame domains

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Fundamental Research 2024年

作者： Jiang, Yu Wang, Yuehang Zhao, Minghao Zhang, Yongji Qi, Hong The College of Computer Science and Technology Jilin University Changchun 130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China The College of Earth Sciences Jilin University Changchun 130012 China

Intelligent perception is crucial in Intelligent Transportation Systems (ITS), with vision cameras as critical components. However, traditional RGB cameras exhibit a significant decline in performance when capturing nighttime traffic scenes, limiting their effectiveness in supporting ITS. In contrast, event cameras possess a high dynamic range (140 dB vs. 60 dB for traditional cameras), enabling them to overcome frame degradation in low-light conditions. Recently, multimodal learning paradigms have made substantial progress in various vision tasks, such as image-text retrieval. Motivated by this progress, we propose an adaptive selection and fusion detection method that leverages both event and RGB frame domains to optimize nighttime traffic object detection jointly. To address the challenge of unbalanced multimodal data fusion, we design a learnable adaptive selection and fusion module. This module performs feature ranking and fusion in the channel dimension, allowing efficient multimodal fusion. Additionally, we construct a novel multi-level feature pyramid network based on multimodal attention fusion. This network extracts potential features to enhance robustness in detecting nighttime traffic objects. Furthermore, we curate a dataset for nighttime traffic scenarios comprising RGB frames and corresponding event streams. Through experiments, we demonstrate that our proposed method outperforms current state-of-the-art techniques in event-based, frame-based, and event and frame fusion methods. This highlights the effectiveness of integrating the event and frame domains in enhancing nighttime traffic object detection. © 2023 The Authors

关键词： Event camera Intelligent transportation Multimodal learning Nighttime object detection

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A Novel Local Search Algorithm Based on Connectivity Method and Vertex Penalty Mechanism for Minimum Weighted Connected Dominating Set Problem

SSRN

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SSRN 2024年

作者： Li, Ruizhi He, Jintao Yin, Minghao Hu, Shuli Ding, Nan Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

The minimum weighted connected dominating set problem is a classical NP-hard problem with wide applications in practice. In this paper, an efficient local search algorithm is proposed to solve this problem. In this algorithm, firstly, an initial solution construction method based on greedy randomized adaptive strategy is adopted, which can balance the quality and the diversity of the initial solutions. Second, two new scoring functions, Ascore and Cscore, are proposed to increase the diversity of deleted vertices. Third, a new connectivity method is proposed so that the algorithm no longer relies on the shortest path between two connected components. Fourth, a vertex penalty mechanism is introduced, which helps the algorithm to jump out of the local optimum effectively. Fifth, two scoring functions, Ascore and Cscore, are combined with the vertex penalty mechanism to propose an effective vertex selection method, which helps the algorithm to select suitable vertices to be added to or removed from the current candidate solution. Finally, the propsoed local search algorithm is compared with seven state-of-the-art algorithms on three types of benchmark instances. The experimental results show that the algorithm exhibits better performance on these three types of classical benchmark instances. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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CMRM: Collaborative Multi-agent Reinforcement Learning for Multi-objective Traffic Signal Control

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IEEE Transactions on Consumer Electronics 2025年

作者： Nie, Lei Qi, Dandan Liu, Bingyi Li, Peng Bao, Haizhou He, Heng Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks. © 1975-2011 IEEE.

关键词： Traffic congestion

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Tc-Sepm: Characterizing Soft Error Resilience of Cnns on Tensor Cores from Program and Microarchitecture Perspectives

SSRN

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SSRN 2023年

作者： Wei, Xiaohui Zhou, Changbao Yue, Hengshan Zhou, Joey Tianyi College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China Singapore138632 Singapore Singapore138632 Singapore

As an architectural CNN accelerator integrated into NVIDIA's GPUs, existing research mainly focus on improving the performance of Tensor Core. However, the highly integrated Tensor Cores are vulnerable to transient faults (i.e., soft errors), causing catastrophic consequences in safety-critical applications like automatic driving. Thus, it is imperative to estimate the reliability of CNNs on Tensor Cores. However, it is expensive to obtain a statistically significant resilience profile of CNNs on Tensor Cores with the existing fault injection(FI)-based reliability estimation methods. To this end, we build TC-SEPM to predict the error resilience of CNNs on Tensor Cores instead of FI methods. To ensure the accuracy of TC-SEPM, we first investigate resilience-related features from microarchitecture and program perspectives. Then, leveraging these heuristic features, we train machine learning models to learn the hidden relationship between error resilience and the investigated features, enabling us to predict the impact of soft errors in Tensor Cores on CNN output. Experimental results show that TC-SEPM achieves high accuracy for individual soft error resiliency prediction and overall program resilience estimation while its overhead is only 1/27 of FI methods. Additionally, TC-SEPM can provide valuable insights for programmers or architects to design more robust CNN models on Tensor Cores. © 2023, The Authors. All rights reserved.

关键词： Error correction

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Unknown Task Selection and Worker Recruitment Using Two-Stage Multi-Armed Bandit in Crowdsensing

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IEEE Internet of Things Journal 2025年

作者： Song, Haoyuan Li, Peng Du, Rui Yu, Hai Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hubei Polytechnic University School of Computer Huangshi435003 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Mobile Crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This paper tackles these challenges by modeling the long-term multi-task, multi-worker selection problem as a Two-Stage Multi-Armed Bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a Combinatorial MAB (CMAB) problem. We propose an extended Upper Confidence Bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods. © 2014 IEEE.

关键词： Personnel selection

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