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An Evolutionary Multi-Objective Neural Architecture Search Approach to Advancing Cognitive Diagnosis in intelligent Education

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Yang, Shangshang Ma, Haiping Bi, Ying Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi School of Artificial Intelligence Anhui University Hefei China Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electrical and Information Engineering Computational Intelligence Laboratory Zhengzhou University Zhengzhou China School of Computer Science and Technology Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei Anhui China School of Engineering Westlake University Hangzhou China

As a pivotal technique in intelligent education systems, cognitive diagnosis (CD) serves to reveal students’ knowledge proficiency for better tackling subsequent tasks. Unfortunately, due to pursuing high model interpretability, existing manually designed models for CD often hold simplistic architectures, which cannot cope with intricate data in modern education platforms. Furthermore, the bias of human design limits the emergence of novel and effective CD models. To develop interpretable and more effective models, thus this paper proposes an evolutionary multi-objective neural architecture search (NAS) approach for CD. Specifically, we first adopt a comprehensive search space for the NAS task of CD: all candidate models can be encompassed by a general model that deals with three distinct types of inputs. Then, an innovative model interpretability objective is devised to formulate the architecture search task as a bi-objective optimization problem (BOP). To solve the BOP, we employ a multi-objective genetic programming (MOGP) as the search strategy to explore the search space. To make the employed MOGP search well, all architectures are first encoded by trees for easy optimization, and we devise a genetic operation and a population initialization strategy to expedite its convergence. Finally, the proposed approach is actually a MOGP-based NAS approach for CD. Extensive experiments show that CD models searched by the proposed approach exhibit significantly better performance than existing models and hold as good interpretability as handcrafted models. Besides, the effectiveness of the proposed MOGP search strategy, the devised objective, and tailored strategies are validated. IEEE

关键词： Computer architecture

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Bi-Level Meta-Learning for Few-Shot Domain Generalization

Bi-Level Meta-Learning for Few-Shot Domain Generalization

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaorong Qin Xinhang Song Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing

The goal of few-shot learning is to learn the generalization from seen to unseen data with only a few samples. Most previous few-shot learning methods focus on learning the generalization within particular domains. However, the more practical scenarios may also require the generalization ability across domains. In this paper, we study the problem of few-shot domain generalization (FSDG), which is a more challenging variant of few-shot classification. FSDG requires additional generalization with larger gap from seen domains to unseen domains. We address FSDG problem by meta-learning two levels of meta-knowledge, where the lower-level meta-knowledge is domain-specific embedding spaces as subspaces of a base space for intra-domain generalization, and the upper-level meta-knowledge is the base space and a prior subspace over domain-specific spaces for inter-domain generalization. We formulate the two levels of meta-knowledge learning problem with bi-level optimization, and further develop an optimization algorithm without higher-order derivative information to solve it. We demonstrate our method is significantly superior to the previous works by evaluating it on the widely used benchmark Meta-Dataset.

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Nonmonotonic propositional logic

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Frontiers of Computer Science 2021年第3期15卷 215-216页

作者： Wei LI Yuefei SUI Yuhui WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049China Information Technology Center China Reinsurance(Group)CorporationBeijing 100033China

Propositional logic[1]is basic,based on which other logics are *** deduction system for propositional logic is *** logics are a class of logics which deduction systems are *** ones are default logic[2,3],R-calculus[4]... 详细信息

关键词： default propositional Nonmonotonic

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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification

arXiv

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arXiv 2024年

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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A Generalized Depression Recognition Framework Based on Cross-Center and Cross-Task Eeg Signals

SSRN

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SSRN 2024年

作者： Liu, Xuesong Qu, Shanshan Luo, Gang Yan, Chang Wang, Dixin Chu, Na Tian, Fuze Zhu, Jing Li, Xiaowei Sun, Shuting Hu, Bin Key Laboratory of Brain Health Intelligent Evaluation and Intervention Beijing Institute of Technology Ministry of Education Beijing100081 China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou730000 China

This study developed a generalized framework for automatic depression recognition using a Dempster Shafer Theory-based Classification Fusion (DSTCF) method. We collected resting-state electroencephalography (EEG) data using 128 electrodes from 24 Major Depressive Disorder (MDD) patients and 29 Normal Controls (NC), as well as additional data from 47 MDD and 47 NC in both resting-state and dot-probe task conditions. By extracting eight linear and three nonlinear features and applying three feature selection methods, we conducted cross-center recognition and validation. Cross-center validation using the DSTCF method showed that after adopting the Relief method, EEG data in the beta frequency band achieved optimal performance. The best average accuracy was 96.18% across three training datasets and 67.82% across six validation datasets, representing improvements of 7% and 8%, respectively, compared to traditional methods. Furthermore, Ppmean and activity features demonstrated high discriminative capability and significant correlation with depressive levels. Based on these two features, abnormal parieto-occipital lobe activation served as a task-independent feature for MDD identification. Additionally, abnormal activation in the frontal lobe’s beta frequency band and the temporal lobe’s theta frequency band can effectively distinguish MDD from NC under resting and task conditions, respectively. These findings not only help in understanding the atypical neural mechanisms of depression but also provide reliable EEG biomarkers for its identification and diagnosis. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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A Stealthy False Data Injection Attack Scheme Against Sensor Measurements Using Partial System Knowledge

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IEEE Transactions on Automatic Control 2025年

作者： Guo, Haibin Pang, Zhong-Hua Sun, Jian Han, Qing-Long Liu, Guo-Ping North China University of Technology Key Laboratory of Fieldbus Technology and Automation of Beijing Beijing100144 China Beijing University of Technology School of Information Science and Technology Beijing100124 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing100081 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China

Attack design is indispensable for analyzing potential risks of networked control systems (NCSs). However, the remote control center is usually well protected and its knowledge is difficult to be disclosed, which becomes a major obstacle in developing stealthy false data injection (FDI) attack scheme because only partial system knowledge (i.e., the system matrices of the physical plant) could be used. To meet this challenge, a novel stealthy FDI attack scheme against the sensor measurement is proposed by employing the normal and compromised self-governed filters held by malicious attackers, where the normal one is adopted to estimate the system state and the compromised one is used as the virtual attacked target. The corresponding attack strategy is obtained by maximizing the estimation error of the compromised self-governed filter. Then, the residual of the compromised system is derived to prove attack stealthiness. Next, it is derived and found that the attack impact on system estimation performance is the same as that based on full system knowledge. Furthermore, the divergence condition of NCSs under the attack is presented. Finally, all the theoretical analyses are verified by simulation results. © 1963-2012 IEEE.

关键词： Kalman filters

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SILTD: Structural information for LLM-Generated Text Detection

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International Journal of Machine Learning and Cybernetics 2025年

作者： Yang, Jing Wang, Shi Zi, Kangli Sun, Yanshun Huang, Yuwei Luo, Tianyu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

The rapid development of large language models (LLMs) has significantly improved the quality and diversity of AI-generated content(AIGC). LLM-Generated text detection plays an important role in preventing the harmful misuse of large language models. Existing approaches primarily analyze texts individually, overlooking the structural relationships between them. This limitation restricts their ability to generalize across diverse LLMs, as they fail to capture the shared statistical patterns inherent in generated texts. To address this, an unsupervised-based structural information for LLM-generated text detection (SILTD) method is proposed. The key insight is that texts from different LLMs exhibit latent similarities in their generative statistical space, which can be modeled to improve cross-model generalization. First, we construct a multi-relational text graph based on the similarity of text features, which aims to model the intricate similarities and correlations between texts. Second, we propose a novel unsupervised graph clustering method. The multi-relational graph is transformed into an encoding tree, which is then optimized based on a two-dimensional structure entropy minimization algorithm to achieve hierarchical clustering of texts. Structural entropy minimization enables achieving high-quality clusters, by measuring the uncertainty of random walks within the graph. Finally, we introduce a new method that measures text similarity and computes the intensity of text aggregation within each cluster, to perform in-cluster label inference. Extensive experiments show that, compared to baseline methods, our approach is more effective and generalizable in detecting six popular LLMs across five datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Clustering LLM-generated text detection Multi-relational graph Structural information

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Performer: A High-Performance Global-Local Model-Augmented with Dual Network Interaction Mechanism

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Tan, Dayu Hao, Rui Hua, Linfeng Xu, Qi Su, Yansen Zheng, Chunhou Zhong, Weimin Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

In deep learning, Convolutional Neural Networks (CNNs) focus on local information through convolutional kernels, while transformers attend to global information using self-attention mechanisms. The union of these distinct approaches enables a more comprehensive extraction of image features. However, the feature map dimensions of CNN and Transformer differ, leading to dimension mismatch issues when combining these architectures. Additionally, the parameter size of the hybrid model integrating both architectures remains large, making it difficult to train. To further augmenting the interpretation of complex image patterns, we present Performer, a dual-network architecture that seamlessly combines CNNs and transformers, resulting in a novel and efficient representation learning model. In the Performer model, we innovate by devising a unique interaction methodology for CNN and transformer architectures to enhance the image feature extraction capabilities mutually. To counteract issue of dimensionality mismatch, we also introduce a refined transformer block, a advancement over the transformer block of ViT. To validate the effectiveness of Performer, we conduct extensive experiments on both classification and segmentation tasks. Performer achieve an accuracy of 83.37% on the ImageNet-200 dataset. For semantic segmentation, Performer excels on the CamVid and Hippocampus datasets. On CamVid, our model achieves a mean Intersection over Union (mIoU) of 63.27% and Pixel Accuracy of 92.11%, demonstrating superior performance in capturing fine details and handling complex scenes effectively. The code is available at https://***/hlfthh/ Performer. © 2016 IEEE.

关键词： Semantic Segmentation

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Optimizing Model-Driven Federated Learning for Rational and Data-Efficient in Social Mobile Network

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IEEE Network 2024年

作者： Lu, Jianfeng Zhang, Ying Cao, Shuqin Wang, Wei Tang, Changbing Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China

With the rise of data-intensive services in mobile networks, the demand for intelligent and efficient learning frameworks has grown exponentially. Federated Learning (FL), as an emerging paradigm, enables decentralized edge devices to collaboratively train large models without sharing raw data, ensuring privacy preservation. However, deploying FL in resource-constrained mobile environments faces significant challenges, such as free-riding and malicious behavior. These challenges hinder model performance and raise concerns about data integrity and inter-user cooperation. To address these issues, we propose a layered dynamic framework for social mobile networks, called FedSMN, which aims to enhance the generalization ability of collaboratively trained large models in FL environments. FedSMN leverages Bayesian inference to evaluate user behavior and employs evolutionary game theory to simulate dynamic interactions, thereby optimizing cooperation strategies and reducing communication overhead. Unlike traditional approaches that assume fully rational actors, FedSMN accounts for irrational behavior and fluctuating resources, designing an efficient incentive mechanism to improve data utilization. Extensive experiments on large-scale datasets demonstrate that FedSMN increases model accuracy by 21% and reduces communication overhead by 49%, paving the way for scalable, intelligent mobile networks powered by large models. © 1986-2012 IEEE.

关键词： Collaborative learning

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

引用

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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