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Exploring implicit influence for social recommendation based on GNN

Soft computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics

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Science China(Information Sciences) 2023年第8期66卷 269-270页

作者： Wenkai XU Li WANG Shiwen SUN Chengyi XIA Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology The Engineering Research Center of Learning-Based Intelligent System Ministry of Education College of Artificial Intelligence Nankai University

At present, an increasing number of researchers have noticed the importance of optimal consensus control(OCC) of multiagent systems(MASs) because of their rich practical applications in various areas [1–4]. To accomp...

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Avionics system Architecture Design of a Manned eVTOL Aircraft

Avionics System Architecture Design of a Manned eVTOL Aircra...

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2024 China Automation Congress, CAC 2024

作者： Wang, Xiaobo Liang, Wenyi Yu, Fenfen Hu, Yiren Cheng, Libo Xie, Anhuan Research Center for Intelligent Equipment Zhejiang Lab Hangzhou China Online Marketing Service Center China Mobile Communications Co. Ltd. Hangzhou China Space-based Computing System Research Center Zhejiang Lab Hangzhou China Frontier Basic Research Center Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350368604

The avionics system plays a critical role in ensuring the operation of an aircraft, facilitating communication between the aircraft and ground, detecting and displaying aircraft status, as well as coordinating data exchange and processing among various systems. This paper presents an architecture of the avionics system for a manned electric vertical takeoff and landing (eVTOL) aircraft, which mainly includes the flight control system, navigation sensor system, communication system, intelligent onboard computer system, and cloud monitoring system. Detailed descriptions are provided for the redundant flight control system, sensor redundancy design, and intelligent onboard computer system. Various flight tests, including manned flight, cargo transportation, endurance, range, speed, and fault tolerance tests, are conducted on the aircraft equipped with this avionics system. The flight tests have exceeded a thousand. Analysis of flight log data demonstrates that the designed avionics system meets the safety and functionality requirements of the manned eVTOL aircraft. © 2024 IEEE.

关键词： Aircraft communication

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LEMTL: Enhancing the pharmacokinetic predictions of multitask learning with existing pharmacokinetics

LEMTL: Enhancing the pharmacokinetic predictions of multitas...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Ying Deng, Xiao Lv, Jingsong Chen, Hongyang Yang, Yao Zhejiang Lab Research Center for Space Computing System Zhejiang Province Hangzhou China Zhejiang Lab Research Center for Data Hub and Security Zhejiang Province Hangzhou China

ISBN: (纸本)9798350386226

Evaluation of pharmacokinetic properties such as absorption, distribution, metabolism, excretion, and toxicity is essential but costly and time-consuming in the research and development of new drug candidates. This challenge has driven many researchers to use multitask learning for in silico predictions. Given extra information such as SMILES (simplified molecular input line entry system) as input data, existing multitask learning methods can usually have a good performance in predicting pharmacokinetics, which are served as labels/tasks. However, most of these methods are based solely on additional information, overlooking potentially available pharmacokinetics, which can be utilized to further enhance the prediction of unavailable ones. To address this limitation, we propose Label Enhanced MultiTask Learning (LEMTL), a framework to improve the prediction accuracy of unknown pharmacokinetics of existing multitask learning methods by aggregating shared feature representations learned from additional information and from known pharmacokinetics. Experiments on synthetic and real datasets demonstrate significant improvements over the associated multitask learning approaches when incorporated within the LEMTL framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Falcon: A Remote Sensing Vision-Language Foundation Model

arXiv

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arXiv 2025年

作者： Yao, Kelu Xu, Nuo Yang, Rong Xu, Yingying Gao, Zhuoyan Kitrungrotsakul, Titinunt Ren, Yi Zhang, Pu Wang, Jin Wei, Ning Li, Chao Research Center for Space Computing System ZhejiangLab Hangzhou China

This paper introduces a holistic vision-language foundation model tailored for remote sensing, named Falcon. Falcon offers a unified, prompt-based paradigm that effectively executes comprehensive and complex remote sensing tasks. Falcon demonstrates powerful understanding and reasoning abilities at the image, region, and pixel levels. Specifically, given simple natural language instructions and remote sensing images, Falcon can produce impressive results in text form across 14 distinct tasks, i.e., image classification, object detection, segmentation, image captioning, and etc. To facilitate Falcon’s training and empower its representation capacity to encode rich spatial and semantic information, we developed Falcon SFT, a large-scale, multi-task, instruction-tuning dataset in the field of remote sensing. The Falcon SFT dataset consists of approximately 78 million high-quality data samples, covering 5.6 million multi-spatial resolution and multi-view remote sensing images with diverse instructions. It features hierarchical annotations and undergoes manual sampling verification to ensure high data quality and reliability. Extensive comparative experiments are conducted, which verify that Falcon achieves remarkable performance over 67 datasets and 14 tasks, despite having only 0.7B parameters. We release the complete dataset, code, and model weights at https://***/TianHuiLab/Falcon, hoping to help further develop the open-source community. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Depth Estimation Method for Monocular Images Combined with Position Estimation 13

Depth Estimation Method for Monocular Images Combined with P...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Wang, Hongliang Liu, Xingyu Li, Yang Xing, Guoyang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

—In this paper, we propose a monocular image depth estimation method based on ResNet and bitmap estimation. Traditional monocular depth estimation methods usually utilize convolutional neural networks (CNNs) for end-to-end training, but often require large amounts of labeled data. To overcome the problem of difficult data labeling, we use a competitive cooperative learning approach to jointly train depth estimation networks, camera motion estimation, optical flow estimation, and motion segmentation networks. We use ResNet as the backbone network for depth estimation, which achieves efficient feature transfer through residual concatenation. In addition, we introduce a bit-position estimation module to assist the depth estimation task by estimating camera motion and bit-position information. Experimental results show that our proposed method achieves good performance in the monocular depth estimation task and still achieves satisfactory results in the absence of labeled data. ©2024 IEEE.

关键词： Convolutional neural networks

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FairSteer: Inference Time Debiasing for LLMs with Dynamic Activation Steering

arXiv

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arXiv 2025年

作者： Li, Yichen Fan, Zhiting Chen, Ruizhe Gai, Xiaotang Gong, Luqi Zhang, Yan Liu, Zuozhu Zhejiang University China Research Center for Space Computing System Zhejiang Lab China

Large language models (LLMs) are prone to capturing biases from training corpus, leading to potential negative social impacts. Existing prompt-based debiasing methods exhibit instability due to their sensitivity to prompt changes, while fine-tuning-based techniques incur substantial computational overhead and catastrophic forgetting. In this paper, we propose FairSteer, a novel inference-time debiasing framework without requiring customized prompt design or model retraining. Motivated by the linear representation hypothesis, our preliminary investigation demonstrates that fairness-related features can be encoded into separable directions in the hidden activation space. FairSteer operates in three steps: biased activation detection, debiasing steering vector (DSV) computation, and dynamic activation steering. Specifically, it first trains a lightweight linear classifier to detect bias signatures in activations, and then computes DSVs as intervention directions derived from small contrastive prompt pairs. Subsequently, it performs debiasing by adjusting activations with DSVs in the inference stage. Comprehensive evaluation with six LLMs demonstrates the superiority of FairSteer across question-answering, counterfactual input evaluation and open-ended text generation tasks. Code will be released. © 2025, CC BY.

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Biogas Fermentation Monitoring research based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning 13

Biogas Fermentation Monitoring Research Based on Borderline-...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Xiu, Pengfei Fu, Lijun Sun, Yukun Wang, Hongliang Li, Xuerao Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model. ©2024 IEEE.

关键词： Decision making

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All Muscle Activity Test and Evaluation (AMATE): An analytical tool to assess lower limb muscle activation for stroke patients 46

All Muscle Activity Test and Evaluation (AMATE): An analytic...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Xiong, Xiyan Gao, Nan Zhang, Qing Ma, Xiao Smirnov, Pavel Zeng, Duchun Xue, Junxiao Yang, Chun Lin, Feng Research Center for Frontier Fundamental Studies Zhejiang Lab Zhejiang Hangzhou China School of Aerospace Engineering Tsinghua University Beijing China Research Center for Astronomical Computing Zhejiang Lab Zhejiang Hangzhou China Zhejiang Provincial People's Hospital Department of Physical Medicine and Rehabilitation Hangzhou China Research Center for Space Computing System Zhejiang Lab Zhejiang Hangzhou China

ISBN: (纸本)9798350371499

Assessing muscle activation and monitoring stroke recovery progress are vital aspects of evaluating motor impairment. Surface electromyography, a common tool for measuring muscle activity, has practical limitations for clinical implementation and fails to capture deep muscle activities, leading to challenges in accurately identifying muscle activation patterns in stroke patients. To address these limitations, we introduce an All Muscle Activity Test and Evaluation (AMATE) system, combining wearable sensor technology with a musculoskeletal model. AMATE computes activation levels of all lower limb muscles during various activities without requiring surface electrodes. It also generates an Activation Dissimilarity Index (ADI) to illustrate muscle activation patterns among individuals. We evaluated AMATE's effectiveness through a pilot study involving 22 walking trials with healthy subjects and stroke patients. The results highlight AMATE's capability in aiding clinicians to analyze and understand diverse lower limb muscle activation patterns in stroke patients, particularly identifying muscles with abnormal activation for targeted rehabilitative training. © 2024 IEEE.

关键词： Electromyography

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Hierarchical Action Recognition: A Contrastive Video-Language Approach with Hierarchical Interactions

arXiv

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arXiv 2024年

作者： Zhang, Rui Li, Shuailong Xue, Junxiao Lin, Feng Zhang, Qing Ma, Xiao Yan, Xiaoran Research Center for Data Hub and Security Zhejiang Lab China Research Centre for Frontier Fundamental Studies Zhejiang Lab China Research Center of Space based Computing System Zhejiang Lab China

Video recognition remains an open challenge, requiring the identification of diverse content categories within videos. Mainstream approaches often perform flat classification, overlooking the intrinsic hierarchical structure relating categories. To address this, we formalize the novel task of hierarchical video recognition, and propose a video-language learning framework tailored for hierarchical recognition. Specifically, our framework encodes dependencies between hierarchical category levels, and applies a top-down constraint to filter recognition predictions. We further construct a new fine-grained dataset based on medical assessments for rehabilitation of stroke patients, serving as a challenging benchmark for hierarchical recognition. Through extensive experiments, we demonstrate the efficacy of our approach for hierarchical recognition, significantly outperforming conventional methods, especially for fine-grained subcategories. The proposed framework paves the way for hierarchical modeling in video understanding tasks, moving beyond flat categorization. Copyright © 2024, The Authors. All rights reserved.

关键词： Patient rehabilitation

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