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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ren, Hao Lan, Xiao Tang, Rui Chen, Xingshu School of Cyber Science and Engineering Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China Cyber Science Research Institute Sichuan University Chengdu China

ISBN: (纸本)157735897X

Deep Neuro-Fuzzy Inference Systems (DNFIS) seamlessly fuse neural networks with the fuzzy inference system enabling intricate decision-making and knowledge representation, while upholding a commendable degree of adaptability and interpretability. However, the challenge of privacy-preserving inference (PI) over DNFIS has remained largely uncharted, with no prior research addressing this critical issue. In this paper, we embark on an exploration of this issue. We introduce an efficient and secure PI framework for DNFIS, named PrivDNFIS, which leverages the post-quantum lattice-based homomorphic encryption to implement secure computation protocols for PI over DNFIS. Our work incorporates several non-trivial performance enhancements. Firstly, it consolidates multiple elements of input feature vectors into a single message, reducing encryption/decryption overhead. Secondly, building upon this novel encoding approach, PrivDNFIS can perform ciphertext aggregation and vector-vector inner production without necessitating time-consuming ciphertext rotation operations. Thirdly, we replace the softmax function in the DNFIS layer with a quadratic function to further enhance inference efficiency, without compromising the inference accuracy. Under the given threat model, we provide formal security proof for PrivDNFIS. In comprehensive experimental results, PrivDNFIS demonstrates an approximately 1.9 to 4.4 times reduction in end-to-end time cost compared to the benchmark. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Personalized Glycemic Control Program for Patients with Type I Diabetes Mellitus

Personalized Glycemic Control Program for Patients with Type...

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2024 China Automation Congress, CAC 2024

作者： Zhou, Ying Song, Rui Zhuo Wei, Qing Lai Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing Beijing100083 China

ISBN: (纸本)9798350368604

In this paper, an adaptive fuzzy sliding mode controller is designed to regulate the glucose level in type I diabetes, where its estimation is based on Bergman minimum glucose-insulin Model. All the underlying data in this paper was obtained from the UVA/Padova Type I Diabetes Simulator. The study included a cohort of nine diabetic patient from the simulation platform, comprising three adults, three adolescents, and three children. Firstly, simulation conditions were set, and blood glucose data before meals were obtained from nine patients. Subsequently, parameter identification techniques were employed, utilizing the general parameters of the Bergman Minimal Model as a foundation to derive personalized parameters for each patient’s Bergman Minimal Model. Furthermore, in response to the requirements of blood glucose control, we proposed a control strategy combining fuzzy theory and sliding mode control. Considering the individualized parameters among patients, we derived the control strategy of the controller and constructed a SIMULINK simulation system in MATLAB. Through experimentation, we validated the effectiveness of the controller, which effectively controlled blood glucose levels in type I diabetes patients. © 2024 IEEE.

关键词： Sliding mode control

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Propagation of terahertz waves in nonuniform plasma slab under “electromagnetic window”

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Chinese Physics B 2022年第3期31卷 399-405页

作者： Hao Li Zheng-Ping Zhang Xin Yang College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Semiconductor Power Device Reliability Engineering Research Center of Ministry of Education Guizhou UniversityGuiyang 550025China Key Laboratory of Micro-Nano-Electronics and Software Technology of Guizhou Province Guiyang 550025China

The application of magnetic fields, electric fields, and the increase of the electromagnetic wave frequency are upand-coming solutions for the blackout problem. Therefore, this study considers the influence of the external magnetic field on the electron flow and the effect of the external electric field on the electron density distribution, and uses the scattering matrix method(SMM) to perform theoretical calculations and analyze the transmission behavior of terahertz waves under different electron densities, magnetic field distributions, and collision frequencies. The results show that the external magnetic field can improve the transmission of terahertz waves at the low-frequency end. Magnetizing the plasma from the direction perpendicular to the incident path can optimize the right-hand polarized wave transmission. The external electric field can increase the transmittance to some extent, and the increase of the collision frequency can suppress the right-hand polarized wave cyclotron resonance caused by the external magnetic field. By adjusting these parameters, it is expected to alleviate the blackout phenomenon to a certain extent.

关键词： terahertz wave scattering matrix method(SMM) nonuniform magnetized plasma electric field

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Face micro-expression recognition algorithm based on ResNet depth model

Face micro-expression recognition algorithm based on ResNet ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Liquan Wang Shu Zhan School of Computer and Information Engineering Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education Hefei China

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted; SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASMEⅡ, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition.

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Rethinking data-Free Quantization as a Zero-Sum Game

arXiv

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arXiv 2023年

作者： Qian, Biao Wang, Yang Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. However, such sample generation process is totally independent of Q, specialized as failing to consider the adaptability of the generated samples, i.e., beneficial or adversarial, over the learning process of Q, resulting into non-ignorable performance loss. Building on this, several crucial questions — how to measure and exploit the sample adaptability to Q under varied bit-width scenarios? how to generate the samples with desirable adaptability to benefit the quantized network? — impel us to revisit DFQ. In this paper, we answer the above questions from a game-theory perspective to specialize DFQ as a zero-sum game between two players — a generator and a quantized network, and further propose an Adaptability-aware Sample Generation (AdaSG) method. Technically, AdaSG reformulates DFQ as a dynamic maximization-vs-minimization game process anchored on the sample adaptability. The maximization process aims to generate the sample with desirable adaptability, such sample adaptability is further reduced by the minimization process after calibrating Q for performance recovery. The Balance Gap is defined to guide the stationarity of the game process to maximally benefit Q. The theoretical analysis and empirical studies verify the superiority of AdaSG over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaSG. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction 31

Unified Grid Tagging Scheme for Aspect Sentiment Quad Predic...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Su, Guixin Zhang, Yongcheng Wang, Tongguan Wu, Mingmin Sha, Ying Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798891761964

Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance. © 2025 Association for Computational Linguistics.

关键词： Semantics

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Adaptive data-Free Quantization

arXiv

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arXiv 2023年

data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions - how to measure the sample adaptability to Q under varied bit-width scenarios? how to generate the samples with large adaptability to improve Q's generalization? whether the largest adaptability is the best? To answer the above questions, in this paper, we propose an Adaptive data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players - a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin is optimized to address the over-and-under fitting issues, so as to generate the samples with adaptive adaptability to Q. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization;2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaDFQ. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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LuoJia-Explorer: Unmanned Collaborative Localization and Mapping System 1

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Wu, Weitong Chen, Chi Yang, Bisheng State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Engineering Research Center of Space-Time Data Capturing and Smart Application The Ministry of Education of P.R.C. Wuhan430079 China

ISBN: (数字)9789819710997

ISBN: (纸本)9789819710980

Over the past two decades, numerous single-robot SLAM systems have been developed and proposed. However, the problem of collaborative localization and mapping with a team of robots remains an open challenge. Collaborative localization and mapping plays a crucial role in enhancing mapping efficiency and has a wide application in search and rescue, infrastructure monitoring, extraterrestrial planetary cave exploration, etc. In this paper, we propose LuoJia-Explorer, an unmanned collaborative localization and mapping system for ground platforms. The proposed system is centralized with a main control center. Each ground platform is equipped with a multi-beam LiDAR and IMU to perform LiDAR-inertial odometry in its own coordinate system. Through mesh networking communication technology, the main control center receives the mapping result and trajectory from each platform. At the control center, global SLAM is performed, which includes loop detection, relative pose estimation and global pose graph optimization, to obtain a globally consistent mapping result. Two ground platforms were built to verify the proposed system. Field experiments have preliminarily verified the effectiveness of this system. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Mapping

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DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution 43

DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framew...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Mei Jin, Ye Zhu, Jinhui South China University of Technology School of Automation Science and Engineering Guangzhou510641 China South China University of Technology School of Software Engineering Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581581

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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